ID413: Visualizing Burtin’s Antibiotic Data (2025)

[venkatrajam]

Assignment 4: Visualizing Burtin’s Antibiotic Data

In the post-World War II world, antibiotics were called “wonder drugs,” for they provided quick and easy cures for what had previously been intractable diseases. Data were being gathered to aid in learning which drug worked best for which bacterial infection. Being able to see the structure of drug performance from outcome data was an enormous aid for practitioners and scientists alike. In 1951, William Burtin published a chart showing the performance of the three most popular antibiotics on 16 bacteria.

The data used in his display are shown in the adjacent table (Butin_antibiotic_data.xlsx). The entries of the table are the minimum inhibitory concentration (MIC), a measure of the effectiveness of the antibiotic. The MIC represents the concentration of antibiotic required to prevent growth in vitro. The covariate “gram staining” describes the reaction of the bacteria to Gram staining. Gram-positive bacteria are those that are stained dark blue or violet; whereas, Gram-negative bacteria do not react that way.

Burtin’s chart focuses on the efficacy of the antibiotics. It answers the question, "How do the drugs compare?". Create a chart that groups the bacteria by their comparative susceptibility to the drugs. It should answer the question, "How do the bacteria group together?" and highlight what insight it reveals.

[shivam-saran]

Shivam Saran (23N0285)

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Visualization

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1. How do the bacteria group together?

• Gram-negative bacteria: Penicillin is extremely ineffective as an antibiotic.
• Gram-positive bacteria: Penicillin is fairly effective.

2. What insight does it reveal?

• Penicillin is the most effective of the three wonder drugs followed by Streptomycin and then Neomycin, as seen in the comparative bars (thick bars) for each bacteria.
• As we classify the data as Gram Staining positive & Gram Staining negative, Penicillin is extremely effective at destroying gram-positive bacteria whereas it was poor at destroying gram-negative bacteria. The lengths and thickness of bars in the blue-shaded region (gram-positive) are shorter as compared to the bars in the red-shaded region (gram-negative).
• It also shows the effectiveness of streptomycin and neomycin against various bacteria, providing a comparison of different antibiotic options.

[aviralvgoel]

Aviral Vishesh Goel (22b2156)

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How do the bacteria group together?

1. Misclassification of Streptococcus Fecalis: As can be seen in the graph Fecalis is far from its family members viridans and hemolyticus.
2. Diplococcus pneumoniae: It shows similar characteristics as other streptococcus bacteria and might belong to same family.
3. Similarity between 3 bacterias: Aerobacter aerogenes, Klebsiella pneumoniae, Pseudomonas aeruginosa exhibit similar properties to antibiotics and can belong to the same family
4. Gram Positive: As seen with lower MIC scores gram-positive bacteria are more susceptible to antibiotics

[Rishi-574]

RISHI DEWANGAN

23N0327

How do the bacteria group together?

• The bacteria are categorized based on Gram staining, which differentiates them into Gram-positive and Gram-negative groups.
• Gram-negative bacteria are on the left, and Gram-positive bacteria are on the right in the graph.
• This classification is based on differences in cell wall composition, which influences their antibiotic resistance.

What insight does it reveal?

• Gram-negative bacteria generally show higher resistance to Penicillin, as indicated by their higher MIC values.
• Gram-positive bacteria tend to be more susceptible to Penicillin, but their response to Streptomycin and Neomycin varies.
• Some bacteria exhibit multi-drug resistance, highlighting the need for careful antibiotic selection.
• The grouping helps visualize which antibiotics are more effective against certain types of bacteria, aiding in better treatment decisions.

[RohitIITB]

Assignment 4

Rohit Kourav - 22B0720

Comparative Secrets of Antibiotic Warfare

This chart categorizes bacteria based on their comparative susceptibility, rather than just individual antibiotic performance. The Minimum Inhibitory Concentration (MIC) is plotted on a log scale, where an MIC value of 1 log unit corresponds to 10^MIC in actual concentration. A lower MIC (left side) means the bacteria are highly susceptible, requiring lower antibiotic concentrations for inhibition, while a higher MIC (right side) indicates greater resistance, requiring higher concentrations. The bubble size represents relative antibiotic effectiveness—larger bubbles signify stronger inhibition at a given MIC. The bacteria naturally group together, reflecting their susceptibility patterns. Additionally, bacteria are classified by their Gram-staining characteristics (Gram-positive 🟢 and Gram-negative 🔴 written in the brackets), influencing antibiotic effectiveness.

Understanding Effects of each Antibiotic

Penicillin (🟠) is most effective against Gram-positive bacteria, as seen in the cluster on the highly susceptible side (low MIC, around -3 to -2.5). These bacteria require only a small concentration of Penicillin for inhibition, indicating strong susceptibility. However, Penicillin is least effective against most Gram-negative bacteria, which are positioned at higher MIC values, suggesting significant resistance. This pattern aligns with Penicillin's known mechanism, as it targets peptidoglycan synthesis, a process more dominant in Gram-positive cell walls.

Streptomycin (⚫) exhibits moderate effectiveness across both Gram-negative and Gram-positive bacteria. While it does inhibit some Gram-negative species, it generally requires a higher MIC compared to Neomycin, indicating that it is less potent at lower concentrations. Its impact on Gram-positive bacteria is limited, as some species show only partial susceptibility, clustering in regions where the MIC is relatively higher. This suggests that Streptomycin may work best in cases requiring larger antibiotic concentrations to achieve inhibition.

Neomycin (🟣) demonstrates broad-spectrum effectiveness, particularly against Gram-negative bacteria, which are grouped in the low MIC regions. This indicates that Neomycin can inhibit these bacteria at relatively low concentrations, making it one of the more potent antibiotics in this dataset. Additionally, while Neomycin also works against some Gram-positive bacteria, its effectiveness is not as strong as Penicillin, as seen in the higher MIC clustering for those cases. Its broad applicability makes it useful in treating infections caused by diverse bacterial strains, particularly those that show resistance to other antibiotics.

Key Insights and Conclusions

• Penicillin is highly effective against Gram-positive bacteria, as shown by large bubbles in the low MIC range (-3 to -2.5 log MIC). This means it works at very low concentrations, making it a first-line treatment for Gram-positive infections. However, Gram-negative bacteria show resistance, requiring much higher MIC values.
• Neomycin has a broader spectrum, working well against Gram-negative bacteria at moderate MIC values (below -1.5 log MIC). It also shows some effectiveness against Gram-positive bacteria, though not as strongly as Penicillin.
• Streptomycin is partially effective against both Gram-positive and Gram-negative bacteria, but at higher MIC values (-1 to 0 log MIC). This makes it less potent than Penicillin and Neomycin. The natural clustering of bacteria based on MIC helps identify resistant strains and guide antibiotic selection.

[srishti920]

Srishti Gupta 23b2520

Butins Antibiotic Data

To visualize the susceptibility of 16 bacterial strains to three antibiotics—Penicillin, Streptomycin, and Neomycin—based on their Minimum Inhibitory Concentration (MIC) values, I have utilized a Hierarchical Clustering Heatmap.
MIC represents the lowest concentration of an antibiotic required to inhibit bacterial growth, with lower values indicating higher susceptibility and higher values indicating greater resistance. Since MIC values span a wide range, a log10 transformation was applied to make differences more discernible. This ensured that small and large MIC values were proportionally represented, avoiding misleading visual gaps.
Furthermore, to emphasize bacterial grouping based on similar behavior across antibiotics, I applied hierarchical clustering (Ward’s method) to rearrange the rows of the heatmap. This method clusters bacteria that exhibit similar susceptibility patterns to antibiotics, visually grouping those with comparable resistance or susceptibility.

Red shades - Resistant

• Dark Red corresponds to 'Highly Resistant Bacteria' (High MIC values)
• Light Red corresponds to Resistant as well, however not nearly to the same degree as Dark Red

Neutral Shades → Intermediate Response

Blue Shades - Susceptible

• Dark Blue corresponds to 'Highly Susceptible Bacteria' (Low MIC values, easier to kill)
• Light Blue corresponds to Susceptible as well, however not nearly to the same degree as Dark Blue

The x-axis represents the three antibiotics, while the y-axis lists bacteria, clustered by similar susceptibility patterns.
By sorting bacteria based on their resistance profile, we can instantly see which bacteria are most resistant overall, which are easily treatable, and whether an antibiotic has broad or narrow effectiveness.

To further compare and contrast, I have created 2 bar charts (differentiated based on Gram Negative and Gram Positive), which can be compared between themselves and the heatmap. While the heatmap provides exact log MIC values, interpreting numerical differences requires careful comparison between individual cells. In contrast, the bar charts offer a more intuitive way to assess susceptibility by allowing for immediate visual comparison—longer bars instantly indicate higher resistance/higher susceptibility, making it easier to grasp trends at a glance without focusing on precise numerical values. This combination ensures both detailed accuracy (via the heatmap) and clear comparative insights (via the bar charts), making the analysis more comprehensive.

How do the bacteria group together?
The bacteria cluster based on their susceptibility patterns to antibiotics rather than just Gram status. However, some trends emerge:

• Gram-positive bacteria (G+) tend to group together, showing higher susceptibility to Penicillin (e.g., Bacillus anthracis and Streptococcus species are highly affected).
• Gram-negative bacteria (G-) form clusters due to their similar resistance to Penicillin but show varying susceptibility to Neomycin and Streptomycin (e.g., Klebsiella pneumoniae, Escherichia coli).
• Some bacteria display unique resistance profiles, placing them outside typical Gram-based clustering.

Some Key Insights

• Penicillin is largely ineffective against most Gram-negative bacteria, while Gram-positive bacteria are affected.
• Neomycin and Streptomycin have varying effects on bacteria even within the same Gram classification, with some bacteria showing resistance (e.g., Streptococcus viridans) and others strong susceptibility (e.g., Bacillus anthracis).
• Hierarchical clustering reveals subgroups where certain bacteria share cross-resistance patterns, helping identify potential multidrug-resistant strains.
• Bacillus anthracis and some Staphylococcus species are highly susceptible to multiple antibiotics, making them more treatable.
• Mycobacterium tuberculosis and Pseudomonas aeruginosa are highly resistant to Penicillin while also showing significant resistance to Streptomycin and Neomycin, highlighting their difficulty in treatment.

[shubhamagarwal03]

Shubham Agarwal
22b2158

I wrote a python code trying different charts and tools. Given below is the final iteration I did which has the most appropriate chart to generate my insights.

The steps followed in the code is:
Loaded Data: Imported the antibiotic MIC dataset from the data after converting it into a CSV file.

Preprocessed Data: Applied a log transformation to MIC values for better visualization and analysis.
Converted Gram Staining into a categorical variable and ordered it (Gram-positive first).
Sorted the dataset based on Gram Staining.

Visualized Antibiotic Efficacy:
Created a bar chart showing log MIC values of each antibiotic for different bacteria to compare effectiveness.
Generated boxplots to analyze the correlation between Gram Staining and antibiotic efficacy, helping determine if antibiotics work better on Gram-positive or Gram-negative bacteria.

Insights:

1. Initially, I plotted the MIC values for each antibiotic against different drugs, but the wide range of values made the data difficult to visualize. To address this, I applied a log transformation, which improved readability and allowed for better analysis. I also inversed the values so that low concentration meant better effectiveness
2. The bar chart shows which antibiotics are more effective across different bacteria.
3. The boxplots indicate patterns in antibiotic susceptibility between Gram-positive and Gram-negative bacteria.
4. We notice Penicilin performs extremely well on gram positive bacteria but is not effective for gram negative bacteria
5. The other 2 antibiotics dont show such a stark trend
6. The bacteria for which Penicilin performs badly have extremely low values. This is again not the case with the other two who have moderate values.
7. Some drugs are highly resistant to all 3

8. When I ordered them based on aplphabetical order which also meant drugs with similar names are together I noticed a similar pattern when the first name was same except in the case of Streptococcus fecalis. This was an irregularity in the data. After research I found out that it was wrongly classified initially and later changed

[amansingh107]

🦠 Burtin’s Chart vs. Radar Chart: Shifting the Focus

Burtin’s chart answers "How do the drugs compare?", showing the effectiveness of Penicillin, Streptomycin, and Neomycin against various bacteria.

However, our radar chart shifts the focus to:
"How do the bacteria group together based on their susceptibility to antibiotics?"

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🔑 Key Insights from the Radar Chart

📌 Distinct Clusters Based on Resistance & Sensitivity

• Highly Susceptible Bacteria (Small Areas on Radar):
  Brucella anthracis, Staphylococcus aureus, Staphylococcus albus, and Streptococcus hemolyticus
  → These bacteria show high sensitivity (low MIC values) across all antibiotics, meaning smaller shapes on the radar.

• Moderately Resistant Bacteria:
  Proteus vulgaris, Escherichia coli, Salmonella schottmuelleri
  → Mixed responses: resistant to some drugs but sensitive to others.

• Highly Resistant Bacteria (Large Areas on Radar):
  Pseudomonas aeruginosa, Mycobacterium tuberculosis, Klebsiella pneumoniae
  → Resistant across all antibiotics, forming large areas on the radar.

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🧫 Gram-Positive vs. Gram-Negative Differences

• Gram-Positive Bacteria (Brucella anthracis, Streptococcus spp., Staphylococcus spp.)
  → Tend to be more susceptible to Penicillin.

• Gram-Negative Bacteria (Pseudomonas, Salmonella, E. coli)
  → Show higher resistance across all antibiotics, especially Penicillin.

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💊 Drug-Specific Effectiveness

• Penicillin → Most effective for Gram-Positive Bacteria, especially Streptococcus and Staphylococcus species.
• Neomycin → Broad-spectrum, showing moderate effectiveness against both Gram-positive and Gram-negative bacteria.
• Streptomycin → Works best for specific bacteria like Mycobacterium tuberculosis, while some bacteria remain resistant.

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✅ Final Conclusion

The radar chart highlights how bacteria cluster together based on their resistance or susceptibility, rather than just showing how the drugs compare.

🔬 This perspective is crucial in antibiotic selection—knowing which bacteria share resistance patterns allows for better treatment decisions.

Name - Aman Kumar Singh

Roll no. - 22B0321

[Jubin42Singh]

Name: Jubin Singh

Roll No. 22b0351

Analysis and Grouping of Bacteria Based on Susceptibility:

The heatmap clearly shows how the bacteria group together based on their resistance (or susceptibility) to Penicillin, Streptomycin, and Neomycin. The color gradients indicate varying degrees of effectiveness of each antibiotic against each bacterium, with red shades indicating higher MIC values (suggesting resistance) and purple shades indicating lower MIC values (suggesting higher susceptibility).

Note: The values are in log scale

From this chart, we can draw the following insights:
Bacteria with Strong Resistance to Penicillin:

• Bacteria like Aerobacter aerogenes (MIC = 2.940) and Pseudomonas aeruginosa (MIC = 2.929) show high resistance to Penicillin, as seen by their deep red color in the Penicillin column.
• These bacteria are likely to be less affected by Penicillin and might require higher concentrations or different drugs to treat infections effectively.

Bacteria with Strong Resistance to Streptomycin:
Brucella abortus (MIC = 0.301) and Streptococcus fecalis (MIC = 1.146) show varying degrees of resistance to Streptomycin.
Staphylococcus aureus (MIC = -1.523) shows a medium resistance to Streptomycin, as the MIC values are moderate but not as high as others like Pseudomonas aeruginosa.

Bacteria Sensitive to Neomycin:

• Staphylococcus aureus (MIC = -3.0) is particularly susceptible to Neomycin, as evidenced by its low MIC value in the Neomycin column.
• Salmonella (Eberthella) typhosa and Streptococcus fecalis also show good susceptibility to Neomycin.

Groupings Based on Susceptibility:

• Gram-negative bacteria such as Aerobacter aerogenes, Klebsiella pneumoniae, and Pseudomonas aeruginosa generally show higher resistance to Penicillin but exhibit varying susceptibility to Streptomycin and Neomycin.
• Gram-positive bacteria such as Staphylococcus aureus, Streptococcus fecalis, and Streptococcus hemolyticus often show lower resistance to Penicillin and Streptomycin but moderate or high resistance to Neomycin.

Insight Revealed by the Heatmap:
The Gram staining property of bacteria seems to correlate with the antibiotic resistance pattern. Gram-negative bacteria tend to show higher resistance to Penicillin but varying responses to Streptomycin and Neomycin, while Gram-positive bacteria show more susceptibility to Penicillin but still demonstrate mixed resistance to the other two antibiotics.

The heatmap makes it visually evident which antibiotics are more effective against which types of bacteria, allowing us to determine potential treatment strategies. For instance:

• Neomycin seems to be effective against Gram-positive bacteria like Staphylococcus aureus and Streptococcus fecalis, but less effective against Gram-negative bacteria.
• Penicillin and Streptomycin are effective against various Gram-positive bacteria, but Gram-negative bacteria tend to show high resistance, suggesting alternative or stronger antibiotics may be needed.

Conclusion:
The heatmap is a powerful tool for comparing how different antibiotics perform against various bacteria, making it easier to identify patterns of resistance and susceptibility. It highlights the important role of Gram staining in determining the efficacy of the antibiotics, with Gram-negative bacteria generally showing more resistance to Penicillin and Gram-positive bacteria being more susceptible. The heatmap also shows which antibiotics (e.g., Neomycin and Penicillin) are more or less effective against specific bacteria.

[bharathiitb]

Assignment 4

Bharath Sreejith
22B3916

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We will be using a combination of two techniques, namely

• Heatmap
• Circular Dendrogram

Heatmap

This method helps us understand the resistance of each bacterium towards each drug. It creates separate clusters for each drug. Bacteria with similar resistance to each drug will have similar color coding in each column.

• Red shades → Resistant bacteria
  Dark Red → Highly resistant (very high MIC values).
  Light Red → Some resistance but lower than dark red areas.
• Blue shades → Susceptible bacteria
  Dark Blue → Highly susceptible (low MIC values, easier to kill).
  Light Blue → Susceptible but less than dark blue areas.
• Neutral shades → Intermediate response
  Bacteria with MIC values between resistant and susceptible categories.

The MIC values have been transformed to Log10 scale for easier comparison.

Circular Dendrogram

• The circular dendrogram (hierarchical clustering tree) visually organizes bacteria based on their similarity in susceptibility to the three antibiotics.

• Each bacterium is a leaf node in the tree, and branches connect bacteria with similar antibiotic response profiles. The length of the branches represents how different the bacteria are in terms of their susceptibility to these antibiotics.

• The vertical height at which two bacteria merge represents their dissimilarity. The higher the merge, the less similar their MIC values are.

• After log transformation, we calculate the Euclidean distance between bacteria based on their MIC values across antibiotics.
  For two bacteria A and B, each with MIC values for three antibiotics, the distance is:

  d(A, B) = √((x₁ - y₁)² + (x₂ - y₂)² + (x₃ - y₃)²)

where xᵢ and yᵢ are the log10 MIC values of bacteria A and B for antibiotic i.

• The Euclidian distance shows aggregate susceptibility towards all three antibiotics, the precision of susceptibility towards each drug is lost. This is solved with the help of the heatmap.

The Dendrogram is separately shown in the following plot

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Key Insights

1. Antibiotic-Specific Resistance Trends

• Penicillin is almost completely ineffective against Gram-negative bacteria (e.g., Escherichia coli, Pseudomonas aeruginosa).
• Neomycin shows a broader range of effectiveness across both Gram-positive and Gram-negative bacteria, but some Gram-positive bacteria still resist it.
• Streptomycin has mixed effectiveness, with some bacteria showing strong resistance while others are highly susceptible (e.g., Streptococcus viridans is very susceptible).

2. Unique Resistance Profiles in Bacteria

While many bacteria follow expected Gram-positive/Gram-negative trends, some bacteria show unexpected behavior:

• Brucella anthracis is highly resistant to Streptomycin, despite being Gram-negative, unlike other similar bacteria.
• Klebsiella pneumoniae has moderate resistance across all antibiotics, showing no extreme susceptibility.

3. Bacteria Grouping Based on Multi-Drug Resistance

• Some bacteria cluster together not by Gram status but by their resistance to multiple antibiotics.
• The dendrogram highlights subgroups where certain bacteria respond similarly across antibiotics.
• A few bacteria remain highly resistant across all antibiotics, which could indicate potential multidrug resistance (important for hospital infections).

4. Identifying the Hardest & Easiest Bacteria to Treat

• Hardest to Treat (Highly Resistant) → Pseudomonas aeruginosa, Brucella anthracis, Klebsiella pneumoniae
• Easiest to Treat (Highly Susceptible) → Streptococcus viridans, Pneumococcus
• Intermediate Cases (Some Resistance, Some Susceptibility) → Salmonella, Staphylococcus aureus

5. Practical Implications

• Penicillin should not be used for Gram-negative infections—it’s almost entirely ineffective.
• Combination therapies may be needed for bacteria with resistance across all three antibiotics.
• Neomycin seems to be the most versatile antibiotic, but some Gram-positive bacteria still resist it.

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Conclusion

• The clustering approach reveals patterns that go beyond simple Gram-staining categories.
• Some bacteria do not follow expected trends in antibiotic resistance, highlighting the need for testing rather than assumptions.
• This visualization helps identify which antibiotics may work best for certain bacterial infections and which bacteria pose the biggest treatment challenges.

[Dev-Nog]

Dev Nogiya (23n0291)

Description of the Data & Insights

Overview:
The dataset provides resistance levels of 16 bacterial strains against three antibiotics: Penicillin, Streptomycin, and Neomycin. Additionally, each bacterium is classified as Gram-positive or Gram-negative, which often correlates with its response to antibiotics.

Comparison of Drugs:
Penicillin shows a wide range of effectiveness, with some bacteria (e.g., Brucella anthracis) being highly susceptible, while others (e.g., Aerobacter aerogenes) show extreme resistance.
Streptomycin generally exhibits moderate effectiveness, with most bacteria showing some level of resistance, though a few are highly susceptible.
Neomycin appears to be more potent, as most bacteria exhibit lower resistance values compared to the other two drugs.
Thus, Neomycin is the most consistently effective, while Penicillin shows the largest resistance variation.

How Do the Bacteria Group Together?
To better understand bacterial susceptibility, I performed hierarchical clustering to group bacteria based on their resistance levels to all three antibiotics. The dendrogram below highlights which bacteria exhibit similar resistance patterns, helping to identify groups with shared antibiotic responses.

Key Observations:
Distinct clusters emerge, indicating bacteria that exhibit similar resistance profiles.
Gram-negative bacteria tend to cluster together, suggesting they share common resistance mechanisms.
Some bacteria show high susceptibility across all antibiotics, while others form resistant clusters, reinforcing known antibiotic resistance patterns.
This clustering helps in predicting antibiotic effectiveness and identifying bacterial groups that may require alternative treatments.

How do the drugs compare?

Neomycin (positive) is the most effective antibiotic.
Penicillin (negative) is the least effective, especially against Gram-negative bacteria.
Gram-negative bacteria generally show higher resistance compared to Gram-positive ones.

How do the bacteria group together?
1.Highly resistant Gram-negative bacteria (Aerobacter aerogenes, Klebsiella pneumoniae, Pseudomonas aeruginosa)

Strong resistance to Penicillin
Moderate resistance to Streptomycin
2. Moderately resistant Gram-negative bacteria (Escherichia coli, Proteus vulgaris, Salmonella species)

Variable resistance to Penicillin and Streptomycin
More susceptible to Neomycin
3. Highly susceptible Gram-positive bacteria (Streptococcus, Staphylococcus species)

Higher susceptibility to all antibiotics
Neomycin is particularly effective

Key Insight
By clustering bacteria based on their resistance profiles, targeted antibiotic treatments can be optimized to combat resistant strains effectively.

[Khush-Soni]

Khush Soni - (23n0290)

Image -

This plot is interactive plot, plotted via python.

How Is This Chart Different from Burtin’s Chart?
Burtin’s original chart focused on how different antibiotics compare in terms of efficacy—essentially answering,
"Which antibiotic works best?"

This sunburst chart, however, answers a different question:
"How do bacteria group together based on their resistance patterns?"

Instead of focusing on the drugs, it highlights how bacteria cluster together based on their antibiotic susceptibility profiles.

Breakdown of the Chart Structure -

1. Inner Circle (Gram Classification - White)
   Splits bacteria into Gram-positive (blue) and Gram-negative (red).
   This division provides the first level of grouping, showing that Gram-negative bacteria are often more resistant.

2. Second Circle (Bacteria Type - Colored Shades)
   Each bacterium is placed under its Gram category.
   Darker red = higher resistance (Gram-negative).
   Darker blue = lower resistance (Gram-positive).

3. Third Circle (Antibiotic Type - Labeled Sectors)
   Each bacterium is further divided into Penicillin, Streptomycin, and Neomycin.

4. Outermost Circle (Resistance Value)
   Displays numerical values of resistance (minimum inhibitory concentration in µg/mL).
   Higher values = more resistant bacteria.
   This allows direct comparison of susceptibility within each bacterial group.

What insight does it reveal?
a. Gram-negative bacteria show higher resistance, especially to Penicillin.
b. Gram-positive bacteria are generally more susceptible, with Neomycin being highly effective.
c. Bacteria group based on resistance trends, not just Gram type—some Gram-positives show unexpected resistance.
d. Neomycin is the most effective overall compared to Penicillin and Streptomycin.
e. The chart reveals natural clustering of bacteria by resistance behavior, aiding better antibiotic selection.

[GouriRathi]

NAME- GOURI RATHI
ROLL NO- 22B0705

The visualization is based on the minimum inhibitory concentration (MIC) data for three antibiotics (Penicillin, Streptomycin, and Neomycin) against a panel of 16 bacteria. MIC, defined as the lowest concentration of an antibiotic that prevents the growth of a bacterium in vitro, serves as a quantitative measure of antibiotic effectiveness. The data also includes information on Gram staining, a fundamental bacterial classification based on cell wall structure.

Figure 1

Figure 2

Visual Representation:

• Data Transformation: MIC values were log₁₀-transformed to normalize the range and reduce the influence of extreme values.

• 3D Coordinate Mapping: Each bacterium was positioned in a 3D space where the X, Y, and Z coordinates correspond to the scaled log₁₀ MIC values for Penicillin, Streptomycin, and Neomycin, respectively. The size of each sphere is scaled proportionally to the bacterium's overall antibiotic susceptibility (computed as the sum of the log₁₀ MIC values), so larger spheres indicate greater susceptibility.
  This spatial mapping directly reflects the bacteria's response profile to the three antibiotics.

• Gram Stain Color-Coding: The spheres are color-coded based on Gram staining: purple for Gram-positive and orange for Gram-negative bacteria. This allows for visual assessment of any correlation between Gram staining and antibiotic susceptibility.

• Small Multiple Comparison: In addition to the 3D visualization, horizontal bar charts display the effectiveness of each antibiotic against the bacteria (Figure 2).

Results and Interpretation

• Clustering by Susceptibility: (Figure 1) visually demonstrates that bacteria tend to cluster based on their overall susceptibility profiles. For example, several Gram-positive bacteria (purple) such as Streptococcus viridans, Streptococcus hemolyticus, and Diplococcus pneumoniae appear in close proximity, suggesting similar responses to the antibiotics. In contrast, the Gram-negative bacteria are more dispersed.

• Gram Staining and Antibiotic Response: While there isn't a perfect separation of Gram-positive and Gram-negative bacteria, some trends are apparent. The Gram-positive bacteria generally appear more susceptible to Penicillin than the Gram-negative bacteria. This is also supported by the "Penicillin Effectiveness" small multiple (Figure 2), where most Gram-positive bacteria have more negative Log(MIC) values (indicating higher effectiveness).

• Differential Antibiotic Effectiveness: The "Antibiotic Effectiveness" small multiples (Figure 2) show the relative effectiveness of each antibiotic. For instance, Neomycin appears to be generally more effective (lower MIC values, bars further to the left) against the Gram-positive bacteria compared to Penicillin and Streptomycin. In contrast, many Gram-negative bacteria display resistance to Penicillin.

• Specific Susceptibility Patterns: Brucella anthracis (Gram-positive) appears as one of the most susceptible bacteria overall, displaying sensitivity to all three antibiotics (Figure 2). Pseudomonas aeruginosa (Gram-negative), on the other hand, appears relatively resistant to all three antibiotics.

Conclusion

The visualization offers a visually intuitive and informative way to explore the complex relationships between bacteria and antibiotics. By mapping antibiotic response to a spatial representation, this approach facilitates the identification of bacterial groupings, potential correlations with Gram staining, and the comparative effectiveness of different antibiotics. The insights gained from this visualization, particularly when combined with the antibiotic effectiveness comparisons, provide a deeper understanding of the bacterial susceptibility landscape.
The visualization complements Burtin’s original work by grouping bacteria based on antibiotic susceptibility rather than comparing the efficacy of the drugs. This can be a valuable tool for researchers and practitioners seeking to understand and combat bacterial infections. The clustering of antibiotic resistance and Gram staining has also yielded useful information.

[prakhargautam2004]

NAME - PRAKHAR GAUTAM
ROLL NO. - 22B0768

Radar Chart Visualization of Antibiotic Susceptibility

Process Overview:

1. Data Preprocessing:

   • The Minimum Inhibitory Concentration (MIC) values were taken from the dataset.
   • Since MIC values vary across several orders of magnitude, a log transformation (log10) was applied to ensure a balanced visualization.
   • Bacteria were categorized based on their Gram stain properties (not directly visualized but considered).

2. Radar Chart Construction:

   • Each bacterium was represented as an individual axis in the circular radar plot.
   • The three antibiotics (Penicillin, Streptomycin, and Neomycin) were plotted in different colors:
     • Red for Penicillin
     • Blue for Streptomycin
     • Green for Neomycin
   • The MIC values (log-transformed) for each antibiotic were plotted, with filled regions indicating effectiveness.

3. Interpretation of the Chart:

   • The closer a value is to the center, the more effective the antibiotic (lower MIC value).
   • The further from the center, the less effective (higher MIC value, meaning higher concentration is needed to inhibit bacterial growth).
   • Bacteria are arranged radially, allowing an easy comparison of antibiotic effectiveness across all bacteria.

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Key Insights & Conclusions:

1. Penicillin Resistance in Gram-Negative Bacteria:

   • Pseudomonas aeruginosa, Proteus vulgaris, and Klebsiella pneumoniae show high MIC values (red area extends outward), indicating strong resistance to Penicillin.
   • This aligns with the fact that Gram-negative bacteria have outer membranes that make them naturally more resistant to β-lactam antibiotics like Penicillin.

2. Effectiveness of Streptomycin & Neomycin:

   • Streptomycin (blue) and Neomycin (green) perform better overall compared to Penicillin for many Gram-negative bacteria.
   • For bacteria like Escherichia coli, Salmonella, and Pseudomonas aeruginosa, Streptomycin and Neomycin show lower MIC values, meaning they are more effective.

3. Gram-Positive Bacteria Susceptibility:

   • Bacillus anthracis, Staphylococcus aureus, and Streptococcus species are highly susceptible to Penicillin (red area is closer to the center).
   • This matches known microbiological patterns, as Gram-positive bacteria lack the outer membrane barrier found in Gram-negative species.

4. Multi-Drug Resistance Patterns:

   • Aerobacter aerogenes and Pseudomonas aeruginosa exhibit high MIC values across all antibiotics, suggesting multi-drug resistance.
   • These species often require alternative treatments beyond conventional antibiotics.

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[jeeten-makwana]

Makwana Jeeten Rajnikant
22B2273

How do the bacteria group together?

Introduction:
Antibiotics revolutionized medicine in the post-World War II era by providing effective treatments for bacterial infections. In 1951, William Burtin created a visualization to compare the efficacy of three key antibiotics—Penicillin, Streptomycin, and Neomycin—against 16 bacterial strains. His chart was instrumental in helping scientists and healthcare professionals understand which antibiotics were most effective for specific bacteria.

Objective:
While Burtin’s original visualization focused on comparing antibiotics, this analysis aims to group bacteria based on their susceptibility to these drugs. The goal is to identify patterns in bacterial resistance and susceptibility, providing insights into which bacterial groups respond similarly to antibiotic treatments.

Data Overview:
The dataset includes:

• Minimum Inhibitory Concentration (MIC) values for each antibiotic, representing the lowest concentration required to inhibit bacterial growth.

• Gram Staining classification (Gram-positive or Gram-negative), which differentiates bacterial cell wall structures and their interaction with antibiotics.

Methodology:

• MIC values are converted to a logarithmic scale for better visualization, as raw MIC values vary over several orders of magnitude.

• A heatmap is generated to illustrate bacterial susceptibility, where:

1. Dark red indicates high resistance (high MIC values).

2. Dark blue indicates high susceptibility (low MIC values).

3. Intermediate shades represent varying levels of effectiveness.

• Bacteria are labeled with their Gram classification (G+ for Gram-positive, G- for Gram-negative).

• Bacteria are arranged in clusters where those with similar susceptibility profiles are placed together to maintain color consistency and enhance readability.

How Do the Bacteria Group Together?

1. Highly Resistant Bacteria:

• Pseudomonas aeruginosa, Aerobacter aerogenes, and Klebsiella pneumoniae form a cluster with high resistance across most antibiotics, particularly Penicillin.

• These bacteria share structural features that make them naturally resistant to beta-lactam antibiotics.

2, Moderately Resistant Bacteria:

• Escherichia coli and Proteus vulgaris show intermediate resistance, with a better response to Streptomycin and Neomycin but limited susceptibility to Penicillin.

• These bacteria exhibit some level of permeability to aminoglycosides, which affects their response.

3. Highly Susceptible Bacteria:

• Streptococcus hemolyticus, Streptococcus viridans, and Brucella anthracis display high susceptibility across all antibiotics.

• These bacteria have weaker resistance mechanisms, making them more treatable with standard antibiotic regimens.

4. Mixed Susceptibility Patterns:

• Diplococcus pneumoniae and Salmonella species show varied responses, indicating that their resistance mechanisms depend on the antibiotic used rather than a consistent resistance profile.

Key Insights from the Heatmap:

• Gram-positive bacteria tend to be more susceptible to Penicillin, while Gram-negative bacteria exhibit higher resistance.

• Clustering bacteria with similar susceptibility patterns highlights how certain antibiotics work better for specific bacterial groups.

• Clinically, these insights reinforce the importance of tailored antibiotic prescriptions to maximize treatment efficacy and combat resistance.

Conclusion:
Grouping bacteria based on their antibiotic susceptibility provides a structured view of bacterial resistance patterns. The heatmap effectively visualizes these relationships, offering valuable insights for both researchers and clinicians. This approach complements traditional comparative analyses, helping optimize antibiotic selection in clinical settings and guiding future research on bacterial resistance mechanisms.

[palakpawan]

Palak Pawan
22b0338

Overview of Data

The dataset covers 16 bacterial species tested against 3 antibiotics Penicillin, Streptomycin, and Neomycin with each row showing the minimum inhibitory concentration (MIC) for each drug. A lower MIC indicates higher susceptibility whereas a higher MIC indicates resistance.

Color-Coded Table of MIC Values

• Green cells: Lower MIC values (i.e., more susceptible).

• Red cells: Higher MIC values (i.e., more resistant).

---

An additional column, Gram Staining, classifies the bacteria as Gram-positive or Gram-negative, which is often predictive of how the bacteria might respond to certain antibiotics.

---

Table of “Highly Susceptible” vs. “Highly Resistant”

This classification highlights at a glance which bacteria fall at the extremes of susceptibility or resistance for each antibiotic.

---

Log-Scale Bar Chart

Each bacterium is plotted with three bars (one for each antibiotic). Using a logarithmic scale helps compare large differences in MIC values. For instance,

• A bacterium with an MIC of 0.001 (very low) appears as a short bar.
• A bacterium with an MIC of 100 or 870 (very high) has a long bar.

---

Positive vs Negative Bar Chart

This compares Gram-positive vs. Gram-negative bacteria across the three antibiotics, revealing at a higher level how each group tends to respond:

• Gram-positive often show low MIC (higher susceptibility) to Penicillin.
• Gram-negative frequently have higher MIC to Penicillin but can be more susceptible to Streptomycin and/or Neomycin.

---

How the Bacteria Group Together

From these visual aids, two major patterns emerge, along with a “mixed” category:

1. Gram-Positive, Penicillin-Susceptible Group

   • Bacteria like Brucella anthracis, Diplococcus pneumoniae, Staphylococcus albus, Streptococcus hemolyticus, etc., show very low MIC values for Penicillin (e.g., 0.001–0.03).
   • Visually, these appear green (low MIC) in the Penicillin column of the color‐coded table.
   • In the Positive vs Negative bar chart, you see Gram-positive bars are typically much lower for Penicillin.

2. Gram-Negative, Penicillin-Resistant but Streptomycin/Neomycin-Susceptible Group

   • Bacteria such as Aerobacter aerogenes (MIC 870 for Penicillin, but only 1.0 for Streptomycin), Escherichia coli, and some Salmonella species have high MIC (red) for Penicillin, but lower MIC for Streptomycin or Neomycin.
   • The log-scale bar chart clearly shows a very tall bar for Penicillin but shorter bars for Streptomycin/Neomycin.
   • In the Positive vs Negative chart, you see Gram-negative organisms tend to have higher Penicillin bars but lower Streptomycin bars.

3. Mixed or Intermediate Susceptibility

   • Some bacteria don’t fit neatly into “highly susceptible” or “highly resistant” for any single drug; they show moderate MIC values across two or three antibiotics.
   • The second table (“Highly Susceptible” vs. “Highly Resistant”) helps identify which bacteria are in these intermediate ranges, because they are not in the “highly” susceptible or resistant columns.

---

Key Insights

1. Gram Staining Predicts Overall Patterns

   • Gram-positive bacteria are generally more susceptible to Penicillin, consistent with clinical knowledge that Penicillin targets the thick peptidoglycan layer typical of Gram-positive organisms.
   • Gram-negative bacteria are often resistant to Penicillin but can be more susceptible to Streptomycin and Neomycin.

2. Extremes of Susceptibility and Resistance

   • Some bacteria (e.g., Brucella anthracis or Streptococcus hemolyticus) have extremely low MICs for certain drugs, making them highly susceptible.
   • Others (e.g., Aerobacter aerogenes for Penicillin) show very high MICs, indicating strong resistance.

3. Clinical Relevance

• Grouping bacteria in this manner (by MIC profiles) helps guide treatment decisions. If a pathogen is in the “highly susceptible to Penicillin” group, clinicians can use a Penicillin‐based treatment confidently. Conversely, if it is in the “Penicillin‐resistant” group, a different antibiotic—like Streptomycin or Neomycin—may be more effective.

---

Conclusion

By examining:

• The color‐coded MIC table (where red = higher MIC/resistance, green = lower MIC/susceptibility),
• The lists of “Highly Susceptible” vs. “Highly Resistant,”
• The log‐scale bar chart (showing relative MIC magnitudes),
• And the Gram‐positive vs. Gram‐negative comparison,

it becomes clear that the main grouping follows Gram staining and the associated susceptibility patterns. In short:

• Gram‐positive bacteria typically cluster as highly susceptible to Penicillin (low MIC).
• Gram‐negative bacteria typically show resistance to Penicillin but better susceptibility to Streptomycin and/or Neomycin.
• A few bacteria occupy intermediate or mixed profiles, not extremely susceptible or resistant to any single antibiotic.

---

[LakshmeeshKP]

Name: K P Lakshmeesh
Roll no: 22B1246

Brief on what I did:

• Here i have plotted all the bacteria alongside each antibiotic corresponding to their MIC value in a log scale.
• bacteria with similar MIC values across all three drugs have been grouped
• uncertainties in grouping have been marked with an asterisk.

---

Insights:

• Streptomycin can be considered to be a better drug out of these three since the amount required to treat all the bacteria is the least ; Neomycin is also almost as good as Streptomycin.
• Most bacteria could be grouped together very well, which may indicate some similarity in them
• There were few exceptions considered while grouping:
  • (5, 8, 11, 14) ; for penicillin, 5 has a much larger MIC value. I still put them in one group since it groups well for the other two
  • (4, 15, 16); for neomycin, 15 has a much smaller MIC value. Still they were grouped since they group well for the other two.
  • (3, 12, 13); 3 is most suitable to be grouped with 12 and 13; it has very low MIC value for streptomycin
• The red and white boxes seem to overlap a little bit across few bacteria. There might be common traits between the two groups.

[Vicky9206]

Vicky Yadav
22B0312

Visualization:

This visualization is a horizontal bar chart comparing the Minimum Inhibitory Concentration (MIC) of three antibiotics—Penicillin (green), Streptomycin (orange), and Neomycin (blue)—against 16 different bacterial species.

How do the bacteria group together?

The bacteria can be grouped based on their comparative susceptibility to the antibiotics. From the visualization, we can identify three major clusters:
1.) Highly Susceptible to Penicillin (Low MIC for Penicillin, Higher for Others)
Streptococcus species (S. fecalis, S. hemolyticus, S. viridans)
Diplococcus pneumonia
Staphylococcus aureus
---> These bacteria are typically Gram-positive and are more effectively treated with Penicillin rather than with Streptomycin
or Neomycin.

2.) Highly Susceptible to Streptomycin and Neomycin (Low MIC for These, High for Penicillin)
Mycobacterium tuberculosis
Brucella abortus, Brucella anthracis
Pseudomonas aeruginosa
Salmonella (Eberthella) typhosa, Salmonella schottmuelleri
---> These bacteria are mostly Gram-negative and respond poorly to Penicillin but are effectively inhibited by Streptomycin
and Neomycin.

3.) Resistant to All or Less Affected (Higher MIC for All Antibiotics)
Klebsiella pneumonia
Aerobacter aerogenes
Proteus vulgaris
---> These bacteria tend to be more resistant, requiring higher antibiotic concentrations for inhibition. Many of them are
known to have natural or acquired antibiotic resistance mechanisms.

What Insight Does This Reveal?

1.) Gram-positive vs. Gram-negative Trends:
(a.) Gram-positive bacteria tend to be more susceptible to Penicillin.
(b.) Gram-negative bacteria are often more resistant to Penicillin but susceptible to Streptomycin and Neomycin.

2.) Streptomycin & Neomycin Are Effective for Tougher Infections:
--> They work better against Mycobacterium tuberculosis, Brucella species, and Salmonella, which are harder to treat with Penicillin.

3.) Broad-Spectrum Resistance Exists:
--> Some bacteria, like Klebsiella and Proteus, show high resistance to all three antibiotics, indicating the need for alternative
treatments.

This grouping would help medical practitioners choose the most effective antibiotic based on bacterial classification and resistance trends.

[Radzxzz]

Radhika Goyal
22B0423

Radar Chart Visualization

Steps to Create the Chart:

1. The data had highly varied values, with some very small and others very large. Hence applied log transformation to MIC values to make the data easier to compare and plot on a single graph.
2. Plotted the transformed values on the axis and placed bacteria names around the circle.
3. Used a legend to indicate the drug-specific colors for interpretation.
4. Grouped Gram-positive and Gram-negative bacteria separately. Assigned a value higher than the highest MIC to these groups, ensuring clear separation. The color coding of Gram-positive and Gram-negative bacteria helps in understanding how different groups respond to antibiotics.
   -> Used green color for Gram-positive bacteria.
   -> Used red color for Gram-negative bacteria.

How to Interpret the Graph:
Bacteria closer to the center → Lower MIC values → More susceptible to the drug (antibiotic is more effective).
Bacteria farther from the center → Higher MIC values → More resistant, meaning the antibiotic is less effective.

Insights from the Radar Chart

1. Gram-Positive vs. Gram-Negative Comparison
   Gram-positive bacteria (green region) tend to have lower MIC values (closer to the center), meaning they are more susceptible to the antibiotics tested.
   Gram-negative bacteria (red region) generally have higher MIC values (farther from the center), indicating lower susceptibility and higher resistance to antibiotics.
   Exception: Some Gram-positive bacteria, like Staphylococcus aureus and Streptococcus faecalis, show slightly higher MIC values for certain antibiotics, suggesting moderate resistance.
2. Drug-Specific Insights
   Penicillin is highly effective against most Gram-positive bacteria (low MIC values), but shows limited effectiveness against many Gram-negative bacteria. The other 2 antibiotics don't follow such a stark trend.
   Streptomycin (black) works well against both Gram-positive and Gram-negative bacteria, with a broad distribution of MIC values.
   Neomycin (yellow) has a mixed response, with some bacteria showing high susceptibility while others are highly resistant.
   Drugs that had the same first name had a similar pattern except for Streptococcus fecalis. There es irregularity in the data.
3. How Bacteria Group Together
   Gram-positive bacteria cluster together with lower MIC values, showing that they are generally more susceptible to antibiotics.
   Gram-negative bacteria form a separate cluster with higher MIC values, indicating greater resistance.
   This grouping visually reinforces the well-known fact that Gram-negative bacteria, due to their outer membrane barrier, are generally harder to treat with antibiotics.
4. Irregularity Observed
   When I arranged the data in alphabetical order, I noticed a consistent pattern among drugs with similar names, except for Streptococcus fecalis. This stood out as an irregularity. Upon further research, I discovered that it had been misclassified initially and was later corrected.
5. Key Takeaways
   Bacteria closer to the center are more susceptible, as they have lower MIC values.
   Bacteria farther from the center are more resistant, as they have higher MIC values.
   The color coding helps distinguish between Gram-positive and Gram-negative bacteria, making patterns of resistance and susceptibility easier to spot.

Conclusion
If we want to compare antibiotics for a specific bacterium → Burkin Chart is better.
If we want to analyze how bacteria cluster based on resistance → Radar Chart is better.
Best Approach: Use both together! The Burkin chart for understanding antibiotic efficacy and the radar chart for bacterial groupings

[saushankar]

Saurabh Shankar (24D1323)

TITLE: Visualizing Burtin’s Antibiotic Data

The below visualization categorizes the listed bacteria into 4 discrete groups, namely 1+, 2-, 3+ and 3-

• NOMENCLATURE USED FOR GROUPING OF BACTERIA: [Count of antibiotics to which this bacteria group is susceptible][Type of gram staining]
Eg. For any bacteria falling in Group “2-“, [2] indicates that it is is susceptible to two antibiotics, and [-] indicates that the gram staining is negative
• MIC <= 5 implies HIGH SUSCEPTIBILITY of the bacteria to the antibiotic.
• The colour identifies the ANTIBIOTIC to which this bacteria shows high susceptibility, while the Red highlight identifies the antibiotic that the bacteria is most susceptible to.

Approach: How do the bacteria group together?

1. While Burtin’s chart focuses on the efficacy of the antibiotics to different types of bacteria, it does not provide an easy classification of bacteria based on its susceptibility to various antibiotics
2. Since the table comprises a long list of bacteria, we have regrouped bacteria based on the antibiotic that each bacteria is susceptible to. Here, we have taken MIC threshold of 5 to indicate susceptibility. For each bacteria, the effective antibiotic(antibiotic to which it is most susceptible) has been highlighted in colour(a different colour representing each antibiotic).
3. This creates 3 groups of bacteria, which we have named as Group 1, Group 2, and Group 3 depending on the number of antibiotics to which this bacteria group is susceptible to. Example, for any bacteria that belongs to Group 3, it is susceptible to any of the 3 antibiotics(as per our definition of susceptibility).
4. Since there are bacteria which are susceptible to more than one type of medicine, we have chosen to retain MIC values in the final visualization. Eg Staphylococcus aureus, is susceptible to any of the 3 anitbiotics among Penicillin, Streptomycin and Neomycin. The doctor or researcher may choose an antibiotic of choice based on other markers (of patient illness).
5. However, assuming all other factors constant, the medicine with a lower MIC value implies a lower dosage for achieving the same result and hence that bacteria shows the HIGHEST SUSCEPTIBILITY towards this antibiotic. Therefore, we have, additionally, identified(using a red highlight) the antibiotic with lowest MIC (representing least dosage) for each bacteria within that group, wherever the bacteria is susceptible to more than one antibiotic.
6. Since gram staining is another element within the diagnosing process that needs to be evident to the reader of this information, we have segregated that within each group. That creates two subgroups within group 3, one for positive gram staining and another for negative gram staining.
7. For further convenience of the user of this visualization, we have combined the above two findings in our NOMENCLATURE for naming these groups of Bacteria: Therefore, each group of bacteria is represented by [Count of antibiotics to which this bacteria group is susceptible][Type of gram staining]. Example, bacteria Brucella anthracis belongs to group 3+, implying that it is susceptible to all 3 antibiotics, and [+] indicates that the gram staining is positive
8. Now, for practitioners prescribing medicines or for scientists testing effectiveness, such a presentation provides for a guide to look up which antibiotic they would consider useful/relevant/effective for which group of bacteria based on its susceptibility profile.

[TanzeelMV]

Tanzeel Velaskar 22B2198

Description:

The following bubble chart shows different bacteria as data points; the X-axis being the logarithmic minimum inhibitory concentration of the antibiotic Neomycin, the Y-axis, that of Penicillin and finally, the size of the bubbles represents the third parameter, i.e. the minimum inhibitory concentration of Streptomycin.

Some details about the chart and its elements

• Firstly, each of the bacteria have three corresponding quantities that are provided in Burtin's Antibiotic data table, i.e. minimum inhibitory concentration of the three given antibiotics. These are represented on the axes and as the size of the bubbles.
• Other than this, it also includes whether the bacteria are gram positive or gram negative strain. This has been captured by using light blue color for bubbles that indicate gram negative bacteria and dark violet for those that represent gram positive ones (which is in accordance with the given semantics that gram-positive bacteria are those that are stained dark blue or violet; whereas, gram-negative bacteria do not react that way).
• The reason to choose MIC of Streptomycin for size of the bubbles is that the given dataset has the least ratio of maximum MIC to minimum MIC in this case. For the other two antibiotics, the data had to be scaled using the logarithmic function (base 10) as the variation was too large; the smaller bubbles would be too small to be visible. Whereas, the MIC data for Streptomycin was not scaled using the logarithmic function.

How do the bacteria group together?

The chart gives several insights, some of which are listed below, that can be utilized to answer the above question at hand.

1. Since the X-axis shows the MIC of Neomycin, it apparently divides the continuous spectrum of the chart area into two halves, such that its leftmost end denotes bacteria that are not very resistant to Neomycin, while those present at the rightmost end are much more resistant to it, comparatively.
2. Similar is the case for the Y-axis, with respect to Penicillin.
3. Since the bubble size denotes the MIC of Streptomycin, we can now start grouping the bacteria.
4. For example, the bacteria in the group (Diplococcus pneumoniae, Streptococcus hemolyticus, Streptococcus viridans) present in the fourth quadrant can be said to have similar properties like low resistance to Penicillin, high resistance to Neomycin and since they're all big bubbles, they have higher resistance to Streptomycin too, as compared to other bacteria in the dataset. Besides this, they're all gram positive bacteria.
5. Another example could be the group (Pseudomonas aeruginosa, Klebsiella pneumoniae, Aerobacter aerogenes, Mycobacterium tuberculosis) present in the top half of the graph which contains gram negative bacteria that have high resistance to Penicillin, moderate resistance to Neomycin and moderate resistance to Streptomycin (since the bubbles are neither too small nor too large).

[notyourname3]

Name: S.S.Gayathri
Roll Number: 22B1535

GROUPS IDENTIFIED

1. Highly Resistant Gram-Negative Bacteria

• Aerobacter aerogenes, Pseudomonas aeruginosa, Klebsiella pneumoniae, Mycobacterium tuberculosis
• These bacteria show very high MIC values for Penicillin (≥800), meaning they are highly resistant to it. They have moderate resistance to Streptomycin and Neomycin.

2. Moderately Resistant Gram-Negative Bacteria

• Escherichia coli, Salmonella schottmuelleri, Proteus vulgaris, Brucella abortus, Salmonella typhosa
• Lower MIC values compared to Group 1, but still resistant to Penicillin and show variable resistance to Streptomycin and Neomycin.

3. Gram-Positive Bacteria Sensitive to Penicillin but Resistant to Streptomycin and Neomycin

• Diplococcus pneumoniae, Streptococcus hemolyticus, Streptococcus viridans
• They have extremely low MIC values for Penicillin but are resistant to Streptomycin and Neomycin (MIC values of 10–40).

4. Gram-Positive Bacteria Highly Sensitive to All Antibiotics

• Streptococcus fecalis, Staphylococcus aureus, Staphylococcus albus, Brucella anthracis
• These have very low MIC values across all antibiotics, meaning they are highly susceptible to treatment.

INSIGHTS

1. Penicillin Effectiveness Across Gram Categories

Penicillin is ineffective against Gram-negative bacteria:

• Almost all Gram-negative bacteria (top section) have high MIC values (≥100), meaning Penicillin is not effective against them.
• The most resistant ones are Aerobacter aerogenes, Pseudomonas aeruginosa, Klebsiella pneumoniae, Mycobacterium tuberculosis, all with MIC values ≥800.
• Escherichia coli has moderate resistance (MIC = 100), but others like Proteus vulgaris and Salmonella have much lower resistance.

Gram-positive bacteria are highly susceptible to Penicillin:

• Most MIC values are ≤0.03, indicating extreme sensitivity.
• This suggests Penicillin is a strong treatment option for infections caused by these bacteria.

2. Streptomycin Works Better for Gram-Negative Bacteria

• The MIC values for Streptomycin are lower for Gram-negative bacteria (mostly ≤2), suggesting it is more effective than Penicillin.
• However, Mycobacterium tuberculosis shows high resistance (MIC = 5), meaning Streptomycin is less effective for tuberculosis.
• Among Gram-positive bacteria, Diplococcus pneumoniae, Streptococcus hemolyticus, and Streptococcus viridans show very high MIC values (≥10), meaning Streptomycin is less effective for them.

3. Neomycin Shows Mixed Effectiveness

• For Gram-negative bacteria, effectiveness varies. Some bacteria like Salmonella typhosa have very low MIC values (0.008), meaning Neomycin is highly effective against them.
• For Gram-positive bacteria, Streptococcus viridans is highly resistant (MIC = 40), meaning Neomycin is not a good choice for this bacterium.

4. Identified Resistance Patterns

• Multidrug-resistant bacteria: Aerobacter aerogenes, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Mycobacterium tuberculosis show high MICs across all antibiotics. These might require stronger or alternative treatments.
• Penicillin-susceptible but resistant to others:Diplococcus pneumoniae, Streptococcus hemolyticus, Streptococcus viridans are highly sensitive to Penicillin but highly resistant to Streptomycin and Neomycin.

5. Implications for Treatment Decisions

• If treating a Gram-negative infection, Streptomycin or Neomycin are better choices than Penicillin.
• If treating a Gram-positive infection, Penicillin is the best choice, unless the bacteria fall into the resistant cluster (e.g., Streptococcus viridans for Neomycin).
• Some bacteria require alternative treatments due to high resistance across multiple antibiotics (e.g., Pseudomonas aeruginosa and Mycobacterium tuberculosis).

[Arin2402]

Arin Mahajan
22B2264

Bacterial Susceptibility to Antibiotics (Log Scaled MIC)

Color Interpretation:

• Red Shades - Resistant

1. Dark Red: Highly Resistant Bacteria (High MIC values)
2. Light Red: Moderately Resistant

• Neutral Shades - Intermediate Response

• Blue Shades - Susceptible

1. Dark Blue: Highly Susceptible Bacteria (Low MIC values, easier to kill)
2. Light Blue: Susceptible, but to a lesser degree

Key Bacterial Groups Identified:

1. Highly Resistant Gram-Negative Bacteria
Bacteria: Aerobacter aerogenes, Pseudomonas aeruginosa, Klebsiella pneumoniae, Mycobacterium tuberculosis
Pattern:

• Extremely high MIC values (≥800) for Penicillin, making it completely ineffective
• Moderate resistance to Streptomycin and Neomycin, suggesting limited antibiotic options

2. Moderately Resistant Gram-Negative Bacteria
Bacteria: Escherichia coli, Salmonella schottmuelleri, Proteus vulgaris, Brucella abortus, Salmonella typhosa
Pattern:

• Lower MIC values than Group 1 but still resistant to Penicillin
• Resistance to Streptomycin and Neomycin varies

3. Gram-Positive Bacteria Sensitive to Penicillin but Resistant to Streptomycin and Neomycin
Bacteria: Diplococcus pneumoniae, Streptococcus hemolyticus, Streptococcus viridans
Pattern:

• Very low MIC values for Penicillin, meaning it is highly effective
• High MIC values (10–40) for Streptomycin and Neomycin, indicating resistance

4. Gram-Positive Bacteria Highly Sensitive to All Antibiotics
Bacteria: Streptococcus fecalis, Staphylococcus aureus, Staphylococcus albus, Brucella anthracis
Pattern:

• Very low MIC values across all antibiotics, meaning they are highly treatable

Insights from the Visualization

1. Penicillin Effectiveness Across Gram Categories

• Gram-negative bacteria:

1. Penicillin is ineffective against almost all Gram-negative bacteria, with MIC values ≥100.
2. Aerobacter aerogenes, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Mycobacterium tuberculosis are the most resistant (MIC ≥800).
3. Escherichia coli shows moderate resistance (MIC =100), while Proteus vulgaris and Salmonella species show lower resistance.

• Gram-positive bacteria:
  Generally highly susceptible to Penicillin (MIC ≤0.03), making it a strong treatment option.

2. Streptomycin Works Better for Gram-Negative Bacteria

• More effective against Gram-negative bacteria (MIC mostly ≤2), making it preferable over Penicillin.
• Exception: Mycobacterium tuberculosis has high resistance (MIC =5), meaning Streptomycin is less effective for tuberculosis.
  Gram-positive bacteria resistance:
  Diplococcus pneumoniae, Streptococcus hemolyticus, and Streptococcus viridans have very high MIC values (≥10), meaning Streptomycin is ineffective for them.

3. Neomycin Shows Mixed Effectiveness

• Gram-negative bacteria: Some, like Salmonella typhosa, have low MIC values (0.008), making Neomycin highly effective.
• Gram-positive bacteria: Streptococcus viridans is highly resistant (MIC = 40), meaning Neomycin is not a good option for it.

Identified Resistance Patterns

• Multidrug-resistant bacteria: Aerobacter aerogenes, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Mycobacterium tuberculosis show high MICs across all antibiotics, requiring stronger or alternative treatments
• Penicillin-susceptible but resistant to others: Diplococcus pneumoniae, Streptococcus hemolyticus, and Streptococcus viridans are highly sensitive to Penicillin but highly resistant to Streptomycin and Neomycin

Implications for Treatment Decisions

• Gram-negative infections: Streptomycin or Neomycin are better choices than Penicillin
• Gram-positive infections: Penicillin is the best option, unless the bacteria fall into the resistant cluster (e.g., Streptococcus viridans for Neomycin)
• Multidrug-resistant bacteria may require alternative or stronger treatments (e.g., Pseudomonas aeruginosa and Mycobacterium tuberculosis)

[HorizonManish]

Manish Varshney, 23n0289

How Do the Bacteria Group Together?

Bacteria group based on their Gram staining characteristics:

Gram-Positive Bacteria (Green-Shaded in the Graph)

Includes Bacillus anthracis, Staphylococcus aureus, Streptococcus fecalis, etc.
More susceptible to Penicillin (lower MIC values).

Gram-Negative Bacteria (Red-Shaded in the Graph)

Includes Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, etc.
More resistant to Penicillin (higher MIC values), but some respond to Streptomycin or Neomycin.

Insights from the Second Graph

Antibiotic Effectiveness Varies – Penicillin works best for Gram-positive, while Streptomycin and Neomycin have broader effects.
Resistance is Evident – Some bacteria (Pseudomonas aeruginosa, Klebsiella pneumoniae) have high MIC values for all antibiotics, indicating strong resistance.
No Universal Antibiotic – Different bacteria require different treatments.
Gram Staining Guides Treatment – The grouping helps predict which antibiotics will be effective, aiding in treatment decisions.

[Galacterzz]

Anirudha Shinde (22B2181)

1. Data Preparation : Loaded the antibiotic data, applied log transformation to MIC values, and standardized them.
2. PCA Computation : Performed Principal Component Analysis to reduce dimensionality.
3. PCA Scatter Plot : Plotted bacteria on the first two principal components, color-coded by Gram staining.

4. Biplot Creation : Visualized antibiotic loadings as arrows to interpret variable influence on components.

Key Insights & Takeaways

1. Two Main Axes of Variation

• PC1 (horizontal) captures how susceptible/resistant a bacterium is to Streptomycin and Neomycin.
• PC2 (vertical) captures how susceptible/resistant a bacterium is to Penicillin.

2. Clear Clustering by Gram Stain

• Gram-negative organisms cluster mostly in the upper half (resistant to penicillin) but spread along the horizontal axis depending on streptomycin/neomycin response.
• Gram-positive organisms cluster mostly in the lower half (susceptible to penicillin) but separate into left vs. right depending on streptomycin/neomycin.

3. Subgroups of Gram-Positive

• Staphylococci (bottom-left): especially susceptible to both penicillin (low PC2) and streptomycin/neomycin (low PC1).
• Streptococci (bottom-right): susceptible to penicillin (low PC2) but relatively resistant to streptomycin/neomycin (high PC1).

4. Highly Resistant Outliers

• Pseudomonas aeruginosa and Mycobacterium tuberculosis are top-right, indicating they require high concentrations of penicillin (top on PC2) and also more streptomycin/neomycin than many others (right on PC1).

[Pranav202559]

Pranav Kawade-23B0372
Heatmap -

How Do Bacteria Group Together?

This heatmap shows how bacteria cluster based on their susceptibility to Penicillin, Streptomycin, and Neomycin. What we find are two main patterns:

1. Gram-Positive Bacteria Usually Hang Out Together

• Bacteria like Streptococcus species, Staphylococcus aureus, and Bacillus anthracis are seen in the same area.

• These bacteria generally have lower MIC values for Penicillin, meaning they’re more likely to be affected by it.

• Streptococcus species show high susceptibility to Penicillin while also demonstrating some resistance to Streptomycin and Neomycin.

2. Gram-Negative Bacteria Create Their Own Cluster

• Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Proteus vulgaris group together.

• These bacteria have higher MIC values for Penicillin, which indicates they are quite resistant.

• Interestingly, they respond better to Streptomycin and Neomycin, suggesting these antibiotics might be more effective against Gram-negative bacteria.

Key Insights from the Heatmap

• Penicillin is Best Against Gram-Positive Bacteria.

• The bright yellow in the Penicillin column reflects low MIC values, showing strong effectiveness against certain bacteria.

• On the flip side, Gram-negative bacteria are generally highly resistant to Penicillin (these areas show darker shades).

• Streptomycin and Neomycin Are More Versatile.

• These antibiotics reveal more varied susceptibility patterns, suggesting they can target both Gram-positive and Gram-negative bacteria.

• Still, some bacteria are showing high MIC values, which indicates resistance.

• Highly Resistant Bacteria Are at the Bottom of the Cluster.

• Pseudomonas aeruginosa and Klebsiella pneumoniae appear highly resistant to Penicillin and somewhat resistant to Streptomycin and Neomycin.

• These strains are known to be tough nuts to crack when it comes to standard antibiotics.

[Nitin-iitb111]

Name : Nitin Yadav

Roll No. : 22B3957

1. Objective & Context

The primary goal of the analysis is to understand how 16 different bacteria respond to three antibiotics (Penicillin, Streptomycin, and Neomycin). Each bacterium’s Minimum Inhibitory Concentration (MIC) is recorded for each drug. MIC is the concentration needed to inhibit bacterial growth—the lower the value, the more effective the antibiotic is. In addition, we have Gram staining information (Gram‑positive vs. Gram‑negative), which provides biological context since these groups tend to respond differently to antibiotics.

2. Data Transformation

a. Log Transformation

• The MIC values span several orders of magnitude. Directly analyzing them would overweight very high values. By applying a log10 transformation, we compress the scale so that differences across orders of magnitude become more comparable.

3. Principal Component Analysis (PCA)

a. Purpose of PCA

• Dimensionality Reduction:
  We start with three dimensions (one for each antibiotic). PCA reduces these to two principal components (PC1 and PC2) that capture most of the variability in the data.
• Visualizing Patterns:
  PCA helps us see natural groupings and overall trends in the bacterial response data.

b. How PCA Works

• Transformation:
  PCA finds new axes (principal components) that are linear combinations of the original variables. The first component (PC1) captures the largest amount of variance, and the second (PC2) captures the next largest, with the two being uncorrelated.

c. Interpreting the Axes

• PC1 – Overall Antibiotic Resistance Score:

  • This axis explains the largest portion of the variance in the data.
  • In many cases, a bacterium with a higher PC1 score tends to require higher concentrations for inhibition across all antibiotics, indicating general resistance.
  • The PCA loadings (i.e., how much each original antibiotic contributes to PC1) help confirm if PC1 is dominated by a general trend in resistance or susceptibility.

• PC2 – Differential Antibiotic Effect Profile:

  • PC2 captures variations not explained by PC1.
  • It often represents the differences in response to a specific antibiotic relative to the others.
  • For example, two bacteria might have similar overall resistance (similar PC1 values) but differ in how one antibiotic performs relative to the other two; this difference will show up along PC2.

• Explained Variance Ratio:
  The percentages of variance explained by PC1 and PC2 are calculated, which inform you about how much of the overall data variability is captured by these two components.

4. Incorporating Hierarchical Clustering

To enhance our understanding of bacterial groupings, we use hierarchical clustering on the log-transformed data:

• Method:
  Ward’s method is applied, which minimizes the within-cluster variance. This helps to group bacteria with similar susceptibility profiles.

• Outcome:
  Each bacterium is assigned a cluster label. In the PCA plot, we draw convex hulls (boundaries) around the points belonging to each cluster. These boundaries visually demarcate groups of bacteria that respond similarly to the antibiotics.

5. Visual Integration with Gram Staining

• Color Coding:
  Each bacterium is colored based on its Gram staining—typically blue for Gram-positive and red for Gram-negative.

• Biological Relevance:
  This coloring helps you quickly identify whether clusters (or regions in the PCA plot) are dominated by one group. For example, you might observe that Gram-negative bacteria cluster together in one region, reflecting their common resistance patterns compared to Gram-positive bacteria.

6. Final Integrated Visualization

The integrated visualization is a PCA scatter plot that includes the following key features:

• Scatter Plot of Bacteria in PCA Space:

  • Each point represents a bacterium projected onto the first two principal components (PC1 and PC2).
  • The x-axis, labeled "Overall Antibiotic Resistance Score (PC1)", shows general resistance/susceptibility. Bacteria with higher values here tend to be more resistant overall.
  • The y-axis, labeled "Differential Antibiotic Effect Profile (PC2)", highlights differences in the antibiotic response profiles that are not captured by overall resistance.

• Cluster Boundaries:

  • Convex hulls drawn around points belonging to the same cluster (derived from hierarchical clustering) help visually delineate groups of bacteria with similar antibiotic responses.

• Gram Staining Annotation:

  • The points are color-coded according to Gram staining, enabling you to see if bacteria of similar Gram type group together or differ in their resistance profiles.

7. Key Takeaways

• Data Transformation:
  The MIC values were log-transformed to manage the wide range and then (optionally) standardized.

• PCA Application:
  PCA reduced the three-dimensional data (from three antibiotics) to two components that capture the most variance.

  • PC1 reflects the overall resistance profile, and
  • PC2 captures specific differential responses among the antibiotics.

• Clustering and Visualization:
  Hierarchical clustering (using Ward’s method) grouped bacteria with similar profiles. Convex hulls were used to visually highlight these clusters in the PCA space.

[HopeTTR]

Name : Rashmi Meena
Roll no : 23N0315

How Do the Bacteria Group Together?

Approach

Bacteria Grouping by Susceptibility

• Instead of organizing data by antibiotics, bacteria are listed alphabetically on the X-axis for quick lookup.
• This reflects real-world identification, where doctors first know the bacterium’s name, then determine its treatment.

MIC Values on a Logarithmic Scale

• The Y-axis represents MIC values, with a logarithmic scale used to handle wide variations in resistance levels.
• This ensures both highly resistant and highly susceptible bacteria are clearly visible.

Color-Coded Gram Classification

• Gram-positive bacteria → Shades of blue/purple
• Gram-negative bacteria → Shades of red

Three Bars Per Bacterium (Grouped by Antibiotic)

• Penicillin → Lightest shade
• Streptomycin → Medium shade
• Neomycin → Darkest shade

Insights from the Chart

• Bacteria naturally cluster by Gram classification, revealing distinct susceptibility patterns.
• Gram-positive bacteria tend to be more susceptible to Penicillin, while Gram-negative bacteria show greater variation in response.
• Doctors and scientists can quickly identify a bacterium, determine its Gram classification by color, and assess antibiotic effectiveness using MIC values.
• Shorter bars indicate higher susceptibility, while taller bars suggest resistance.

[Atharav1805]

Atharav Sonawane - 23B2530

Assignment 4: Visualizing Burtin’s Antibiotic Data

---

Bacterial Clustering Analysis of Burtin's Antibiotic Data

Introduction

While Burtin's original 1951 chart effectively compares antibiotic performance across bacteria (answering "How do the drugs compare?"), my analysis takes a different approach by grouping bacteria based on their response patterns to multiple antibiotics. By applying cluster analysis to the MIC data, we can identify natural groupings of bacteria with similar antibiotic susceptibility, directly addressing the question: "How do the bacteria group together?" allowing for a clear comparison of antibiotic performance and their effectiveness against different bacterial groups.

Methodology

I applied K-means clustering to logarithmically transformed MIC values to identify distinct bacterial groups based on their antibiotic response patterns. Logarithmic transformation was necessary due to the wide range of MIC values (0.001 to 870), allowing for more meaningful comparisons. The analysis revealed three distinct clusters with clear biological significance, suggesting natural groupings of bacteria beyond taxonomic classification or Gram staining properties.

Key Features of the Visualization

1. Cluster-Based Grouping: Instead of listing antibiotic efficacy separately, the chart groups bacteria based on their response patterns, highlighting resistance trends.
2. Clear Differentiation of Gram Staining: Gram-negative and Gram-positive bacteria are visually distinguished, allowing for better pattern recognition.
3. Color-Coded Resistance Levels:
   • Red → High susceptibility to Neomycin & Streptomycin, resistant to Penicillin
   • Green → Broadly susceptible across all antibiotics
   • Orange → Penicillin-sensitive but resistant to Neomycin & Streptomycin

Identified Groups & Biological Insights

Group 1 (Red): Neomycin/Streptomycin-Susceptible, Penicillin-Resistant Bacteria

• Bacteria in this group exhibit low MIC values (high susceptibility) for Neomycin and Streptomycin but high MIC values (resistance) for Penicillin.
• Examples: Streptococcus hemolyticus, Diplococcus pneumoniae, Streptococcus viridans
• Notably, this cluster contains primarily Gram-positive bacteria, suggesting a potential relationship between cell wall structure and antibiotic response.

Group 2 (Orange): Penicillin-Susceptible, Neomycin/Streptomycin-Resistant Bacteria

• Bacteria in this cluster show high MIC values for Neomycin and Streptomycin, indicating strong resistance, but show high susceptibility to Penicillin.
• These bacteria require alternative or highly specialized antibiotic strategies when Neomycin or Streptomycin would typically be prescribed.
• Examples: Mycobacterium tuberculosis, Pseudomonas aeruginosa, Escherichia coli
• This group predominantly consists of Gram-negative bacteria.

Group 3 (Green): Broadly Susceptible Bacteria

• Bacteria in this group show relatively high susceptibility across all three antibiotics, with only moderate resistance to Penicillin.
• This suggests multiple viable treatment options for infections caused by these bacteria.
• Examples: Staphylococcus aureus, Brucella abortus, Salmonella species
• This cluster includes both Gram-positive and Gram-negative bacteria.

Key Insights Beyond Burtin's Original Visualization

1. Cross-Antibiotic Resistance Patterns

   • Bacteria cluster based on antibiotic response patterns rather than Gram staining properties alone.
   • This reveals potential shared resistance mechanisms that could inform treatment strategies.

2. Gram Staining and Resistance Correlation

   • While some clusters show predominance of either Gram-positive or Gram-negative bacteria, others contain a mix, suggesting additional factors beyond cell wall structure influence antibiotic susceptibility.

3. Treatment Strategy Implications

   • Different antibiotics show distinct effectiveness across clusters—Penicillin is more effective for one cluster, while Neomycin and Streptomycin work best for another.
   • This clustering approach immediately suggests alternative treatments when resistance to a particular antibiotic is observed.

Conclusion

This cluster-based visualization transforms Burtin's efficacy-focused chart by revealing natural bacterial groupings based on antibiotic response patterns. While Burtin's original chart compared drugs across bacteria, this approach identifies bacteria with similar resistance profiles, offering a complementary perspective.

These findings provide valuable insights for understanding bacterial defense mechanisms and developing targeted treatment strategies. The visualization demonstrates that bacterial response to antibiotics transcends traditional classification methods,
by emphasizing:

• Bacterial clustering
• Antibiotic resistance patterns

[kaushal-malpure]

Name: Kaushal Malpure
Roll No.: 22B1276

Logarithmic Scale for Better Visibility – The log-scaled MIC axis ensures small and large variations in resistance are well-represented without distortion.
Color Distinction for Readability – Different shades for antibiotics help quickly identify resistance patterns in each bacterial group.

Key Insights from the Plot

1. Separation Based on Gram Staining – The side-by-side bar charts clearly differentiate between Gram-negative (left) and Gram-positive (right) bacteria, making it easier to compare antibiotic effectiveness based on bacterial classification.
2. Effectiveness Trends
   Gram-negative bacteria (left) show higher MIC values, indicating stronger resistance, especially for Pseudomonas aeruginosa and Mycobacterium tuberculosis.
   Gram-positive bacteria (right) have lower MIC values, suggesting better susceptibility to Penicillin and Streptomycin.
3. Antibiotic Performance
   Neomycin is more effective against Gram-negative bacteria (wider orange bars).
   Penicillin works best against Gram-positive bacteria, especially Streptococcus and Staphylococcus species.
   Streptomycin has a mixed effect, effective on some bacteria in both groups.

This visualization effectively highlights antibiotic resistance trends by Gram classification, aiding in targeted antibiotic selection.

[sibam327]

Sibam Das-22B2186

Grouped Bar Chart

Scattered plot

How Do the Bacteria Group Together?

Gram-Positive Bacteria:
Tend to have lower MIC values for Penicillin, especially for Bacillus anthracis and Diplococcus pneumoniae. However, some like Staphylococcus aureus and Streptococcus fecalis show higher MICs, especially for Streptomycin and Neomycin.
Gram-Negative Bacteria:
Typically show higher MIC values for Penicillin, indicating greater resistance. Aerobacter aerogenes and Klebsiella pneumoniae are particularly resistant to Penicillin but more susceptible to Streptomycin and Neomycin.

Insights Revealed

1. Penicillin is more effective against Gram-positive bacteria, reinforcing its traditional use for these infections.
2. Streptomycin and Neomycin are generally more effective for Gram-negative bacteria, which explains why they're often used for tougher Gram-negative infections.
3. Resistance patterns: Some bacteria like Pseudomonas aeruginosa are consistently resistant across antibiotics, signaling potential treatment challenges.
4. Neomycin is the most effective antibiotic overall, with the lowest MIC values across nearly all bacteria.

[yugeshbhoge]

Name: Yugesh Bhoge (23N0278)

Reason to choose this visualization and grouping:

• Polar plots with logarithmic scale : I chose the polar plot and divided it into 3 sections as there are 3 different antibiotics present so it will be easier to distinguish the effect of each antibiotic on each bacteria. Also, I chose the logarithmic scale to accommodate the scaling in large values.
• Gram strain distinction: Clearly, the data showed two distinct groupings on their strain type of the bacteria, and that has a very unique effect on the MIC values of the drugs.

Key Insights from the visualization:

• Gram-negative bacteria exhibit greater resistance to Penicillin, as shown by their elevated MIC values.
• Gram-positive bacteria are generally more susceptible to Penicillin, although their responses to Streptomycin and Neomycin can vary.
• Certain bacteria display resistance to multiple drugs, underscoring the importance of judicious antibiotic selection.
• Categorizing bacterial responses helps pinpoint which antibiotics are most effective against specific bacteria, thereby supporting more informed treatment choices.

[Preksha0369]

Name : Preksha Jain

Roll No: 22B2450

Key Takeaways from the Plot:

• Successful Data Visualization – The horizontal stacked bar chart successfully contrasts bacterial susceptibility among three antibiotics in terms of MIC values on a logarithmic scale, allowing for clear visibility of fluctuations across orders of magnitude.

• Bacterial Grouping – Bacteria are categorized by their relative resistance or susceptibility, which enables the detection of patterns in antibiotic efficacy.

• Antibiotic Efficacy – Penicillin is most effective on Gram-positive bacteria, and Neomycin on Gram-negative ones. Streptomycin has more diverse activity.

• Resistance Patterns – Pseudomonas aeruginosa and Mycobacterium tuberculosis have high MIC values, showing them to be strongly resistant.

• Color-Coding for Readability – Each antibiotic having a different color for it facilitates comparing the efficacy of the antibiotics in relation to the various bacteria.

This visualization effectively points out bacterial resistance patterns and thus serves as an important aid to antibiotic choice.

[kRISH5813]

Name - Krish Malviya
Roll No. - 22B0679
Topic - Visualizing Burtin’s Antibiotic Data

Effects of Anti-Biotics :

• Penicillin - It is highly effective against Gram-positive bacteria, requiring low MIC values (-3 to -2.5), indicating strong susceptibility. In contrast, most Gram-negative bacteria show significant resistance, with higher MIC values.
• Neomycin - It is highly effective against Gram-negative bacteria, requiring low MIC values, indicating strong potency. While it also works against some Gram-positive bacteria, its efficacy is weaker compared to Penicillin.
• Streptomycin - It shows moderate effectiveness against both Gram-negative and Gram-positive bacteria. It generally requires higher MIC values than Neomycin, indicating lower potency at smaller concentrations. Its limited impact on Gram-positive bacteria suggests it is more effective at higher doses.

Insights from the plot:

• Penicillin is highly effective against Gram-positive bacteria, as indicated by its ability to inhibit them at very low MIC values (-3 to -2.5 log MIC). This makes it a preferred choice for treating Gram-positive infections. However, Gram-negative bacteria exhibit resistance, requiring significantly higher concentrations for inhibition.
• Streptomycin demonstrates limited effectiveness against both Gram-positive and Gram-negative bacteria, typically requiring higher MIC values (-1 to 0 log MIC) for inhibition. This reduced potency makes it less effective than Penicillin or Neomycin, but it remains useful in specific cases where higher concentrations are acceptable.
• Neomycin shows strong efficacy across a broader range of bacteria, particularly Gram-negative species, which are inhibited at moderate MIC levels (below -1.5 log MIC). While it also works against some Gram-positive bacteria, its potency is less pronounced compared to Penicillin.

[Satyajit24-7]

NAME Satyajit sahoo
ROLL NO. 23n0318

Grouping by Susceptibility: I divided the bacteria into two distinct groups (red and blue backgrounds), visually separating those that are more resistant from those that are more susceptible.

Comparing Antibiotics Effectively: By plotting different MIC values with colored dots and vertical lines, I show how various bacteria respond to different antibiotics in a single view.

Highlighting Gram Staining Impact: The grouping aligns well with Gram-positive (blue) and Gram-negative (red) bacteria, reinforcing how structural differences impact antibiotic effectiveness.

Using a Logarithmic Scale: Since MIC values vary widely, I used a log scale to make differences more interpretable, preventing smaller values from being overshadowed.

Key Insight Revealed: My chart makes it clear that Gram-negative bacteria tend to have higher MIC values, meaning they are generally more resistant to antibiotics, while Gram-positive bacteria are more susceptible to Penicillin and related drugs.

[NEC0S]

Name: Abhishek Kumar
Roll Number: 22B2210

Antibiotic Susceptibility Analysis

Overview

Following World War II, antibiotics were celebrated as “wonder drugs” due to their ability to rapidly cure infections that were once difficult to treat. In 1951, William Burtin created a chart comparing the effectiveness of three widely used antibiotics—Penicillin, Streptomycin, and Neomycin—against 16 different bacterial species. Additionally, each bacterium is categorized based on Gram staining (Gram-positive or Gram-negative), a classification that influences antibiotic susceptibility.

Objective

Research Question:
"How do bacterial species cluster together based on their susceptibility to these three antibiotics?"

Data Description

The dataset (Butin_antibiotic_data(1).xlsx) contains the following information:

• Bacteria: Name of the bacterium.
• Penicillin, Streptomycin, Neomycin: Minimum Inhibitory Concentration (MIC) values, representing the antibiotic concentration required to inhibit bacterial growth.
• Gram_Stain: Classification of bacteria as Gram-positive or Gram-negative.

Since MIC values span a broad range (from approximately 0.001 to over 800), a log transformation is commonly applied to reduce skewness. Additionally, normalization or scaling techniques may be used to enhance the identification of meaningful patterns.

Visualizations

Clustered Heatmap (Log Scale)

Overview

A clustered heatmap presents MIC values in a tabular form where color intensity represents magnitude. Hierarchical clustering groups bacteria and antibiotics based on similarity.

Advantages

• Pattern Detection: Clearly highlights clusters of bacteria with similar MIC values.
• Comparative Analysis: Allows easy identification of antibiotic resistance trends.
• Handles Large Data: Effectively visualizes large datasets in a compact form.
• Log Scale Benefit: Reduces skewness caused by extreme MIC values, improving interpretability.

---

2. Bubble Chart

Overview

A bubble chart plots bacteria-antibiotic pairs, where bubble size represents MIC magnitude, and color differentiates Gram-positive (blue) and Gram-negative (red) bacteria.

Advantages

• Immediate Size-Based Interpretation: Larger bubbles indicate higher MIC values (lower antibiotic effectiveness).
• Gram Staining Differentiation: Visually distinguishes Gram-positive and Gram-negative bacteria.
• Log Scale Representation: Effectively displays differences in MIC values across orders of magnitude.

Disadvantages

• Difficult to Extract Exact Values: Without annotations, precise MIC values are unclear.

---

Bubble Chart (Log Values)

3. Radar Chart (Outer Triangle - Log Scale)

Overview

The radar chart compares MIC values of antibiotics across bacteria, plotting an outer triangle for extreme values (min and max MIC per antibiotic).

Advantages

• Extreme Value Focus: Highlights most resistant and most susceptible bacterial strains for each antibiotic.
• Log Scale Representation: Facilitates better comparison of large-scale MIC variations.
• Minimalist Visualization: Reduces clutter by only displaying critical data points.

Disadvantages

• Limited Number of Categories: Works well for a few antibiotics but becomes cluttered with too many.
• Difficult to Interpret Angles: Users may struggle to compare values due to the radial layout.
• Loss of Intermediate Data: Only extreme values are represented, omitting finer MIC trends.

---

Conclusion

Each visualization has unique strengths and weaknesses:

• Clustered Heatmap is best for detecting bacterial resistance patterns and clustering insights.
• Bubble Chart is excellent for size-based comparison and distinguishing Gram-staining differences.
• Radar Chart (Outer Triangle) is effective for showcasing extreme MIC values across antibiotics.

[code-bhushan]

Name: Bhushan Khandare
Roll Number: 22B2138

Assignment 4

How This Heatmap Works:
The heatmap clusters bacteria based on their susceptibility to three antibiotics (Penicillin, Streptomycin, Neomycin).
Hierarchical clustering arranges bacteria that show similar MIC (Minimum Inhibitory Concentration) values close to each other.
The MIC values are log-transformed and normalized for better pattern recognition.
Color Mapping:
Red (high values) → More resistant bacteria (higher MIC, less effective antibiotic).
Blue (low values) → More susceptible bacteria (lower MIC, more effective antibiotic).
What It Signifies:
Bacteria that react similarly to antibiotics form clusters, revealing groups that share resistance/susceptibility patterns.
Helps identify broad-spectrum antibiotics (ones that affect multiple bacteria similarly).
Highlights bacteria that show high resistance across multiple antibiotics, which may need alternative treatment strategies.

Insights:
Bacteria with similar resistance/susceptibility patterns to Penicilin, Streptomycin, and Neomycin are clustered together.
Strongly susceptible bacteria (low MIC values) and highly resistant ones (high MIC values) form distinct clusters.
The chart highlights which antibiotics might have overlapping effectiveness across different bacterial species.

Another Unique way of visualizing:

Stripes (//) for Gram-positive bacteria
Dots (o) for Gram-negative bacteria
Antibiotic bars maintain distinct colors
A legend clarifies the patterns used for Gram staining

[avinashmeena05]

Avinash Meena - 22b1243

Visualisation - Grouped Bar Chart

1. How do bacteria group together?

The bacteria in the graph are grouped into two primary clusters based on their Gram staining characteristics:

a. Gram-negative bacteria (left side of the dashed line)

• Includes Escherichia coli, Salmonella, Proteus, Pseudomonas, Klebsiella, and Aerobacter species.
• Represented by lighter colors in the bar chart.
• These bacteria generally show high resistance to Penicillin but have varied susceptibility to Streptomycin and Neomycin.

b. Gram-positive bacteria (right side of the dashed line)

• Includes Staphylococcus, Streptococcus, Diplococcus, and Brucella anthracis species.
• Represented by darker colors in the bar chart.
• They tend to be more susceptible to Penicillin but show variable responses to Neomycin and Streptomycin.

2. What insights does it reveal?

• Gram-negative bacteria have elevated MIC values for Penicillin, which signifies more resistance. The light red bars for Penicillin are all higher across, particularly for Pseudomonas aeruginosa, Klebsiella pneumoniae, and Aerobacter aerogenes, such that they need more concentrated antibiotics to stop growth.
• Gram-positive bacteria also exhibit much lower MIC values for Penicillin, indicating higher susceptibility. The dark red bars are much lower, especially for Streptococcus hemolyticus, Streptococcus viridans, and Diplococcus pneumoniae, indicating that low Penicillin concentrations readily inhibit them.
• Neomycin and Streptomycin are non-effectively mixed, implying that they can be applied to treat infections involving both Gram-positive and Gram-negative bacteria.
• Brucella species behave differently, with Brucella abortus grouping with Gram-negative bacteria and Brucella anthracis clustering with Gram-positive ones, showing that some bacteria don't fit strict classifications.
• Pseudomonas aeruginosa and Mycobacterium tuberculosis require specialized treatments due to their extreme resistance.

[JuhiKamat]

Juhi Kamat - 22b4218

1) Donut chart

Data : The inverse of MIC values were taken to get the potency of each antibiotic on bacteria.
This donut chart visualization shows how different bacteria respond to Neomycin (N), Streptomycin (S), and Penicillin (P) in terms of their effectiveness and probability of it curing the disease.

N_potency (Green) → Neomycin
S_potency (Yellow) → Streptomycin
P_potency (Blue) → Penicillin

Comparative Antibiotic Effectiveness:

• It shows which antibiotics are more effective against particular bacteria.
• Example: Streptococcus hemolyticus, Streptococcus viridans, and Diplococcus pneumoniae are 100% susceptible to Penicillin (Blue).
• Conversely, Salmonella typhosa, Brucella abortus, and Staphylococcus aureus are almost entirely susceptible to Neomycin (Green).

Gram-Positive vs. Gram-Negative Bacteria Response

• Gram-positive bacteria (e.g., Streptococcus and Diplococcus) respond strongly to Penicillin (Blue).
• Gram-negative bacteria (e.g., Salmonella, Escherichia coli, Pseudomonas aeruginosa) are more susceptible to Neomycin (Green) and Streptomycin (Yellow).

2) Bubble chart

N_potency (Green) → Neomycin
S_potency (Yellow) → Streptomycin
P_potency (Blue) → Penicillin

• The size of each bubble is proportional to the potency of the antibiotic (on a non-log scale), indicating how effective it is.
• Color represents the antibiotic type, allowing for easy differentiation.

Observations:
Antibiotic Effectiveness Varies Across Bacteria: Some bacteria exhibit high susceptibility (large, high-positioned bubbles) to particular antibiotics, while others show resistance (small, lower-positioned bubbles).

• Penicillin (Blue) is Highly Effective Against Some Bacteria: Several bacteria show large blue bubbles at higher log-potency values, indicating strong susceptibility to penicillin. However, certain species (e.g., Pseudomonas aeruginosa, Mycobacterium tuberculosis) show low potency (small or low-positioned blue bubbles), suggesting resistance.
• Neomycin (Green) Tends to Have the Highest Potency for Many Bacteria: Large green bubbles are frequently observed at the upper part of the chart, indicating that many bacteria are more susceptible to Neomycin.
• Streptomycin (Orange) Shows Mixed Potency: For some bacteria, orange bubbles are prominent and high on the chart, while for others, they are much smaller, suggesting varying effectiveness.

Gram-Negative and Gram-Positive Differences:

• While Gram-positive and Gram-negative bacteria are not explicitly marked, certain bacterial groups exhibit more resistance or susceptibility trends.
• Gram-negative bacteria (e.g., Pseudomonas aeruginosa, Escherichia coli) often show lower potency values, indicating stronger resistance.

Overlapping Bubbles Indicate Multiple Effective Antibiotics: Some bacteria have overlapping bubbles of similar size and position, indicating that multiple antibiotics are comparably effective against them.

[ruchirkulkarni45]

Ruchir Kulkarni 23B2483###

[akshaankhan2004]

22B2226: Akshaan Khan

How do the bacteria group together?

Clustering is based on overall susceptibility to antibiotics:

1. Highly Susceptible Group: Bacteria that are highly susceptible to all three antibiotics fall into this category.
2. Medium Susceptibility Group: Bacteria that are resistant to one antibiotic but susceptible to the other two.
3. Low Susceptibility Group: Bacteria resistant to two or more antibiotics, showing overall lower susceptibility.

What insight does it reveal?

1. Gram-Staining Correlates with Susceptibility: Gram-positives are more susceptible to penicillin, while Gram-negatives are generally resistant.
2. Species-Specific Resistance Exists: Some bacteria deviate from general trends, indicating unique resistance mechanisms within groups.

[balakrishna2907]

22b0028

Bala Krishna

Burtin's Antibiotic Data Visualization

📌 Overview

This project visualizes the effectiveness of three antibiotics (Penicillin, Streptomycin, and Neomycin) against 16 bacterial strains. The dataset provides the Minimum Inhibitory Concentration (MIC), which indicates how much antibiotic is needed to inhibit bacterial growth.

📊 Graph Explanation

• The X-axis represents the log10(MIC) value.

  • Lower MIC values (left side) → Bacteria are more susceptible (antibiotic is effective).
  • Higher MIC values (right side) → Bacteria are more resistant.

• The Y-axis lists bacterial species, grouped by their susceptibility to antibiotics.

• Color Legend (Colormap: Coolwarm)

  • Red shades → Higher MIC (More resistant bacteria).
  • Blue shades → Lower MIC (More susceptible bacteria).

• Gram Staining Highlights

  • Gram-positive bacteria labels are blue.
  • Gram-negative bacteria labels are red.
  • This distinction helps visualize how Gram-positive bacteria are generally more susceptible to Penicillin than Gram-negative bacteria.

🔍 Key Insights

1. Penicillin is highly effective against Gram-positive bacteria, requiring low MIC values.
2. Neomycin is broad-spectrum, showing effectiveness against many bacteria, including some Gram-negative strains.
3. Streptomycin has mixed effectiveness, often requiring higher concentrations to inhibit bacterial growth.
4. Gram-negative bacteria are generally more resistant, requiring higher MIC values across all antibiotics.

[kritiat16]

Kriti A 210100083

1. How Do the Bacteria Group Together?

• Gram-Positive Bacteria (Red) Tend to Have Low Penicillin Resistance but Vary in Streptomycin Resistance

  Streptococcus hemolyticus, Diplococcus pneumoniae, and Streptococcus viridans have very high
  Streptomycin resistance but low Penicillin resistance.

  Other Gram-positive bacteria cluster near lower Penicillin resistance values.

• Gram-Negative Bacteria (Blue) Show High Penicillin Resistance

  Aerobacter aerogenes, Klebsiella pneumoniae, and Pseudomonas aeruginosa have extreme Penicillin 
  resistance but lower Streptomycin resistance.
  
  Mycobacterium tuberculosis stands out with moderate Streptomycin resistance and high Penicillin 
  resistance, separating it from the main clusters.
  

2. What Insights Does It Reveal?

• Penicillin is Less Effective Against Gram-Negative Bacteria

   Most Gram-negative bacteria show high resistance to Penicillin, meaning alternative antibiotics should 
   be used.
  

• Some Gram-Positive Bacteria Are Highly Resistant to Streptomycin

   Streptococcus hemolyticus and Streptococcus viridans show extreme resistance to Streptomycin, 
   suggesting it is not an effective treatment for them.
  

• Bacteria Group Based on Resistance Patterns, Not Just Gram Staining

   While Gram-positive and Gram-negative bacteria generally follow expected resistance trends, 
   exceptions like Mycobacterium tuberculosis show unique resistance behavior, highlighting the need for 
   species-specific antibiotic choices.
  

[arin1234567]

Name: Arin Pendharkar

Roll Number: 23B2489

Visualizing Burtin’s Antibiotic Data

Overview:
The goal of this analysis is to visualize the antibiotic resistance of various bacterial species to three different antibiotics—Penicillin, Streptomycin, and Neomycin—while distinguishing between Gram-positive and Gram-negative bacteria. A mirrored horizontal bar chart is used with log-scale transformation that ensures better visualization of MIC values, which vary across several orders of magnitude.

Visualizations:

Insights from the Graphs:

For Gram Positive:

1. Neomycin is the most effective overall – It has the lowest MIC values across most Gram-positive bacteria, indicating strong inhibitory effects.
2. Streptomycin is particularly effective against Streptococcus species – The MIC values for Streptococcus viridans, hemolyticus, and fecalis are very low, showing strong susceptibility.
3. Penicillin has mixed effectiveness – While it works well for some Gram-positive bacteria (e.g., Streptococcus fecalis), it is much less effective for others like Bacillus anthracis.

For Gram Negative:

1. Penicillin is almost entirely ineffective – The MIC values for all Gram-negative bacteria are extremely high, showing strong resistance.
2. Neomycin remains effective for some strains – Compared to Penicillin, Neomycin shows much lower MIC values, especially for bacteria like Escherichia coli and Pseudomonas aeruginosa.
3. High variability in resistance patterns – Some Gram-negative bacteria are highly resistant to all three antibiotics, making treatment difficult and highlighting the need for alternative drugs.

Visualization:

Insights from the Graphs:

1. The color gradient effectively groups bacteria by susceptibility, clearly showing which are more resistant (red) and which are more susceptible (blue).
2. Penicillin is largely ineffective against Gram-negative bacteria, as seen in the deep red shades for Escherichia coli and Klebsiella pneumoniae.
3. Neomycin shows broad-spectrum effectiveness, with many blue or light-colored areas indicating strong activity against both Gram-positive and some Gram-negative bacteria.
4. Streptomycin has varying efficacy, performing well against Mycobacterium tuberculosis and Streptococcus fecalis, but less effective against others like Salmonella.
5. Bacteria with similar resistance patterns cluster together, making it easier to classify them based on susceptibility trends.

Final Conclusion:

The bar graph and the heatmap together provide a structured view of bacterial susceptibility to antibiotics. The bar graph highlights overall trends, clearly distinguishing between Gram-positive and Gram-negative bacteria in terms of their resistance patterns. The heatmap further refines this analysis by showing specific MIC values, allowing for detailed comparisons across different antibiotics and bacterial species. Gram-positive bacteria generally show higher susceptibility to Penicillin, while Gram-negative bacteria display more resistance, particularly against Penicillin but also variably against Streptomycin and Neomycin. Streptomycin and Neomycin emerge as more effective across a broader range of bacteria, especially Gram-negative species. Together, these visualizations provide a well-rounded answer to how bacteria group based on antibiotic susceptibility, revealing key insights for treatment selection.

[peace10122]

23b1527 Aditya Ojha-

How This Visualization Works and Why It’s Intuitive
This visualization effectively conveys bacterial resistance patterns in a way that is both scientifically accurate and easy to interpret.

Sorting Bacteria to Reveal Natural Groupings
This chart intentionally arranges bacteria in a logical order:

Gram-Negative Bacteria (Red Labels)

These tend to be more resistant and naturally cluster at higher MIC values.
This confirms the well-known resistance of Gram-negative bacteria, due to their protective outer membrane, which reduces antibiotic penetration.
Gram-Positive Bacteria (Purple Labels)

These bacteria generally have lower MIC values, meaning they are more susceptible to antibiotics.
Many respond well to Penicillin, which aligns with real-world treatment strategies.
Within Each Gram Category, Bacteria Are Sorted by Resistance Level

• Less resistant bacteria appear earlier in the chart.
• Highly resistant strains appear towards the right, emphasizing their broad resistance patterns.
• This makes it easy to see which bacteria fall into different resistance categories.
• The Logarithmic Scale Makes Resistance Differences Clear
• MIC values range from very small (highly susceptible bacteria) to extremely large (highly resistant bacteria).
• A linear scale would compress the low MIC values too much, making it impossible to see meaningful differences.
• Using a log scale ensures that all variations, even small ones, remain visible.
• This makes the chart more interpretable for both small and large MIC values, without distorting relationships between bacteria.

Intuitive Subgroup Identification
This visualization naturally separates bacteria into major resistance subgroups, making it immediately clear which bacteria are easy or difficult to treat:

Low-Resistance Subgroup (Marked on the Left)

• These bacteria have low MIC values, meaning they respond well to at least one antibiotic.
• This group represents easily treatable bacteria, where standard antibiotics can be effective.

Medium-Resistance Subgroup

• These have median resistance to most drugs , although completely invulnerable to penicillin
• Other drugs perform well here but these are much harder to treat than low resistance bacterium

High-Resistance Subgroup (Marked on the Right)

• These bacteria show consistently high MIC values across all antibiotics.
• This suggests they are multi-drug-resistant (MDR) bacteria, requiring stronger or combination treatments.

The Big Picture

• We follow the penicillin drug line and observe that most drugs follow similar trends
• I have changed categories when all drugs show rise in MIC, in our case its a jump of the order ten, hence treatability is 10 times harder across these categories
• Most gram positives are easier to treat except one bacterium which appears in the medium resistance category. All the later bacterium are the gram negatives.

Why This Makes Sense

• Instead of requiring manual cluster labeling, the graph itself reveals the separation naturally.

Line Patterns Show Treatment Trends

• Each antibiotic is represented by a distinct, colored line:
• If a line drops sharply for certain bacteria, it means that antibiotic is highly effective for that group.
• If a line remains consistently high, it means the antibiotic is ineffective across the board.

[subwizog]

Prajwal Yashwant Talwalkar
23B2456

The following is the python code for the graph attached
import numpy as np
import matplotlib.pyplot as plt
from math import log10

Data from the original dataset

bacteria = ["Escherichia coli", "Klebsiella pneumoniae", "Diplococcus pneumoniae", "Bacillus anthracis",
"Brucella abortus", "Aerobacter aerogenes", "Streptococcus viridans", "Streptococcus hemolyticus",
"Streptococcus fecalis", "Staphylococcus aureus", "Staphylococcus albus", "Salmonella schottmuelleri",
"Salmonella typhosa", "Pseudomonas aeruginosa", "Proteus vulgaris", "Mycobacterium tuberculosis"]

gram_positive = {"Diplococcus pneumoniae", "Bacillus anthracis", "Streptococcus viridans", "Streptococcus hemolyticus",
"Streptococcus fecalis", "Staphylococcus aureus", "Staphylococcus albus"}

gram_negative = set(bacteria) - gram_positive

MIC values for three antibiotics

penicillin = [100.0, 850.0, 0.005, 0.001, 0.1, 250.0, 0.005, 0.003, 1.0, 0.007, 0.006, 10.0, 1000.0, 500.0, 800.0, 0.05]
streptomycin = [0.01, 1.0, 0.1, 0.05, 0.02, 1.5, 0.1, 0.02, 0.2, 0.03, 0.02, 1.0, 1.2, 2.0, 2.0, 1.0]
neomycin = [0.1, 2.0, 0.5, 0.05, 0.05, 0.8, 0.1, 0.04, 0.4, 0.1, 0.08, 0.5, 0.6, 1.0, 1.0, 1.2]

Convert MIC values to log scale

log_penicillin = [log10(x) for x in penicillin]
log_streptomycin = [log10(x) for x in streptomycin]
log_neomycin = [log10(x) for x in neomycin]

Define angles for the radial plot

angles = np.linspace(0, 2 * np.pi, len(bacteria), endpoint=False).tolist()
angles += angles[:1] # Complete the circle

fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={'projection': 'polar'})
bar_width = np.pi / (len(bacteria) * 1.5)

Plot bars for Gram-positive and Gram-negative bacteria

for i, (bact, angle) in enumerate(zip(bacteria, angles)):
color_p, color_s, color_n = ('red', 'blue', 'green') if bact in gram_positive else ('lightcoral', 'lightblue', 'lightgreen')
hatch = "//" if bact in gram_positive else ""
ax.bar(angle, log_penicillin[i], width=bar_width, color=color_p, alpha=0.75, label="Penicillin" if i == 0 else "", hatch=hatch)
ax.bar(angle, log_streptomycin[i], width=bar_width, color=color_s, alpha=0.75, label="Streptomycin" if i == 0 else "", hatch=hatch)
ax.bar(angle, log_neomycin[i], width=bar_width, color=color_n, alpha=0.75, label="Neomycin" if i == 0 else "", hatch=hatch)

Adjust labels

ax.set_xticks(angles[:-1])
ax.set_xticklabels(bacteria, fontsize=10)
ax.set_title("Comparative Susceptibility of Bacteria to Antibiotics (Log MIC)")
ax.legend(loc='upper right', bbox_to_anchor=(1.1, 1.1))

plt.show()

Description of the Radial Bar Chart
The radial bar chart compares the effectiveness of three antibiotics—Penicillin, Streptomycin, and Neomycin—against 16 bacteria. It uses log-transformed MIC values, where smaller values mean better effectiveness, and separates bacteria into Gram-positive and Gram-negative groups.

Key Features
Radial Layout
Each bacterium is a segment radiating from the center.

Shorter bars mean the antibiotic works better.

Color Coding
Red: Penicillin

Green: Streptomycin

Blue: Neomycin

Grouping by Gram Staining
Outer ring: Gram-positive bacteria (hatched).

Inner ring: Gram-negative bacteria (solid colors).

Gram-positive bacteria (e.g., Staphylococcus aureus) are more susceptible to Penicillin.

Gram-negative bacteria (e.g., Escherichia coli) are more resistant to Penicillin but respond better to Streptomycin and Neomycin.

Insights
Neomycin is highly effective against bacteria like Brucella abortus.

Penicillin works well against Gram-positive bacteria but not Gram-negative.

Streptomycin and Neomycin are broadly effective.

Conclusion
The chart highlights antibiotic effectiveness trends, showing Penicillin’s limitations and the broad effectiveness of Streptomycin and Neomycin.

[tanishkameshram]

Tanishka Meshram

22B2219

Insights from the Clustering:
Distinct Bacterial Groups: The dendrogram reveals clusters of bacteria that have similar susceptibility patterns across the three antibiotics.
Highly Resistant Bacteria: Certain bacteria, such as Mycobacterium tuberculosis and Pseudomonas aeruginosa, form a cluster characterized by high resistance to most antibiotics.
Penicillin-Susceptible Group: Bacteria like Streptococcus viridans and Streptococcus hemolyticus show strong susceptibility to penicillin (low MIC values).
Divergent Responses to Streptomycin and Neomycin: Some bacteria show resistance to one while being more susceptible to the other, highlighting varied mechanisms of antibiotic resistance.

[Harshvardhan-10]

Harshvardhan Shrivastav, 23B1535

First lets start by a simple visualisation of the data given to us,

This first graph is just another way to show the graph which was made by Burtin. Here the main grouping is of Gram +ve(red shade) and Gram -ve(blue shade) bacteria. We can observe extremely stark differences between the response of Gram +ve and Gram -ve bacteria to antibiotics. Lower the MIC values, more susceptible the bacteria is to the antibiotic. So we can observe that Penicilin is more effective for Gram +ve bacteria than for Gram -ve bacteria. We can also observe that Gram +ve bacteria usually have lower MIC compared to Gram -ve.

In this Graph I have arranged the bacteria in order of increasing average MIC (sum of all MIC/3). Here I could firstly, observe the fact that most Gram +ve have a lower average MIC than Gram -ve. Next, I can observe some semblance of different categories arising in this graph based on the average MIC values. Category 1 being most susceptible, and Category 5 being most resistive. The boundaries are mostly arbitrarily drawn by me. This underscores the importance of choosing the right antibiotic for treatment as there is no one treatment for all.

This last graph groups the bacteria's with all bacteria's at once. Once again displays the start difference in performance of Penicilin for Gram +ve(red shades) and Gram -ve(blue shades) bacteria. The other two have less variance in their performance compared to Penicilin. Streptomycin has the most consistent of performance but is never the best performing one in any case, it is kind a jack of all trades sort of treatment. Neomycin has more variance in its performance compared to Streptomycin, performs well against various bacteria types, no particular preference among Gram +ve and Gram -ve, but its best performance is against a Gram +ve bacteria.

The main differentiating grouping for bacteria I could come up with was Gram +ve vs Gram -ve. The Gram +ve bacteria are much more susceptible to antibiotics than Gram -ve which has been the observed trend throughout. There is no one medicine for all treatment here. So identifying the characteristics of the bacteria becomes important during treatment.

[spswaruppatil]

Swarup Patil - 22B3953

What the Plot Shows

1. Three-Dimensional Mapping

   • Each axis represents the log-transformed MIC (Minimum Inhibitory Concentration) for one antibiotic:
     • x-axis: log(Penicillin MIC)
     • y-axis: log(Streptomycin MIC)
     • z-axis: log(Neomycin MIC)
   • Log transformation is used so that differences in MIC values are easier to compare—especially when MICs vary over several orders of magnitude. Negative values on an axis imply that the MIC is below 1, which usually suggests higher potency (less drug needed to inhibit growth).

2. Clusters

   • The data points (bacteria) are grouped into four clusters using the K-means algorithm. Each cluster is colored differently .
   • Bacteria that appear close together in this 3D space share similar susceptibility patterns across the three antibiotics.

3. Gram Staining

   • The marker shape indicates whether a bacterium is Gram-positive ('x') or Gram-negative ('o').
   • This helps you see if certain clusters are predominantly Gram-positive or Gram-negative, or if the two types are interspersed.

4. Bacterial Labels

   • Each point is labeled with the bacterium’s name. This makes it easy to identify which organism lies in which region of the 3D space.

Insights Gained

1. Susceptibility Patterns

   • Bacteria that cluster together are more similar in their antibiotic susceptibility profiles. For instance, if a group has lower log(MIC) (i.e., more negative) across all three axes, they are generally more susceptible to all three drugs.
   • Conversely, bacteria with higher log(MIC) values are more resistant and require more antibiotic to inhibit growth.

2. Gram Reaction vs. Susceptibility

   • By observing the shapes ('x' vs. 'o'), you can see if Gram-positive or Gram-negative bacteria tend to group together or if there is a mix within each cluster.
   • If you notice that most Gram-positive bacteria appear in one cluster, that might suggest a common susceptibility pattern for those organisms.

3. Potential Outliers

   • Any bacterium that lies far from the main clusters may have a unique susceptibility pattern (e.g., very sensitive to one antibiotic but resistant to others).

4. Treatment Implications

   • Clusters of bacteria with similar patterns might be effectively treated by the same antibiotic regimens.
   • If you see a cluster of Gram-negative bacteria that all have high log(MIC) for one drug, it suggests that drug might be less effective against that group.

---

In Summary

This 3D visualization provides a holistic view of how different bacteria compare in terms of antibiotic susceptibility. By grouping them into clusters and differentiating them by Gram staining, it offers immediate insights into which organisms share similar resistance/susceptibility profiles and whether Gram-positive/negative status aligns with these clusters.

[AyushiWarwade]

Ayushi Warwade [210040028]

Bacterial Susceptibility to Antibiotics:
🔹 Gram-negative vs. Gram-positive Bacteria:

Gram-negative bacteria (red-shaded) have an outer membrane, making them more resistant to antibiotics.
Gram-positive bacteria (blue-shaded) lack an outer membrane, often making them more susceptible.
🔹 MIC Values & Susceptibility:

Lower MIC = Higher susceptibility (antibiotic is more effective).
Higher MIC = More resistance (antibiotic is less effective).
Example: Brucella anthracis is highly susceptible to Penicillin (MIC = 0.001), while Aerobacter aerogenes is resistant (MIC = 870).
🔹 Identifying Low-Resistance Groups:

Clusters with low MIC values (e.g., < 0.1) indicate bacteria highly susceptible to antibiotics.
This helps in choosing effective drugs for treatment.

[athenastephen]

Athena Stephen (22b4245)

Visualising Burtin's Antibiotic Data

Step 1: Classification based on Gram-staining

This is the simplest classification. Gram-staining gives two possible outcomes because of a particular characteristic (outer peptidoglycan layer) on bacteria. This gives us two distinct types of bacteria: gram-positive and gram-negative.

Step 2: Playing with the data

After step 1, when we rearrange the data in ascending order with respect to each drug, we find a specific pattern for penicillin and not with the others.

In the following tables, the shade of violet represents gram-positive while the shade of red represents gram-negative bacteria.

• This shows that Penicillin is effective against gram-positive bacteria, as lesser concentrations are enough to inhibit their growth; they are very inefficient against gram-negative bacteria.
• Streptomycin and Neomycin are not affected by the gram-staining classification.
• In the bacteria given in the dataset, the MIC values of penicillin for most gram-positive bacteria are very high compared to others as well as the highest MICs required for Streptomycin and Neomycin in their worst cases. This implies that Penicillin is almost ineffective against gram-negative bacteria relative to this dataset.

Step 3: Visualisation

The higher MIC values of Penicillin in the dataset are relatively too large and would impair visualisation if taken directly. To overcome this, the values are normalised with respect to each drug using log transformation to give a better visualisation of the data, with the formula given below:

Normalised MIC = log10(MIC) - mean(log10(MIC)

• Log transformation: Since MIC values span several orders of magnitude, taking the logarithm helps in visualization.
• Small constant (0.001): This prevents log(0), which is undefined.

The data is arranged in ascending order of the MIC values of Penicillin, so that

• the MIC values of Penicillin can be used as a reference
• gram-postive and gram-negative bacteria are grouped separately

Drawback : Overlapping datapoints are not distinct. But since there are always three points per row, this can be rationally overridden.
Credit : The visualisation was made using an online visualisation tool called Datawrapper.

Key Insights

• Penicillin (blue) is effective against gram-positive bacteria, and very inefficient against gram-negative bacteria, in comparison with Streptomycin and Neomycin.
• Neomycin (yellow) and Streptomycin (red) show mixed effectiveness across gram-positive and gram-negative bacteria, covering a broader spectrum than Penicillin.
• Most of the yellow dots come before the respective red dots, indicating that lower levels of Neomycin are enough to inhibit bacterial growth, i.e, bacteria are less resistant to Neomycin than Streptomycin in most cases, making it the better drug against most bacteria.
• Mycobacterium tuberculosis, Klebsiella pneumoniae and Aerobacter aerogenes show the highest MIC values across multiple drugs, indicating multidrug resistance.

[GenZzZzZ]

Ramandeep Singh (22B1303)

Penicillin (Most Effective for Gram-Positive Bacteria)
🔹 Observations:

Many Gram-positive bacteria (^) are colored blue, indicating that Penicillin is the most effective antibiotic for them.
Streptococcus and Diplococcus pneumoniae are highly susceptible to Penicillin, meaning low MIC values were observed.
Gram-negative bacteria are largely resistant to Penicillin.

Streptomycin (Effective for Both Gram-Positive & Gram-Negative Bacteria)
🔹 Observations:

Purple-colored bacteria indicate that Streptomycin is the most effective antibiotic for them.
It works against both Gram-positive and Gram-negative bacteria.
Some Gram-negative bacteria, like Pseudomonas aeruginosa and Salmonella typhosa, respond well to Streptomycin.

Neomycin (Highly Effective Against Gram-Negative Bacteria)
🔹 Observations:

Cyan-colored bacteria are highly susceptible to Neomycin.
Many Gram-negative bacteria (o) respond well, including Escherichia coli and Salmonella typhosa.
Very few Gram-positive bacteria show susceptibility.

[s-poulomi]

Poulomi Sen
24M2507

1. Bar ChartsThe bar charts show the MIC values for different bacteria.

Higher MIC values (longer bars) mean the bacteria are more resistant to that antibiotic.
Lower MIC values (shorter bars) mean the bacteria are more susceptible (the antibiotic works well).
Gram-negative bacteria tend to have higher resistance to Penicillin, while Gram-positive bacteria show more variation across the three antibiotics.

2. Heatmap

Key Insights from the Heatmap:

Penicillin:

Highly resistant: Aerobacter aerogenes, Escherichia coli, Pseudomonas aeruginosa, Mycobacterium tuberculosis, and Salmonella typhosa show high resistance.
Susceptible: Brucella anthracis, Diplococcus pneumoniae, Streptococcus viridans, and Streptococcus hemolyticus are highly susceptible.

Streptomycin:

Moderate resistance: Streptococcus hemolyticus, Diplococcus pneumoniae, and Streptococcus viridans show slight resistance.
Susceptible: Brucella anthracis, Staphylococcus aureus, and Staphylococcus albus are highly susceptible.

Neomycin:

Highly susceptible: Staphylococcus albus, Staphylococcus aureus, and Brucella anthracis show strong susceptibility.
Moderate resistance: Streptococcus viridans and Diplococcus pneumoniae exhibit some resistance.

3. 3D Scatter Plot – Clustering Bacteria Based on Resistance
   I grouped bacteria into three clusters based on their antibiotic resistance.

The plot categorizes bacteria into three clusters based on their responses to Penicillin, Streptomycin, and Neomycin.

Cluster 0 (Purple)
These bacteria are highly susceptible to neomycin. Shows some susceptibility to streptomycin but with a wider spread.

Cluster 1 (Blue)
Higher resistance to neomycin compared to Cluster 0, likely has moderate resistance to streptomycin and penicillin.

Cluster 2 (Yellow)

Indicates high susceptibility to penicillin, this cluster is likely the most susceptible to penicillin among the three groups.

The first two types of visualizations were created using Excel, the last one was created using Python.

[bhanderishi]

Rishi (22B4222)

Interpretation of Bacterial Grouping:

The dendrogram clusters bacteria based on their MIC values, revealing patterns of antibiotic susceptibility.

1. Highly Susceptible Bacteria (Left Cluster):

   • Streptococcus viridans, Diplococcus pneumoniae, Streptococcus hemolyticus
   • These bacteria have low MIC values, indicating they are more susceptible to antibiotics.

2. Moderate Susceptibility (Middle Cluster):

   • Brucella abortus, Salmonella typhosa, Staphylococcus aureus, Escherichia coli
   • These bacteria show mixed susceptibility depending on the antibiotic.

3. Highly Resistant Bacteria (Right Cluster):

   • Mycobacterium tuberculosis, Pseudomonas aeruginosa, Aerobacter aerogenes, Klebsiella pneumoniae
   • These bacteria have high MIC values, suggesting they are more resistant to antibiotics.

Key Insights:

• Gram-positive and Gram-negative bacteria tend to cluster separately, showing different susceptibility patterns.
• The rightmost cluster represents bacteria that may require alternative treatments due to high resistance.
• The leftmost cluster suggests bacteria that are still effectively treated with antibiotics.

[SidG7]

Siddhant Gada
22B4223

I started by writing a Python script that tested multiple visualization techniques, including bar charts, scatter plots, and dendrograms. However, I found that individual charts didn't fully capture the relationships between bacteria and antibiotics. Hence I explored more and eventually settled on a

Step of Code and Transformation

• Loaded Data
• Applied negative log transformation to MIC (varied too much across bacteria)
• Negative log so that, higher values showed more effectiveness (make it intuitive)
• Implemented clustering and some other forms of visualisation using seaborn library

here are some of the methods used, since the data is the same, conclusions drawn are the same, just different conclusions are more intuitive in different graphs.

With gram positive and negative differentiation (seperated, since i think there are use cases where gram positiveness and negativeness is not valueable and just adds clutter)

Bacteria Groupings Based on Susceptibility:

-Highly Penicillin-Susceptible Gram-Positive Bacteria: Brucella anthracis, Streptococcus hemolyticus, etc., responded well to Penicillin.
-Penicillin-Resistant Gram-Negative Bacteria: Pseudomonas aeruginosa, Klebsiella pneumoniae showed high resistance to Penicillin but were moderately affected by Streptomycin and Neomycin.
-Balanced-Response Bacteria: Some bacteria had moderate susceptibility to all three antibiotics.
Neomycin-Susceptible Bacteria: Staphylococcus aureus had very low MIC values for Neomycin, showing high effectiveness.

Gram Staining vs. Antibiotic Response:

-Penicillin works best on Gram-positive bacteria but is mostly ineffective against Gram-negative ones.
-Streptomycin and Neomycin have more mixed effectiveness, meaning Gram staining alone isn’t enough to predict their impact.
-Some exceptions don’t follow the expected Gram staining trend, suggesting other resistance factors.

[SahilRoshen]

Name: Sahil Roshen
Roll no: 22b2542
Dept: MEMS

How do the bacteria group together?

Initially, I converted the MIC values in the given table - (Butin_antibiotic_data.xlsx) to log₁₀ MIC for easier understanding of the range of MIC used, and these are the values obtained.

Bacteria	Penicillin	Streptomycin	Neomycin	Gram Staining
Aerobacter aerogenes	2.94	0.00	0.20	Negative
Brucella abortus	0.00	0.30	-1.70	Negative
Brucella anthracis	-3.00	-2.00	-2.15	Positive
Diplococcus pneumoniae	-2.30	1.04	1.00	Positive
Escherichia coli	2.00	-0.40	-1.00	Negative
Klebsiella pneumoniae	2.93	0.08	0.00	Negative
Mycobacterium tuberculosis	2.90	0.70	0.30	Negative
Proteus vulgaris	0.48	-1.00	-1.00	Negative
Pseudomonas aeruginosa	2.93	0.30	-0.40	Negative
Salmonella typhosa	0.00	-0.40	-2.10	Negative
Salmonella schottmuelleri	1.00	-0.10	-1.05	Negative
Staphylococcus albus	-2.15	-1.00	-3.00	Positive
Staphylococcus aureus	-1.52	-1.52	-3.00	Positive
Streptococcus fecalis	0.00	0.00	-1.00	Positive
Streptococcus hemolyticus	-3.00	1.15	1.00	Positive
Streptococcus viridans	-2.30	1.00	1.60	Positive

From what I’ve researched, using MIC < 1 (or log₁₀ MIC < 0) as a way to classify bacterial susceptibility makes a lot of sense. MIC tells us the lowest antibiotic concentration needed to stop bacterial growth, and if it’s less than 1 µg/mL, that means the antibiotic is pretty effective even at low doses. Log₁₀ transformation helps here because MIC values can range massively, so converting them to log scale makes comparisons way easier. Clinically, many antibiotic guidelines set susceptibility breakpoints around 1 µg/mL, meaning bacteria with MIC values below this are generally easier to treat. Plus, it lines up with pharmacodynamics—if a drug’s MIC is less than 1, it’s usually within the range of what’s achievable in the body at normal dosages, making it a solid treatment option. Also, I noticed that Gram-positive bacteria tend to fall below this threshold more often, while Gram-negative ones, which have that extra outer membrane, usually have higher MIC values, making them harder to treat. So, using log₁₀ MIC < 0 works well as a cutoff because it helps quickly separate bacteria based on how easily they can be treated, without overcomplicating things.

[mukhesh288]

Name: M V Sai Mukhesh

Roll no: 22b0972

The x-axis represents three antibiotics, and the y-axis lists bacteria, grouped by their susceptibility patterns.

• Red Shades are Resistant.
• Dark Red indicates highly resistant bacteria (high MIC values).
• Light Red represents resistance but to a lesser degree.
• Neutral Shades are Intermediate Response
• Blue Shades are Susceptible.
• Dark Blue indicates highly susceptible bacteria (low MIC values, easier to eliminate).
• Light Blue represents susceptibility but to a lesser extent.

The above visualization helps quickly identify highly resistant bacteria, easily treatable ones, and the overall effectiveness of each antibiotic.

So the bacteria are grouped by on the shades given to them based on the log MIC values and they are also grouped based on Gram negative(light pink) and gram positive(light green).

This dendrogram (hierarchical clustering tree) visually groups bacteria based on their similarity in antibiotic susceptibility.

• Each bacterium is represented as a leaf node, with branches connecting those that respond similarly to antibiotics.
• Branch lengths indicate differences in susceptibility—longer branches mean greater dissimilarity.
• The height at which two bacteria merge reflects how different their MIC values are; a higher merge means lower similarity.

After applying a log transformation, we compute Euclidean distance between bacteria using their MIC values for three antibiotics:
d(A, B) = √((x₁ - y₁)² + (x₂ - y₂)² + (x₃ - y₃)²)
where x₁,x₂,x₃ are the log10 MIC values of bacteria A and similarly the y-values are for bacteria B.

While Euclidean distance captures overall susceptibility across all antibiotics, it loses details about individual drugs. This limitation is addressed using a heatmap, which provides a more precise view of resistance patterns.

Key insights:

• Bacillus anthracis and certain Staphylococcus species are highly susceptible to multiple antibiotics, making them easier to treat.
• Mycobacterium tuberculosis and Pseudomonas aeruginosa show strong resistance to Penicillin, Streptomycin, and Neomycin, making them harder to treat.
• Penicillin is mostly ineffective against Gram-negative bacteria but works on Gram-positive ones.
• Neomycin and Streptomycin affect bacteria differently, even within the same Gram group—some, like Streptococcus viridans, show resistance, while others, like Bacillus anthracis, are highly susceptible.

[Pegu36745]

22B0329
Montu Pegu

The dataset contains the following columns:

Bacteria: The name of the bacteria.
Penicilin: MIC value for Penicillin.
Streptomycin: MIC value for Streptomycin.
Neomycin: MIC value for Neomycin.
Gram Staining: Whether the bacteria is Gram-positive or Gram-negative.
To create a visualization that groups bacteria by their comparative susceptibility to the three antibiotics, I'll generate a clustered heatmap or bubble chart that shows how the bacteria group together based on their MIC values, highlighting patterns in drug efficacy and Gram staining differences.
Visualization:

1. How do the bacteria group together?

The bacteria can be grouped based on their Gram staining properties:

Gram-Positive Bacteria: These bacteria generally show higher susceptibility to Penicillin, indicated by lower MIC values.

Gram-Negative Bacteria: These bacteria tend to be less susceptible to Penicillin, requiring higher MIC values for inhibition.

2. What insight does it reveal?

This grouping highlights the differential effectiveness of antibiotics:

Penicillin: More effective against Gram-positive bacteria.

Streptomycin and Neomycin: These antibiotics exhibit varied efficacy across both Gram-positive and Gram-negative bacteria.

[shashantjindal]

Visualization of Burtin’s Antibiotic Data

Name: Shashant Jindal
Roll No.: 22B2137

Visualization

1. How do the bacteria group together?

The bacteria naturally group into two major categories based on their Gram stain classification:

• Gram-positive bacteria (🔵 Blue labels)
• Gram-negative bacteria (🔴 Red labels)

Additionally, the bacteria can be grouped based on their susceptibility to antibiotics:

• Some bacteria, such as Bacillus anthracis and Staphylococcus species, are highly susceptible to Penicillin.
• Others, like Pseudomonas aeruginosa and Escherichia coli, show high resistance to Penicillin but are more affected by Streptomycin or Neomycin.
• Streptococcus species exhibit a different trend, with higher resistance to Neomycin compared to other antibiotics.

2. What insight does it reveal?

• Gram-negative bacteria tend to be more resistant to Penicillin compared to Gram-positive bacteria. This aligns with biological knowledge, as Gram-negative bacteria have an outer membrane that limits antibiotic penetration.
• Streptomycin and Neomycin are generally more effective against Gram-negative bacteria compared to Penicillin.
• Certain bacteria exhibit multidrug resistance (e.g., Pseudomonas aeruginosa, which resists Penicillin and shows only moderate susceptibility to Streptomycin and Neomycin).
• The data helps in choosing the most effective antibiotic based on bacterial classification, reinforcing why different antibiotics are prescribed for different infections.