LiverDeconv

liver-specific deconvolution model for mouse

Data

• RNA-seq data (fastq files) are available in GEO dataset. The accession number is GSE237801.
• /Data directory
  • facs_true_population.csv: The ground truth cell type proportion matrix obtained by flow cytometry. (P)
  • mix_processed.csv: Bulk gene expression for 11588 genes across 57 mouse liver injury samples. (Y)
  • ref_13types.csv: Specific gene expression profiles for 13 cell types. (X)

├─Data
│  │  facs_true_population.csv
│  │  mix_processed.csv
│  │  ref_13types.csv
│  │  tpm_mix_raw.csv
│  │
│  └─info
│          batch_info.csv
│          blood_biochemistry_values.csv
│          Mouse_stable2MouseMGI.csv
│          Sample_Summary.xlsx

Sample Code

• /Sample_Codes directory
  • 0_data_preprocessing.ipynb: guides the processing method of the acquired raw files and the storage location of the various data.
  • 1_input_data.ipynb: provides the shape of the data assumed for input.
  • 2_simple_deconv_with_LM13.ipynb: explains the procedure for performing a simple deconvolution method.
  • 3_reference_comb_optimization.ipynb: provides an example of reference optimization in the paper.

Publication

• peer-reviewed article
  • Not yet
• preprint

Citation

Please cite the following if you use any contents of this repository:

Azuma I^*, Mizuno T^*,§, Morita K, Kusuhara H. Investigation of the usefulness of liver-specific deconvolution method toward legacy data utilization. bioRxiv 2023.04.19.537436; doi: https://doi.org/10.1101/2023.04.19.537436 0 Citations

Authors

• Iori Azuma
  • main contributor
• Tadahaya Mizuno
  • correspondence

Contact

If you have any questions or comments, please feel free to create an issue on github here, or email us:

• phazuma19980625[at]gmail.com
• tadahaya[at]gmail.com
  • lead contact