All the processed data and codes from the manuscript "Deconvolving clinically relevant cellular immune crosstalk from bulk gene expression using CODEFACS and LIRICS stratifies melanoma patients to anti-PD-1 therapy" are deposited here: 1. Eestimated cell fractions across 21 cancer types in TCGA: cell_frac_TCGA.zip 2. List of cell fraction file names: cell_frac_TCGA_files.txt 3. The primary tissue/cancer type names of processed TCGA data: tumor_type_TCGA_deconv.txt 4. Deconvolved cell type specific expression for each sample across 21 cancer types in TCGA: “CANCER TYPE/TISSUE TYPE.rds” e.g. melanoma.rds and breast.rds etc. To merge all the datasets across 21 cancer types as a single RData file, you can run the code "merge_TCGA_deconvolved_data.r". 5. Curated benchmark datasets: benchmark_datasets.RData 6. Performance of CODEFACS on benchmark datasets: acc_benchmark_codefacs_12.20.RData and pred_benchmark_codefacs_12.20.RData 7. Estimated mean gene expression in each cell type based on publicly available single cell datasets across cancer types: sc_mean_datasets.RData 8. The three Deconvolved ICB datasets (Riaz N. et al, Cell 2017, Gide T. et al, Cancer Cell 2019 and Liu D. et al, Nature Medicine 2019): pred_ICB_codefacs_rsem.RData 9. Compressed signatures files for SKCM, GBM and NSCLC: signature_files.zip 10. Data for supplementary figure S12, S13, S14, S15, S16, S30 and S34: data_S12.S13.zip, data_simulated_sc_S14.S15.S16.zip, dat_stages_conf, coff_gene_gene_correlation_skcm.rds, coff_gene_gene_correlation_gbm.rds and coff_gene_gene_correlation_nsclc.rds 11. Gene list file: gene_name_livnat.txt 12. All the relevant codes, scripts and curated database for CODEFACS and LIRICS: CODEFACS&LIRICS-master.zip