Fig. 1

quanTIseq method and validation based on blood-cell mixtures. a quanTIseq characterizes the immune contexture of human tumors from expression and imaging data. Cell fractions are estimated from expression data and then scaled to cell densities (cells/mm²) using total cell densities extracted from imaging data. b Heatmap of quanTIseq signature matrix, with z scores computed from log₂(TPM+1) expression values of the signature genes. c The quanTIseq pipeline consists of three modules that perform (1) pre-processing of paired- or single-end RNA-seq reads in FASTQ format; (2) quantification of gene expression as transcripts-per-millions (TPM) and gene counts; and (3) deconvolution of cell fractions and scaling to cell densities considering total cells per mm² derived from imaging data. The analysis can be initiated at any step. Optional files are shown in grey. Validation of quanTIseq with RNA-seq data from blood-derived immune cell mixtures generated in [46] (d) and in this study (e). Deconvolution performance was assessed with Pearson’s correlation (r) and root-mean-square error (RMSE) using flow cytometry estimates as ground truth. The grey and blue lines represent the linear fit and the “x = y” line, respectively. B, B cells; CD4, non-regulatory CD4⁺ T cells; CD8, CD8⁺ T cells; DC, dendritic cells; M1, classically activated macrophages; M2, alternatively activated macrophages; Mono, monocytes; Neu, neutrophils; NK, natural killer cells; T, T cells; Treg, regulatory T cells