FASTQ files of RNA-seq reads were pre-processed with Trimmomatic [22 (link)] to remove adapter sequences and read ends with Phred quality scores lower than 20, to discard reads shorter than 36 bp, and to trim long reads to a maximum length of 50 bp. This analysis is implemented in the “Preprocessing” module of quanTIseq (step 1 in Fig. 1c), which also allows selecting different parameters for data preprocessing.

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 (link)] (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

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