Chapter 7 References

7.1 IOBR

IOBR: Zeng D, Ye Z, Shen R, et al. (2021). IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures. Frontiers in Immunology, 12, 687975. https://doi.org/10.3389/fimmu.2021.687975

IOBR 2.0: Zeng D, Fang Y, Qiu W, et al. (2024). Enhancing immuno-oncology investigations through multidimensional decoding of tumor microenvironment with IOBR 2.0. Cell Reports Methods, 4(12), 100910. https://doi.org/10.1016/j.crmeth.2024.100910

7.2 FASTQ preprocessing and read mapping

fastp: Chen S, Zhou Y, Chen Y, Gu J. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 34(17), i884-i890. https://doi.org/10.1093/bioinformatics/bty560

fastp 1.0: Chen S. (2025). fastp 1.0: An ultra-fast all-round tool for FASTQ data quality control and preprocessing. iMeta, 4(5), e70078. https://doi.org/10.1002/imt2.70078

fastp: Chen S. (2023). Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta, 2(2), e107. https://doi.org/10.1002/imt2.107

MultiQC: Ewels P, Magnusson M, Lundin S, Kaller M. (2016). MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19), 3047-3048. https://doi.org/10.1093/bioinformatics/btw354

Salmon: Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. (2017). Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods, 14(4), 417-419. https://doi.org/10.1038/nmeth.4197

STAR: Dobin A, Davis CA, Schlesinger F, et al. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15-21. https://doi.org/10.1093/bioinformatics/bts635

7.3 TME deconvolution and scoring

CIBERSORT: Newman AM, Liu CL, Green MR, et al. (2015). Robust enumeration of cell subsets from tissue expression profiles. Nature Methods, 12(5), 453-457. https://doi.org/10.1038/nmeth.3337

EPIC: Racle J, de Jonge K, Baumgaertner P, Speiser DE, Gfeller D. (2017). Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. eLife, 6, e26476. https://doi.org/10.7554/eLife.26476

quanTIseq: Finotello F, Mayer C, Plattner C, et al. (2019). Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Medicine, 11(1), 34. https://doi.org/10.1186/s13073-019-0638-6

MCP-counter: Becht E, Giraldo NA, Lacroix L, et al. (2016). Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biology, 17(1), 218. https://doi.org/10.1186/s13059-016-1070-5

IPS: Charoentong P, Finotello F, Angelova M, et al. (2017). Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Reports, 18(1), 248-262. https://doi.org/10.1016/j.celrep.2016.12.019

ESTIMATE: Yoshihara K, Shahmoradgoli M, Martinez E, et al. (2013). Inferring tumour purity and stromal and immune cell admixture from expression data. Nature Communications, 4(1), 2612. https://doi.org/10.1038/ncomms3612

BayesPrism: Chu T, Wang Z, Pe’er D, Danko CG. (2022). Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nature Cancer, 3(4), 505-517. https://doi.org/10.1038/s43018-022-00356-3

7.4 Signature analysis

ssGSEA: Barbie DA, Tamayo P, Boehm JS, et al. (2009). Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature, 462(7269), 108-112. https://doi.org/10.1038/nature08460

GSEApy: Fang Z, Liu X, Peltz G. (2023). GSEApy: a comprehensive package for performing gene set enrichment analysis in Python. Bioinformatics, 39(1), btac757. https://doi.org/10.1093/bioinformatics/btac757

PCA: Ringner M. (2008). What is principal component analysis? Nature Biotechnology, 26(3), 303-304. https://doi.org/10.1038/nbt0308-303

7.5 HLA typing and immune repertoire

SpecHLA: Wang S, Wang M, Chen L, et al. (2023). SpecHLA enables full-resolution HLA typing from sequencing data. Cell Reports Methods, 3(9), 100589. https://doi.org/10.1016/j.crmeth.2023.100589

TRUST4: Song L, Cohen D, Ouyang Z, Cao Y, Hu X, Liu XS. (2021). TRUST4: immune repertoire reconstruction from bulk and single-cell RNA-seq data. Nature Methods, 18(6), 627-630. https://doi.org/10.1038/s41592-021-01142-2

7.6 Example dataset

PRJDB16684: Lin YK, Coppo R, Onuma K, et al. (2024). Growth pattern of de novo small clusters of colorectal cancer is regulated by Notch signaling at detachment. Cancer Science, 115(11), 3648-3659. https://doi.org/10.1111/cas.16299