13  Citation

13.1 IOBR

  1. IOBR: IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures. Frontiers in Immunology (2021). https://doi.org/10.3389/fimmu.2021.687975

  2. IOBR 2.0: Enhancing immuno-oncology investigations through multidimensional decoding of tumor microenvironment with IOBR 2.0. Cell Reports Methods (2024). https://doi.org/10.1016/j.crmeth.2024.100910

13.2 RNA-seq preprocessing and quantification

  1. fastp: fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics (2018). https://doi.org/10.1093/bioinformatics/bty560

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

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

  4. MultiQC: MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics (2016). https://doi.org/10.1093/bioinformatics/btw354

  5. Salmon: Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods (2017). https://doi.org/10.1038/nmeth.4197

  6. STAR: STAR: ultrafast universal RNA-seq aligner. Bioinformatics (2013). https://doi.org/10.1093/bioinformatics/bts635

13.3 TME deconvolution and scoring

  1. CIBERSORT: Robust enumeration of cell subsets from tissue expression profiles. Nature Methods (2015). https://doi.org/10.1038/nmeth.3337

  2. EPIC: Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. eLife (2017). https://doi.org/10.7554/eLife.26476

  3. quanTIseq: Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Medicine (2019). https://doi.org/10.1186/s13073-019-0638-6

  4. MCP-counter: Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biology (2016). https://doi.org/10.1186/s13059-016-1070-5

  5. IPS: Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Reports (2017). https://doi.org/10.1016/j.celrep.2016.12.019

  6. ESTIMATE: Inferring tumour purity and stromal and immune cell admixture from expression data. Nature Communications (2013). https://doi.org/10.1038/ncomms3612

  7. BayesPrism: Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nature Cancer (2022). https://doi.org/10.1038/s43018-022-00356-3

13.4 Signature scoring and enrichment

  1. ssGSEA: Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature (2009). https://doi.org/10.1038/nature08460

  2. GSEApy: GSEApy: a comprehensive package for performing gene set enrichment analysis in Python. Bioinformatics (2023). https://doi.org/10.1093/bioinformatics/btac757

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

13.5 HLA typing

  1. SpecHLA: SpecHLA enables full-resolution HLA typing from sequencing data. Cell Reports Methods (2023). https://doi.org/10.1016/j.crmeth.2023.100589

13.6 TCR/BCR repertoire analysis

  1. TRUST4: TRUST4: immune repertoire reconstruction from bulk and single-cell RNA-seq data. Nature Methods (2021). https://doi.org/10.1038/s41592-021-01142-2