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Notes on Immunology-related tools and databases

License: MIT PR's Welcome

Mostly cancer-related. Please, contribute and get in touch! See MDmisc notes for other programming and genomics-related notes.

Table of content

Pipelines

  • nf-core/airrflow - an adaptive immune receptor repertoire (AIRR) analysis workflow, the Immcantation framework wrapper. B-cell and T-cell repertoire sequencing data, bulk (FASTQ) and single-cell (cellranger). QC, sequence assembly, VDJ annotation, clonal inference, reporting. Benchmarked on simulated and experimental (COVID) data against MiXCR.
    Paper Gabernet, Gisela, Susanna Marquez, Robert Bjornson, Alexander Peltzer, Hailong Meng, Edel Aron, Noah Y Lee, Cole Jensen, David Ladd, and Friederike Hanssen. “Nf-Core/Airrflow: An Adaptive Immune Receptor Repertoire Analysis Workflow Employing the Immcantation Framework,” n.d.

Deconvolution

  • Benchmarking six cell type-specific differential expression analysis methods (CellDMC, CARseq, TOAST, LRCDE, CeDAR and TCA), together with two classical methods, csSAM and DESeq2 (Table 1 - summary of methods). Evaluating the effect of baseline expression levels, sample sizes, cell type compositions, expression level alterations, technical noises and biological dispersions - these parameters strongly affect performance. TOAST and LRCDE share the highest number of cell type-specific consensus differentially expressed genes. CARseq, TOAST, CellDMC and TCA are the most reliable methods in terms of precision and sensitivity.
    Paper Meng, Guanqun, Wen Tang, Emina Huang, Ziyi Li, and Hao Feng. “A Comprehensive Assessment of Cell Type-Specific Differential Expression Methods in Bulk Data.” Briefings in Bioinformatics, December 6, 2022, bbac516. https://doi.org/10.1093/bib/bbac516.
  • Review of immune cell deconvolution methods from bulk RNA-seq data. Table 1 - list and links to deconbolution tools (TIminer, xCELL, MCP-counter, DeconRNASeq, PERT, CIBERSORT, TIMER, EPIC, quantTIseq, deconf, ssKL, ssFrobenius, DSA, MMAD). Detailed description of each.
    Paper Finotello, Francesca, and Zlatko Trajanoski. “Quantifying Tumor-Infiltrating Immune Cells from Transcriptomics Data.” Cancer Immunology, Immunotherapy: CII 67, no. 7 (2018): 1031–40. https://doi.org/10.1007/s00262-018-2150-z.
  • CIBERSORTx - cell type deconvolution of bulk RNA-seq using scRNA-seq signatures (minimizing batch effect). Extends original CIBERSORT. Robust to platform-specific variation.
    Paper Newman, Aaron M., Chloé B. Steen, Chih Long Liu, Andrew J. Gentles, Aadel A. Chaudhuri, Florian Scherer, Michael S. Khodadoust, et al. “Determining Cell Type Abundance and Expression from Bulk Tissues with Digital Cytometry.” Nature Biotechnology 37, no. 7 (July 2019): 773–82. https://doi.org/10.1038/s41587-019-0114-2.

  • immunedeconv - an R package and a review and benchmarking of 7 immune deconvolution tools. Table 1 - overview of deconvolution methods, Table 2 - recommendations for different immune cell types. Using mix of gene expression from known proportions and types of single cells, mean expression.

  • DWLS - dampened weighted least squares approach for cell-type deconvolution of bulk RNA-seq data from scRNA-seq-derived cell-type signatures. Accounts for average gene expression, downweights low expressed genes in deconvolution. More accurate for rare cell types, compared with v-support vector regression, quadratic programming on 27 simulated bulk datasets and four real mouse datasets using the Mouse Cell Atlas for scRNA-seq signatures. Two performance evaluation metrics. R package.

    Paper Tsoucas, Daphne, Rui Dong, Haide Chen, Qian Zhu, Guoji Guo, and Guo-Cheng Yuan. “Accurate Estimation of Cell-Type Composition from Gene Expression Data.” Nature Communications 10, no. 1 (December 2019). https://doi.org/10.1038/s41467-019-10802-z.

  • xCell - bulk gene expression deconvolution into 64 immune and stromal cell types. Cell signatures (6573 genes) were generated from 1822 pure human cell type transcriptomes (six sources). Single-sample GSEA, spillover compensation between closely related cell types. Precalculated xCell scores are available for 9947 samples from TCGA and TARGET. Input - any length-normalized gene expression (FPKM, TPM, RSEM). Additional files include gene signatures, TCGA/TARGET xCell scores.

  • Deconvolution using methylation profiles. Reference-free and reference-based. Table 1 overview of published methods. Description of Houseman algorithm, CIBERSORT. Problems with reference signatures. SVA performs well. Methylation is highly tissue-specific.

    • Teschendorff, Andrew E, and Shijie C Zheng. “Cell-Type Deconvolution in Epigenome-Wide Association Studies: A Review and Recommendations.” Epigenomics, March 14, 2017. https://doi.org/10.2217/epi-2016-0153.

  • Classical deconvolution paper, estimating cell proportions using cell signatures. IRIS cell signatures. Condition number, selection of biomarkers to minimize (improve) it. 

    • Abbas, Alexander R., Kristen Wolslegel, Dhaya Seshasayee, Zora Modrusan, and Hilary F. Clark. “Deconvolution of Blood Microarray Data Identifies Cellular Activation Patterns in Systemic Lupus Erythematosus.” Edited by Patrick Tan. PLoS ONE 4, no. 7 (July 1, 2009): e6098. https://doi.org/10.1371/journal.pone.0006098.

  • Statistical derivation of deconvolution.

    • Venet, D., F. Pecasse, C. Maenhaut, and H. Bersini. “Separation of Samples into Their Constituents Using Gene Expression Data.” Bioinformatics (Oxford, England) 17 Suppl 1 (2001): S279-287.

Stand-alone tools

  • immunedeconv - A unified interface to immune deconvolution methods (CIBERSORT, EPIC, quanTIseq, TIMER, xCell, MCPcounter) https://icbi-lab.github.io/immunedeconv, https://github.com/icbi-lab/immunedeconv

  • MIXTURE - an R package and a Shiny app to deconvolve cell proportions, Based on Support Vector Regression with a noise-constrained recursive feature selection. Compared with ABBAS, ABIS, CIBERSORT, DTANGLE, it detects less cell types, avoids overestimation of noisy detections. https://github.com/elmerfer/MIXTURE.App

    • Fernandez, Elmer, Yamil D Mahmoud, Florencia Veigas, Dario Rocha, Monica Balzarini, Hugo D Lujan, Gabriel A Rabinovich, and Maria Romina Girotti. “MIXTURE: An Improved Algorithm for Immune Tumor Microenvironment Estimation Based on Gene Expression Data.” BioRxiv, August 7, 2019. https://doi.org/10.1101/726562.

  • Linseed - Mathematically elegant approach for cell type deconvolution when the number of cell types and cell signatures are unknown (Methods). Based on mutual linearity of cell type-specific genes. Mixed gene expression profiles form a simplex structure in the normalized sample-specific expression space, with corners representing normalized cell proportions. Implemented in R, Linseed (LINear Subspace identification for gene Expresion Deconvolution) is a package that provides tools and interface to explore gene expression datasets in linear space and perform complete gene expression deconvolution. https://github.com/ctlab/linseed

    • Zaitsev, Konstantin, Monika Bambouskova, Amanda Swain, and Maxim N. Artyomov. “Complete Deconvolution of Cellular Mixtures Based on Linearity of Transcriptional Signatures.” Nature Communications 10, no. 1 (May 17, 2019): 2209. https://doi.org/10.1038/s41467-019-09990-5.

  • MuSiC - cell type deconvolution method to use scRNA-seq data (pre-determined cell types) to deconvolve bulk RNA-seq data. Gene weighting to prioritize stable and reliably expressed genes to build cell signatures. Compared with CIBERSORT, Nonnegative least squares (NNLS), BSEQ-sc. https://github.com/xuranw/MuSiC

    • Wang, Xuran, Jihwan Park, Katalin Susztak, Nancy R. Zhang, and Mingyao Li. “Bulk Tissue Cell Type Deconvolution with Multi-Subject Single-Cell Expression Reference.” Nature Communications 10, no. 1 (December 2019). https://doi.org/10.1038/s41467-018-08023-x.

  • MethylCIBERSORT - methylation-based cell type deconvolution, using CIBERSORT model, reformatting methylation matrices for it. https://zenodo.org/record/1298968#.W9-iaHpKj-Y

    • Chakravarthy, Ankur, Andrew Furness, Kroopa Joshi, Ehsan Ghorani, Kirsty Ford, Matthew J. Ward, Emma V. King, et al. “Pan-Cancer Deconvolution of Tumour Composition Using DNA Methylation.” Nature Communications 9, no. 1 (December 2018). https://doi.org/10.1038/s41467-018-05570-1.

  • CDSeq - Complete Deconvolution of cell proportions using bulk RNA-seq only, without prior knowledge of cell type-specific profiles, only need the number of cells. Uses Latent Dirichlet Annotation, extends it to account for gene length and differences of RNA per cell if cell sizes differ. Similar to deriving abstract but meaningful topics from text. Compared with CIBERSORT, csSAM using synthetic and experimental datasets with known proportions. MATLAB implementation, https://github.com/kkang7/CDSeq

  • ImmQuant - Deconvolution of human/mouse gene expression, output - immune cell proportions. Download from https://csgi.tau.ac.il/ImmQuant/download.html, run as java -jar ImmQuant.jar

    • Frishberg, Amit, Avital Brodt, Yael Steuerman, and Irit Gat-Viks. “ImmQuant: A User-Friendly Tool for Inferring Immune Cell-Type Composition from Gene-Expression Data.” Bioinformatics 32, no. 24 (December 15, 2016): 3842–43. https://doi.org/10.1093/bioinformatics/btw535.

  • DeconRNAseq - deconvolution of RNA-seq datasets into cell proportions using cell signatures. Non-negative decomposition algorithm (X = AS) solved using quadratic programming.  https://bioconductor.org/packages/release/bioc/html/DeconRNASeq.html

    • Gong, Ting, and Joseph D. Szustakowski. “DeconRNASeq: A Statistical Framework for Deconvolution of Heterogeneous Tissue Samples Based on MRNA-Seq Data.” Bioinformatics (Oxford, England) 29, no. 8 (April 15, 2013): 1083–85. https://doi.org/10.1093/bioinformatics/btt090.

Web-based tools

  • ABIS-seq - ABsolute Immune Signal (ABIS) deconvolution, Shiny app https://giannimonaco.shinyapps.io/ABIS/ and local installationm https://github.com/giannimonaco/ABIS.

    • Monaco, Gianni, Bernett Lee, Weili Xu, Seri Mustafah, You Yi Hwang, Christophe Carré, Nicolas Burdin, et al. “RNA-Seq Signatures Normalized by MRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types.” Cell Reports 26, no. 6 (February 2019): 1627-1640.e7. https://doi.org/10.1016/j.celrep.2019.01.041.

  • TCIA - The Cancer Immunome Atlas, https://tcia.at/home. Immunophenograms, cell type fraction table of TCGA samples. Survival analysis based on immune cell signatures. All analyses are on TCGA data.

  • scTIME portal - Single-cell pan-cancer Tumor-Immune Microenvironments database and analysis portal. Improved cell type classification, ligand-receptor network, correlating gene expression and immune cell fractions, visualization.

    Paper Hong, Fang, Qianqian Meng, Weiyu Zhang, Ruiqin Zheng, Xiaoyun Li, Tao Cheng, Deqing Hu, and Xin Gao. “Single-Cell Analysis of the Pan-Cancer Immune Microenvironment and ScTIME Portal.” Cancer Immunology Research 9, no. 8 (August 2021): 939–51. https://doi.org/10.1158/2326-6066.CIR-20-1026.

  • TIMER - immune cell-oriented exploration of TCGA cancers. prehensive resource for systematical analysis of immune infiltrates across diverse cancer types. Exploring the abundances of six immune infiltrates (B cells, CD4+ T cells, CD8+ T cells, Neutrphils, Macrophages and Dendritic cells) with gene expression, survival, mutations, copy number variants and more. Six analysis modules: Gene correlation with immune cell proportions, immune proportions and survival, and mutations, and somatic copy number alterations, simple boxplot expression of a gene across all cancer/normal samples, correlation between two genes adjusted for tumor purity or age, deconvolution of user-provided gene expression, estimation of immune proportions in all TCGA samples. https://cistrome.shinyapps.io/timer/. Video tutorial at https://youtu.be/94v8XboCrXU

    • Li, Taiwen, Jingyu Fan, Binbin Wang, Nicole Traugh, Qianming Chen, Jun S. Liu, Bo Li, and X. Shirley Liu. “TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells.” Cancer Research 77, no. 21 (November 1, 2017): e108–10. https://doi.org/10.1158/0008-5472.CAN-17-0307.

  • CIBERSORT - cell type identification. Support Vector Regression. Excellent methods description. Non-log-linear space. p-value for the overall goodness of deconvolution (H0 - no cell types are present in a given gene expression profile), also Pearson and RMSE for estimating goodness of fit. References to datasets for benchmarking. https://cibersort.stanford.edu/index.php

    • Newman, Aaron M., Chih Long Liu, Michael R. Green, Andrew J. Gentles, Weiguo Feng, Yue Xu, Chuong D. Hoang, Maximilian Diehn, and Ash A. Alizadeh. “Robust Enumeration of Cell Subsets from Tissue Expression Profiles.” Nature Methods 12, no. 5 (May 2015): 453–57. https://doi.org/10.1038/nmeth.3337.

Immune markers

  • HLA analysis tutorial - Introduction into the MHC locus structure (three subclasses), location and structure of HLA genes. International Immunogenomics database, HLA 4-field nomenclature. Available HLA imputation panels (Table 2, SNP2HLS, HLA-TAPAS, HIBAG, HLAIMP), new Multi-ancestry reference panel (admixed African, East Asian, European and Latino populations). Imputation recommendations, from per-individual and per-variant QC to tools (SNP2HLA, Michigan Imputation Server). Association tests between HLA genotype and human traits.
    Paper Sakaue, Saori, Saisriram Gurajala, Michelle Curtis, Yang Luo, Wanson Choi, Kazuyoshi Ishigaki, Joyce B. Kang, et al. “Tutorial: A Statistical Genetics Guide to Identifying HLA Alleles Driving Complex Disease.” Nature Protocols 18, no. 9 (September 2023): 2625–41. https://doi.org/10.1038/s41596-023-00853-4.
  • 10K immunomes project - immunology reference dataset from 83 studies, 10 data types (CyTOF, proteomics, gene expression, others). Formatted (standard units of measurement) and normalized (batch-corrected, ComBat) data for visualization and download.
    Paper Kelly A. Zalocusky et al., “The 10,000 Immunomes Project: Building a Resource for Human Immunology,” Cell Reports 25, no. 2 (October 2018): 513-522.e3, https://doi.org/10.1016/j.celrep.2018.09.021.

  • 63_immune_cells - Gene expression profiles of 63 immune cell types. From Bonnal RJP, Ranzani V, Arrigoni A, Curti S, Panzeri I, Gruarin P, Abrignani S, Rossetti G, Pagani M: De novo transcriptome profiling of highly purified human lymphocytes primary cells. Sci Data 2015, 2:150051.

  • Database of Immune Cell Expression (DICE) - cis-interactome project, promoter-interacting eQTLs in five immune cell types, their target genes. H3K27ac HiChIP and promoter-capture Hi-C data (FitHiChIP analysis, two background models). Many eQTLs are long-distance (median distance 120-235kb, better captured by HiChIP), significantly enriched for disease-associated variants. ~40% of promoters interact with other promoters. Supplementary tables - hg19 genomic coordinates of H3K27ac in different cell types, summary of promoter interactions (H3K27ac HiChIP), promoter-interacting eQTLs (pieQTLs) in different cell types. GitHub.

    Paper Chandra, Vivek, Sourya Bhattacharyya, Benjamin J. Schmiedel, Ariel Madrigal, Cristian Gonzalez-Colin, Stephanie Fotsing, Austin Crinklaw, et al. “Promoter-Interacting Expression Quantitative Trait Loci Are Enriched for Functional Genetic Variants.” Nature Genetics, December 21, 2020. https://doi.org/10.1038/s41588-020-00745-3.
  • Tumor-associated macrophage (TAM) gene signature (Breast and Endometrial cancers), associated with poor clinical outcome. SIGLEC1 and CCL8 are markers of TAMs. Supplementary material, Table S1 - Differentially expressed genes between normal monocytes (Mo) and tumor-educated monocytes (TEMo). Table S2 - Genes between TAMs and residential macrophages (RMs), M1 and M2 macrophage signatures. Table S3 - immune, TAM, macrophage gene lists.
    Paper Cassetta, Luca, Stamatina Fragkogianni, Andrew H. Sims, Agnieszka Swierczak, Lesley M. Forrester, Hui Zhang, Daniel Y.H. Soong, et al. “Human Tumor-Associated Macrophage and Monocyte Transcriptional Landscapes Reveal Cancer-Specific Reprogramming, Biomarkers, and Therapeutic Targets.” Cancer Cell 35, no. 4 (April 2019): 588-602.e10. https://doi.org/10.1016/j.ccell.2019.02.009.
  • Haemopedia - a database of blood cell gene expression profiles from human (12 types) and mouse (57 types) hematopoietic cells. Includes Immgen and other studies. Search for gene expression profiles, correlated genes. Connecting cells into hierarchy using minimum spanning trees. Download.
    Paper Choi, Jarny, Tracey M. Baldwin, Mae Wong, Jessica E. Bolden, Kirsten A. Fairfax, Erin C. Lucas, Rebecca Cole et al. "Haemopedia RNA-seq: a database of gene expression during haematopoiesis in mice and humans." Nucleic Acids Research 47, no. D1 (08 January 2019): D780-D785. https://doi.org/10.1093/nar/gky1020 Graaf, Carolyn A. de, Jarny Choi, Tracey M. Baldwin, Jessica E. Bolden, Kirsten A. Fairfax, Aaron J. Robinson, Christine Biben, et al. “Haemopedia: An Expression Atlas of Murine Hematopoietic Cells.” Stem Cell Reports, August 3, 2016. https://doi.org/10.1016/j.stemcr.2016.07.007.
  • The cytotoxic gene list consists of 12 genes that translate to effector cytotoxic proteins (GZMA, GZMB, GZMH, GZMK, GZMM, GNLY, PRF1 and FASLG) and well-described cytotoxic T cell activation markers (IFNG, TNF, IL2R and IL2). Source

Purity

  • ESTIMATE - tumor-stroma purity detection. 141 immune and stromal genes. single-sample GSEA analysis. ESTIMATE score as a combination of immune and stromal scores. - R package https://bioinformatics.mdanderson.org/estimate/rpackage.html

    • Yoshihara, Kosuke, Maria Shahmoradgoli, Emmanuel Martínez, Rahulsimham Vegesna, Hoon Kim, Wandaliz Torres-Garcia, Victor Treviño, et al. “Inferring Tumour Purity and Stromal and Immune Cell Admixture from Expression Data.” Nature Communications 4 (2013): 2612. https://doi.org/10.1038/ncomms3612.

  • ISOpureR - Deconvolution of Tumour Profiles to purify tumor samples. Regression-based, uses purified tumor profile to estimate the proportion of tumor samples. Discussion of overfitting due to overparametrization. https://cran.r-project.org/web/packages/ISOpureR/index.html

    • Quon, Gerald, Syed Haider, Amit G Deshwar, Ang Cui, Paul C Boutros, and Quaid Morris. “Computational Purification of Individual Tumor Gene Expression Profiles Leads to Significant Improvements in Prognostic Prediction.” Genome Medicine 5, no. 3 (2013): 29. https://doi.org/10.1186/gm433.

  • PurityEst - proportion of somatic mutations, averaged across all chromosomes, to estimate tumor purity. https://odin.mdacc.tmc.edu/~xsu1/PurityEst.html

    • Su, Xiaoping, Li Zhang, Jianping Zhang, Funda Meric-Bernstam, and John N. Weinstein. “PurityEst: Estimating Purity of Human Tumor Samples Using next-Generation Sequencing Data.” Bioinformatics (Oxford, England) 28, no. 17 (September 1, 2012): 2265–66. https://doi.org/10.1093/bioinformatics/bts365.

  • ABSOLUTE - infers tumor purity, ploidy from SNPs, CNVs. Also detects subclonal heterogeneity. https://archive.broadinstitute.org/cancer/cga/ABSOLUTE

    • Carter, Scott L., Kristian Cibulskis, Elena Helman, Aaron McKenna, Hui Shen, Travis Zack, Peter W. Laird, et al. “Absolute Quantification of Somatic DNA Alterations in Human Cancer.” Nature Biotechnology 30, no. 5 (May 2012): 413–21. https://doi.org/10.1038/nbt.2203.

Data folder

  • BRCA - Data from Azizi, Elham, Ambrose J. Carr, George Plitas, Andrew E. Cornish, Catherine Konopacki, Sandhya Prabhakaran, Juozas Nainys, et al. “Single-Cell Immune Map of Breast Carcinoma Reveals Diverse Phenotypic States Driven by the Tumor Microenvironment.” BioRxiv, January 1, 2017. https://doi.org/10.1101/221994. - scRNA-seq of immune cells in BRCA. inDrop single-cell technology. SEQC processing pipeline, Bisquit Bayesian clustering and normalization that removes confounding technical effects. Heterogeneity of immune cell composition, clusters of immune cell subpopulations, covariance among them. Supplementary Material at https://www.biorxiv.org/content/early/2017/11/25/221994.figures-only

    • 221994-2.xlsx - Table S2. Annotations of clusters inferred in full breast immune atlas (across all patients and tissues) and their proportions across tissues and patients.
    • 221994-3.xlsx - Table S3. List of differentially expressed genes in clusters listed in Table S2 (sheet 1); the subset of differentially expressed immune-related genes (sheet 2).
    • 221994-2.xlsx - Table S4. List of gene signatures (sources listed in STAR Methods)

  • Cibersort - Data from Newman, Aaron M., Chih Long Liu, Michael R. Green, Andrew J. Gentles, Weiguo Feng, Yue Xu, Chuong D. Hoang, Maximilian Diehn, and Ash A. Alizadeh. “Robust Enumeration of Cell Subsets from Tissue Expression Profiles.” Nature Methods 12, no. 5 (May 2015): 453–57. https://doi.org/10.1038/nmeth.3337. - CIBERSORT - cell type identification. Support Vector Regression. Methods description. Non-log-linear space. p-value for the overall goodness of deconvolution (H0 - no cell types are present in a given gene expression profile), also Pearson and RMSE for estimating goodness of fit. https://cibersort.stanford.edu/index.php

    • LM22.txt - 547 genes X 22 immune cell types matrix of cell type specific gene signatures

  • ESTIMATE - Yoshihara, Kosuke, Maria Shahmoradgoli, Emmanuel Martínez, Rahulsimham Vegesna, Hoon Kim, Wandaliz Torres-Garcia, Victor Treviño, et al. “Inferring Tumour Purity and Stromal and Immune Cell Admixture from Expression Data.” Nature Communications 4 (2013): 2612. doi:10.1038/ncomms3612. https://www.nature.com/articles/ncomms3612#supplementary-information

    • ncomms3612-s2.xlsx - A gene list of stromal and immune signatures
    • ncomms3612-s3.xlsx - A list of stromal, immune, and ESTIMATE scores in TCGA data sets. All cancers, all gene expression plaforms.

  • ImmQuant - Frishberg, Amit, Avital Brodt, Yael Steuerman, and Irit Gat-Viks. “ImmQuant: A User-Friendly Tool for Inferring Immune Cell-Type Composition from Gene-Expression Data.” Bioinformatics 32, no. 24 (December 15, 2016): 3842–43. https://doi.org/10.1093/bioinformatics/btw535. - Deconvolution of immune cell lineages. https://csgi.tau.ac.il/ImmQuant/downloads.html. The log2-scaled reference data files. https://csgi.tau.ac.il/ImmQuant/download.html

    • ImmGen.txt - mouse reference data (Heng and Painter, 2008)
    • DMAP.txt - human reference data (Novershtern et al., 2011)
    • IRIS.txt - human reference data (Abbas et al., 2005)

  • immune_cell_signature_genes - Repository for signature genes from Immune Cell Atlas. Also, R code for Exploratory data analysis of SCSig collection: Signatures of Single Cell Identities, by Caleb Lareau.

  • quanTIseq - Finotello, Francesca, Clemens Mayer, Christina Plattner, Gerhard Laschober, Dietmar Rieder, Hubert Hackl, Anne Krogsdam, et al. “QuanTIseq: Quantifying Immune Contexture of Human Tumors.” BioRxiv, January 1, 2017. https://doi.org/10.1101/223180. https://www.biorxiv.org/content/early/2017/11/22/223180

    • 223180-4.xlsx - Immune cell signatures, 170 genes x 10 immune cell types. Source

  • TIMER - Li, Taiwen, Jingyu Fan, Binbin Wang, Nicole Traugh, Qianming Chen, Jun S. Liu, Bo Li, and X. Shirley Liu. “TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells.” Cancer Research 77, no. 21 (November 1, 2017): e108–10. https://doi.org/10.1158/0008-5472.CAN-17-0307.

  • 29_signatures.xlsx - Well-Conditioned Signature Matrices for RNA-Seq (ABIS-Seq) and Microarray (ABIS-Microarray) Deconvolution. Table S5.

    • Monaco, Gianni, Bernett Lee, Weili Xu, Seri Mustafah, You Yi Hwang, Christophe Carré, Nicolas Burdin, et al. “RNA-Seq Signatures Normalized by MRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types.” Cell Reports 26, no. 6 (February 2019): 1627-1640.e7. https://doi.org/10.1016/j.celrep.2019.01.041. - Expression signatures of 29 immune subsets (FACS sorted). Modules of co-expressed, housekeeping genes (Table S3). Their robust normalization method (RLM) better suited for normalizing heterogeneous cell populations. Deconvolution for PBMC transcriptomic data. RNA-seq (ABIS-seq, 1296 genes) and microarray (ABIS-microarray, 819 genes) deconvolution panels. Outperforms five other methods (LM, non-negative LM, RLM, QP, CIBERSORT).TPM download at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE107011.

  • EINAV_INTERFERON_SIGNATURE_IN_CANCER.txt - A gene expression signature found in a subset of cancer patients suggestive of a deregulated immune or inflammatory response. Source

  • Hong_et_al_2021_TableS2.xlsx - T NK, Macrophage marker genes, all cell types (clusters) marker genes. From Hong, Fang, Qianqian Meng, Weiyu Zhang, Ruiqin Zheng, Xiaoyun Li, Tao Cheng, Deqing Hu, and Xin Gao. “Single-Cell Analysis of the Pan-Cancer Immune Microenvironment and ScTIME Portal.” Cancer Immunology Research 9, no. 8 (August 2021): 939–51. https://doi.org/10.1158/2326-6066.CIR-20-1026

  • Immune_signatures.xlsx - List of gene signatures for "Treg", "CD8 T Cell Activation", "Anti-inflammatory", "Anergy", "Pro inflammatory", "Lipid mediators", "Glycolysis", "TCA cycle", "Pentose Phosphate Pathway", "Glycogen Metabolism", "Glucose Deprivation", "M1 Macrophage Polarization", "M2 Macrophage Polarization", "Cytolytics effector pathway", "Type I Interferon response", "Type II Interferon Response", "Hypoxia/HIF regulated", "TCell Terminal Differentiation", "G1/S", "G2/M". Sheets 2 and 3 - macrophage M1 and M2 (suppressive) signatures. Table S4 from https://doi.org/10.1016/j.cell.2018.05.060.

  • IRIS.xlsx - Gene signatures of six immune cell types (T-cells, NK cells, B cells, monocytes and macrophages, Dendritic cells, Neutrophils). Microarray data). Latest gene expression data for twelve cell types, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE22886. Table S1.

    • Abbas, A. R., D. Baldwin, Y. Ma, W. Ouyang, A. Gurney, F. Martin, S. Fong, et al. “Immune Response in Silico (IRIS): Immune-Specific Genes Identified from a Compendium of Microarray Expression Data.” Genes and Immunity 6, no. 4 (June 2005): 319–31. https://doi.org/10.1038/sj.gene.6364173.

  • TCGA_immune_classification.xlsx - PanImmune Feature Matrix of Immune Characteristics. TCGA sample IDs, clinical characteristice, scores for key immune charactics, six immune signatures, individual cell types. Table S1

    • Thorsson, Vésteinn, David L. Gibbs, Scott D. Brown, Denise Wolf, Dante S. Bortone, Tai-Hsien Ou Yang, Eduard Porta-Pardo, et al. “The Immune Landscape of Cancer.” Immunity 48, no. 4 (April 2018): 812-830.e14. https://doi.org/10.1016/j.immuni.2018.03.023.

Misc Notes

  • Immuneatlas.org - differences in immune cell composition and immune responses between humans, mice, and non-human primates. Example - mice have ~10-times fewer neutrophils than all primates, and ~10-times more B cells. Results, data.
    Paper Bjornson-Hooper, Zachary B, Gabriela K Fragiadakis, Matthew H Spitzer, Deepthi Madhireddy, Dave McIlwain, and Garry P Nolan. “A Comprehensive Atlas of Immunological Differences between Humans, Mice and Non-Human Primates.” BioRxiv, March 11, 2019. https://doi.org/10.1101/574160.
  • OptiType - an HLA genotyping algorithm from any sequencing data. Based on integer linear programming. Based on the assumption that the correct HLA genotype explains the highest number of mapped reads. Reads are mapped against an HLA allele reference, a binary matrix is created for which alleles a specific read could be aligned to with the least mismatches, an integer linear program maximizes the number of mapped reads that can be explained by the predicted genotype. 97% accuracy. Other tools - HLAminer, seq2HLA, ATHLATES, HLAforest. Python implementation.
    Paper Szolek, András, Benjamin Schubert, Christopher Mohr, Marc Sturm, Magdalena Feldhahn, and Oliver Kohlbacher. “OptiType: Precision HLA Typing from next-Generation Sequencing Data.” Bioinformatics (Oxford, England) 30, no. 23 (December 1, 2014): 3310–16. https://doi.org/10.1093/bioinformatics/btu548.

  • T cell signature: CD8A, CCL2, CCL3, CCL4, CXCL9, CXCL10, ICOS, GZMK, IRF1, HLA-DMA, HLA-DMB, HLA-DOA, and HLA-DOB

  • CTNNB1 score: mean expression of TCF1, TCF12, MYC, EFNB3, VEGFA, and APC2, to be correlated with CD8b expression

    • Spranger, Stefani, Jason J. Luke, Riyue Bao, Yuanyuan Zha, Kyle M. Hernandez, Yan Li, Alexander P. Gajewski, Jorge Andrade, and Thomas F. Gajewski. “Density of Immunogenic Antigens Does Not Explain the Presence or Absence of the T-Cell-Inflamed Tumor Microenvironment in Melanoma.” Proceedings of the National Academy of Sciences of the United States of America 113, no. 48 (29 2016): E7759–68. https://doi.org/10.1073/pnas.1609376113.

  • Markers used to type cells: NCAM1, NCR1, NKG2 (NK-cells), GNLY, PFN1, GZMA, GZMB, GMZM, GZMH (cytotoxic T, NK), FOXP3, CTLA4, TIGIT, TNFRSF4, LAG3, PDCD1 (Exhausted T cell, T-regulatory Cell), CD8, CD3, CD4 (T cells), IL7R (Naive T cells), CD19 (B cells), ENPP3, KIT (Mast cells), IL3RA, LILRA4 (plasmacytoid DC), HLA-DR, FCGR3A, CD68, ANPEP, ITGAX, CD14, ITGAM, CD33 (Monocytic Lineage).

    • Azizi, Elham, Ambrose J. Carr, George Plitas, Andrew E. Cornish, Catherine Konopacki, Sandhya Prabhakaran, Juozas Nainys, et al. “Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment.” Cell, June 2018. https://doi.org/10.1016/j.cell.2018.05.060.

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