Notes on Immunology-related tools and databases

Mostly cancer-related.

• Web-based tools
• Stand-alone tools
• Data
  • BRCA
  • Cibersort
  • ESTIMATE
  • ImmQuant
  • quanTIseq
• Notes

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.

• DICE - Database of Immune Cell eQTLs, Expression, Epigenomics. https://dice-database.org/

• Haemosphere - mostly murine immune cell signatures, downloadable data. http://haemosphere.org/datasets/show

• 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.

• 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.

Stand-alone tools

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

Data

• 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.- 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. http://10kimmunomes.org/

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

  • 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.

• data/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. http://software.broadinstitute.org/gsea/msigdb/geneset_page.jsp?geneSetName=EINAV_INTERFERON_SIGNATURE_IN_CANCER

• data/Immune_signatures.xlsx - Table S4 from https://doi.org/10.1016/j.cell.2018.05.060. 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.

• data/IRIS.xlsx - Table S1. 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

  • 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.

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

  • 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.

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. http://csgi.tau.ac.il/ImmQuant/downloads.html. The log2-scaled reference data files. http://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)

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.
  • ImmuneEstimation.xlsx - Proportions of six immune cell types in all TCGA samples, downloaded from "Estimates" tab at TIMER web site https://cistrome.shinyapps.io/timer/

Notes

• 63_immune_cells - Gene expression profiles of 63 immune cell types. https://github.com/mdozmorov/63_immune_cells

• 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.

  • 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

• 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.

  • 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).