Robust enumeration of cell subsets from tissue expression profiles

By Aaron M. Newman, C. Liu, M. Green, Andrew J. Gentles, Weiguo Feng, Yue Xu, Chuong D. Hoang, M. Diehn, Ash A. Alizadeh. Nature Methods, 2015

5,813 citations recorded by the source corpus.

We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu/).

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ed0dbdbf192829018592a7be649b2e403cbd92fa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images (2018, neutral): “The molecular estimate of TIL fraction is obtained by multiplying an estimate of the overall leukocyte fraction, based on DNA methylation arrays, with an estimate of the lymphocyte proportion within the immune compartment obtained by applying CIBERSORT (Newman et al., 2015) to RNA sequencing data.”
Enabling Privacy Preserving GWAS in Heterogeneous Human Populations (2016, methodology): “An open questions was posed by van Iersel et al . asking whether or not the TCP is solvable in polynomial time for binary reticulation-visible networks [1, 2, 4].”
Profiling tumor infiltrating immune cells with CIBERSORT (2018, methodology): “In benchmarking experiments, CIBERSORT was more accurate than other methods in resolving closely related cell subsets and in mixtures with unknown cell types (e.g., solid tissues) [16].”
Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures (2016, methodology): “Compared to CIBERSORT and DeconRNA-Seq, our decomposition method has the advantages of (1) being compatible with both microarray and RNA-Seq platforms, and (2) not requiring reference expression vectors, which actually reduce the robustness of a method due to the fact that even small changes in the ”
A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade (2018, critical): “However, while current methods use bulk genomics to deconvolve the tumor’s composition (Newman et al., 2015), they cannot recover the salient intracellular programs of malignant cells.”
TIMER: A web server for comprehensive analysis of tumor-infiltrating immune cells (2017, methodology): “One influential deconvolution method, CIBERSORT, utilizes microarray data with a predefined immune signature matrix to estimate the fraction of 22 tumor-infiltrating immune cells (TIIC) within a given sample (5).”
Tumor evolution of glioma intrinsic gene expression subtype associates with immunological changes in the microenvironment (2017, methodology): “We explored this possibility by comparing the tumor-associated microenvironment in primary and recurrent GBMs using CIBERSORT (Table S6) (Newman et al., 2015).”
An Introduction to Machine Learning (2020, methodology): “Concretely in a more recent example, SVR was used for delineating cell compositions from bulk transcriptomics data.(48) (4) P(Fever=High|Flu) ⋅P(Pain=Strong|Flu)⋅”
TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment (2020, methodology): “The marker genes of each cell type were collected from the published articles (23-25) and curated manually (Supplementary Table S3, S4).”
Integrative Clinical Genomics of Metastatic Cancer (2017, neutral): “Unsupervised clustering revealed groups of samples with significant differences in TIL composition, based on bulk tumor transcriptome data 35 (Fig.”
Deep learning in cancer diagnosis, prognosis and treatment selection (2021, methodology): “using transcriptomic [141, 142] or methylation data [143].”
Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma (2017, neutral): “The analysis was performed for all CIBERSORT immune cell types but only the significant ones are labeled on the plot. and implicated pathways downstream of receptor tyrosine kinases (RTKs) (Figures S7C–S7H, Table S7).”
Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data (2017, methodology): “For CIBERSORT, DeconRNASeq and ISOpure, when run based on our gene expression reference profiles, we used the reference profiles from peripheral blood immune cells for the predictions in blood and the reference profiles from tumor infiltrating cells for the predictions in solid tumors.”
Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab (2017, neutral): “…by request to https:// fasttrack.bms.com and christine.horak@bms.com Software and Algorithms ABSOLUTE Carter et al., 2012; PMID: 22544022 http://archive.broadinstitute.org/cancer/cga/absolute CIBERSORT Newman et al., 2015; PMID: 25822800 https://github.com/zomithex/CIBERSORT deconstructSigs v1.”