04--CIBERSORT: Web-tools of deconvolution

Overview of CIBERSORT

CIBERSORT is a deconvolution method for complex tissues especially for human leukocyte subsets,based on linear support vector regression((SVR) from gene expression profiles. It performed better than other six gene expression profiles (LLSR,LLSR,PERT,RLR,MMAD,DSA) deconvolution methods with respect to noise, unknown mixture content and closely related cell types.

other six gene expression profiles deconvolution methods

• linear least-squares regression (LLSR),
• quadratic programming (QP),
• perturba- tion model for gene expression deconvolution (PERT),
• robust linear regression (RLR),
• microarray microdissection with analysis of differences (MMAD) ,
• digital sorting algorithm (DSA)

Methods

CIBERSORT requires an input matrix of reference gene expression signatures, like leukocyte gene signature matrix, termed LM22. which offers a web-based software (http://cibersort.stanford.edu/).

General pipeline like below

1. register this software and then can sign in it.
2. Prepare Data

• Mixture File
  The mixture file is the gene expression profiles of the samples to be analyzed , column is sample, each row is gene.
• Signature Genes File
  It is a matrix of reference gene expression signatures, or signature matrix. Each data column is a component reference sample cell population class, and each row is a barcode gene.

1. upload data files
2. run CIBERSORT

Limitations

• The fidelity of refer- ence profiles, which could deviate in cells undergoing heterotypic interactions, phenotypic plasticity or disease-induced dysregula- tion(all signature gene–based methods).
• CIBERSORT does not provide P values for detection limits of individual cell types.
• Despite CIBERSORT show- ing a considerably lower estimation bias than other approaches, it systematically over- or underestimated some cell types
• Although CIBERSORT was not explicitly tested on RNA-seq data, the linearity assumptions made by our method are likely to hold.