Cibersort 算法分析肿瘤样本免疫细胞组分

最新推荐文章于 2024-12-31 08:21:37 发布

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最新推荐文章于 2024-12-31 08:21:37 发布

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分类专栏：生信肿瘤 TCGA 文章标签：算法 r语言

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CIBERSORT是一种由斯坦福大学研发的分析混合细胞群体中细胞类型丰度的方法，尤其适用于肿瘤样本。通过输入基因表达矩阵，它可以估算不同细胞类型的比例。CIBERSORTx是其最新版本，增强了对大规模组织样本的处理能力。本文介绍了使用CIBERSORT的四个步骤：准备R包和算法，准备表达矩阵，计算细胞类型丰度，以及结果的可视化。

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2015年斯坦福大学医学院的一个研究团队，提出了一种分析单细胞类型的新方法。这种方法类似于分析一杯奶昔，以找到什么水果加入其中。本研究描述的方法称为Cibersort，在线发表于三月三十日的《自然方法》（Nature Methods）

Newman, Aaron M et al. “Robust enumeration of cell subsets from tissue expression profiles.” Nature methods vol. 12,5 (2015): 453-7. doi:10.1038/nmeth.3337

在Pubmed简单搜索，就有 1300+ 的记录，可见该方法很值得学习

CIBERSORTx 新版官网入口：CIBERSORTx 官网https://cibersortx.stanford.edu/

增加了适配大量组织(bulk tissue)样本单细胞测序方面的功能。

CIBERSORTx is an analytical tool from the Alizadeh Lab and Newman Lab to impute gene expression profiles and provide an estimation of the abundances of member cell types in a mixed cell population, using gene expression data. （输入基因表达矩阵，输出样本中的各种细胞类型的丰度。比较有意思的是，CIBERSORT的设计之初的核心目标是“predicting fractions of multiple cell types in gene expression profiles (GEPs)”，可能例子里22个免疫细胞 signature让人太过印象深刻，几乎全互联网的教程都是基于免疫细胞的，CIBERSORT也被认为是专门用来量化免疫浸润的，其实signature是可以按需求自行选择的。）

source.R文件：

#' CIBERSORT R script v1.03 (last updated 07-10-2015)
#' Note: Signature matrix construction is not currently available; use java version for full functionality.
#' Author: Aaron M. Newman, Stanford University (amnewman@stanford.edu)
#' Requirements:
#'       R v3.0 or later. (dependencies below might not work properly with earlier versions)
#'       install.packages('e1071')
#'       install.pacakges('parallel')
#'       install.packages('preprocessCore')
#'       if preprocessCore is not available in the repositories you have selected, run the following:
#'           source("http://bioconductor.org/biocLite.R")
#'           biocLite("preprocessCore")
#' Windows users using the R GUI may need to Run as Administrator to install or update packages.
#' This script uses 3 parallel processes.  Since Windows does not support forking, this script will run
#' single-threaded in Windows.
#'
#' Usage:
#'       Navigate to directory containing R script
#'
#'   In R:
#'       source('CIBERSORT.R')
#'       results <- CIBERSORT('sig_matrix_file.txt','mixture_file.txt', perm, QN)
#'
#'       Options:
#'       i)  perm = No. permutations; set to >=100 to calculate p-values (default = 0)
#'       ii) QN = Quantile normalization of input mixture (default = TRUE)
#'
#' Input: signature matrix and mixture file, formatted as specified at http://cibersort.stanford.edu/tutorial.php
#' Output: matrix object containing all results and tabular data written to disk 'CIBERSORT-Results.txt'
#' License: http://cibersort.stanford.edu/CIBERSORT_License.txt
#' Core algorithm
#' @param X cell-specific gene expression
#' @param y mixed expression per sample
#' @export
CoreAlg <- function(X, y){

  #try different values of nu
  svn_itor <- 3

  res <- function(i){
    if(i==1){nus <- 0.25}
    if(i==2){nus <- 0.5}
    if(i==3){nus <- 0.75}
    model<-e1071::svm(X,y,type="nu-regression",kernel="linear",nu=nus,scale=F)
    model
  }

  if(Sys.info()['sysname'] == 'Windows') out <- parallel::mclapply(1:svn_itor, res, mc.cores=1) else
    out <- parallel::mclapply(1:svn_itor, res, mc.cores=svn_itor)

  nusvm <- rep(0,svn_itor)
  corrv <- rep(0,svn_itor)

  #do cibersort
  t <- 1
  while(t <= svn_itor) {
    weights = t(out[[t]]$coefs) %*% out[[t]]$SV
    weights[which(weights<0)]<-0
    w<-weights/sum(weights)
    u <- sweep(X,MARGIN=2,w,'*')
    k <- apply(u, 1, sum)
    nusvm[t] <- sqrt((mean((k - y)^2)))
    corrv[t] <- cor(k, y)
    t <- t + 1
  }

  #pick best model
  rmses <- nusvm
  mn <- which.min(rmses)
  model <- out[[mn]]

  #get and normalize coefficients
  q <- t(model$coefs) %*% model$SV
  q[which(q<0)]<-0
  w <- (q/sum(q))

  mix_rmse <- rmses[mn]
  mix_r <- corrv[mn]

  newList <- list("w" = w, "mix_rmse" = mix_rmse, "mix_r" = mix_r)

}

#' do permutations
#' @param perm Number of permutations
#' @param X cell-specific gene expression
#' @param y mixed expression per sample
#' @export
doPerm <- function(perm, X, Y){
  itor <- 1
  Ylist <- as.list(data.matrix(Y))
  dist <- matrix()

  while(itor <= perm){
    #print(itor)

    #random mixture
    yr <- as.numeric(Ylist[sample(length(Ylist),dim(X)[1])])

    #standardize mixture
    yr <- (yr - mean(yr)) / sd(yr)

    #run CIBERSORT core algorithm
    result <- CoreAlg(X, yr)

    mix_r <- result$mix_r

    #store correlation
    if(itor == 1) {dist <- mix_r}
    else {dist <- rbind(dist, mix_r)}

    itor <- itor + 1
  }
  newList <- list("dist" = dist)
}

#' Main functions
#' @param sig_matrix file path to gene expression from isolated cells
#' @param mixture_file heterogenous mixed expression
#' @param perm Number of permutations
#' @param QN Perform qua