你怎么能重叠基因列表中提取基因名称

问题描述 投票:0回答:1

我现在有一个剧本在看不同的基因列表之间的基因重叠。那么什么代码返回是一个八乘八矩阵与不同数量的发现重叠基因(即共有的列表)。有没有一种方法,我可以看看这些特定基因,并找出他们的基因符号?

#-------------------------------------------------------------------------------
# Set the working directory and load the data files
#-------------------------------------------------------------------------------
setwd("~/Desktop/R_Project/Gene_overlap")
getwd()
files <- list.files(pattern="*.txt", full.names = TRUE)
files

data.list <- lapply(files, function(fil) {
  scan(file=fil, what=character())
})

names(data.list) <- basename(files) %>% stringr::str_remove("\\.txt$")

str(data.list)
# List of 8
# $ GSE108363_BCGdown_D:chr [1:350] "IL1B" "IL6" "IL1A" "CCL20" ...
# $ GSE108363_BCGdown_V: chr [1:267] "IL6" "CCL20" "IL1A" "CXCL5" ...
# $ GSE108363_BCGup_D  : chr [1:250] "FABP4" "CMTM2" "FUCA1" "CD36" ...
# $ GSE108363_BCGup_V  : chr [1:429] "FCN1" "FCGR3B" "MNDA" "CPVL" ...
# $ GSE108363_MTBdown_D: chr [1:86] "CCL20" "IL1B" "IL1A" "IL6" ...
# $ GSE108363_MTBdown_V: chr [1:244] "IL1B" "IL1A" "CCL20" "IL6" ...
# $ GSE108363_MTBup_D  : chr [1:128] "FUCA1" "FGL2" "TGFBI" "CPVL" ...
# $ GSE108363_MTBup_V  : chr [1:286] "FABP4" "RNASE1" "MNDA" "CPVL" ...

intersect(data.list$GSE108363_BCGdown_D, data.list$GSE108363_BCGdown_V) %>% length

sapply(data.list, length)



#-------------------------------------------------------------------------------
# Using the intersect function to see the overlaps 
#-------------------------------------------------------------------------------

data.file1 <- "GSE108363_BCGdown_D.txt"
data.file2 <- "GSE108363_BCGdown_V.txt"
data.file3 <- "GSE108363_BCGup_D.txt"
data.file4 <- "GSE108363_BCGup_V.txt"
data.file5 <- "GSE108363_MTBdown_D.txt"
data.file6 <- "GSE108363_MTBdown_V.txt"
data.file7 <- "GSE108363_MTBup_D.txt"
data.file8 <- "GSE108363_MTBup_V.txt"

genevect1 <- scan(data.file1, what=character(), sep="\n")
genevect2 <- scan(data.file2, what=character(), sep="\n")
genevect3 <- scan(data.file3, what=character(), sep="\n")
genevect4 <- scan(data.file4, what=character(), sep="\n")
genevect5 <- scan(data.file5, what=character(), sep="\n")
genevect6 <- scan(data.file6, what=character(), sep="\n")
genevect7 <- scan(data.file7, what=character(), sep="\n")
genevect8 <- scan(data.file8, what=character(), sep="\n")


filelist <- list(data.file1, data.file2, data.file3, data.file4, data.file5, data.file6, data.file7, data.file8)
all(sapply(filelist, file.exists))

#-------------------------------------------------------------------------------
# read files:
#-------------------------------------------------------------------------------

gene.lists <- lapply(filelist, function(f) {
  scan(file=f, what=character())
})


#-------------------------------------------------------------------------------
# set up empty matrix for overlaps
#-------------------------------------------------------------------------------
x <- (length(gene.lists))^2
x
y <- rep(NA, x)
mx <- matrix(y, ncol=length(gene.lists))
mx
row.names(mx) <- sapply(filelist, basename) %>% stringr::str_remove('.txt$')
colnames(mx) <- sapply(filelist, basename) %>% stringr::str_remove('.txt$')
mx

mx.overlap.count <- mx

#-------------------------------------------------------------------------------
# Overlaps
#-------------------------------------------------------------------------------
for (i in seq_along(gene.lists)) {
  g1 <- gene.lists[[i]]
  for (j in seq_along(gene.lists)) {
    g2 <- gene.lists[[j]]
    a <- intersect(g1, g2)
    b <- length(a)
    mx.overlap.count[j,i] <- b
  }
}

mx.overlap.count
round(as.numeric(mx.overlap.count),digits = 1)
View(mx.overlap.count)

目前,该代码返回的数值。不过,我想产生某种列表中每两个基因列表进行比较(或类似的东西),这样我可以看看,看看到底哪些基因是常见的两种。

r
1个回答
0
投票

我将通过对比的矩阵循环,存储矩阵,就像你做了路口的长度,并以列表存储在基因名称,genes.overlap解决问题。像这样:

# Load files. 
file_names <- list.files(pattern=".txt")

# Extract gene lists. 
gene.lists <- lapply(file_names, function(f) {
  scan(file=f, what=character())
})

# Name the entries in the list. 
names(gene.lists) <- file_names
names(gene.lists)

# Initiate an empty list and matrix for storing output of loop.
genes.overlap <- list()
nfiles <- length(gene.lists)
mx.overlap.count <- matrix(NA,nrow=nfiles)

# Generate contrasts:
contrasts <- combn(nfiles,2)

# Loop to determine intersection:
for (i in 1:dim(contrasts)[2]){
  list1 <- contrasts[1,i]
  list2 <- contrasts[2,i]
  g1 <- gene.lists[[list1]]
  g2 <- gene.lists[[list2]]
  comparison_name <- paste(names(gene.lists[list1]),names(gene.lists[list2]),sep="_")
  genes.overlap[[i]] <- intersect(g1, g2)
  names(genes.overlap)[i] <- comparison_name
  b <- length(genes.overlap[[i]])
  mx.overlap.count[i] <- b
}

# You can index into a list like a df with the $ operator. 
genes.overlap$List.txt_List1.txt
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