title: 与 Deseq2 斗智斗勇 tags:
今天要做一个差异表达分析，理所当然的用上 Deseq2 包。
<!--more-->此次的分析方式与之前有差别，需要先进行 fpkm 标准化后再进行差异表达分析，于是读入 counts 矩阵：
rm(list = ls())
counts <- read.csv(
'rna-seq.counts',
header = TRUE,
sep = '\t',
# row.names = "Geneid",
comment.char = '#',
check.names = FALSE
)
然后使用一个 for 循环进行 fpkm 计算：
for (clm in colnames(counts)[6:ncol(counts)]) {
col_fpkm <- paste0(clm, "_FPKM") # 新列的名称，加上"_FPKM"后缀
total <- sum(counts[, clm]) # 计算每个样本的总读数
counts[col_fpkm] <- (counts[, clm] * 10^6) / (counts[, "Length"] / 1000) # 使用相应样本的长度值计算 FPKM 并添加 FPKM 列
}
计算之后，删去矩阵里没用的数据和原来的 counts 矩阵：
counts = counts[,-c(2:19)]
看了一下数据，有很多的 0，为了防止意外，全部给它加一个不怎么影响结果的数值 1，然后再写入文件：
numeric_mask <- sapply(counts, is.numeric)
counts[numeric_mask] <- lapply(counts[numeric_mask], function(x) ifelse(x < 1, x + 1, x))
write.table(counts, file = 'fpkm_output', sep = '\t', row.names = FALSE)
拿到 fpkm 矩阵后开始进行差异表达分析，还是使用之前的代码：
# 数据示例：
# "Geneid" "Normal-1-1_FPKM" "Normal-1-2_FPKM" "Normal-1-3_FPKM" "HS-1-1_FPKM" "HS-1-2_FPKM" "HS-1-3_FPKM" "Normal-2-1_FPKM" "Normal-2-2_FPKM" "Normal-2-3_FPKM" "HS-2-1_FPKM" "HS-2-2_FPKM" "HS-2-3_FPKM"
# "Os01g0100100" 254855195.911414 242248722.316865 273253833.049404 587052810.902896 594548551.959114 539011925.042589 186030664.39523 145826235.093697 125383304.940375 103918228.279387 110391822.827939 100851788.756388
# "Os01g0100200" 1 887311.446317657 887311.446317657 1774622.89263531 3549245.78527063 5323868.67790595 1 887311.446317657 1 1 1 1
# "Os01g0100300" 1 1 1 1 1 1 1 1 1 1 1 1
# "Os01g0100400" 48588616.3813049 32855159.648311 51365108.7459509 136973623.322536 130032392.410921 124479407.681629 75890791.3003239 51827857.473392 59694585.8398889 9254974.54881999 16658954.187876 15270708.005553
# "Os01g0100466" 1 1 1 1 1 1 1 1 1 1 1 1
# "Os01g0100500" 610261026.10261 571107110.711071 669216921.692169 919441944.19442 886138613.861386 851485148.514851 698019801.980198 548154815.481548 536903690.369037 458595859.585959 527452745.274527 504500450.045004
# "Os01g0100600" 188775510.204082 179591836.734694 202040816.326531 398469387.755102 369387755.102041 339795918.367347 218367346.938776 152551020.408163 142346938.77551 1.75e+08 186224489.795918 137755102.040816
# 使用 fpkm 标准化之后的数据
# setwd('./pub_share/')
rm(list = ls())
Sys.setenv(LANGUAGE = "en")
library(DESeq2)
fpkm = read.csv(
'fpkm_output',
header = T,
sep = '\t',
row.names = "Geneid",
comment.char = '#',
check.name = F
)
samples = data.frame(
sampleID = c("Normal-1-1_FPKM", "Normal-1-2_FPKM", "Normal-1-3_FPKM", "HS-1-1_FPKM", "HS-1-2_FPKM", "HS-1-3_FPKM", "Normal-2-1_FPKM", "Normal-2-2_FPKM", "Normal-2-3_FPKM", "HS-2-1_FPKM", "HS-2-2_FPKM", "HS-2-3_FPKM"),
sample = c("sample1", "sample1", "sample1", "sample2", "sample2", "sample2", "sample3", "sample3", "sample3", "sample4", "sample4", "sample4")
)
rownames(samples) = samples$sampleID
samples$sample = factor(samples$sample, levels = c('sample1', 'sample2', 'sample3', 'sample4'))
dds = DESeqDataSetFromMatrix(countData = fpkm, colData = samples, design = ~sample)
dds_count <- DESeq(dds, fitType = 'mean', minReplicatesForReplace = 7, parallel = FALSE)
sampl1_vs_sample2 <- results(dds_count, contrast = c('sample', 'sample1', 'sample2'))
sampl3_vs_sample4 <- results(dds_count, contrast = c('sample', 'sample3', 'sample4'))
result1 <- data.frame(sampl1_vs_sample2, stringsAsFactors = FALSE, check.names = FALSE)
result2 <- data.frame(sampl3_vs_sample4, stringsAsFactors = FALSE, check.names = FALSE)
write.table(result1, 'sampl1_vs_sample2.DESeq2.txt', col.names = NA, sep = '\t', quote = FALSE)
write.table(result2, 'sampl3_vs_sample3.DESeq4.txt', col.names = NA, sep = '\t', quote = FALSE)
报错说是 Deseq2 处理的数据得是整数，于是加上：
fpkm <- round(fpkm)
将数值四舍五入为整数。
这里突然报错说有 NA 空值converting counts to integer mode Error in validObject(.Object) : invalid class “DESeqDataSet” object: NA values are not allowed in the count matrix In addition: Warning message: In mde(x) : NAs introduced by coercion to integer range，那就奇怪了，我不是全部加了 1 吗？于是这里判断空值，给空值加 1，再加一个判断，如果加 1 后还是空值那就直接删掉该行：
numeric_mask <- sapply(fpkm, is.numeric)
fpkm[numeric_mask] <- lapply(fpkm[numeric_mask], function(x) ifelse(is.numeric(x) & x < 1, x + 100, x))
missing_values <- sum(is.na(fpkm))
if (missing_values > 0) {
fpkm <- na.omit(fpkm)
}
还是会报同样的错误 ... 于是 Google 了一下，发现原来是某些比较大的数值仍然会保留小数点，例如：
as.character(x)
[1] "8903.00000000001" "946.999999999999"
[3] "9113.99999999999" "9451.00000000001"
[5] "9156.99999999999" "875.999999999999"
直接给我无语住了，于是根据网上的资料加上：
fpkm = apply(fpkm, 2, as.integer)
成功跑出差异表达分析，查看分析结果的时候突然发现，原本第一列的基因 ID 居然全部变成了数字？？
排查了半天才发现，哦，原来fpkm = apply(fpkm, 2, as.integer)会直接把 ID 也给转化数字 ... 于是把这一句改成：
fpkm[-1, ] <- apply(fpkm[-1, ], 2, as.integer)
问题就解决了~真的费劲~