我有一个来自两个人群的基因的集合数据集，然后我想评估两个群体是否具有这些基因的差异表达。这就是为什么我想获得调整后的P值。
我对编码一无所知，但我尝试没有任何成功。我尝试了Python和R。
我拥有的结构的数据：
,CONTROL1,CONTROL2,CONTROL3,PROBLEM1,PROBLEM2,PROBLEM3
gene1,31.7,6.31,0.632,0.021,0.159,0.026,
gene2,31.7,6.31,0.632,0.021,0.159,0.026,
gene3,31.7,6.31,0.632,0.021,0.159,0.026,
...
gene_n,31.7,6.31,0.632,0.021,0.159,0.026,
我尝试了我在Internet中发现的所有内容，但没有成功。
我想获得与此类似的列表：
gene_name, adjusted_p-value
gene1, 0.001
gene2, 0.3
gene3, 0.9
...
gene_n, 0.004
如果有人可以给我任何提示在哪里检查或如何做，我将非常感谢。
谢谢！！
I have a set data set from genes derived from two populations, then I want to assess if the two populations have these genes differentially expressed. That's why I want to obtain the adjusted p-value.
I know little about coding, but I have tried without any success. I have tried Python and R.
The data that I have have this structure:
,CONTROL1,CONTROL2,CONTROL3,PROBLEM1,PROBLEM2,PROBLEM3
gene1,31.7,6.31,0.632,0.021,0.159,0.026,
gene2,31.7,6.31,0.632,0.021,0.159,0.026,
gene3,31.7,6.31,0.632,0.021,0.159,0.026,
...
gene_n,31.7,6.31,0.632,0.021,0.159,0.026,
I have tried all I found in Internet, but nothing succeeds.
I want to obtain a list similar to this:
gene_name, adjusted_p-value
gene1, 0.001
gene2, 0.3
gene3, 0.9
...
gene_n, 0.004
if someone could give me any hint where to check or how to do it I would be very grateful.
Thanks!!
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评论（1）
简短的答案是，如果您使用的是不同的表达数据，则可能值得使用deseq2。
https://bioconductor.or.orgg/packages/packages/release/release/release/bioc/bioc/bioc/html/deseq-2 html
教程非常好：
它可以从基本上是您一组一组的矩阵数据来介绍如何从计数矩阵数据中转移调整后的P值。
对于这样的将来的事情，您可能有更好的时间去 https://bioinformatics.stackexchange.com/ 比这里。
Short answer is if you're using differential expression data it's probably worth using deseq2.
https://bioconductor.org/packages/release/bioc/html/DESeq2.html
The tutorial is pretty good: https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html
It walks you through how to go from a count matrix data which is basically what you have to a set of adjusted p-values.
For future things like this you might have a better time going to https://bioinformatics.stackexchange.com/ than here.