paper
Microbiome differential abundance methods produce different results across 38 datasets
data link
https://figshare.com/articles/dataset/16S_rRNA_Microbiome_Datasets/14531724
code link
https://github.com/nearinj/Comparison_of_DA_microbiome_methods
This person's github homepage has data and code for other papers
https://github.com/jnmacdonald/differential-abundance-analysis This link has a lot of code titled differential abundance analysis
In the past two days, I was watching the metagenome using otu abundance data for differential abundance analysis. I found this paper and read the abstract. It seems to compare the similarities and differences of the results obtained by different differential abundance analysis methods. Repeat the code here for differential abundance analysis using DESeq2
The dataset I use here is
Abundance data
ArcticFireSoils_genus_table.tsvpacket data
ArcticFireSoils_meta.tsv
Here is a question: There are two abundance table data provided by the paper, and the other is with a rare suffix. I don't know the difference between the two for the time being.
The first is to read the dataset
ASV_table <- read.table("metagenomics/dat01/ArcticFireSoils_genus_table.tsv",
sep="\t",
skip=1,
header=T,
row.names = 1,
comment.char = "",
quote="", check.names = F)
groupings <- read.table("metagenomics/dat01/ArcticFireSoils_meta.tsv",
sep="\t",
row.names = 1,
header=T,
comment.char = "",
quote="",
check.names = F)
dim(ASV_table)
dim(groupings)
groupings$Fire<-factor(groupings$Fire)Here you need to assign a factor to represent the grouping, otherwise there will be a warning message in the step of deseq2 later
Determine whether the sample names of the abundance data and the sample names of the grouped data are in the same order
identical(colnames(ASV_table), rownames(groupings))return false inconsistent
Take the intersection of two sample names
rows_to_keep <- intersect(colnames(ASV_table), rownames(groupings))Reselect samples based on the result of taking the intersection
groupings <- groupings[rows_to_keep,,drop=F]
ASV_table <- ASV_table[,rows_to_keep]Question: What is the function of the drop parameter in the brackets here?
Judge the order of the sample names in the two datasets again
identical(colnames(ASV_table), rownames(groupings))This time returns TRUE
Modify the column names of the grouping file
colnames(groupings)[1] <- "Groupings"Differential abundance analysis
library(DESeq2)
dds <- DESeq2::DESeqDataSetFromMatrix(countData = ASV_table,
colData=groupings,
design = ~ Groupings)
dds_res <- DESeq2::DESeq(dds, sfType = "poscounts")
res <- results(dds_res,
tidy=T,
format="DataFrame",
contrast = c("Groupings","Fire","Control"))
head(res)
volcano map code
DEG<-res
logFC_cutoff<-2
DEG$change<-as.factor(ifelse(DEG$pvalue<0.05&abs(DEG$log2FoldChange)>logFC_cutoff,
ifelse(DEG$log2FoldChange>logFC_cutoff,"UP","DOWN"),
"NOT"))
this_title <- paste0('Cutoff for logFC is ',round(logFC_cutoff,3),
'\nThe number of up gene is ',nrow(DEG[DEG$change =='UP',]) ,
'\nThe number of down gene is ',nrow(DEG[DEG$change =='DOWN',]))
DEG<-na.omit(DEG)
library(ggplot2)
ggplot(data=DEG,aes(x=log2FoldChange,
y=-log10(pvalue),
color=change))+
geom_point(alpha=0.8,size=3)+
labs(x="log2 fold change")+ ylab("-log10 pvalue")+
ggtitle(this_title)+theme_bw(base_size = 20)+
theme(plot.title = element_text(size=15,hjust=0.5),)+
scale_color_manual(values=c('#a121f0','#bebebe','#ffad21')) -> p1
p1+xlim(NA,10)+ylim(NA,30) -> p2
library(patchwork)
p1+p2
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