ExactTest vs QLFTest
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Entering edit mode
fr8712ca-s • 0
@38b1deaf
Last seen 16 days ago
Sweden

Hi! I did the code for exactTest and for glmQLFTest for my 153 samples. I used raw counts (DGE) for the DE analysis. Resuts from the exactTest: 10 genes with FDR<0.05 and 1651 genes with p-value< 0.05. With glmQLFTest results, all the p-values are >= 0.99 and all the FDR values are =1. Why are the results so different?

glm approach

library(edgeR)
library(tidyverse)
library(readxl)
library(writexl)

setwd("C:/Users/catel/Documents/DBT_DEG/Analysis_CC")

# read the phenotype features
data <- read.csv("C:/Users/catel/Documents/DBT_DEG/Analysis_CC/Data/Case_Control_Phenotype_Features_NA_BMI_excluded_cellCounts.csv")

group <- as.factor(data$T2D)
age <-as.numeric(data$Age_exact)
sex <-as.factor(data$Sex)
BMI <-as.numeric(gsub(",",".",data$BMI))
cd8t <- as.numeric(data$CD8T)
cd4t <- as.numeric(data$CD4T)
nk <- as.numeric(data$NK)
bcell <- as.numeric(data$Bcell)
mono <- as.numeric(data$Mono)
gran <- as.numeric(data$Gran)

x <-read.csv("C:/Users/catel/Documents/DBT_DEG/Analysis_CC/Data/case_control_gene_counts_filtered_gene_name_type_chr_NA_BMI_excluded_cellCounts.csv") 
# gene counts is a csv file obtained from FeatureCounts: first column contains the ENSids (from row 2), column two has in row 1 the sample ID and the rest are the counts for each ENSid. 

y <- DGEList(counts=x,group=group)
y <- normLibSizes(y)

keep <- filterByExpr(y, group = group, min.prop = 0.8, min.count = 1)
table(keep)
y <- y[keep, , keep.lib.sizes=FALSE]

y <- normLibSizes(y)
design <- model.matrix(~group + sex + age + BMI + cd8t + cd4t + nk + bcell + mono+ gran)
y <- estimateDisp(y, design)
fit <- glmQLFit(y,design)
qlf <- glmQLFTest(fit,coef=2)

p_values <- qlf$table$PValue
FDR_values <- p.adjust(p_values, method = "BH")
FDR_results <- data.frame(FDR = FDR_values)
all_results_with_FDR <- cbind(qlf, FDR_results)

classical approach


setwd("C:/Users/catel/Documents/DBT_DEG/Analysis_CC")
library("edgeR")
library(tidyr)

data <- read.csv("C:/Users/catel/Documents/DBT_DEG/Analysis_CC/Data/Case_Control_Phenotype_Features_NA_BMI_excluded_cellCounts.csv")

#group include individuals with T2D (YES) and non-diabetics (NO)
group <- as.factor(data$T2D)
age <-as.numeric(data$Age_exact)
sex <-as.factor(data$Sex)
BMI <-as.numeric(gsub(",",".",data$BMI))
cd8t <- as.numeric(data$CD8T)
cd4t <- as.numeric(data$CD4T)
nk <- as.numeric(data$NK)
bcell <- as.numeric(data$Bcell)
mono <- as.numeric(data$Mono)
gran <- as.numeric(data$Gran)

x <-read.csv("C:/Users/catel/Documents/DBT_DEG/Analysis_CC/Data/case_control_gene_counts_filtered_gene_name_type_chr_NA_BMI_excluded_cellCounts.csv") 
# gene counts is a csv file obtained from FeatureCounts: first column contains the ENSids (from row 2), column two has in row 1 the sample ID and the rest are the counts for each ENSid. 

y <- DGEList(counts=x,group=group)

# Normalizes libraries
y <- calcNormFactors(y)

#Filtration
# this calculates number of genes that fulfill the conditions of cpm >=1 and >= 80% of samples
keep <- filterByExpr(y, group = group, min.prop = 0.8, min.count = 1)
table(keep)

# Apply filtration, add the logical vectors and sort out everything that is false
y <- y[keep, , keep.lib.sizes=FALSE]

#Recalculate library sizes after  values are filtered out
y <- calcNormFactors(y)

#Estimate the dispersion to model the biological and technical variability in your data
design <- model.matrix(~group+sex+age+BMI+cd8t+cd4t+nk+bcell+mono+gran)
y <- estimateDisp(y, design)

# Perform exact testing for differential expression 
et <- exactTest(y)

# Differential Expression Analysis: 
write.table(topTags(et, 65000L), file = "C:/Users/catel/Documents/DBT_DEG/Analysis_CC/Results/Age_sex_BMI_cellCounts_filtrerGene_c_approach/outputfile_A1_BMI_DEG.csv")
results <-read.table("C:/Users/catel/Documents/DBT_DEG/Analysis_CC/Results/Age_sex_BMI_cellCounts_filtrerGene_c_approach/outputfile_A1_BMI_DEG.csv")

write.table(topTags(et, 65000L), file = "C:/Users/catel/Documents/DBT_DEG/Analysis_CC/Results/Age_sex_BMI_cellCounts_filtrerGene_c_approach/outputfile_A1_DEG.txt", row.names = TRUE, col.names=TRUE)
results <- read.table("C:/Users/catel/Documents/DBT_DEG/Analysis_CC/Results/Age_sex_BMI_cellCounts_filtrerGene_c_approach/outputfile_A1_DEG.txt", header = TRUE)

results$FDR<-p.adjust(results$PValue,"fdr")

write.table(results$FDR, "C:/Users/catel/Documents/DBT_DEG/Analysis_CC/Results/Age_sex_BMI_cellCounts_filtrerGene_c_approach/outputfile_A1_BMI_FDR.csv")

Best Regards Francesca

edgeR • 220 views
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Entering edit mode
@gordon-smyth
Last seen 3 minutes ago
WEHI, Melbourne, Australia

The two tests you have conducted are quite different so it's no surprise they give different results.

In the first case (glmQLFTest) you tested for T2D differential expression adjusting for 9 other covariates and factors. If the covariates and factors are correlated with T2D status, then it is not surprising that there might be no T2D effect after adjusting for all those variables.

The second case (exactTest) is different for two reasons. First, you are testing T2D differential expression without any adjustment for other factors. Secondly, the dispersions used for the exactTest are incorrect. In edgeR, you must always use the same design matrix for estimateDisp as for the statistical test. It is not correct to use a much larger model for estimateDisp compared to the test and a mismatch like that will give artificially too many DE results. If you use exactTest, then you should not use a design matrix for estimateDisp.

I'm not sure why you would use exactTest at all here. It doesn't seem appropriate for your problem.

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