vignettes/edgeR.md
In MalteThodberg/ABC2018: Datasets and examples for Introduction to Advanced Topics in Bioinformatics 2018
title: "Introduction to edgeR"
author: "Malte Thodberg"
date: "2018-09-07"
output:
ioslides_presentation:
smaller: true
highlight: tango
transition: faster
vignette: >
%\VignetteEngine{knitr::knitr}
%\VignetteIndexEntry{EDA}
%\usepackage[UTF-8]{inputenc}
Introduction
This presentation will show the basic usage of edgeR:
- Normalization.
- Dispersion estimate.
- Fitting GLMs.
- Testing coefficients and contrasts.
- Testing the results.
# Load the data
library(ABC2018)
library(ggplot2)
theme_set(theme_minimal())
library(edgeR)
data("zebrafish")
Trimming and normalizing the data.
Just as before, we started by loading, trimming and normalizing the data:
# Trim
EM_zebra <- subset(zebrafish$Expression, rowSums(zebrafish$Expression >= 5) >= 3)
# Calculate normalization factors
dge_zebra <- DGEList(EM_zebra)
dge_zebra <- calcNormFactors(dge_zebra, method="TMM")
Building the model matrix
We use model.matrix to make a simple model matrix:
mod <- model.matrix(~gallein, data=zebrafish$Design)
mod
## (Intercept) galleintreated
## Ctl1 1 0
## Ctl3 1 0
## Ctl5 1 0
## Trt9 1 1
## Trt11 1 1
## Trt13 1 1
## attr(,"assign")
## [1] 0 1
## attr(,"contrasts")
## attr(,"contrasts")$gallein
## [1] "contr.treatment"
Estimating the dispersion
Now we move on to estimating dispersion or BCV:
disp_zebra <- estimateDisp(dge_zebra, design=mod, robust=TRUE)
The 3-step estimation is all done by a single function in the newest version of edgeR: estimateDisp, which replaces the three previous functions of
estimateCommonDispestimateTrendedDispestimateTagwiseDisp
Plotting the dispersion
We can then plot the estimates:
plotBCV(y=disp_zebra)
Fiting gene-wise GLMs and testing a coefficient.
Now we can fit gene-wise GLMs:
fit_zebra <- glmFit(disp_zebra, design=mod)
Finally, we can test a coefficient in the model for DE:
# Using the name of the coefficient
gallein <- glmLRT(fit_zebra, coef="galleintreated")
# Using the index of the coefficient
gallein <- glmLRT(fit_zebra, coef=2)
Inspecting the results
edgeR has several useful functions for inspecting and plotting the results:
Inspecting the top hits with topTags:
topTags(gallein)
## Coefficient: galleintreated
## logFC logCPM LR PValue FDR
## ENSDARG00000075505 -7.050266 0.91117648 23.80888 1.063907e-06 0.02098875
## ENSDARG00000002508 -6.899411 1.98202488 22.20389 2.451759e-06 0.02296418
## ENSDARG00000091349 -8.890033 2.67150109 20.55248 5.801872e-06 0.02296418
## ENSDARG00000069441 -9.167127 -0.06217613 20.49904 5.966113e-06 0.02296418
## ENSDARG00000071565 7.114211 0.34425608 20.11517 7.291619e-06 0.02296418
## ENSDARG00000087178 -9.267206 0.03576790 19.88517 8.223557e-06 0.02296418
## ENSDARG00000040128 7.387525 6.64510937 19.77201 8.725111e-06 0.02296418
## ENSDARG00000090689 5.949190 4.67990013 19.49232 1.010048e-05 0.02296418
## ENSDARG00000000906 -8.272182 1.30746230 19.42254 1.047636e-05 0.02296418
## ENSDARG00000076643 -7.572011 0.42975453 18.64519 1.574441e-05 0.02741095
Inspecting the results
Summarize the number of up- and down-regulated genes with decideTestsDGE:
is_de <- decideTestsDGE(gallein, p.value=0.1)
head(is_de)
## galleintreated
## ENSDARG00000000001 0
## ENSDARG00000000002 0
## ENSDARG00000000018 0
## ENSDARG00000000019 0
## ENSDARG00000000068 0
## ENSDARG00000000069 0
summary(is_de)
## galleintreated
## Down 68
## NotSig 19581
## Up 79
Inspecting the results
MA-plot, showing relation between mean expression and logFC, :
plotSmear(gallein, de.tags=rownames(gallein)[is_de != 0])
Inspecting the results
There is no built-in function for making a volcano plot, but it's easy to do yourself:
plot(-log10(PValue) ~ logFC, data = topTags(gallein, n = Inf, sort.by = "none"),
col = factor(decideTestsDGE(gallein) != 0), pch = 16)
Quasi-likelihood: Fitting models
Recently, edgeR introduced an alternative DE pipeline using Quasi-likelihood. It should be slightly more conservative than the standard edgeR pipeline. Code-wise, though, they are almost identical:
fitQL <- glmQLFit(y=disp_zebra, design=mod, robust=TRUE)
galleinQL <- glmQLFTest(fitQL, coef="galleintreated")
summary(decideTestsDGE(galleinQL, p.value=0.1))
## galleintreated
## Down 0
## NotSig 19728
## Up 0
Quasi-likehood: Plotting dispersions
The QL-pipeline estimates slightly different dispersions:
plotQLDisp(fitQL)
Final exercises:
Use the above code as a template for conducting your own analysis. For the pasilla dataset:
- Load, trim and normalize the data
- Inspect the study design, and decide on a model matrix to use with edgeR
- Use edgeR to estimate dispersions and fit gene-wise GLMs
- Report the number of up- and down-regulated genes for the tests of interests
- Generate smear and volcano plots summarizing the analysis.
- Does the number of DE genes change when you model the batch effect or not?
If you have time:
- Investigate the effect of different normalization methods on the number of DE genes.
- Try out the QL-pipeline by replacing
glmFit with glmQLFit and glmLRT with glmQLFTest
Once again - the next couple of slides contain cheat sheets...
Cheatsheet: Experimenting with model matrices
# Load the data
data("pasilla")
# This will makes "treated the intercept!"
mod <- model.matrix(~condition, data=pasilla$Design)
mod
## (Intercept) conditionuntreated
## treated1fb 1 0
## treated2fb 1 0
## treated3fb 1 0
## untreated1fb 1 1
## untreated2fb 1 1
## untreated3fb 1 1
## untreated4fb 1 1
## attr(,"assign")
## [1] 0 1
## attr(,"contrasts")
## attr(,"contrasts")$condition
## [1] "contr.treatment"
Cheatsheet: Experimenting with model matrices
# Relevel to make "untreated" intercept
pasilla$Design$condition <- relevel(pasilla$Design$condition, "untreated")
mod <- model.matrix(~condition, data=pasilla$Design)
mod
## (Intercept) conditiontreated
## treated1fb 1 1
## treated2fb 1 1
## treated3fb 1 1
## untreated1fb 1 0
## untreated2fb 1 0
## untreated3fb 1 0
## untreated4fb 1 0
## attr(,"assign")
## [1] 0 1
## attr(,"contrasts")
## attr(,"contrasts")$condition
## [1] "contr.treatment"
Cheatsheet: Experimenting with model matrices
# Relevel to make "untreated" intercept
pasilla$Design$condition <- relevel(pasilla$Design$condition, "untreated")
mod_batch <- model.matrix(~condition+type, data=pasilla$Design)
mod_batch
## (Intercept) conditiontreated typesingle-read
## treated1fb 1 1 1
## treated2fb 1 1 0
## treated3fb 1 1 0
## untreated1fb 1 0 1
## untreated2fb 1 0 1
## untreated3fb 1 0 0
## untreated4fb 1 0 0
## attr(,"assign")
## [1] 0 1 2
## attr(,"contrasts")
## attr(,"contrasts")$condition
## [1] "contr.treatment"
##
## attr(,"contrasts")$type
## [1] "contr.treatment"
Cheatsheet: Fitting the models
Fit and test model without batch correction:
# Trim
EM <- subset(pasilla$Expression, rowSums(pasilla$Expression >= 5) >= 3)
# Calculate normalization factors
dge <- calcNormFactors(DGEList(EM), method="TMM")
# Calculate dispersion
disp <- estimateDisp(dge, design=mod, robust=TRUE)
# Fit models
fit <- glmFit(disp, design=mod)
# Perform the test of the given coefficient
res <- glmLRT(fit, coef="conditiontreated")
Cheatsheet: Fitting the models
Fit and test model without batch correction:
# Calculate dispersion
disp_batch <- estimateDisp(dge, design=mod_batch, robust=TRUE)
# Fit models
fit_batch <- glmFit(disp_batch, design=mod_batch)
# Perform the test of the given coefficient
res_batch <- glmLRT(fit_batch, coef="conditiontreated")
Cheatsheet: Inspecting the results
Plot dispersions
par(mfrow=c(1,2))
plotBCV(disp)
plotBCV(disp_batch)
Cheatsheet: Inspecting the results
Number of DE genes
summary(decideTestsDGE(res))
## conditiontreated
## Down 320
## NotSig 7728
## Up 349
summary(decideTestsDGE(res_batch))
## conditiontreated
## Down 519
## NotSig 7342
## Up 536
Cheatsheet: Inspecting the results
Top genes
topTags(res)
## Coefficient: conditiontreated
## logFC logCPM LR PValue FDR
## FBgn0039155 -4.408111 5.984445 506.9701 2.893583e-112 2.429742e-108
## FBgn0029167 -2.194997 8.238072 278.8209 1.356904e-62 5.696961e-59
## FBgn0035085 -2.473314 5.680246 247.0205 1.158840e-55 3.243593e-52
## FBgn0034736 -3.312828 4.059977 203.4360 3.715723e-46 7.800232e-43
## FBgn0029896 -2.569995 5.177376 179.5330 6.128996e-41 1.029304e-37
## FBgn0011260 2.350697 4.318489 147.1858 7.146857e-34 1.000203e-30
## FBgn0000071 2.659627 4.673300 145.2512 1.892539e-33 2.270236e-30
## FBgn0034434 -3.739335 3.426241 127.9909 1.127592e-29 1.183549e-26
## FBgn0001226 1.702006 6.590252 125.6806 3.611838e-29 3.369844e-26
## FBgn0040091 -1.538557 6.415788 122.1507 2.139567e-28 1.796595e-25
topTags(res_batch)
## Coefficient: conditiontreated
## logFC logCPM LR PValue FDR
## FBgn0039155 -4.436497 5.982619 682.8152 1.631955e-150 1.370353e-146
## FBgn0026562 -2.461267 11.674381 412.8130 8.949932e-92 3.757629e-88
## FBgn0029167 -2.201287 8.237568 395.2902 5.837388e-88 1.633885e-84
## FBgn0035085 -2.500920 5.677681 367.4798 6.620961e-82 1.389905e-78
## FBgn0034736 -3.340042 4.056757 256.3267 1.084488e-57 1.821289e-54
## FBgn0029896 -2.568352 5.177267 226.6557 3.196773e-51 4.473884e-48
## FBgn0034434 -3.798349 3.412107 225.7582 5.017097e-51 6.018366e-48
## FBgn0000071 2.635277 4.675388 185.2609 3.442314e-42 3.613139e-39
## FBgn0011260 2.334747 4.320301 181.7895 1.971071e-41 1.839009e-38
## FBgn0001226 1.688841 6.590962 160.4543 9.003123e-37 7.559922e-34
Cheatsheet: Inspecting the results
MA-plots:
par(mfrow=c(1,2))
plotSmear(res_batch, de.tags=rownames(res)[decideTestsDGE(res) != 0])
plotSmear(res_batch, de.tags=rownames(res_batch)[decideTestsDGE(res_batch) != 0])
Cheatsheet: Inspecting the results
Volcano plots:
par(mfrow = c(1, 2))
plot(-log10(PValue) ~ logFC, data = topTags(res, n = Inf, sort.by = "none"),
col = factor(decideTestsDGE(res) != 0), pch = 16)
plot(-log10(PValue) ~ logFC, data = topTags(res_batch, n = Inf, sort.by = "none"),
col = factor(decideTestsDGE(res_batch) != 0), pch = 16)
Cheatsheet: Quasi-likehood
Trying the more conservative QL-pipeline
fitQL_batch <- glmQLFit(y=disp_batch, design=mod_batch, robust=TRUE)
testQL_batch <- glmQLFTest(fitQL_batch, coef=2)
summary(decideTestsDGE(testQL_batch, p.value=0.05))
## conditiontreated
## Down 428
## NotSig 7542
## Up 427
Cheatsheet: Quasi-likehood
The QL-pipeline estimates slightly different dispersions:
plotQLDisp(fitQL)
MalteThodberg/ABC2018 documentation built on May 27, 2019, 11:42 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
title: "Introduction to edgeR" author: "Malte Thodberg" date: "2018-09-07" output: ioslides_presentation: smaller: true highlight: tango transition: faster vignette: > %\VignetteEngine{knitr::knitr} %\VignetteIndexEntry{EDA} %\usepackage[UTF-8]{inputenc}
Introduction
This presentation will show the basic usage of edgeR:
- Normalization.
- Dispersion estimate.
- Fitting GLMs.
- Testing coefficients and contrasts.
- Testing the results.
# Load the data
library(ABC2018)
library(ggplot2)
theme_set(theme_minimal())
library(edgeR)
data("zebrafish")
Trimming and normalizing the data.
Just as before, we started by loading, trimming and normalizing the data:
# Trim
EM_zebra <- subset(zebrafish$Expression, rowSums(zebrafish$Expression >= 5) >= 3)
# Calculate normalization factors
dge_zebra <- DGEList(EM_zebra)
dge_zebra <- calcNormFactors(dge_zebra, method="TMM")
Building the model matrix
We use model.matrix to make a simple model matrix:
mod <- model.matrix(~gallein, data=zebrafish$Design)
mod
## (Intercept) galleintreated
## Ctl1 1 0
## Ctl3 1 0
## Ctl5 1 0
## Trt9 1 1
## Trt11 1 1
## Trt13 1 1
## attr(,"assign")
## [1] 0 1
## attr(,"contrasts")
## attr(,"contrasts")$gallein
## [1] "contr.treatment"
Estimating the dispersion
Now we move on to estimating dispersion or BCV:
disp_zebra <- estimateDisp(dge_zebra, design=mod, robust=TRUE)
The 3-step estimation is all done by a single function in the newest version of edgeR: estimateDisp, which replaces the three previous functions of
estimateCommonDispestimateTrendedDispestimateTagwiseDisp
Plotting the dispersion
We can then plot the estimates:
plotBCV(y=disp_zebra)
Fiting gene-wise GLMs and testing a coefficient.
Now we can fit gene-wise GLMs:
fit_zebra <- glmFit(disp_zebra, design=mod)
Finally, we can test a coefficient in the model for DE:
# Using the name of the coefficient
gallein <- glmLRT(fit_zebra, coef="galleintreated")
# Using the index of the coefficient
gallein <- glmLRT(fit_zebra, coef=2)
Inspecting the results
edgeR has several useful functions for inspecting and plotting the results:
Inspecting the top hits with topTags:
topTags(gallein)
## Coefficient: galleintreated
## logFC logCPM LR PValue FDR
## ENSDARG00000075505 -7.050266 0.91117648 23.80888 1.063907e-06 0.02098875
## ENSDARG00000002508 -6.899411 1.98202488 22.20389 2.451759e-06 0.02296418
## ENSDARG00000091349 -8.890033 2.67150109 20.55248 5.801872e-06 0.02296418
## ENSDARG00000069441 -9.167127 -0.06217613 20.49904 5.966113e-06 0.02296418
## ENSDARG00000071565 7.114211 0.34425608 20.11517 7.291619e-06 0.02296418
## ENSDARG00000087178 -9.267206 0.03576790 19.88517 8.223557e-06 0.02296418
## ENSDARG00000040128 7.387525 6.64510937 19.77201 8.725111e-06 0.02296418
## ENSDARG00000090689 5.949190 4.67990013 19.49232 1.010048e-05 0.02296418
## ENSDARG00000000906 -8.272182 1.30746230 19.42254 1.047636e-05 0.02296418
## ENSDARG00000076643 -7.572011 0.42975453 18.64519 1.574441e-05 0.02741095
Inspecting the results
Summarize the number of up- and down-regulated genes with decideTestsDGE:
is_de <- decideTestsDGE(gallein, p.value=0.1)
head(is_de)
## galleintreated
## ENSDARG00000000001 0
## ENSDARG00000000002 0
## ENSDARG00000000018 0
## ENSDARG00000000019 0
## ENSDARG00000000068 0
## ENSDARG00000000069 0
summary(is_de)
## galleintreated
## Down 68
## NotSig 19581
## Up 79
Inspecting the results
MA-plot, showing relation between mean expression and logFC, :
plotSmear(gallein, de.tags=rownames(gallein)[is_de != 0])
Inspecting the results
There is no built-in function for making a volcano plot, but it's easy to do yourself:
plot(-log10(PValue) ~ logFC, data = topTags(gallein, n = Inf, sort.by = "none"),
col = factor(decideTestsDGE(gallein) != 0), pch = 16)
Quasi-likelihood: Fitting models
Recently, edgeR introduced an alternative DE pipeline using Quasi-likelihood. It should be slightly more conservative than the standard edgeR pipeline. Code-wise, though, they are almost identical:
fitQL <- glmQLFit(y=disp_zebra, design=mod, robust=TRUE)
galleinQL <- glmQLFTest(fitQL, coef="galleintreated")
summary(decideTestsDGE(galleinQL, p.value=0.1))
## galleintreated
## Down 0
## NotSig 19728
## Up 0
Quasi-likehood: Plotting dispersions
The QL-pipeline estimates slightly different dispersions:
plotQLDisp(fitQL)
Final exercises:
Use the above code as a template for conducting your own analysis. For the pasilla dataset:
- Load, trim and normalize the data
- Inspect the study design, and decide on a model matrix to use with edgeR
- Use edgeR to estimate dispersions and fit gene-wise GLMs
- Report the number of up- and down-regulated genes for the tests of interests
- Generate smear and volcano plots summarizing the analysis.
- Does the number of DE genes change when you model the batch effect or not?
If you have time:
- Investigate the effect of different normalization methods on the number of DE genes.
- Try out the QL-pipeline by replacing
glmFitwithglmQLFitandglmLRTwithglmQLFTest
Once again - the next couple of slides contain cheat sheets...
Cheatsheet: Experimenting with model matrices
# Load the data
data("pasilla")
# This will makes "treated the intercept!"
mod <- model.matrix(~condition, data=pasilla$Design)
mod
## (Intercept) conditionuntreated
## treated1fb 1 0
## treated2fb 1 0
## treated3fb 1 0
## untreated1fb 1 1
## untreated2fb 1 1
## untreated3fb 1 1
## untreated4fb 1 1
## attr(,"assign")
## [1] 0 1
## attr(,"contrasts")
## attr(,"contrasts")$condition
## [1] "contr.treatment"
Cheatsheet: Experimenting with model matrices
# Relevel to make "untreated" intercept
pasilla$Design$condition <- relevel(pasilla$Design$condition, "untreated")
mod <- model.matrix(~condition, data=pasilla$Design)
mod
## (Intercept) conditiontreated
## treated1fb 1 1
## treated2fb 1 1
## treated3fb 1 1
## untreated1fb 1 0
## untreated2fb 1 0
## untreated3fb 1 0
## untreated4fb 1 0
## attr(,"assign")
## [1] 0 1
## attr(,"contrasts")
## attr(,"contrasts")$condition
## [1] "contr.treatment"
Cheatsheet: Experimenting with model matrices
# Relevel to make "untreated" intercept
pasilla$Design$condition <- relevel(pasilla$Design$condition, "untreated")
mod_batch <- model.matrix(~condition+type, data=pasilla$Design)
mod_batch
## (Intercept) conditiontreated typesingle-read
## treated1fb 1 1 1
## treated2fb 1 1 0
## treated3fb 1 1 0
## untreated1fb 1 0 1
## untreated2fb 1 0 1
## untreated3fb 1 0 0
## untreated4fb 1 0 0
## attr(,"assign")
## [1] 0 1 2
## attr(,"contrasts")
## attr(,"contrasts")$condition
## [1] "contr.treatment"
##
## attr(,"contrasts")$type
## [1] "contr.treatment"
Cheatsheet: Fitting the models
Fit and test model without batch correction:
# Trim
EM <- subset(pasilla$Expression, rowSums(pasilla$Expression >= 5) >= 3)
# Calculate normalization factors
dge <- calcNormFactors(DGEList(EM), method="TMM")
# Calculate dispersion
disp <- estimateDisp(dge, design=mod, robust=TRUE)
# Fit models
fit <- glmFit(disp, design=mod)
# Perform the test of the given coefficient
res <- glmLRT(fit, coef="conditiontreated")
Cheatsheet: Fitting the models
Fit and test model without batch correction:
# Calculate dispersion
disp_batch <- estimateDisp(dge, design=mod_batch, robust=TRUE)
# Fit models
fit_batch <- glmFit(disp_batch, design=mod_batch)
# Perform the test of the given coefficient
res_batch <- glmLRT(fit_batch, coef="conditiontreated")
Cheatsheet: Inspecting the results
Plot dispersions
par(mfrow=c(1,2))
plotBCV(disp)
plotBCV(disp_batch)
Cheatsheet: Inspecting the results
Number of DE genes
summary(decideTestsDGE(res))
## conditiontreated
## Down 320
## NotSig 7728
## Up 349
summary(decideTestsDGE(res_batch))
## conditiontreated
## Down 519
## NotSig 7342
## Up 536
Cheatsheet: Inspecting the results
Top genes
topTags(res)
## Coefficient: conditiontreated
## logFC logCPM LR PValue FDR
## FBgn0039155 -4.408111 5.984445 506.9701 2.893583e-112 2.429742e-108
## FBgn0029167 -2.194997 8.238072 278.8209 1.356904e-62 5.696961e-59
## FBgn0035085 -2.473314 5.680246 247.0205 1.158840e-55 3.243593e-52
## FBgn0034736 -3.312828 4.059977 203.4360 3.715723e-46 7.800232e-43
## FBgn0029896 -2.569995 5.177376 179.5330 6.128996e-41 1.029304e-37
## FBgn0011260 2.350697 4.318489 147.1858 7.146857e-34 1.000203e-30
## FBgn0000071 2.659627 4.673300 145.2512 1.892539e-33 2.270236e-30
## FBgn0034434 -3.739335 3.426241 127.9909 1.127592e-29 1.183549e-26
## FBgn0001226 1.702006 6.590252 125.6806 3.611838e-29 3.369844e-26
## FBgn0040091 -1.538557 6.415788 122.1507 2.139567e-28 1.796595e-25
topTags(res_batch)
## Coefficient: conditiontreated
## logFC logCPM LR PValue FDR
## FBgn0039155 -4.436497 5.982619 682.8152 1.631955e-150 1.370353e-146
## FBgn0026562 -2.461267 11.674381 412.8130 8.949932e-92 3.757629e-88
## FBgn0029167 -2.201287 8.237568 395.2902 5.837388e-88 1.633885e-84
## FBgn0035085 -2.500920 5.677681 367.4798 6.620961e-82 1.389905e-78
## FBgn0034736 -3.340042 4.056757 256.3267 1.084488e-57 1.821289e-54
## FBgn0029896 -2.568352 5.177267 226.6557 3.196773e-51 4.473884e-48
## FBgn0034434 -3.798349 3.412107 225.7582 5.017097e-51 6.018366e-48
## FBgn0000071 2.635277 4.675388 185.2609 3.442314e-42 3.613139e-39
## FBgn0011260 2.334747 4.320301 181.7895 1.971071e-41 1.839009e-38
## FBgn0001226 1.688841 6.590962 160.4543 9.003123e-37 7.559922e-34
Cheatsheet: Inspecting the results
MA-plots:
par(mfrow=c(1,2))
plotSmear(res_batch, de.tags=rownames(res)[decideTestsDGE(res) != 0])
plotSmear(res_batch, de.tags=rownames(res_batch)[decideTestsDGE(res_batch) != 0])
Cheatsheet: Inspecting the results
Volcano plots:
par(mfrow = c(1, 2))
plot(-log10(PValue) ~ logFC, data = topTags(res, n = Inf, sort.by = "none"),
col = factor(decideTestsDGE(res) != 0), pch = 16)
plot(-log10(PValue) ~ logFC, data = topTags(res_batch, n = Inf, sort.by = "none"),
col = factor(decideTestsDGE(res_batch) != 0), pch = 16)
Cheatsheet: Quasi-likehood
Trying the more conservative QL-pipeline
fitQL_batch <- glmQLFit(y=disp_batch, design=mod_batch, robust=TRUE)
testQL_batch <- glmQLFTest(fitQL_batch, coef=2)
summary(decideTestsDGE(testQL_batch, p.value=0.05))
## conditiontreated
## Down 428
## NotSig 7542
## Up 427
Cheatsheet: Quasi-likehood
The QL-pipeline estimates slightly different dispersions:
plotQLDisp(fitQL)
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