Question

FDR does not change between genes in edgeR

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apfelbapfel ▴ 30

@apfelbapfel-15149

Last seen 5.3 years ago

HI all

I am analyzing an RNAseq dataset to find DE genes using edgeR. Strangely I get no significantly differentially expressed genes, and the same FDR for every gene when summarizing the results.

I ran the same code on a public dataset and got fine results, so I guess it has something to do with the specs of the dataset but I cant figure out what.

The data is from 8 mice, genetically homogeneous, 4 of them treated and 4 untreated. What I know is that the biological material used for sequencing was very little, so the quality is maybe not the best.

Any help would be appreciated to find out where I am going wrong, many thanks!

My Code:

library(edgeR)
d <- DGEList(counts=data_matrix[2:46079,3:10], group=group)
d$samples
isexpr <- rowSums(cpm(d)>1) >= 4
y <- d[isexpr, , keep.lib.sizes=FALSE]
y$samples
y <- calcNormFactors(y)

plotMDS(y, method="bcv", col=as.numeric(y$samples$group))
legend("bottomleft", as.character(unique(d$samples$group)), col=1:3, pch=20)

design<- model.matrix(~ 0 + y$samples$group)
y <- estimateDisp(y, design, robust=TRUE)
plotBCV(y)

fit <- glmQLFit(y, design, robust=TRUE)
plotQLDisp(fit)

qlf <- glmQLFTest(fit)
topTags(qlf,n=15)

Results:

	logFC	logCPM	F	PValue	FDR
Gene1	0.929197	7.740159	21.96436	0.000363	0.998617
Gene2	1.056017	5.989199	19.84983	0.000562	0.998617
Gene3	1.172161	5.667213	17.50894	0.000944	0.998617
Gene4	0.897815	7.162813	16.90687	0.001086	0.998617
Gene5	1.523411	5.06676	16.61661	0.001164	0.998617
Gene6	-1.72696	4.947108	16.4548	0.00121	0.998617
Gene7	-0.903	6.165918	16.37441	0.001233	0.998617
Gene8	-3.23804	4.054428	16.32435	0.001248	0.998617
Gene9	-3.75089	5.074982	19.1382	0.001352	0.998617
Gene10	0.970776	6.206563	15.89572	0.001385	0.998617
Gene11	0.749168	7.167152	15.84059	0.001403	0.998617
Gene12	0.799071	6.561417	15.34004	0.001588	0.998617

RNAseq edgeR • 2.0k views

ADD COMMENT • link updated 5.3 years ago by Gordon Smyth 50k • written 5.3 years ago by apfelbapfel ▴ 30

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The following specs are relevant:

After filtering (<1% was removed)

group lib.size norm.factors
1     1  7814960    0.9844539
2     1  3638388    0.9664794
3     2 12624890    1.0205424
4     2 10289325    1.0223115
5     1  5545574    0.9718041
6     1  8192437    1.0549536
7     2  4185595    0.9658630
8     2  8608568    1.0173494

ADD REPLY • link 5.3 years ago apfelbapfel ▴ 30

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please just tell me if any further info would be helpful!

ADD REPLY • link 5.3 years ago apfelbapfel ▴ 30

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Well, the results you report are very surprising. It is very unusual that you could start with 46,000 genes but filter less than 1%. When we analyse mouse data we typically filter nearly half the genes using a similar filtering rule to you. I don't understand how you could be filtering less than 1% of genes.

Secondly, as we've already discussed, your code sequence is actually testing whether the counts in the last group are equal to 1 rather than testing DE. It seems incredible that none of the counts could be judged different from 1, so I conclude that the dispersion in your data must be enormous. I would view any NB common dispersion greater than about 0.1 for genetically identical mice to represent a poor quality experiment or an error in the analysis.

ADD REPLY • link 5.3 years ago Gordon Smyth 50k

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Hey Gordon. sorry for the late reply, was traveling.

i have to say that this comment of yours confuses me, will i test for DE if using

design <- model.matrix(~ y$samples$group)?

regarding filtering, library sizes changed for example from 7849976 to 7814960, which I would argue is not much.

looking at my dispersion plot I agree that its not optimally set up, but since i am a beginner i have to admit I am a bit struggling to find an adequate solution, can you advise a better strategy?

Thanks for your effort!

https://imgur.com/a/b2H2hQ4

and

https://imgur.com/a/zXhOJuW

ADD REPLY • link 5.3 years ago apfelbapfel ▴ 30

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What is important is the number of genes that are filtered, not the number of reads. I thought you were saying that only 1% of genes were filtered, which would be crazy. The percentage of genes filtered should be much greater than the percentage of reads filtered.

The dispersion shown in the BCV plot is huge. No RNA-seq experiment with genetically identical mice, good quality RNA and controlled lab conditions should be as variable as this. You should trouble-shoot your data using MDS plots and so on. Maybe you have a low RNA quality problem, which you might not be able to much about at this stage.

Also, if you have used Ensembl genes, you could also consider using our recommended Rsubread read mapping and summarization pipeline with builtin Rsubread Entrez Gene annotation. That does tend to reduce variability because it concentrates on better annotated exons.

ADD REPLY • link 5.3 years ago Gordon Smyth 50k

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My apologies for unclear terminology. You are absolutely correct, the number of genes goes down from around 46000 to 12000.

I will work on your suggestions, but the experimentalists have now told me that they barely had any RNA to begin with, so I assume its really a problem of low quality data.

Thanks!

ADD REPLY • link 5.3 years ago apfelbapfel ▴ 30

score 3 · Accepted Answer · 2019-01-01

This is entirely expected due to the enforced monotonicity of the FDR calculation. See, for example:

A: FDR calculation by combineTests in csaw

Limma Voom produces many identical adjusted p-values

A: All same padj of contrast results in the DESeq2

The conclusion here is that there is no differential expression at a FDR threshold of 5% (or, really, any sensible threshold that you might choose).

My only suggestion would be to check whether there are additional factors of variation that you haven't accounted for, e.g., batches. These would inflate your dispersion estimates and reduce power to detect DE genes. Otherwise, it seems that your samples are too variable to allow you to pick up any subtle DE that may (or may not!) be present.

Gordon Smyth · Accepted Answer · 2019-01-02

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Gordon Smyth 50k

@gordon-smyth

Last seen 1 hour ago

WEHI, Melbourne, Australia

Aaron has answered your FDR question, but your analysis code is not correct and you are not actually testing for DE between the treated and untreated groups. The code would work if you replaced the design matrix with:

design <- model.matrix(~ y$samples$group)

As always with edgeR or limma, you can use "0+" in your model formula and then form a test contrast, or you can omit the "0+" and test a coefficient directly, but you can't use "0+" and then test a coefficient.

ADD COMMENT • link 5.3 years ago Gordon Smyth 50k

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Many thanks to Aaron and Gordon. That mistake with the code was really important to know. Unfortunately still nothing significant to be found, the PI wont be happy.

ADD REPLY • link updated 5.3 years ago by Gordon Smyth 50k • written 5.3 years ago by apfelbapfel ▴ 30

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Well, happiness and truth are rarely correlated. A harsh lesson that comes with age, I guess.

Here's some possible excuses: perhaps the treatment effect is not transcriptional? Or maybe the treatment has no effect, in which case you can save everyone some time by working on something else. You can always blame it on statistical power, or specifically the lack thereof to pick up subtle DE that will (inevitably) be present when dealing with different biological conditions. But if the DE is so subtle, how much biological effect can you expect it to have? Food for thought.

Also, I'm surprised you managed to get your result table with your incorrect design matrix. When someone makes this mistake, usually everything is detected as DE (with log-fold changes similar to log-CPMs) because the null hypothesis is nonsensical. I suppose that's my excuse for not picking this up in the first place.

ADD REPLY • link 5.3 years ago Aaron Lun ★ 28k

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Good points. I am assuming its due to the heterogeneity in the treated individuals, the dont cluster at all. I had run it now in DEseq2, bc I am still learning and thought its good to practice.

DEseq2 finds 26 DE genes, how is your experience with regards to trustworthiness of such diverging results? The fitted trend in the dispersion plot in DEseq2 looks actually worse than in EdgeR, and I heard its better to use it only when one has more biological replicates than 6 per group(I have 4)? I am therefore very hesitant to trust those results?

Many thanks!

ADD REPLY • link 5.3 years ago apfelbapfel ▴ 30

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The edgeR QL pipeline provides rigorous control of the FDR rate while DESeq2 does not. DESeq2 is comparable to the edgeR LRT pipeline, and both are slightly anti-conservative so that a nominal 5% FDR might actually be 7% or so. It is normal for edgeR-LRT or DESeq2 to give more DE genes than edgeR-QL, but Aaron and I recommend the latter because of the stricter FDR control.

ADD REPLY • link 5.3 years ago Gordon Smyth 50k