How to run allelic imbalance in apeglm
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euge.eugenio ▴ 20
@eugeeugenio-15908
Last seen 6 weeks ago
United Kingdom

I'm trying to perform an allelic imbalance analysis on some ATAC-seq data generated from a population of human individuals. As such for each SNP there will be some individuals that are heterozygous and some that are homozygous. In this case my table contains counts for the individuals that are heterozygous but NA values for the individuals that are homozygous.

I've tried following the apeglm vignette, Modeling ratios of counts section to perform a beta-binomial fit, but if I present a table with NA values it will fail with the following error:

Error in seq_len(sum(nonzero)): argument must be coercible to non-negative integer
Traceback:

1. system.time({
 .     for (i in 1:niter) {
 .         param <- cbind(theta.hat, cts)
 .         fit.mle <- apeglm(Y = ase.cts, x = x, log.lik = NULL, 
 .             param = param, no.shrink = TRUE, log.link = FALSE, 
 .             method = "betabinCR")
 .         theta.hat <- bbEstDisp(success = ase.cts, size = cts, 
 .             x = x, beta = fit.mle$map, minDisp = 0.01, maxDisp = 5000)
 .     }
 . })
2. apeglm(Y = ase.cts, x = x, log.lik = NULL, param = param, no.shrink = TRUE, 
 .     log.link = FALSE, method = "betabinCR")   # at line 7-8 of file <text>
3. betabinCppRoutine(Y, x, weights, offset, param, prior.control, 
 .     method, result, bounds, optim.method)
4. sapply(seq_len(sum(nonzero)), function(i) {
 .     betabinFn(initC, x = x, y = YNZ[, i], size = sizeNZ[, i], 
 .         theta = theta[i], weights = weightsNZ[, i], sigma = sigma, 
 .         S = S, no.shrink = no.shrink, shrink = shrink, cnst = 0)
 . })
5. lapply(X = X, FUN = FUN, ...)

What's the correct procedure to analyse this data? Do I need to fill in the NA values with something?

I wonder if there is any code/tutorial to run this kind of analysis, including the estimation of p-values?

apeglm • 570 views
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Michael Love could you help me? Thanks!

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@mikelove
Last seen 12 hours ago
United States

You can put 1 / 1 for the ratio when you are missing counts and then set the weight for that matrix cell to 1e-6.

Let me know if that's clear or I can post code.

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I think it's been coded correctly, but now it takes forever to run. It's been running for 2 days and it still hasn't finished. Is that normal?

size of cts is: A matrix: 90049 x 54 of type dbl

This is the code for the first bit, and it's stuck there:

# fill NA
weights <- ifelse(is.na(ase.cts), 1e-6, 1)
ase.cts[is.na(ase.cts)] <- 1
cts[is.na(cts)] <- 2

theta.hat <- 100 # rough initial estimate of dispersion
X=matrix(rep(1,ncol(cts)))
niter <- 3
system.time({
  for (i in 1:niter) {
    param <- cbind(theta.hat, cts)
    fit.mle <- apeglm(Y=ase.cts, x=X, log.lik=NULL, param=param,
                      no.shrink=TRUE, log.link=FALSE, method="betabinCR", weights = weights)
    theta.hat <- bbEstDisp(success=ase.cts, size=cts,
                           x=X, beta=fit.mle$map,
                           minDisp=1, maxDisp=500, weights = weights)
  }
})
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apeglm was coded in C++, such that 3000 x 8 matrix in the vignette takes <3 seconds.

Try for the first 100 features or so, just one iteration of the loop (fit.mle then theta.hat updates), and see how long it is taking.

You could also likely do more pre-filtering. What are the 90k features?

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Hi, so I've tried running it on 1000 samples and the 3 iterations take about 30 seconds to run. With 2000 samples it takes 1.5 minutes. I've added a few print statements and it basically never completes the bbEstDisp step for even the first loop. CPU usage is still 100% and memory is about 1.2 gb.

The 90k features are all the SNPs that have enough coverage (10 reads) in at least 3 patients. Do you think if I ran it per chromosome the dispersion estimate would still be valid?

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So I had a look at the source code of bbEestDisp and I feel the meaning of weights in apeglm is different from bbEstDisp. It seems that it's fitting theta.hat per row, but it's multiplying the weights matrix for the whole result rather than doing it per row of weights. Maybe that's why it's going exponential?

I tried setting the weights of bbEstDisp to 1 and that seems to run fine, but is that statistically correct (having weights for apeglm but not for bbEstDisp)?

That's the results for chromosome 1 enter image description here

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I agree, I think weights hadn't been tested in this step of the beta binomial GLM (in the paper we didn't include weights, and this was added posthoc for support of some experiments).

I've tried to address it here, can you install_github():

https://github.com/azhu513/apeglm/commit/0abaccbfaad42996f4dad3cb5fba89597428e7ac

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Yes, that seems to work. The number of significant SNPs with allelic imbalance went down slightly, but I guess that's expected since all the filled in data would have 1/1 counts so no dispersion?

Just last question, am I understanding correctly that I can use the final S-values as if they were FDR corrected p-values?

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Yes and yes.

Thanks for posting the bug! The patch should be in devel and release apeglm soon.

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Thank you for the help!

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