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lauringlab / variant_pipeline

Work on the variant_pipeline and initial r analysis used in calling variants from NGS data
Apache License 2.0
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failed to install some R packages with packrat #13

Open grendon opened 5 years ago

grendon commented 5 years ago

First of all, I am installing the pipeline on a cluster to my local space as I am a regular user rather than someone with root privileges. Some R packages like Rhtslib are failing to install and the issue seems to be that packrat cannot direct the installation to happen on my local path. This problem has been known and discussed in places like this one: https://github.com/rstudio/packrat/issues/418

Wouldn't it make more sense to use BiocManager in order to install that long list of R libraries that this pipeline requires?

alauring commented 5 years ago

Thanks for the comment. We had some issues with this ourselves and were able to get a workaround with the folks who manage our cluster. Feel free to fork our repository and use BiocManager. Please let us know if that provides a more robust solution and perhaps send a pull request?

grendon commented 5 years ago

I am trying my luck with modules and nextflow instead of using your approach with conda and packrat. If I get anywhere I will be happy to post my version as a branch (perhaps?)

grendon commented 5 years ago

Would it be possible for you to post results files within the tutorial so that we can compare our results to your, please?

This is what deepsnv is generating for us in the output.csv file:

chr pos ref var p.val freq.var sigma2.freq.var n.tst.fw cov.tst.fw n.tst.bw cov.tst.bw n.ctrl.fw cov.ctrl.fw n.ctrl.bw cov.ctrl.bw raw.p.val Id mutation
HA 784 A G 0 1 0.000697 1018 1018 416 416 0 1146 0 215 0 HA-22_S7_L001 HA_A784G
M 4 G A 0.008994 0.147545 0.000885 421 883 1 3 309 937 2 9 1.66E-07 HA-22_S7_L001 M_G4A
NP 4 G A 0.000134 0.192342 0.001148 334 725 1 3 138 518 1 1 2.47E-09 HA-22_S7_L001 NP_G4A
alauring commented 5 years ago

That looks like a typical output file for DeepSNV. I do not know if the data are exactly like what we get in our tutorial, but suspect that everything is working properly here!