STEP 18 Errors when running Bayesian Network Model on HMM predicted CNVs

[mluciarr]

Hi, I ran into an error when infercnv::run is doing Bayesian Network Model on HMM predicted CNV's. Here is the error message:

STEP 18: Run Bayesian Network Model on HMM predicted CNVs

INFO [2023-06-08 15:13:55] Initializing new MCM InferCNV Object.
INFO [2023-06-08 15:13:55] validating infercnv_obj
INFO [2023-06-08 15:13:55] Total CNV's:  4005
INFO [2023-06-08 15:13:55] Loading BUGS Model.
INFO [2023-06-08 15:14:00] Running Sampling Using Parallel with  4 Cores
INFO [2023-06-08 15:48:06] Obtaining probabilities post-sampling
**Error in do.call(rbind, mcmc[[j]]) : second argument must be a list**

I am using R version 4.3.0 (2023-04-21) Platform: aarch64-apple-darwin20 (64-bit) Running under: macOS Ventura 13.4

The infercnv version is infercnv_1.16.0 . Many thanks,


Lucia

[GeorgescuC]

Hi @mluciarr ,

I am not sure of why this is happening. The only guess would be that one of the threads does not return results because it was killed/errored but that is not being reported. What version of JAGS are you using?

Looking at the number of CNVs you have however, I would check if your subclusters are not too fragmented because the count seems rather high. You can check the video tutorial to see how to inspect and tune subclustering.

Regards, Christophe.

[mluciarr]

Hi @GeorgescuC !

Thank you so much for your reply, I have the JAGS 4.3.2 version, I think it's the last one. I am not certain that it may be the issue.

Regarding you second observation perhaps makes sense. maybe it is linked to the fact that the deprogram doesn't look very nice. I am not sure which is the reason that it seems it's not working properly. Both sample are 2 different tumoral samples, and both have these dendrogram shape which from my point of view aren't doing anything or were no built properly. In both cases I used normal B cells as a reference.

Moreover, I wanted to change the order of the clusters in the heatmap. How can I do that?

Thanks you so much again! 👍🏼