Question: CIBERSORT deconvolution advice
gravatar for joker33
11 months ago by
joker3330 wrote:

Dear community,

I was wondering if any of you could give me some advice with the following issues:

  1. All results are different when performing "absolute" deconvolution using legacy CIBERSORT source code R version (no.sumto1 & sig.score), legacy CIBERSORT online tool and the online CIBERSORTx tool (see example copied below). Has there been a change in the code between versions?
  2. I have very large matrices to deconvolute (up to 780MB). Is it possible to split up matrices for deconvolution of samples I want to compare?
  3. How does CIBERSORT accomplish to exclude non-hematopoietic genes during signature matrix genetartion? Is there an internal database of "hematopoietic" and "non-hematopoietic" genes? In this case, I am wondering which gene annotation tool and which snapshot thereof was used to define "non-hematopoietic" genes? The reason why I am asking is, that I am concerned this will affect signature matrix generation depending on which annotation tool and version I use for gene annotation of the reference file due to tool-specific annotation differences in gene names (alterantive gene names, novel transcripts etc...) that don't match the CIBERSORT internal record.
  4. For the imputation of cell fractions, how important is it to match gene annotation of signature and mixture in regard to gene-annotation differences caused by different annotation tools/versions. Do I have to assume that the results are always penalised in case the mixture and signature/reference files were annotated in different labs (with a different annotation tool / version)? How would an annotation difference of more than 15% of genes between signature and mixture affect deconvolution performance?
  5. Error during the imputation of cell fractions using CIBERSORTx: "WARNING: reaching max number of iterations". What is the cause and how can I solve it?

CIBERSORT absolute deconvolution results

Thank you!!

ADD COMMENTlink modified 4 months ago by starick.marick0 • written 11 months ago by joker3330

Hi, I'm using CIBERSORTx for check cell types on RNA-Seq data, and I'm not sure about which count normalization it's better for input: CPM or TPM. I'm using LM22 as the signature matrix. The tutorial recommends that we should normalize the mixture file same as signature matrix, however, LM22 is microarray and I don't know how it was normalized. I also have access only to count matrix and using CPM will be easier, but I'm not sure if this is the best way. What signature matrix did you use? How did you normalize your counts?

ADD REPLYlink written 4 months ago by starick.marick0

Hi, I'm pretty sure LM22 is RMA-normalised as I have read that somewhere in the CIBERSORTx paper, could be under the supplementary information section. CIBERSORTx has a batch correction option that removes technical differences between signature matrix and mixture file that derived from different platforms (like your case - a signature matrix derived from microarray [LM22] and mixture file from RNA-seq data), so I think either CPM or TPM is fine. However, I personally would go for TPM because the authors of CIBERSORTx mainly used TPM files for their research/analysis, as reported in their paper... As you can see, I am only following the protocols set by CIBERSORTx team, if anyone has better explanation to which normalisation method to use, I would love to hear it!

ADD REPLYlink written 6 weeks ago by jill.syx0

Hi! I was wondering if there's any follow-ups to all the questions posted? Because I am having the same questions in mind as well, especially the second question. I'd be really grateful if you could please share any updates/findings with me. If not, have you tried contacting the cibersortx team? (because I did but haven't heard anything back from them...).

ADD REPLYlink written 6 weeks ago by jill.syx0
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