livnatje / immuneresistance Goto Github PK

In file Mel.T.CD4.QC.rds you have classifed cells as "exhausted_Treg".

As far as I've read in the paper, there is no indication on how you get this very specific immune cell state.
Is this based on the combined expression of two gene sets from TableS3A?

As far as I know only naive, exhausted and regulatory CD4 + T cell subsets are indicated in the supplementary.

Thanks

I'm trying to reproduce OE score you produce in this study, which by the way is impressive !
The idea behind the OE was also recently reused elsewhere https://rdcu.be/czHtb.

I got some hints from ImmRes_OE.R but I don't understand this little part of code before the whole process, precisely the content of r$cd. What does it stand for ? The raw counts data ? If you could highlight me a little bit on this, that could help me to understand this little preprocess.

If one value is inferior to zero, you use the raw counts contained in r$cd otherwise you transforme to counts but still your r$genes.mean is computed from r$tpm as r$zscores .
You kind of mixing both approach with counts and tpm and i must say that I'm a bit lost here :/

Thanks.

  if(any(r$tpm<0)){ 
    print("Using counts to bin genes!!!")
    r$genes.dist<-rowMeans(r$cd>0)
  }else{
    X<-10*((2^r$tpm)-1)
    r$genes.dist<-log2(rowMeans(X,na.rm = T)+1)
  }

Hi,
which code was used to calculate TCGA sample resistance w or w/o non-malignant cell filtered in FigS7
thx

Hello Livnat,

I am interested to use the bulk-RNA-seq data from validation cohort 2 that I found here:

https://portals.broadinstitute.org/single_cell/study/SCP109/melanoma-immunotherapy-resistance#study-download

However, some gene expression data is missing, especially the gene expression status of CXCL10, IFNG, CXCL9, IDO1, HLA-DRA, STAT1 . Is it available somewhere else?

Mostapha

Hi.

In table S3B (denovo.cell.type.sig) you show the cell type signatures derived from the scRNA-seq analyses.

I've also downloaded the scRNA-seq annotation data from GEO, this is annotated by cell type but I do not see the annotation for cell subsets (cytotoxic, exhausted, naïve).

Is there any way to know which specific cells were considered as cytotoxic, exhausted, naïve, undetermined.

Thanks!

Hello,

I would like to apply your approach to other datasets, as suggested here:
https://github.com/livnatje/ImmuneResistance/wiki/Applying-the-approach-to-other-datasets

In particular, I want to predict ICI results for melanoma. Can I use this approach with only bulk-RNA data from each patient?

If yes, I only have bulk.tpm: where to find the other inputs?

Can you give a concrete example of application to other datasets?

I am especially interested in plugging bulk RNA seq data from Riaz et al.

I cannot find the ImmRes_Rfiles.zip in Single Cell Portal, as indicated in the link you provided. Did you remove it? Thank you!

Hello Livnat,

How to access your predictions of the clinical response to immunotherapy for the previous studies: Hugo et al. 2016, Riaz et al. 2017, Van Allen et al. 2015?

(your tutorial suggests to look at "ImmRes4_predictICBresponses.R", but there are a lot of rds files and abbreviations inside, and I am a bit confused)

Hello. Aside from the scRNAseq data after Anti-PD1 treatment, do you have scRNAseq data before the treatment? I have only seen an accessible antiPD1 treated scRNAseq data at Single Cell portal. Thank you!