Comments (4)
I will take charge of this task.
Here is what I woudl like to implement:
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Deconvolution-based methods:
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Use of CIBERSORT with custom signature matrix
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Use of EPIC with custom signature matrix
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Scoring-based methods:
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Use of xCell with custom signature
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Use of BASE with custom pseudo-signature matrix: this method has been introduced alongside the mouse methods, but by changing the signature we can easily use it for human -> this methods does somehow a GSEA but more refined
When custom signatures will be used there is no distinction between human and mouse based methods
Feel free to leave any suggestion!
EDIT: This is intended to be a preliminary plan. I will then check if the implementatio is easily feasible and does not require too complex data
from immunedeconv.
Hello,
Two other important things to keep in mind concerning if allowing for custom signatures:
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The cell_type_mapping.xlsx file will not necessarily include the cell types defined in the custom signature. In such a case, you should make sure that all cell types derived in the signature will be reported.
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Even between methods accepting matrices there are structural and conceptual differences: for CIBERSORT, the signature matrix should only contain the gene expression of the signature genes, while for EPIC there should be a reference profile matrix containing all the genes and a list of marker genes to use (because some normalization on all the genes is performed within EPIC). Moreover for EPIC, there should ideally also be some gene variability matrix.
So it's indeed not easy to reconcile everything in a unified framework (but the same list of marker genes can certainly be used in EPIC, MCP and xCell, so it'd make that you'd need to give the full reference profiles, the signature genes and possibly the reference profiles variability matrix).
Cheers,
Julien
from immunedeconv.
Hi Julien,
I agree with your last comment! Another conceptual difference regards the usage of all signature genes or just a subset of them. CIBERSORT performs feature selection on the LM22 matrix, whereas methods like quanTIseq and EPIC, if run with the LM22 matrix, might obtain noisy results.
Also, quanTIseq removes some genes from the TIL10 signature depending on the "---tumor" and "--arrays" options. This is not easily transferable to other signatures, for now.
Cheers,
Francesca
from immunedeconv.
Update:
xCell technically allows for a set of user-specified signature genes, but they are quite difficult to prepare. It requires a GMT object with the genes, and a Spillover object for the correction of the sores. All these elements are required and I think they are difficult to deal with in the case of custom signatures.
What I will do is that I will offer the possibility to use ConsensusTME with user-specified gene signatures. These can be provided as a list
signatures <- list(
cell type 1 = c(...),
cell type 2 = c(...),
etc)
Then ConsensusTME will compute the scores with a GSEA approach
from immunedeconv.
Related Issues (20)
- Modernize CI HOT 4
- Support for mouse deconvolution HOT 3
- patched mcp counter dependencies
- quantiseqr / immundeconv differences HOT 2
- quanTIseq update HOT 2
- Error in Installation of R Package HOT 2
- Error in the mapping of cell types HOT 1
- TPM-normalized HOT 1
- genes not being identical between reference and my gene expression data HOT 15
- GLIBCXX_3.4.30 not found when trying to use timer HOT 1
- Issue with ESTIMATE inner_join() HOT 3
- Wrong names of ESTIMATE scores HOT 7
- deconvolute_consensus_tme won't work with "Unfiltered" indications HOT 1
- xcell reporting all zeros HOT 5
- Error in if (max(Y) < 50) { : missing value where TRUE/FALSE needed - CIBERSORT HOT 9
- add path to geneset for MCPCOUNTER HOT 2
- Warning pops up in the "Getting started" section HOT 1
- Documentation for mouse methods HOT 3
- Return missing genes for each tool HOT 1
- Add developer docs
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