Comments (1)
Yes, you want to ensure that interesting signal contributes to the majority of the NNLS objective. You can do this two ways:
- Perform some sort of row-wise normalization (just as you perform column-wise normalization). There are some good ideas about how to do this in the Bioconductor deseq2 package.
- Remove features that you just aren't interested in (i.e. mtRNA and rRNA), and any features that have overwhelmingly high counts.
Note that it is NOT a good idea to select only variable features -- you will lose a lot of interesting information and statistical power.
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Related Issues (16)
- do we need to remove batch effect? HOT 4
- RcppML::project renamed? HOT 4
- crossValidate aborting R HOT 3
- how to check the top features in each rank? HOT 2
- Functions don't work unless Matrix is loaded HOT 3
- How to perform orthogonal NMF and other questions. HOT 4
- Clarification wrt L1/Lasso penalties HOT 3
- Initialization HOT 2
- compilation of the development version failing HOT 3
- Matrix package deprecations
- Example code is broken, need to run library(RcppML) first HOT 1
- Upper-bounded NNLS HOT 14
- model failures HOT 2
- `SparseMatrix::t()` doesn't work unless Matrix is fully attached HOT 8
- R code example in the README is broken? HOT 1
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