A prefiltering module that reduces the number of input features that go into the model and improve memory footprint of predictor. Would make use of gene-level or unit-level features feasible.
It could do the following:
canned unit-wise t-tests (e.g. limma) that prune out units that aren't distinguishable among groups (e.g. pruning RNA datapoints after diffEx test)
create meta-features such as eigen-genes by PCA or shrinking centroids
pruning genotypes by GWAS pvalue.
pruning genotypes by excluding those not associated with a GSEA signal.
A prefiltering module that reduces the number of input features that go into the model and improve memory footprint of predictor. Would make use of gene-level or unit-level features feasible.
It could do the following:
canned unit-wise t-tests (e.g. limma) that prune out units that aren't distinguishable among groups (e.g. pruning RNA datapoints after diffEx test)
create meta-features such as eigen-genes by PCA or shrinking centroids
pruning genotypes by GWAS pvalue.
pruning genotypes by excluding those not associated with a GSEA signal.