Fixed the sparse matrix issue #89
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This reverts commit abb4e0a.
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Thanks, and sorry for a late reply, I will need to test this first and then merge. |
nh3
reviewed
Apr 12, 2019
| @@ -149,7 +149,7 @@ sc3_prepare.SingleCellExperiment <- function(object, gene_filter, pct_dropout_mi | |||
| f_data <- rowData(object) | |||
| f_data$sc3_gene_filter <- TRUE | |||
| if (gene_filter) { | |||
| dropouts <- rowSums(counts(object) == 0)/ncol(object)*100 | |||
| dropouts <- Matrix::rowSums(counts(object) == 0)/ncol(object)*100 | |||
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I am aware of the discussion in #53 , but I am still not sure if hardcoding it this way without checking the type of counts(object) is a good idea. For hdf5-backed object, the returned matrix can be of class "DelayedMatrix", which is not handled correctly by Matrix::rowSums(), just as an example.
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Nice, thanks for the comment! That's why I didn't rush to merge it, the issue is quite big and requires some time to think and decide.
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Issue was discussed here:
This is a temp fix. The correlation function
corandED2also need regular matrix as input, and converting the sparse matrix to regular isn't memory efficient.