- Consistent file headers for scFilterStats and scCountQC, to allow multiQC parsing - Argparse and docs improved - Deduplication: avoid overcounting due to chimeric fragments or mismatched reference pos
0.3
- *New* in scClusterCells: - glmPCA with several distributions. - LSA/LDA moved to the topicmodels class - Bugfix: - scFilterBarcodes: outfile - scBAMops: renamed tool (WIP for v0.4: adding cell groups by barcode) - Code re-factored to new python style (pep621)
0.2
More features:
- **scCombineCounts**: to concatenate counts from multiple runs of scCountReads - scCountReads: Ability to group by SM tag, ability to output region name in case of BED/GTF input - other minor updates and bugfixes
0.1
Following tools are supported:
scFilterBarcodes Identify and filter cell barcodes from BAM file (for droplet-based single-cell seq) scFilterStats Produce per-cell statistics after filtering reads by user-defined criteria. scCountReads Counts reads for each barcode on genomic bins or user-defined features. scCountQC Perform quality control and filter the output of scCountReads. scCombineCounts Concatenate/merge the counts from different samples/batches or modalities scClusterCells Perform dimensionality reduction and clustering on the output of scCountReads. scBulkCoverage Get pseudo-bulk coverage per group using a user-supplied cell->group mapping (output of scClusterCells).