scbs 0.6.3 - fixes a rare crash in scbs diff

This minor release fixes a rare ZeroDivisionError in scbs diff.

scbs 0.6.2 - fixes an issue with FDR estimation of DMRs

This minor release fixes an issue with the calculation of adjusted p-values in scbs diff.

scbs 0.6.1 - improved output of scbs diff

This minor release addresses issues #15 and #16.

scbs diff now reports a lot more information and summary statistics for each DMR, including the raw p-value, the mean methylation level in both cell groups, the number of CpG sites in the DMR, the number of cells that had sequencing coverage in each group, etc.
Both scbs diff and scbs scan now optionally write a header with the --write-header flag.
Also scbs filter no longer just throws away the log_info.txt file and instead copies it to the filtered directory, as it should.

scbs 0.6.0 - improved output of scbs scan

This release fixes #17 , now scbs scan reports, for each VMR, how many CpG sites the VMR contains, and how many cells have sequencing coverage in the VMR.
VMRs with low coverage, i.e. VMRs with data in very few cells, can now also be filtered automatically with the new ---min-cells option.

scbs 0.5.3 - DMR detection and overhauled matrix generation

This release comes with multiple new features and improvements:

• The new command scbs diff allows you to scan the whole genome for differentially methylated regions (DMRs) between two user-defined groups of cells. This works by sliding a window across the genome, performing a t-test for each window, and merging windows above a threshold. To control the false discovery rate, the same procedure is repeated on permutations of the data which are then used to calculate an adjusted p-value for each DMR.
• scbs matrix now reports the methylation matrix in a more convenient format (wide matrices instead of a huge long table).
• scbs matrix can now use multiple threads, which means it runs much faster when quantifying a large number of genomic intervals.
• scbs prepare now supports biscuit .BED files as input.

scbs 0.4.0 - lower memory requirements

This is a performance update that lowers the amount of RAM required by scbs prepare. Instead of scipy.sparse.tocsr() we now use a custom conversion algorithm that reads each chromosome in chunks, instead of loading the whole chromosome into memory.

scbs 0.3.4 - initial release

This is the initial release of scbs, including all the features described in our bioRxiv preprint.
The following commands are available in this release:

• scbs prepare: Collect and store sc-methylation data for quick access
• scbs filter: Filter low-quality cells based on coverage and mean methylation
• scbs smooth: Smooth the pseudobulk of single cell methylation data
• scbs scan: Scan the genome to discover regions with variable methylation
• scbs matrix: Make a methylation matrix, similar to a count matrix in scRNA-seq
• scbs profile: Plot mean methylation around a group of genomic features