Tools for feature barcoding analyses
Tools for feature barcoding analysis
$ pip install fba
$ fba usage: fba [-h] ... Tools for feature barcoding analyses Version: 0.0.6 optional arguments: -h, --help show this help message and exit functions: extract extract cell and feature barcodes map map enriched transcripts filter filter extracted barcodes count count feature barcodes per cell demultiplex demultiplex cells based on feature abundance qc quality control of feature barcoding assay kallisto_wrapper deploy kallisto/bustools for feature barcoding quantification
extract: extract cell and feature barcodes from paired fastq files. For single cell assays, read 1 usually contains cell partitioning and UMI information, and read 2 contains feature information.
map: quantify enriched transcripts (through hybridization or PCR amplification) from parent single cell libraries. Read 1 contains cell partitioning and UMI information, and read 2 contains transcribed regions of enriched/targeted transcripts of interest. Bowtie2 (Langmead, B., et al. 2012) is used for read 2 alignment. The quantification (UMI deduplication) of enriched/targeted transcripts is powered by UMI-tools (Smith, T., et al. 2017).
filter: filter extracted cell and feature barcodes (output of
qc). Additional fragment filter/selection can be applied through
count: count UMIs per feature per cell (UMI deduplication), powered by UMI-tools (Smith, T., et al. 2017). Take the output of
demultiplex: demultiplex cells based on the abundance of features (matrix generated by
qc: generate diagnostic information. If
-1is omitted, bulk mode is enabled and only read 2 will be analyzed.
kallisto_wrapper: deploy kallisto/bustools for feature barcoding quantification (just a wrapper) (Bray, N.L., et al. 2016).
Cell surface protein labeling
Targeted transcript enrichment
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