Calculate the per gene UMI count matrix by parsing the genome alignment file.
Usage
quantify_gene(
annotation,
outdir,
infq,
n_process,
pipeline = "sc_single_sample",
samples = NULL,
random_seed = 2024
)Arguments
- annotation
The file path to the annotation file in GFF3 format
- outdir
The path to directory to store all output files.
- infq
The input FASTQ file.
- n_process
The number of processes to use for parallelization.
- pipeline
The pipeline type as a character string, either
sc_single_sample(single-cell, single-sample),- samples
A vector of sample names, default to the file names of input fastq files, or folder names if
fastqsis a vector of folders.bulk(bulk, single or multi-sample), orsc_multi_sample(single-cell, multiple samples)- random_seed
The random seed for reproducibility.
Details
After the genome alignment step (do_genome_align), the alignment file will be parsed to
generate the per gene UMI count matrix. For each gene in the annotation file, the number of reads
overlapping with the gene’s genomic coordinates will be assigned to that gene. If a read overlaps
multiple genes, it will be assigned to the gene with the highest number of overlapping nucleotides.
If exon coordinates are included in the provided annotation, the decision will first consider the
number of nucleotides aligned to the exons of each gene. In cases of a tie, the overlap with introns
will be used as a tiebreaker. If there is still a tie after considering both exons and introns,
a random gene will be selected from the tied candidates.
After the read-to-gene assignment, the per gene UMI count matrix will be generated. Specifically, for each gene, the reads with similar mapping coordinates of transcript termination sites (TTS, i.e. the end of the the read with a polyT or polyA) will be grouped together. UMIs of reads in the same group will be collapsed to generate the UMI counts for each gene.
Finally, a new fastq file with deduplicated reads by keeping the longest read in each UMI.