Pipeline for Multi-sample Single Cell Data
Source:R/sc_long_multisample_pipeline.R
sc_long_multisample_pipeline.Rd
Semi-supervised isoform detection and annotation for long read data.
This variant is for multi-sample single cell data. By default, this pipeline demultiplexes input
fastq data (match_cell_barcode = TRUE
). Specific parameters relating to
analysis can be changed either through function arguments, or through a
configuration JSON file.
Usage
sc_long_multisample_pipeline(
annotation,
fastqs,
outdir,
genome_fa,
sample_names = NULL,
minimap2 = NULL,
k8 = NULL,
barcodes_file = NULL,
expect_cell_numbers = NULL,
config_file = NULL
)
Arguments
- annotation
The file path to the annotation file in GFF3 format
- fastqs
The input fastq files for multiple samples. It can be provided in different way: 1) a single path to the folder containing fastq files, each fastq file will be treated as a sample; or 2) a vector of paths to each fastq file, each fastq file will be treated as a sample; or 3) a vector of paths to folders containing fastq files, each folder will be treated as a sample.
- outdir
The path to directory to store all output files.
- genome_fa
The file path to genome fasta file.
- sample_names
A vector of sample names, Default to the file names of input fastq files, or folder names if
fastqs
is a vector of folders.- minimap2
Path to minimap2, if it is not in PATH. Only required if either or both of
do_genome_align
anddo_read_realign
areTRUE
.- k8
Path to the k8 Javascript shell binary. Only required if
do_genome_align
isTRUE
.- barcodes_file
The file path to the reference csv used for demultiplexing in flexiplex. If not specified, the demultiplexing will be performed using BLAZE. Default is
NULL
.- expect_cell_numbers
A vector of roughly expected numbers of cells in each sample E.g., the targeted number of cells. Required if using BLAZE for demultiplexing, specifically, when the
do_barcode_demultiplex
areTRUE
in the the JSON configuration file andbarcodes_file
is not specified. Default isNULL
.- config_file
File path to the JSON configuration file. If specified,
config_file
overrides all configuration parameters
Value
a list of SingleCellExperiment
objects if "do_transcript_quantification" set to true.
Otherwise nothing will be returned.
Details
By default FLAMES use minimap2 for read alignment. After the genome alignment step (do_genome_align
), FLAMES summarizes the alignment for each read in every sample by grouping reads
with similar splice junctions to get a raw isoform annotation (do_isoform_id
). The raw isoform
annotation is compared against the reference annotation to correct potential splice site
and transcript start/end errors. Transcripts that have similar splice junctions
and transcript start/end to the reference transcript are merged with the
reference. This process will also collapse isoforms that are likely to be truncated
transcripts. If isoform_id_bambu
is set to TRUE
, bambu::bambu
will be used to generate the updated annotations (Not implemented for multi-sample yet).
Next is the read realignment step (do_read_realign
), where the sequence of each transcript from the update annotation is extracted, and
the reads are realigned to this updated transcript_assembly.fa
by minimap2. The
transcripts with only a few full-length aligned reads are discarded (Not implemented for multi-sample yet).
The reads are assigned to transcripts based on both alignment score, fractions of
reads aligned and transcript coverage. Reads that cannot be uniquely assigned to
transcripts or have low transcript coverage are discarded. The UMI transcript
count matrix is generated by collapsing the reads with the same UMI in a similar
way to what is done for short-read scRNA-seq data, but allowing for an edit distance
of up to 2 by default. Most of the parameters, such as the minimal distance to splice site and minimal percentage of transcript coverage
can be modified by the JSON configuration file (config_file
).
The default parameters can be changed either through the function
arguments are through the configuration JSON file config_file
. the pipeline_parameters
section specifies which steps are to be executed in the pipeline - by default, all
steps are executed. The isoform_parameters
section affects isoform detection - key
parameters include:
Min_sup_cnt
which causes transcripts with less reads aligned than it's value to be discarded
MAX_TS_DIST
which merges transcripts with the same intron chain and TSS/TES distace less than
MAX_TS_DIST
strand_specific
which specifies if reads are in the same strand as the mRNA (1), or the reverse complemented (-1) or not strand specific (0), which results in strand information being based on reference annotation.
See also
bulk_long_pipeline()
for bulk long data,
SingleCellExperiment()
for how data is outputted
Examples
reads <- ShortRead::readFastq(system.file("extdata/fastq/musc_rps24.fastq.gz", package = "FLAMES"))
outdir <- tempfile()
dir.create(outdir)
dir.create(file.path(outdir, "fastq"))
bc_allow <- file.path(outdir, "bc_allow.tsv")
genome_fa <- file.path(outdir, "rps24.fa")
R.utils::gunzip(filename = system.file("extdata/bc_allow.tsv.gz", package = "FLAMES"), destname = bc_allow, remove = FALSE)
R.utils::gunzip(filename = system.file("extdata/rps24.fa.gz", package = "FLAMES"), destname = genome_fa, remove = FALSE)
ShortRead::writeFastq(sample(reads, size = 500, replace = TRUE), file.path(outdir, "fastq/sample1.fq.gz"), mode = "w", full = FALSE)
ShortRead::writeFastq(sample(reads, size = 500, replace = TRUE), file.path(outdir, "fastq/sample2.fq.gz"), mode = "w", full = FALSE)
ShortRead::writeFastq(sample(reads, size = 500, replace = TRUE), file.path(outdir, "fastq/sample3.fq.gz"), mode = "w", full = FALSE)
if (!any(is.na(sys_which(c("minimap2", "k8"))))) {
sce_list <- FLAMES::sc_long_multisample_pipeline(
annotation = system.file("extdata/rps24.gtf.gz", package = "FLAMES"),
fastqs = file.path(outdir, "fastq", list.files(file.path(outdir, "fastq"))),
outdir = outdir,
genome_fa = genome_fa,
barcodes_file = rep(bc_allow, 3)
)
}