Description Usage Arguments Details Value References See Also Examples
This founction is to obtained gene reads count by Rsubread given Gene Anotation File (GTF) or in-built annotation.
1 2 3 4 5 6 7 8 | Get_express_data(INPUT_BAM,
TREATED_INPUT_BAM=character(0),
species="human",
annot_file="hg19",
isPairedEnd=FALSE,
GENE_ANNO_GTF = NULL,
isGTFAnnotationFile=FALSE
)
|
INPUT_BAM |
a character vector giving names of INPUT samples in MeRIP-seq data, which specifies a path for allignment reads in BAM format. |
TREATED_INPUT_BAM |
a character vector giving names of treated INPUT samples in MeRIP-seq data, which specifies a path for allignment reads in BAM format. These files are only provided in differential methylation context. |
annot_file |
a character string specifying an in-built annotation used to quantify each gene's reads count for expression data. It has four possible values including |
species |
a character string specifying the name of species, which could be |
GENE_ANNO_GTF |
A character string giving name of a user-provided annotation file in GTF format. Note that |
isGTFAnnotationFile |
logical indicating whether the annotation provided via the |
isPairedEnd |
logical indicating if counting should be performed on read pairs or reads. FALSE by default. If TRUE, read pairs will be counted instead of individual reads. |
Get_expression_data
is a general-purpose read summarization function that can assign mapped reads from RNA sequencing to genomic features (gene).
The function takes as input a set of BAM files containing read mapping results.
Get_expression_data
accepts GTF/GFF annotation formats or in-built annotation for human "hg19"
, "hg38"
or mouse muscle "mm9"
or "mm10"
.
Get_expression_data
will return a list data format, which includes a dataframe with reads count for each gene in each INPUT sample and a vector of size factor for each INPUT sample whose columans.
gene_reads_count |
A dataframe format, which includes the reads count for each gene in each INPUT sample. |
size_factor |
A vector in float type, which are used to normalize samples. |
Yang Liao, Gordon K Smyth and Wei Shi (2019). The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. Nucleic Acids Research, 47(8):e47. http://www.ncbi.nlm.nih.gov/pubmed/30783653
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ## Not run:
##Get gene expression data for each INPUT BAM file.
f1 <- system.file("extdata", "Input1.bam", package="m6Aexpress")
f2 <- system.file("extdata", "Input2.bam", package="m6Aexpress")
f3 <- system.file("extdata", "Input3.bam", package="m6Aexpress")
f4 <- system.file("extdata", "Input4.bam", package="m6Aexpress")
INPUT_BAM <- c(f1,f2,f3,f4)
# Input the annotation file
gtf <- system.file("extdata", "hg19toy.gtf", package="m6Aexpress")
# Get reads count for each gene of each sample by given GTF file.
get_gene_express <- Get_express_data(INPUT_BAM=INPUT_BAM,
isPairedEnd=FALSE,
GENE_ANNO_GTF = gtf)
## Not run:
# Get reads count for each gene of each sample by in-built annotation file.
# get_gene_express <- Get_express_data(INPUT_BAM=INPUT_BAM,
species="human",
annot_file="hg19",
isPairedEnd=FALSE,
GENE_ANNO_GTF = NULL,
isGTFAnnotationFile=FALSE)
## End(Not run)
|
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