Runs differential analyses and enrichment pipelines
Usage
cinaR(
matrix,
contrasts,
experiment.type = "ATAC-Seq",
DA.choice = 1,
DA.fdr.threshold = 0.05,
DA.lfc.threshold = 0,
comparison.scheme = "OVO",
save.DA.peaks = FALSE,
DA.peaks.path = NULL,
norm.method = "cpm",
filter.method = "custom",
library.threshold = 2,
cpm.threshold = 1,
TSS.threshold = 50000,
show.annotation.pie = FALSE,
reference.genome = NULL,
batch.correction = FALSE,
batch.information = NULL,
additional.covariates = NULL,
sv.number = NULL,
run.enrichment = TRUE,
enrichment.method = NULL,
enrichment.FDR.cutoff = 1,
background.genes.size = 20000,
geneset = NULL,
verbose = TRUE
)Arguments
- matrix
either bed formatted consensus peak matrix (peaks by 3+samples) CHR, START, STOP and raw peak counts OR count matrix (genes by 1+samples).
- contrasts
user-defined contrasts for comparing samples
- experiment.type
The type of experiment either set to "ATAC-Seq" or "RNA-Seq"
- DA.choice
determines which pipeline to run: (1) edgeR, (2) limma-voom, (3) limma-trend, (4) DEseq2. Note: Use limma-trend if consensus peaks are already normalized, otherwise use other methods.
- DA.fdr.threshold
fdr cut-off for differential analyses
- DA.lfc.threshold
log-fold change cutoff for differential analyses
- comparison.scheme
either one-vs-one (OVO) or one-vs-all (OVA) comparisons.
- save.DA.peaks
saves differentially accessible peaks to an excel file
- DA.peaks.path
the path which the excel file of the DA peaks will be saved, if not set it will be saved to current directory.
- norm.method
normalization method for consensus peaks
- filter.method
filtering method for low expressed peaks
- library.threshold
number of libraries a peak occurs so that it is not filtered default set to 2
- cpm.threshold
count per million threshold for not to filter a peak
- TSS.threshold
Distance to transcription start site in base-pairs. Default set to 50,000.
- show.annotation.pie
shows the annotation pie chart produced with ChipSeeker
- reference.genome
genome of interested species. It should be 'hg38', 'hg19' or 'mm10'.
- batch.correction
logical, if set will run unsupervised batch correction via sva (default) or if the batch information is known `batch.information` argument should be provided by user.
- batch.information
character vector, given by user.
- additional.covariates
vector or data.frame, this parameter will be directly added to design matrix before running the differential analyses, therefore won't affect the batch corrections but adjust the results in down-stream analyses.
- sv.number
number of surrogate variables to be calculated using SVA, best left untouched.
- run.enrichment
logical, turns off enrichment pipeline
- enrichment.method
There are two methodologies for enrichment analyses, Hyper-geometric p-value (HPEA) or Geneset Enrichment Analyses (GSEA).
- enrichment.FDR.cutoff
FDR cut-off for enriched terms, p-values are corrected by Benjamini-Hochberg procedure
- background.genes.size
number of background genes for hyper-geometric p-value calculations. Default is 20,000.
- geneset
Pathways to be used in enrichment analyses. If not set vp2008 (Chaussabel, 2008) immune modules will be used. This can be set to any geneset using `read.gmt` function from `qusage` package. Different modules are available: https://www.gsea-msigdb.org/gsea/downloads.jsp.
- verbose
prints messages through running the pipeline
Examples
# \donttest{
data(atac_seq_consensus_bm) # calls 'bed'
# a vector for comparing the examples
contrasts <- sapply(strsplit(colnames(bed), split = "-", fixed = TRUE),
function(x){x[1]})[4:25]
results <- cinaR(bed, contrasts, reference.genome = "mm10")
#> >> Experiment type: ATAC-Seq
#> >> Matrix is filtered!
#>
#> >> preparing features information... 2024-05-22 10:40:28
#> >> identifying nearest features... 2024-05-22 10:40:29
#> >> calculating distance from peak to TSS... 2024-05-22 10:40:30
#> >> assigning genomic annotation... 2024-05-22 10:40:30
#> >> assigning chromosome lengths 2024-05-22 10:40:45
#> >> done... 2024-05-22 10:40:45
#> >> Method: edgeR
#> FDR:0.05& abs(logFC)<0
#> >> Estimating dispersion...
#> >> Fitting GLM...
#> >> DA peaks are found!
#> >> No `geneset` is specified so immune modules (Chaussabel, 2008) will be used!
#> >> enrichment.method` is not selected. Hyper-geometric p-value (HPEA) will be used!
#> >> Mice gene symbols are converted to human symbols!
#> >> Enrichment results are ready...
#> >> Done!
# }