View source: R/run_deeptools.R
run_deeptools | R Documentation |
Runs the deeptools suite of programs
run_deeptools(
command = NULL,
input = NULL,
control = NULL,
output = NULL,
output.format = NULL,
binsize = NULL,
sample.names = NULL,
gtf = NULL,
region = NULL,
before.region = NULL,
region.bases = NULL,
after.region = NULL,
plotType = NULL,
kmeans = NULL,
effective.genome.size = NULL,
normalization = NULL,
scale.factors = NULL,
threads = 10,
parallel = FALSE,
cores = 4,
execute = TRUE,
deeptools = NULL,
version = FALSE
)
command |
deeptools command to run, at present can choose from bamCoverage, computeMatrix and plotHeatmap, required |
input |
List of files to be processed, required |
control |
List of input control files, for bamCompare only |
output |
Name of output directory |
output.format |
Output format, bigwig or bedgraph, default bigwig |
binsize |
Size of the bins, in bases, default 50 |
sample.names |
list of the sample name |
gtf |
Path to the gtf file |
region |
Region of the genome to limit the operation to,format is chr:start:end |
before.region |
Distance upstream of the reference-point selected |
region.bases |
Distance in bases to which all regions will be fit |
after.region |
Distance downstream of the reference-point selected |
plotType |
Type of plot can select lines, fill, se, std, overlapped_lines or heatmap |
kmeans |
Number of kmeans clusters to compute. |
effective.genome.size |
The effective genome size is the portion of the genome that is mappable. Large fractions of the genome are stretches of NNNN that should be discarded. A table of values is available here: http://deeptools.readthedocs.io/en/latest/content/feature/effectiveGenomeSize.html |
normalization |
Possible choices: RPKM (Reads Per Kilobase per Million mapped reads), CPM (Counts Per Million mapped reads), BPM (Bins Per Million mapped reads, similar to TPM), RPGC (reads per genomic content) |
scale.factors |
Method to use to scale the samples. Possible choices: readCount, SES and None |
threads |
Number of threads for each instance of deeptools to use, default set to 10 |
parallel |
Run in parallel, default set to FALSE |
cores |
Number of cores/threads to use for parallel processing, default set to 4 |
execute |
Whether to execute the commands or not, default set to TRUE |
deeptools |
Path to the where the deeptools programs are sorted (usually /usr/local/bin), required |
version |
Returns the version number |
A list with the deeptools commands
## Not run:
# Version
command <- "deeptools"
deeptools.cmd <- run_deeptools(command = command,
deeptools.path = path,
version = TRUE)
deeptools.cmd
# Bam to bigwig
command <- "bamCoverage"
input.names <- "list of input file paths"
output.dir <- "directoryname"
output.names <- "list of names for samples"
deeptools.cmd <- run_deeptools(command = command,
input = input,
output = output,
sample.names = sample.names,
deeptools = path)
deeptools.cmd
# Bigwig to matrix
command <- "computeMatrix"
gtf <- "path/to/gtf"
input.names <- "list of input file paths"
output.dir <- "directoryname"
output.names <- "list of names for samples"
deeptools.cmd <- run_deeptools(command = command,
input = matrix.lists,
output = output,
sample.names = output.names,
gtf = gtf,
# parallel = TRUE,
# cores = 6,
deeptools = path)
deeptools.cmd
# plot heatmap
command <- "plotHeatmap"
input.names <- "list of input file paths"
output.dir <- "directoryname"
output.names <- "list of names for samples"
deeptools.cmd <- run_deeptools(command = command,
input = input.names,
output = output.dir
sample.names = output.names,
# parallel = TRUE,
# cores = 6,
deeptools = path)
deeptools.cmd
## End(Not run)
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