IsomirDataSeqFromFiles | R Documentation |
This function parses output of seqbuster tool to allow isomiRs/miRNAs analysis of samples in different groups such as characterization, differential expression and clustering. It creates an IsomirDataSeq object.
IsomirDataSeqFromFiles(
files,
coldata,
rate = 0.2,
canonicalAdd = TRUE,
uniqueMism = TRUE,
uniqueHits = FALSE,
design = ~1L,
minHits = 1L,
header = TRUE,
skip = 0,
quiet = TRUE,
...
)
files |
files with the output of seqbuster tool |
coldata |
data frame containing groups for each sample |
rate |
minimum counts fraction to consider a mismatch a real mutation |
canonicalAdd |
|
uniqueMism |
|
uniqueHits |
|
design |
a |
minHits |
Minimum number of reads in the sample to consider it in the final matrix. |
header |
boolean to indicate files contain headers |
skip |
skip first line when reading files |
quiet |
boolean indicating to print messages
while reading files. Default |
... |
arguments provided to
|
This function parses the output of http://seqcluster.readthedocs.org/mirna_annotation.html for each sample to create a count matrix for isomiRs, miRNAs or isomiRs grouped in types (i.e all sequences with variations at 5' but ignoring any other type). It creates IsomirDataSeq object (see link to example usage of this class) to allow visualization, queries, differential expression analysis and clustering. To create the IsomirDataSeq, it parses the isomiRs files, and generates an initial matrix having all isomiRs detected among samples. As well, it creates a summary for each isomiR type (trimming, addition and substitution) to visualize general isomiRs distribution.
IsomirDataSeq class object.
path <- system.file("extra", package="isomiRs")
fn_list <- list.files(path, pattern="mirna", full.names = TRUE)
de <- data.frame(row.names=c("f1" , "f2"),
condition = c("newborn", "newborn"))
ids <- IsomirDataSeqFromFiles(fn_list, coldata=de)
head(counts(ids))
IsomirDataSeqFromRawData(metadata(ids)[["rawData"]], de)
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