IsomirDataSeqFromFiles
loads miRNA annotation from seqbuster toolThis 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, design = ~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 |
|
design | a |
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
|
IsomirDataSeq
class object.
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.
path <- system.file("extra", package="isomiRs") fn_list <- list.files(path, full.names = TRUE) de <- data.frame(row.names=c("f1" , "f2"), condition = c("newborn", "newborn")) ids <- IsomirDataSeqFromFiles(fn_list, coldata=de) head(counts(ids))#> f1 f2 #> hsa-let-7a-5p 0 18 #> hsa-let-7b-5p 0 6 #> hsa-let-7c-5p 0 61 #> hsa-let-7f-5p 8 7 #> hsa-let-7g-5p 74 3 #> hsa-let-7i-5p 0 2