IsomirDataSeqFromFiles.Rd
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 isomiRs::IsomirDataSeq object.
IsomirDataSeqFromFiles(files, coldata, rate = 0.2, canonicalAdd = TRUE, uniqueMism = TRUE, uniqueHits = FALSE, design = ~1L, minHits = 1L, header = TRUE, skip = 0, quiet = TRUE, ...) IsomirDataSeqFromRawData(rawdata, coldata, design = ~1L, pct = 0.1, whitelist = NULL, ...)
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
|
rawdata | data.frame stored in metadata slot of IsomirDataSeq object. |
pct | numeric used to remove isomiRs with an importance lower than this value. Importance is calculated by dividing the isomiR count by the total counts of the miRNA to which it maps. |
whitelist | character vector with sequences to keep even
if the filtering step would have removed them. They have to match
the |
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 isomiRs::IsomirDataSeq object (see link to example usage of this class) to allow visualization, queries, differential expression analysis and clustering. To create the isomiRs::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)#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: 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may be incorrect#>#> f1 f2 #> hsa-let-7a-5p 0 14 #> hsa-let-7b-5p 0 6 #> hsa-let-7c-5p 0 60 #> hsa-let-7f-5p 5 5 #> hsa-let-7g-5p 62 0 #> hsa-let-7i-5p 0 2IsomirDataSeqFromRawData(metadata(ids)[["rawData"]], de)#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> Warning: Chi-squared approximation may be incorrect#> class: IsomirDataSeq #> dim: 42 2 #> metadata(2): sequences rawData #> assays(1): counts #> rownames(42): hsa-let-7a-5p hsa-let-7b-5p ... hsa-miR-99a-5p #> hsa-miR-99b-5p #> rowData names(0): #> colnames(2): f1 f2 #> colData names(1): condition