IsomirDataSeqFromFiles {isomiRs} | R Documentation

## Loads miRNA annotation from seqbuster tool or pre-processed data.

### Description

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.

### Usage

    IsomirDataSeqFromFiles(
      files,
      coldata,
      rate = 0.2,
      canonicalAdd = TRUE,
      uniqueMism = TRUE,
      uniqueHits = FALSE,
      design = ~1L,
      minHits = 1L,
      header = TRUE,
      skip = 0,
      quiet = TRUE,
      ...
    )

### Arguments

`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` | `boolean` only keep A/T non-template addition. All non-template nucleotides at the 3' end will be removed if they contain C/G nts.
`uniqueMism` | `boolean` only keep mutations that have a unique hit to one miRNA molecule. For instance, if the sequence map to two different miRNAs, then it would be removed.
`uniqueHits` | `boolean` whether filtering ambigous sequences or not.
`design` | a `formula` to pass to DESeq2::DESeqDataSet
`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 `FALSE`.
`...` | arguments provided to `SummarizedExperiment` and IsomirDataSeqFromRawData. including rowData.

### Details

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.

### Value

IsomirDataSeq class object.

### Examples

    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)

[Package isomiRs version 1.22.1 Index]