Load small RNA-seq bcbio-nextgen run

Simply point to the final upload directory output by bcbio-nextgen, and this function will take care of the rest. It automatically imports small RNA-seq counts, metadata, and program versions used.

loadSmallRnaRun(projectDir = "date-final",
  interestingGroups = "sample", maxSamples = 50, dataDir = NULL,
  colData = NULL, ...)

Arguments

projectDir	Path to final upload directory. This path is set when running bcbio_nextgen -w template.
interestingGroups	Character vector of interesting groups. First entry is used for plot colors during quality control (QC) analysis. Entire vector is used for PCA and heatmap QC functions.
maxSamples	Optional. Maximum number of samples to calculate rlog and variance stabilization object from DESeq2.
dataDir	Folder to keep a cache of the object.
colData	Optional External metadata to be used while reading samples.
...	Additional arguments, saved as metadata.

Value

bcbioSmallRnaDataSet.

Note

When working in RStudio, we recommend connecting to the bcbio-nextgen run directory as a remote connection over sshfs.

Examples

path <- system.file("extra", package="bcbioSmallRna")
sbcb <- loadSmallRnaRun(file.path(path, "geu_tiny", "final",
                                  "2018-09-29_geu_tiny"), "population")
#> /Users/lpantano/repos/hbc/bcbioSmallRna/inst/extra/geu_tiny/final/2018-09-29_geu_tiny
#> Reading project summary YAML
#> laboratorypopulationsample
#> 10 samples detected
#> Genome: hg19
#> value for "which" not specified, defaulting to c("rows", "cols")
#> Warning: NAs introduced by coercion
#> Warning: NAs introduced by coercion
#> Reading bcbio run information
#> Parsed with column specification:
#> cols(
#>   genome = col_character(),
#>   resource = col_character(),
#>   version = col_character()
#> )
#> Parsed with column specification:
#> cols(
#>   program = col_character(),
#>   version = col_character()
#> )
#> Reading miRNA count files
#> Total samples filtered due to low number of hits: 0
#> converting counts to integer mode
#> converting counts to integer mode
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#>    function: y = a/x + b, and a local regression fit was automatically substituted.
#>    specify fitType='local' or 'mean' to avoid this message next time.