gbCounts {ASpli} | R Documentation

## Summarize read overlaps

### Description

Summarize read overlaps against all feature levels

### Usage

      gbCounts( features, targets, minReadLength,
                maxISize, minAnchor = 10, libType="SE",
                strandMode=0, alignFastq = FALSE, dropBAM = FALSE)

### Arguments

`features` | An object of class ASpliFeatures. It is a list of GRanges at gene, bin and junction level
`targets` | A dataframe containing sample, bam and experimental factors columns
`minReadLength` | Minimum read length of sequenced library. It is used for computing E1I and IE2 read summarization. Make sure this number is smaller than the maximum read length in every bam file, otherwise no E1I or IE2 will be found.
`maxISize` | Maximum intron expected size. Junctions longer than this size will be dicarded
`minAnchor` | Minimum percentage of read that should be aligned to an exon-intron boundary.
`libType` | Defines how reads will be treated according their sequencing lybrary type (paired (PE, default) or single end (SE))
`strandMode` | controls the behavior of the strand getter. It indicates how the strand of a pair should be inferred from the strand of the first and last alignments in the pair. 0: strand of the pair is always *. 1: strand of the pair is strand of its first alignment. This mode should be used when the paired-end data was generated using one of the following stranded protocols: Directional Illumina (Ligation), Standard SOLiD. 2: strand of the pair is strand of its last alignment. This mode should be used when the paired-end data was generated using one of the following stranded protocols: dUTP, NSR, NNSR, Illumina stranded TruSeq PE protocol. For more information see ?strandMode
`alignFastq` | Experimental (that means it's highly recommended to, leave the default, FALSE): executes an alignment step previous to Bam summarization. Useful if not enough space on local disks for beans so fasts can be aligned on the fly, even from a remote machine, and then the BAMs can be deleted after each summarization. If set to TRUE, targets data frame must have a column named alignerCall with complete call to aligner for each sample. ie: STAR –runMode alignReads –outSAMtype BAM SortedByCoordinate –readFilesCommand zcat –genomeDir /path/to/STAR/genome/folder -runThreadN 4 –outFileNamePrefix sample name –readFilesIn /path/to/R1 /path/to/R2. Output must match bam files provided in targets.
`dropBAM` | Experimental (that means it's highly recommended to leave the default, FALSE): If alignFastq is TRUE, deletes BAMs after sumarization. Used in conjunction with alignFastq to delete BAMs when there's not enough free space on disk. Use with caution as it will delete all files in "bam" column in targets dataframe.

### Value

An object of class ASpliCounts. Each slot is a dataframe containing features metadata and read counts. Summarization is reported at gene, bin, junction and intron flanking regions (E1I, IE2).

`countsg` | symbol: gene symbol locus_overlap: other genes overlaping this locus gene_coordinates: gene coordinates start: gene start end: gene end length: gene length effective_length: gene effective lengh From effective_length to the end, gene counts for all samples
`countsb` | feature: bin type event: type of event asigned by ASpli when bining. locus: gene locus locus_overlap: genes overlaping the same locus symbol: gene symbol gene_coordinates: gene coordinates start: bin start end: bin end length: bin length From length to the end, bin counts for all samples
`countsj` | junction: annotated junction matching the current junction. gene: gene matching the current junction. strand: gene strand for the current junction in case a gene matches with the junction. multipleHit: semicolon separated list of junctions matching the current junction. symbol: gene symbol. gene_coordinates: gene coordinates. bin_spanned: semicolon separated list of all the bins spaned by this junction. j_within_bin: other junctions in the bins. From j_within_bin to the end, junction counts for all samples.
`countse1i` | event: type of event asigned by ASpli when bining. locus: gene locus locus_overlap: genes overlaping the same locus symbol: gene symbol gene_coordinates: gene coordinates start: bin start end: bin end length: bin length From length to the end, bin counts for all samples
`countsie2` | event: type of event asigned by ASpli when bining. locus: gene locus locus_overlap: genes overlaping the same locus symbol: gene symbol gene_coordinates: gene coordinates start: bin start end: bin end length: bin length From length to the end, bin counts for all samples
`rdsg` | symbol: gene symbol locus_overlap: other genes overlaping this locus gene_coordinates: gene coordinates start: gene start end: gene end length: gene length effective_length: gene effective lengh From effective_length to the end, gene counts/effective_length for all samples
`countsb` | feature: bin type event: type of event asigned by ASpli when bining. locus: gene locus locus_overlap: genes overlaping the same locus symbol: gene symbol gene_coordinates: gene coordinates start: bin start end: bin end length: bin length From length to the end, bin counts/length for all samples
`condition.order` | The order in which ASpli is reading the conditions. This is useful for contrast tests, in order to make sure which conditions are being contrasted.

### Author(s)

Estefania Mancini, Andres Rabinovich, Javier Iserte, Marcelo Yanovsky, Ariel Chernomoretz

### See Also

Accesors: `countsg`, `countsb`, `countsj`, `countse1i`, `countsie2`, `rdsg`, `rdsb`, `condition.order`, Export: `writeCounts`

### Examples

      # Create a transcript DB from gff/gtf annotation file.
      # Warnings in this examples can be ignored.
      library(GenomicFeatures)
      genomeTxDb <- makeTxDbFromGFF( system.file('extdata','genes.mini.gtf',
                                     package="ASpli") )

      # Create an ASpliFeatures object from TxDb
      features <- binGenome( genomeTxDb )

      # Define bam files, sample names and experimental factors for targets.
      bamFileNames <- c( "A_C_0.bam", "A_C_1.bam", "A_C_2.bam",
                         "A_D_0.bam", "A_D_1.bam", "A_D_2.bam" )

      targets <- data.frame(
                   row.names = paste0('Sample_',c(1:6)),
                   bam = system.file( 'extdata', bamFileNames, package="ASpli" ),
                   factor1 = c( 'C','C','C','D','D','D'),
                   subject = c(0, 1, 2, 0, 1, 2))

      # Read counts from bam files
      gbcounts  <- gbCounts( features = features,
                               targets = targets,
                               minReadLength = 100, maxISize = 50000,
                               libType="SE",
                               strandMode=0)
      # Access summary and gene and bin counts and display them
      gbcounts
      countsg(gbcounts)
      countsb(gbcounts)

      # Export data
      writeCounts( gbcounts, output.dir = paste0(tempdir(), "/only_counts") )

[Package ASpli version 2.4.0 Index]