TCA: TCA class and constructor

In MengjunWu/TCseq: Time course sequencing data analysis

TCA class and constructor

Description

TCA is a S4 class for storing input data, results of differential analysis and clustering analysis. A TCA object can be created by the constructor function taking a table of sample information, a table of the genomic coordinates of features, and read count table (optional).

Usage

TCA(design, counts = matrix(0L, 0L, 0L), genomicFeature, zero.based = TRUE)

TCAFromSummarizedExperiment(se, genomicFeature = NULL)

Arguments

design	a data frame containing information of samples/libraries. For time course analysis, design table should contain at least three columns (case insensitive): sampleid, timepoint and group providing time point and group information of each sample/library. If counts is not provided when creating TCA object, an optional column bamfile can be used to provide BAM filename of each sample/library and generate count table using countReads function later.
counts	an integer matrix containing read counts. Rows correspond to genomic features and columns to samples/libraries. The name of column s should be the same as the time points in design.
genomicFeature	a data frame or a GRanges object containing genomic coordinates of features of interest (e.g. genes in RNA-seq, binding regions in ChIP-seq). If genomicFeature is a data frame, four columns are required in genomicFeature: id, chr, start, end; if genomicFeature is a Granges object, the metadata column "id" is required. For TCAFromSummarizedExperiment, genomicFeature must be provided if se is a SummarizedExperiment object.
zero.based	Logical. If TRUE, the start positions of the genomic ranges in the returned TCA object are 0-based, if FALSE, the start positions will be 1-based.
se	A SummarizedExperiment or a RangedSummarizedExperiment object. The object might contain multiple assays in the assay list, only the first one will be taken to construct TCA object. For SummarizedExperiment object, genomicFeature must be provided while for RangedSummarizedExperiment object, the genomic features will be extracted directly from the object.

Details

A TCA object can be created without providing read counts, read counts can be provided by counts or generated by countReads. For the read counts, the number of rows should equal to that in 'genomicFeature and the number of columns should equal to number of rows in design; in addition, the name of column names should be the same as the time points in design. Input data and analysis results in a TCA object can be accessed by using corresponding accessors and functions. The TCA objects also have a show method printing a compact summary of their contents see counts, TCA.accessors, DBresult, tcTable, timeclust. clust

Value

A TCA object

Author(s)

Mengjun Wu

See Also

counts, TCA.accessors, DBresult, timeclust, clust

Examples

#create data frame of experiment design: 4 time points and 2 replicates for each time point.
d <- data.frame(sampleID = 1:8, group = rep(c(1, 2, 3, 4), 2),
               timepoint = rep(c('0h', '24h', '48h', '72h'), 2))


#create data frame of genomic intervals of interest
gf <- data.frame(chr = c(rep('chr1', 3), rep('chr2', 2), rep('chr4', 2)),
                start = seq(100, 2000, by = 300),
                end = seq(100, 2000, by = 300) + 150,
                id = paste0('peak', 1:7))
tca <- TCA(design = d, genomicFeature = gf)
genomicFeature(tca)

#if count table is available
c <- matrix(sample(1000, 56), nrow = 7, dimnames = list(paste0('peak', 1:7), 1:8))
tca <- TCA(design = d, counts = c, genomicFeature = gf)
# replace the count table of a \code{TCA} object
c2 <- matrix(sample(500, 56), nrow = 7, dimnames = list(paste0('peak', 1:7), 1:8))
counts(tca) <- c2

MengjunWu/TCseq documentation built on May 15, 2023, 9:47 p.m.