TCA: TCA class and constructor
In TCseq: Time course sequencing data analysis

Description Usage Arguments Details Value Author(s) See Also Examples

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

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

TCAFromSummarizedExperiment(se, genomicFeature = NULL)

`design`	a data frame containing information about samples/libraries, For time course analysis, design 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, the column `BAMfile` can be included in the `design` data frame, providing corresponding BAM filename of each sample/library, this information can be used for generating count table by using `countReads` function.
`counts`	an integer matrix containing read counts. Rows correspond to genomic features and columns to samples/libraries.
`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 (count table) 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.

A TCA object can be created without providing read counts, read counts can be provided by counts or generated by countReads, the number of rows should equal to that in genomicFeature and the number of columns should equal to number of rows 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

A TCA object

Mengjun Wu

counts, TCA.accessors, DBresult, timeclust, clust

#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