This function performs structure learning for count data using various PC algorithms adapted for different distributional assumptions including Poisson, Negative Binomial, and Zero-Inflated Negative Binomial models.
Usage
PCzinb(
x,
method = c("poi", "nb", "zinb0", "zinb1"),
alpha = NULL,
maxcard = 2,
extend = TRUE,
nCores = 1,
whichAssay = "processed",
...
)Arguments
- x
A matrix of count data (n × p), SummarizedExperiment, or SingleCellExperiment object. For matrix input, rows are samples and columns are genes.
- method
The algorithm used to estimate the graph:
poi(Poisson),nb(Negative Binomial),zinb0(Zero-Inflated NB with structure only in mu), orzinb1(Zero-Inflated NB with structure in both mu and pi).- alpha
The significance level of the tests. Default: 2 * pnorm(nrow(x)^0.2, lower.tail = FALSE).
- maxcard
The upper bound of the cardinality of the conditional sets K. Default: 2.
- extend
If TRUE, considers the union of the tests; if FALSE, considers the intersection. Default: TRUE.
- nCores
Number of cores for parallel processing. Default: 1.
- whichAssay
The assay to use as input (for SummarizedExperiment or SingleCellExperiment objects). Default: "processed".
- ...
Additional arguments (currently unused).
Value
If x is a matrix: the estimated adjacency matrix of the graph
If x is a SummarizedExperiment: the object with adjacency matrix stored in metadata as
adj_matIf x is a SingleCellExperiment: the object with adjacency matrix stored as rowPair
Details
PCzinb performs structure learning using PC algorithms for count data. Different methods handle different distributional assumptions:
poi: Poisson distributionnb: Negative Binomial distributionzinb0: Zero-Inflated NB with structure only in mean parameterzinb1: Zero-Inflated NB with structure in both mean and zero-inflation parameters
For SummarizedExperiment and SingleCellExperiment inputs, if the specified
whichAssay is "processed" but not found, the function will use the first
assay and issue a warning recommending QPtransform().
Examples
# Matrix input
mat <- matrix(rpois(50, 5), nrow = 10)
PCzinb(mat, method = "poi")
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0 0 1 0 0
#> [2,] 0 0 0 0 1
#> [3,] 1 0 0 0 0
#> [4,] 0 0 0 0 0
#> [5,] 0 1 0 0 0
# SummarizedExperiment input
library(SummarizedExperiment)
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#>
#> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’:
#>
#> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#> colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#> colWeightedMeans, colWeightedMedians, colWeightedSds,
#> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#> rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#> rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> Loading required package: generics
#>
#> Attaching package: ‘generics’
#> The following objects are masked from ‘package:base’:
#>
#> as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
#> setequal, union
#>
#> Attaching package: ‘BiocGenerics’
#> The following objects are masked from ‘package:stats’:
#>
#> IQR, mad, sd, var, xtabs
#> The following objects are masked from ‘package:base’:
#>
#> Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
#> as.data.frame, basename, cbind, colnames, dirname, do.call,
#> duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
#> mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
#> rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
#> unsplit, which.max, which.min
#> Loading required package: S4Vectors
#>
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:utils’:
#>
#> findMatches
#> The following objects are masked from ‘package:base’:
#>
#> I, expand.grid, unname
#> Loading required package: IRanges
#> Loading required package: Seqinfo
#> Loading required package: Biobase
#> Welcome to Bioconductor
#>
#> Vignettes contain introductory material; view with
#> 'browseVignettes()'. To cite Bioconductor, see
#> 'citation("Biobase")', and for packages 'citation("pkgname")'.
#>
#> Attaching package: ‘Biobase’
#> The following object is masked from ‘package:MatrixGenerics’:
#>
#> rowMedians
#> The following objects are masked from ‘package:matrixStats’:
#>
#> anyMissing, rowMedians
se <- SummarizedExperiment(matrix(rpois(50, 5), ncol = 10))
se_result <- PCzinb(se, method = "poi")
#> Warning: We recommend to use QPtransform() before learning the graph.
# SingleCellExperiment input
library(SingleCellExperiment)
sce <- SingleCellExperiment(matrix(rpois(50, 5), ncol = 10))
sce_result <- PCzinb(sce, method = "poi")
#> Warning: We recommend to use QPtransform() before learning the graph.
rowPair(sce_result)
#> SelfHits object with 4 hits and 1 metadata column:
#> from to | x
#> <integer> <integer> | <numeric>
#> [1] 1 3 | 1
#> [2] 2 5 | 1
#> [3] 3 1 | 1
#> [4] 5 2 | 1
#> -------
#> nnode: 5