Container for expression data that has been pre-processed using the 'CMprep' function. A 'CellMapperList' object can be provided directly to the 'CMsearch' function to predict genes expressed selectively in specific cell types.
'CMprep' transforms an expression matrix using singular value decomposition (SVD), resulting in a matrix, 'B', with the left-singular vectors of original data matrix and a vector, 'd', with the singular values. Singular vectors that account for less variance than an individual sample in the original dataset have been trimed (Kaiser's criterion), thereby removing singular vectors that mainly account for noise. The advantage of this transformation is that it reduces dataset size, and avoids the need to perform a time-consuming SVD transformation before running 'CMsearch'.
A 'CellMapperList' object contains the transformed left singular value matrix, 'B', and singular values, 'd', as well as meta-data associated with the original expression matrix.
'CellMapperList' instances are usually created through the 'CMprep' function. See ?CMprep
To create a 'CellMapperList' object directly, the following constructor is provided:
CellMapperList(B, d, meta = list())
where B is a numeric matrix containing left-singular vectors, d is a numeric vector containing singular values, and meta is a list object containing metadata.
Same as for SimpleList objects. See ?SimpleList
The sample metadata can be extracted using metadata().
B.D. Nelms, L. Waldron, L.A. Barrera, A.W. Weflen, J.A. Goettel, G. Guo, R.K. Montgomery, M.R. Neutra, D.T. Breault, S.B. Snapper, S.H. Orkin, M.L. Bulyk, C. Huttenhower and W.I. Lencer. CellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types. Genome Biology 2016, 17(1).
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# Create a mock expression dataset with random expression values ngenes <- 1000 narrays <- 100 x <- matrix(rnorm(ngenes*narrays), ngenes, narrays) rownames(x) <- 1:ngenes # Prepare a CellMapperList object using the CMprep function data <- CMprep(x, DataSource = "Mock Expression Matrix") show(data) metadata(data)
Loading required package: S4Vectors Loading required package: stats4 Loading required package: BiocGenerics Loading required package: parallel Attaching package: 'BiocGenerics' The following objects are masked from 'package:parallel': clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB 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, append, as.data.frame, cbind, colMeans, colSums, colnames, do.call, duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply, union, unique, unsplit, which, which.max, which.min Attaching package: 'S4Vectors' The following object is masked from 'package:base': expand.grid Warning messages: 1: replacing previous import 'stats::sd' by 'S4Vectors::sd' when loading 'CellMapper' 2: replacing previous import 'stats::var' by 'S4Vectors::var' when loading 'CellMapper' * Scaling the data matrix... * Computing the singular-value decomposition of the data matrix... * Preparing data for CellMapper... An object of class "CellMapperList" # Provide as input to the 'CMsearch' function of the 'CellMapper' package # Derived from an expression dataset with 1000 genes and 100 samples # Dataset source: 'Mock Expression Matrix' # The type of gene ID used is: 'unknown' # Example gene IDs: '1', '2', '3', '4', '5', '6', ... $nrow  1000 $ncol  100 $GeneIDType  "" $DataSource  "Mock Expression Matrix"
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