M3DropConvertData {M3Drop} | R Documentation |
Recognizes a variety of R objects/classes and extracts expression matrices from them then converts that to a normalized but not-log transformed matrix appropriate for input into M3Drop functions.
M3DropConvertData(input, is.log=FALSE, is.counts=FALSE, pseudocount=1)
input |
a matrix, data.frame or object |
is.log |
has the data been log-transformed? (assumes log-base 2 with pseudocount of 1) |
is.counts |
is the data raw unnormalized counts? (raw counts will be CPM normalized) |
pseudocount |
pseudocount added before log-transformation |
You must have loaded the respective packages (in parentheses) into your namespace before running this function on the respective objects. Note that to maintain scalability sparse matrices will remain as such.
Supported classes/objects: SCESet (scater <= 1.4.0) : uses "exprs" or if unavailable then "counts" SingleCellExperiment (scater >= 1.6.0) : uses "logcounts", which is assumed to be log-normalized, or if unavailable then "counts" CellDataSet (monocle) : uses "exprs", specify log/counts using arguments ExpressionSet (Biobase) : uses "exprs", specify log/counts using arguments seurat (Seurat) : uses "raw.data" as counts.
Matrix/Dataframe classes : dgCMatrix (Matrix) : specify log/counts using arguments data.table (data.table) : specify log/counts using arguments DataTable (S4Vectors) : specify log/counts using arguments DataFrame (S4Vectors): specify log/counts using arguments AnnotatedDataFrame (Biobase) : specify log/counts using arguments matrix (base-r) : specify log/counts using arguments data.frame (base-r) : specify log/counts using arguments
A normalized but not log-transformed matrix appropriate for input into M3Drop functions.
# Simulated raw count matrix: set.seed(42) counts <- matrix(rpois(200, lambda=3), ncol=10) expr_mat <- M3DropConvertData(counts, is.counts=TRUE) # log normalized data frame lognorm <-log2( t(t(counts)/colSums(counts)*100)+1 ) lognorm <- as.data.frame(lognorm) expr_mat <- M3DropConvertData(lognorm) # Sparse matrix require("Matrix") counts <- Matrix(counts, sparse=TRUE) expr_mat <- M3DropConvertData(counts, is.counts=TRUE) # SingleCellExperiment Object require("SingleCellExperiment") SCE <- SingleCellExperiment(assays=list(counts=counts)) expr_mat <- M3DropConvertData(SCE) # monocle Object require("monocle") obj <- suppressWarnings(newCellDataSet(as.matrix(lognorm))) expr_mat <- M3DropConvertData(obj, is.log=TRUE)