findDropouts | R Documentation |

Determines which zero values within a transposed, log-normalized expression matrix are likely dropout events. The identification is based on a weighted cell voting scheme, where weights are derived from a processed consensus matrix.

```
findDropouts(logX, consMtx)
```

`logX` |
A (sparse or dense) numeric matrix representing the transpose of a log-normalized gene expression matrix. Rows correspond to cells, and columns correspond to genes. |

`consMtx` |
A numeric matrix representing the processed consensus matrix obtained from clustering analysis. |

A two-column matrix (or data frame) where each row indicates the location (row index, column index) of a potential dropout event in the input matrix 'logX'.

```
library(scater)
library(BiocParallel)
library(splatter)
sce <- splatSimulate(group.prob = rep(1, 5)/5, sparsify = FALSE,
batchCells=100, nGenes=1000, method = "groups", verbose = FALSE,
dropout.type = "experiment")
sce <- logNormCounts(sce)
cores <- 2
logX <- as.matrix(logcounts(sce))
w <- rowVars_fast(logX, cores)
corMat <- getCorM("spearman", logcounts(sce), w, cores)
v <- doSVD(corMat, nCores=cores)
BPPARAM = MulticoreParam(cores)
consMtx <- runKM(logX, v, BPPARAM=bpparam())
dropIds <- findDropouts(logX, consMtx)
```

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