ccImpute | R Documentation |

Performs imputation of dropout values in single-cell RNA sequencing (scRNA-seq) data using a consensus clustering-based algorithm (ccImpute). This implementation includes performance enhancements over the original ccImpute method described in the paper "ccImpute: an accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data" (DOI: https://doi.org/10.1186/s12859-022-04814-8).

Defines the generic function 'ccImpute' and a specific method for 'SingleCellExperiment' objects.

```
ccImpute.SingleCellExperiment(
object,
dist,
nCeil = 2000,
svdMaxRatio = 0.08,
maxSets = 8,
k,
consMin = 0.75,
kmNStart,
kmMax = 1000,
fastSolver = TRUE,
BPPARAM = bpparam(),
verbose = TRUE
)
ccImpute(
object,
dist,
nCeil = 2000,
svdMaxRatio = 0.08,
maxSets = 8,
k,
consMin = 0.75,
kmNStart,
kmMax = 1000,
fastSolver = TRUE,
BPPARAM = bpparam(),
verbose = TRUE
)
## S4 method for signature 'SingleCellExperiment'
ccImpute(
object,
dist,
nCeil = 2000,
svdMaxRatio = 0.08,
maxSets = 8,
k,
consMin = 0.75,
kmNStart,
kmMax = 1000,
fastSolver = TRUE,
BPPARAM = bpparam(),
verbose = TRUE
)
```

`object` |
A |

`dist` |
(Optional) A distance matrix used for cell similarity. calculations. If not provided, a weighted Spearman correlation matrix is calculated. |

`nCeil` |
(Optional) The maximum number of cells used to compute the
proportion of singular vectors (default: |

`svdMaxRatio` |
(Optional) The maximum proportion of singular vectors
used for generating subsets (default: |

`maxSets` |
(Optional) The maximum number of sub-datasets used for
consensus clustering (default: |

`k` |
(Optional) The number of clusters (cell groups) in the data. If not provided, it is estimated using the Tracy-Widom Bound. |

`consMin` |
(Optional) The low-pass filter threshold for processing the
consensus matrix (default: |

`kmNStart` |
nstart parameter passed to |

`kmMax` |
iter.max parameter passed to |

`fastSolver` |
(Optional) Whether to use mean of
non-zero values for calculating dropout values or a linear equation solver
(much slower and did show empirical difference in imputation performance)
(default: |

`BPPARAM` |
(Optional) A |

`verbose` |
(Optional) Whether to print progress messages
(default: |

A `SingleCellExperiment`

class object with the imputed
expression values stored in the '"imputed"' assay.

```
library(BiocParallel)
library(splatter)
library(scater)
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
BPPARAM = MulticoreParam(cores)
sce <- ccImpute(sce, BPPARAM=BPPARAM)
```

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.