Methods for Single-Cell RNA-Seq Data Analysis

buildSNNGraph | Build a nearest-neighbor graph |

cleanSizeFactors | Sanitize size factors |

clusterModularity | Compute the cluster-wise modularity |

combineMarkers | Combine DE results to a marker set |

combinePValues | Combine p-values |

combineVar | Combine variance decompositions |

computeSpikeFactors | Normalization with spike-in counts |

computeSumFactors | Normalization by deconvolution |

convertTo | Convert to other classes |

correlatePairs | Test for significant correlations |

cosineNorm | Cosine normalize |

cyclone | Cell cycle phase classification |

decomposeVar | Decompose the gene-level variance |

denoisePCA | Denoise expression with PCA |

DM | Compute the distance-to-median statistic |

doubletCells | Detect doublet cells |

doubletCluster | Detect doublet clusters |

fastMNN | Fast mutual nearest neighbors correction |

findMarkers | Find marker genes |

gene_selection | Gene selection |

improvedCV2 | Stably model the technical coefficient of variation |

makeTechTrend | Make a technical trend |

mnnCorrect | Mutual nearest neighbors correction |

multiBatchNorm | Per-batch scaling normalization |

multiBatchPCA | Multi-batch PCA |

multiBlockNorm | Per-block scaling normalization |

multiBlockVar | Per-block variance statistics |

overlapExprs | Overlap expression profiles |

pairwiseTTests | Perform pairwise t-tests |

pairwiseWilcox | Perform pairwise Wilcoxon rank sum tests |

parallelPCA | Parallel analysis for PCA |

quickCluster | Quick clustering of cells |

sandbag | Cell cycle phase training |

scaledColRanks | Compute scaled column ranks |

simpleSumFactors | Quickly compute size factors via summation |

technicalCV2 | Model the technical coefficient of variation |

testVar | Test for significantly large variances |

trendVar | Fit a variance trend |

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