Package ‘Seurat’ - cran.r-project.org

1 Package Seurat'. October 3, 2019. Version Date 2019-09-23. Title Tools for Single Cell Genomics Description A toolkit for quality control, analysis, and exploration of single cell RNA sequenc- ing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from sin- gle cell transcriptomic measurements, and to integrate diverse types of sin- gle cell data. See Satija R, Farrell J, Gennert D, et al (2015) < >, Ma- cosko E, Basu A, Satija R, et al (2015) < >, and But- ler A and Satija R (2017) < > for more details. URL , BugReports Additional_repositories Depends R (>= ), methods, Imports ape, cluster, cowplot, fitdistrplus, future, , ggplot2 (>= ), ggrepel, ggridges, graphics, grDevices, grid, httr, ica, igraph, irlba, KernSmooth, leiden (>= ), lmtest, MASS, Matrix (>= ), metap, pbapply, plotly, png, RANN, RColorBrewer, Rcpp, RcppAnnoy, reticulate, rlang, ROCR, rsvd, Rtsne, scales, sctransform (>= ), SDMT ools, stats, tools, tsne, utils, uwot LinkingTo Rcpp (>= ), RcppEigen, RcppProgress License GPL-3 | file LICENSE.

2 LazyData true Collate ' ' ' ' ' ' ' '. ' ' ' ' ' '. ' ' ' ' ' '. ' ' ' ' ' ' ' '. RoxygenNote Encoding UTF-8. 1. 2 R topics documented: Suggests loomR, testthat, hdf5r, S4 Vectors, SummarizedExperiment, SingleCellExperiment, MAST, DESeq2, BiocGenerics, GenomicRanges, GenomeInfoDb, IRanges, rtracklayer, monocle, Biobase, VGAM. NeedsCompilation yes Author Rahul Satija [aut] (< >), Andrew Butler [aut] (< >), Paul Hoffman [aut, cre] (< >), Tim Stuart [aut] (< >), Jeff Farrell [ctb], Shiwei Zheng [ctb] (< >), Christoph Hafemeister [ctb] (< >), Patrick Roelli [ctb], Yuhan Hao [ctb] (< >). Maintainer Paul Hoffman Repository CRAN. Date/Publication 2019-10-03 12:40:02 UTC. R topics documented: Seurat- Package .

3 5. AddMetaData .. 6. AddModuleScore .. 7. ALRAC hooseKPlot .. 8. AnchorSet-class .. 9.. 10.. 10.. 11.. 12.. 13.. 15.. 16. Assay-class .. 17. Assays .. 17. AugmentPlot .. 18. AverageExpression .. 18. BarcodeInflectionsPlot .. 19. BlackAndWhite .. 20. BuildClusterTree .. 21. CalculateBarcodeInflections .. 22. CaseMatch .. 23.. 24.. 24. CellCycleScoring .. 25. Cells .. 26. CellScatter .. 27. R topics documented: 3. CellSelector .. 28. CollapseEmbeddingOutliers .. 29. CollapseSpeciesExpressionMatrix .. 30. ColorDimSplit .. 31. CombinePlots .. 32. Command .. 33. CreateAssayObject .. 34. CreateDimReducObject .. 34. CreateGeneActivityMatrix .. 35. CreateSeuratObject .. 36. CustomDistance .. 37.

4 DefaultAssay .. 38. DietSeurat .. 39. DimHeatmap .. 39. DimPlot .. 41. DimReduc-class .. 42. DoHeatmap .. 43. DotPlot .. 44. ElbowPlot .. 46. Embeddings .. 46. ExpMean .. 47. ExportToCellbrowser .. 48. ExpSD .. 49. ExpVar .. 49. FeaturePlot .. 50. FeatureScatter .. 52. FetchData .. 53. FindAllMarkers .. 54. FindClusters .. 56. FindConservedMarkers .. 58. FindIntegrationAnchors .. 59. FindMarkers .. 60. FindNeighbors .. 63. FindTransferAnchors .. 65. FindVariableFeatures .. 67. GetAssay .. 69. GetAssayData .. 69. GetIntegrationData .. 70. GetResidual .. 71. Graph-class .. 72. HoverLocator .. 72. HTOD emux .. 73. HTOH eatmap .. 74. HVFInfo .. 75. Idents .. 76. IntegrateData .. 78. IntegrationData-class.

5 80. JackStraw .. 80. 4 R topics documented: JackStrawData-class .. 81. JackStrawPlot .. 81. JS .. 82. Key .. 83. L2 CCA .. 84. L2 Dim .. 85. LabelClusters .. 85. LabelPoints .. 86. Loadings .. 87. LocalStruct .. 88. LogNormalize .. 89. LogSeuratCommand .. 89. LogVMR .. 90. MapQuery .. 90.. 92. MetaFeature .. 93. MinMax .. 94. Misc .. 94. MixingMetric .. 95. MULTIseqDemux .. 96. NormalizeData .. 97. OldWhichCells .. 98. PairwiseIntegrateReference .. 99. pbmc_small .. 101. PCASigGenes .. 102. PercentageFeatureSet .. 103. PlotClusterTree .. 104. PolyDimPlot .. 104. PolyFeaturePlot .. 105. PrepSCTI ntegration .. 106.. 106. project .. 107. ProjectDim .. 108. Read10X .. 109. Read10X_h5 .. 110.

6 ReadAlevin .. 110. ReadAlevinCsv .. 111. ReadH5AD .. 112. Reductions .. 113. RegroupIdents .. 114. RelativeCounts .. 115. RenameCells .. 115. RidgePlot .. 117. RunALRA .. 118. RunCCA .. 120. RunICA .. 121. RunLSI .. 122. RunPCA .. 123. Seurat- Package 5. RunTSNE .. 124. RunUMAP .. 126. SampleUMI .. 129. ScaleData .. 130. ScoreJackStraw .. 131. SCTransform .. 132. SelectIntegrationFeatures .. 134. SetAssayData .. 134. SetIntegrationData .. 135. Seurat-class .. 136. seurat-class .. 137. SeuratCommand-class .. 138. SeuratTheme .. 138. SplitObject .. 140. Stdev .. 141. StopCellbrowser .. 142. SubsetByBarcodeInflections .. 142. SubsetData .. 143.. 144. Tool .. 145. TopCells .. 146. TopFeatures.

7 147. TransferData .. 147. UpdateSeuratObject .. 148. UpdateSymbolList .. 149. VariableFeaturePlot .. 150. VariableFeatures .. 151. VizDimLoadings .. 152. VlnPlot .. 153. WhichCells .. 154. [.Seurat .. 155. Index 157. Seurat- Package Tools for single-cell genomics Description Tools for single-cell genomics Package options Seurat uses the following [options()] to configure behaviour: global option to call gc() after many operations. This can be helpful in cleaning up the memory status of the R session and prevent use of swap space. However, it does add to the computational overhead and setting to FALSE can speed things up if you're working in an environment where RAM availabiliy is not a concern.]

8 6 AddMetaData Show warning about the default backend for RunUMAP changing from Python UMAP via reticulate to UWOT. For functions that have .. as a parameter, this controls the behavior when an item isn't used. Can be one of warn, stop, or silent. AddMetaData Add in metadata associated with either cells or features. Description Adds additional data to the object. Can be any piece of information associated with a cell (examples include read depth, alignment rate, experimental batch, or subpopulation identity) or feature (ENSG. name, variance). To add cell level information, add to the Seurat object. If adding feature-level metadata, add to the Assay object ( object[["RNA"]])). Usage AddMetaData(object, metadata, = NULL).

9 ## S3 method for class 'Assay'. AddMetaData(object, metadata, = NULL). ## S3 method for class 'Seurat'. AddMetaData(object, metadata, = NULL). ## S4 replacement method for signature 'Assay'. x[[i, j, ..]] <- value ## S4 replacement method for signature 'Seurat'. x[[i, j, ..]] <- value Arguments x, object An object i, Name to store metadata or object as j Ignored .. Arguments passed to other methods value, metadata Metadata or object to add Value An object with metadata or and object added AddModuleScore 7. Examples cluster_letters <- LETTERS[Idents(object = pbmc_small)]. names(cluster_letters) <- colnames(x = pbmc_small). pbmc_small <- AddMetaData(. object = pbmc_small, metadata = cluster_letters, = ' '.)

10 Head(x = pbmc_small[[]]). AddModuleScore Calculate module scores for feature expression programs in single cells Description Calculate the average expression levels of each program (cluster) on single cell level, subtracted by the aggregated expression of control feature sets. All analyzed features are binned based on averaged expression, and the control features are randomly selected from each bin. Usage AddModuleScore(object, features, pool = NULL, nbin = 24, ctrl = 100, k = FALSE, assay = NULL, name = "Cluster", seed = 1, search = FALSE, ..). Arguments object Seurat object features Feature expression programs in list pool List of features to check expression levels agains, defaults to rownames(x =.)