Package index

Object interaction

Functions for interacting with a Seurat object

AnchorSet-class AnchorSet: The AnchorSet Class
BridgeReferenceSet-class BridgeReferenceSet: The BridgeReferenceSet Class The BridgeReferenceSet is an output from PrepareBridgeReference
Cells(<SCTModel>) Cells(<SlideSeq>) Cells(<STARmap>) Cells(<VisiumV1>): Get Cell Names
CreateSCTAssayObject(): Create a SCT Assay object
DietSeurat(): Slim down a Seurat object
FilterSlideSeq(): Filter stray beads from Slide-seq puck
GetAssay(): Get an Assay object from a given Seurat object.
GetImage(<SlideSeq>) GetImage(<STARmap>) GetImage(<VisiumV1>) GetImage(<VisiumV2>): Get Image Data
GetIntegrationData(): Get integration data
GetTissueCoordinates(<SlideSeq>) GetTissueCoordinates(<STARmap>) GetTissueCoordinates(<VisiumV1>) GetTissueCoordinates(<VisiumV2>): Get Tissue Coordinates
HVFInfo(<SCTAssay>): Get Variable Feature Information
IntegrationAnchorSet-class IntegrationAnchorSet: The IntegrationAnchorSet Class
IntegrationData-class IntegrationData: The IntegrationData Class
ModalityWeights-class ModalityWeights: The ModalityWeights Class
Radius(<SlideSeq>) Radius(<STARmap>) Radius(<VisiumV1>) Radius(<VisiumV2>): Get Spot Radius
RenameCells(<SCTAssay>) RenameCells(<SlideSeq>) RenameCells(<STARmap>) RenameCells(<VisiumV1>): Rename Cells in an Object
levels(<SCTAssay>) `levels<-`(<SCTAssay>): The SCTModel Class
SCTResults() `SCTResults<-`(): Get SCT results from an Assay
STARmap-class STARmap: The STARmap class
ScaleFactors() scalefactors(): Get image scale factors
SetIntegrationData(): Set integration data
SplitObject(): Splits object into a list of subsetted objects.
TopCells(): Find cells with highest scores for a given dimensional reduction technique
TopFeatures(): Find features with highest scores for a given dimensional reduction technique
TopNeighbors(): Get nearest neighbors for given cell
TransferAnchorSet-class TransferAnchorSet: The TransferAnchorSet Class
UpdateSCTAssays(): Update pre-V4 Assays generated with SCTransform in the Seurat to the new SCTAssay class
VisiumV1-class VisiumV1: The VisiumV1 class
VisiumV2-class VisiumV2: The VisiumV2 class
as.CellDataSet(): Convert objects to CellDataSet objects
as.Seurat(<CellDataSet>) as.Seurat(<SingleCellExperiment>): Convert objects to Seurat objects
as.SingleCellExperiment(): Convert objects to SingleCellExperiment objects
as.sparse(<H5Group>) as.data.frame(<Matrix>): Cast to Sparse
merge(<SCTAssay>): Merge SCTAssay objects
subset(<AnchorSet>): Subset an AnchorSet object

Preprocessing

Functions for preprocessing single-cell data

CalculateBarcodeInflections(): Calculate the Barcode Distribution Inflection
FetchResiduals(): Get the Pearson residuals from an sctransform-normalized dataset.
FindSpatiallyVariableFeatures(): Find spatially variable features
FindVariableFeatures(): Find variable features
GetResidual(): Calculate pearson residuals of features not in the scale.data
HTODemux(): Demultiplex samples based on data from cell 'hashing'
Load10X_Spatial(): Load a 10x Genomics Visium Spatial Experiment into a Seurat object
LoadAteraCodewords(): Load per-codeword transcript breakdowns for specific genes
LoadAteraMolecules(): Fetch additional Atera transcript molecules for genes not yet loaded
LoadAteraSegmentations(): Add segmentation polygons to an Atera field of view, for only the cells currently present in it
LoadCurioSeeker(): Load Curio Seeker data
LoadSTARmap(): Load STARmap data
LogNormalize(): Normalize Raw Data
MULTIseqDemux(): Demultiplex samples based on classification method from MULTI-seq (McGinnis et al., bioRxiv 2018)
NormalizeData(): Normalize Data
Read10X(): Load in data from 10X
Read10X_Coordinates(): Load 10X Genomics Visium Tissue Positions
Read10X_HD_GeoJson(): Load 10X Genomics GeoJson
Read10X_Image(): Load a 10X Genomics Visium Image
Read10X_ScaleFactors(): Load 10X Genomics Visium Scale Factors
Read10X_Segmentations(): Load 10X Genomics Visium Cell Segmentations
Read10X_h5(): Read 10X hdf5 file
Read10X_probe_metadata(): Read10x Probe Metadata
ReadAkoya() LoadAkoya(): Read and Load Akoya CODEX data
ReadAtera() LoadAtera(): Load Atera spatial data
ReadMtx(): Load in data from remote or local mtx files
ReadNanostring() LoadNanostring(): Read and Load Nanostring SMI data
ReadSlideSeq(): Load Slide-seq spatial data
ReadVitessce() LoadHuBMAPCODEX(): Read Data From Vitessce
ReadVizgen() LoadVizgen(): Read and Load MERFISH Input from Vizgen
LoadXenium() ReadXenium(): Read and Load 10x Genomics Xenium in-situ data
RelativeCounts(): Normalize raw data to fractions
RunMarkVario(): Run the mark variogram computation on a given position matrix and expression matrix.
RunMoransI(): Compute Moran's I value.
SCTransform(): Perform sctransform-based normalization
SampleUMI(): Sample UMI
ScaleData(): Scale and center the data.
SubsetByBarcodeInflections(): Subset a Seurat Object based on the Barcode Distribution Inflection Points

Differential expression

Functions for testing differential gene (feature) expression

FindAllMarkers(): Gene expression markers for all identity classes
FindConservedMarkers(): Finds markers that are conserved between the groups
FindMarkers(): Gene expression markers of identity classes
FoldChange(): Fold Change
PrepSCTFindMarkers(): Prepare object to run differential expression on SCT assay with multiple models

Dimensional reduction

Functions to reduce the dimensionality of datasets

JackStraw(): Determine statistical significance of PCA scores.
L2CCA(): L2-Normalize CCA
L2Dim(): L2-normalization
PCASigGenes(): Significant genes from a PCA
ProjectDim(): Project Dimensional reduction onto full dataset
ProjectUMAP(): Project query into UMAP coordinates of a reference
RunCCA(): Perform Canonical Correlation Analysis
RunDiffusionMap(): Run Diffusion Map
RunGraphLaplacian(): Run Graph Laplacian Eigendecomposition
RunICA(): Run Independent Component Analysis on gene expression
RunPCA(): Run Principal Component Analysis
RunPLS(): Run Partial Least Squares (PLS) on Seurat Objects
RunSLSI(): Run Supervised Latent Semantic Indexing
RunSPCA(): Run Supervised Principal Component Analysis
RunTSNE(): Run t-distributed Stochastic Neighbor Embedding
RunUMAP(): Run UMAP
ScoreJackStraw(): Compute Jackstraw scores significance.

Clustering

Functions to cluster single-cell data

BuildNicheAssay(): Construct an assay for spatial niche analysis
FindClusters(): Cluster Determination
FindMultiModalNeighbors(): Construct weighted nearest neighbor graph
FindNeighbors(): (Shared) Nearest-neighbor graph construction
FindSubCluster(): Find subclusters under one cluster
RunLeiden(): Run Leiden clustering algorithm

Integration

Functions related to the Seurat v3 integration and label transfer algorithms

AnnotateAnchors(): Add info to anchor matrix
BridgeCellsRepresentation(): Construct a dictionary representation for each unimodal dataset
CCAIntegration(): Seurat-CCA Integration
FastRPCAIntegration(): Perform integration on the joint PCA cell embeddings.
FindBridgeIntegrationAnchors(): Find integration bridge anchors between query and extended bridge-reference
FindBridgeTransferAnchors(): Find bridge anchors between query and extended bridge-reference
FindIntegrationAnchors(): Find integration anchors
FindTransferAnchors(): Find transfer anchors
GetTransferPredictions(): Get the predicted identity
HarmonyIntegration(): Harmony Integration
IntegrateData(): Integrate data
IntegrateEmbeddings(): Integrate low dimensional embeddings
IntegrateLayers(): Integrate Layers
JointPCAIntegration(): Seurat-Joint PCA Integration
LocalStruct(): Calculate the local structure preservation metric
MapQuery(): Map query cells to a reference
MappingScore(): Metric for evaluating mapping success
MixingMetric(): Calculates a mixing metric
NNtoGraph(): Convert Neighbor class to an asymmetrical Graph class
PredictAssay(): Predict value from nearest neighbors
PrepSCTIntegration(): Prepare an object list normalized with sctransform for integration.
PrepareBridgeReference(): Prepare the bridge and reference datasets
ProjectDimReduc(): Project query data to reference dimensional reduction
ProjectIntegration(): Integrate embeddings from the integrated sketched.assay
SelectIntegrationFeatures(): Select integration features
SelectIntegrationFeatures5(): Select integration features
SelectSCTIntegrationFeatures(): Select SCT integration features
TransferData(): Transfer data
UnSketchEmbeddings(): Transfer embeddings from sketched cells to the full data

Spatial

Functions related to the analysis of spatially-resolved single-cell data

Cells(<SCTModel>) Cells(<SlideSeq>) Cells(<STARmap>) Cells(<VisiumV1>): Get Cell Names
FilterSlideSeq(): Filter stray beads from Slide-seq puck
FindSpatiallyVariableFeatures(): Find spatially variable features
GetImage(<SlideSeq>) GetImage(<STARmap>) GetImage(<VisiumV1>) GetImage(<VisiumV2>): Get Image Data
GetTissueCoordinates(<SlideSeq>) GetTissueCoordinates(<STARmap>) GetTissueCoordinates(<VisiumV1>) GetTissueCoordinates(<VisiumV2>): Get Tissue Coordinates
ISpatialDimPlot(): Visualize clusters spatially and interactively
ISpatialFeaturePlot(): Visualize features spatially and interactively
ImageDimPlot(): Spatial Cluster Plots
ImageFeaturePlot(): Spatial Feature Plots
InteractiveSpatialPlot(): Interactive Spatial Cell Selection Tool
LinkedDimPlot() LinkedFeaturePlot(): Visualize spatial and clustering (dimensional reduction) data in a linked, interactive framework
LoadAteraCodewords(): Load per-codeword transcript breakdowns for specific genes
LoadAteraMolecules(): Fetch additional Atera transcript molecules for genes not yet loaded
LoadAteraSegmentations(): Add segmentation polygons to an Atera field of view, for only the cells currently present in it
PolyFeaturePlot(): Polygon FeaturePlot
Radius(<SlideSeq>) Radius(<STARmap>) Radius(<VisiumV1>) Radius(<VisiumV2>): Get Spot Radius
STARmap-class STARmap: The STARmap class
ScaleFactors() scalefactors(): Get image scale factors
SlideSeq-class SlideSeq: The SlideSeq class
SpatialPlot() SpatialDimPlot() SpatialFeaturePlot(): Visualize spatial clustering and expression data.
VisiumV1-class VisiumV1: The VisiumV1 class
VisiumV2-class VisiumV2: The VisiumV2 class

Sketching

Functions for flexible analysis of massively scalable datasets

LeverageScore(): Leverage Score Calculation
ProjectData(): Project full data to the sketch assay
SketchData(): Sketch Data
SketchDataByGroup(): Sketch Data by Group
TransferSketchLabels(): Transfer data from sketch data to full data

Mixscape

Functions related to the mixscape algorithm

CalcPerturbSig(): Calculate a perturbation Signature
DEenrichRPlot(): DE and EnrichR pathway visualization barplot
MixscapeHeatmap(): Differential expression heatmap for mixscape
MixscapeLDA(): Linear discriminant analysis on pooled CRISPR screen data.
PlotPerturbScore(): Function to plot perturbation score distributions.
PrepLDA(): Function to prepare data for Linear Discriminant Analysis.
RunLDA(): Run Linear Discriminant Analysis
RunMixscape(): Run Mixscape

Sample-level analysis

Functions for sample-level analysis with scSLIDE

FindmmNN(): Find Multi-Modal Nearest Neighbors
FindsmNN(): Find Single-Modality Nearest Neighbors
GenerateSampleObject(): Generate a sample-level count matrix based on landmark assay and its weighted.nn object
NormalizeChiSquared(): Apply Chi-squared normalization to a Seurat object
PlotAnnotatedHeatmap(): Plot an annotated expression heatmap of features across cells
PrepareSampleObject(): Perform preprocessing procedures for a Seurat object to prepare for the sample-level analysis
QuickCorTest(): Perform cor.test between each gene and a response variable given a Seurat object
TrajDETest(): Trajectory Differential Expression Test

Visualization

Functions for plotting data and adjusting

AugmentPlot(): Augments ggplot2-based plot with a PNG image.
AutoPointSize(): Automagically calculate a point size for ggplot2-based scatter plots
BGTextColor(): Determine text color based on background color
BarcodeInflectionsPlot(): Plot the Barcode Distribution and Calculated Inflection Points
CellScatter(): Cell-cell scatter plot
CellSelector() FeatureLocator(): Cell Selector
CollapseEmbeddingOutliers(): Move outliers towards center on dimension reduction plot
ColorDimSplit(): Color dimensional reduction plot by tree split
CombinePlots(): Combine ggplot2-based plots into a single plot
BlackAndWhite() BlueAndRed() CustomPalette() PurpleAndYellow(): Create a custom color palette
DimHeatmap() PCHeatmap(): Dimensional reduction heatmap
DimPlot() PCAPlot() TSNEPlot() UMAPPlot(): Dimensional reduction plot
DiscretePalette(): Discrete colour palettes from pals
DoHeatmap(): Feature expression heatmap
DotPlot(): Dot plot visualization
ElbowPlot(): Quickly Pick Relevant Dimensions
FeaturePlot(): Visualize 'features' on a dimensional reduction plot
FeatureScatter(): Scatter plot of single cell data
GroupCorrelationPlot(): Boxplot of correlation of a variable (e.g. number of UMIs) with expression data
HTOHeatmap(): Hashtag oligo heatmap
HoverLocator(): Hover Locator
IFeaturePlot(): Visualize features in dimensional reduction space interactively
ISpatialDimPlot(): Visualize clusters spatially and interactively
ISpatialFeaturePlot(): Visualize features spatially and interactively
ImageDimPlot(): Spatial Cluster Plots
ImageFeaturePlot(): Spatial Feature Plots
InteractiveSpatialPlot(): Interactive Spatial Cell Selection Tool
JackStrawPlot(): JackStraw Plot
LabelClusters(): Label clusters on a ggplot2-based scatter plot
LabelPoints(): Add text labels to a ggplot2 plot
LinkedDimPlot() LinkedFeaturePlot(): Visualize spatial and clustering (dimensional reduction) data in a linked, interactive framework
NNPlot(): Highlight Neighbors in DimPlot
PlotClusterTree(): Plot clusters as a tree
PolyDimPlot(): Polygon DimPlot
PolyFeaturePlot(): Polygon FeaturePlot
RidgePlot(): Single cell ridge plot
SeuratTheme() CenterTitle() DarkTheme() FontSize() NoAxes() NoLegend() NoGrid() SeuratAxes() SpatialTheme() RestoreLegend() RotatedAxis() BoldTitle() WhiteBackground(): Seurat Themes
SpatialPlot() SpatialDimPlot() SpatialFeaturePlot(): Visualize spatial clustering and expression data.
VariableFeaturePlot(): View variable features
VizDimLoadings(): Visualize Dimensional Reduction genes
VlnPlot(): Single cell violin plot
Intensity() Luminance(): Get the intensity and/or luminance of a color

Function related to tree-based analysis of identity classes

BuildClusterTree(): Phylogenetic Analysis of Identity Classes

Utility functions

Useful functions to help with a variety of tasks

AddAzimuthResults(): Add Azimuth Results
AddModuleScore(): Calculate module scores for feature expression programs in single cells
AggregateExpression(): Aggregated feature expression by identity class
AverageExpression(): Averaged feature expression by identity class
CaseMatch(): Match the case of character vectors
CellCycleScoring(): Score cell cycle phases
CollapseSpeciesExpressionMatrix(): Slim down a multi-species expression matrix, when only one species is primarily of interenst.
CreateCategoryMatrix(): Create one hot matrix for a given label
CustomDistance(): Run a custom distance function on an input data matrix
ExpMean(): Calculate the mean of logged values
ExpSD(): Calculate the standard deviation of logged values
ExpVar(): Calculate the variance of logged values
FastRowScale(): Scale and/or center matrix rowwise
GroupCorrelation(): Compute the correlation of features broken down by groups with another covariate
LoadAnnoyIndex(): Load the Annoy index file
LogVMR(): Calculate the variance to mean ratio of logged values
MetaFeature(): Aggregate expression of multiple features into a single feature
MinMax(): Apply a ceiling and floor to all values in a matrix
PercentAbove(): Calculate the percentage of a vector above some threshold
PercentageFeatureSet(): Calculate the percentage of all counts that belong to a given set of features
PseudobulkExpression(): Pseudobulk Expression
RPCAIntegration(): Seurat-RPCA Integration
RegroupIdents(): Regroup idents based on meta.data info
SaveAnnoyIndex(): Save the Annoy index
SetQuantile(): Find the Quantile of Data
GeneSymbolThesarus() UpdateSymbolList(): Get updated synonyms for gene symbols
as.sparse(<H5Group>) as.data.frame(<Matrix>): Cast to Sparse
getThreads(): Get the number of threads being used for parallel processing in Seurat
setThreads(): Set the number of threads to use for parallel processing in Seurat

Data

Descriptions of data included with Seurat

cc.genes: Cell cycle genes
cc.genes.updated.2019: Cell cycle genes: 2019 update

Convenience functions

Functions included for user convenience and to keep maintain backwards compatability

DimHeatmap() PCHeatmap(): Dimensional reduction heatmap
DimPlot() PCAPlot() TSNEPlot() UMAPPlot(): Dimensional reduction plot
ReadParseBio(): Read output from Parse Biosciences
ReadSTARsolo(): Read output from STARsolo
SpatialPlot() SpatialDimPlot() SpatialFeaturePlot(): Visualize spatial clustering and expression data.

Sketching

Functions for flexible analysis of massively scalable datasets

LeverageScore(): Leverage Score Calculation
ProjectData(): Project full data to the sketch assay
SketchData(): Sketch Data
SketchDataByGroup(): Sketch Data by Group
TransferSketchLabels(): Transfer data from sketch data to full data

Re-exports

Functions re-exported from other packages

components() `%||%` `%iff%`: Objects exported from other packages
Seurat-class: The Seurat Class
Assay-class: The Assay Class
DimReduc-class: The DimReduc Class
Neighbor-class: The Neighbor Class
Graph-class: The Graph Class
SpatialImage-class: The SpatialImage Class
JackStrawData-class: The JackStrawData Class
SeuratCommand-class: The SeuratCommand Class
Seurat Seurat-package: Seurat: Tools for Single Cell Genomics

Object interaction

Preprocessing

Differential expression

Dimensional reduction

Clustering

Integration

Spatial

Sketching

Mixscape

Sample-level analysis

Visualization

Tree-related functions

Utility functions

Data

Convenience functions

Sketching

Re-exports