Seurat整合不同條件、技術和物種的單細胞轉錄組數據

Seurat相關連結

Seurat教程
Seurat的github頁面
基於Seurat的擴展包
Seurat函數參考
老版本的Seurat
Seurat文獻：https://doi.org/10.1038/nbt.4096

Seurat的安裝

Seurat包發布在CRAN官方上。

安裝最新版Seurat

安裝代碼：

install.packages('Seurat')
library(Seurat)

如果出現以下提示，請輸入y

package which is only available in source form, and may need compilation of C/C++/Fortran: 'Seurat'
Do you want to attempt to install these from sources?
y/n:y

安裝較早版本的Seurat

### 安装Seurat3的包
remotes::install_version("Seurat", version = "3.X.X")

### 安装Seurat2的包
source("https://z.umn.edu/archived-seurat")
# 安装remotes包 
install.packages('remotes')
# 替换'2.3.0'为你需要的版本
remotes::install_version(package = 'Seurat', version = package_version('2.3.0'))
library(Seurat)

安裝開發中的Seurat

install.packages('remotes')
remotes::install_github(repo = 'satijalab/seurat', ref = 'develop')
library(Seurat)

Docker安裝Seurat

docker pull satijalab/seurat:latest
FROM satijalab/seurat:latest

Seurat的函數

對象交互：用於與 Seurat 對象交互的函數

`AnchorSet-class`	AnchorSet類
`Cells(<SCTModel>)` `Cells(<SlideSeq>)` `Cells(<STARmap>)` `Cells(<VisiumV1>)`	獲取細胞名
`CreateSCTAssayObject()`	創建SCT Assay對象
`DietSeurat()`	縮小Seurat對象
`FilterSlideSeq()`	Filter stray beads from Slide-seq puck
`GetAssay()`	Get an Assay object from a given Seurat object.
`GetImage(<SlideSeq>)` `GetImage(<STARmap>)` `GetImage(<VisiumV1>)`	Get Image Data
`GetIntegrationData()`	Get integration data
`GetTissueCoordinates(<SlideSeq>)` `GetTissueCoordinates(<STARmap>)` `GetTissueCoordinates(<VisiumV1>)`	Get Tissue Coordinates
`IntegrationAnchorSet-class`	The IntegrationAnchorSet Class
`IntegrationData-class`	The IntegrationData Class
`ModalityWeights-class`	The ModalityWeights Class
`Radius(<SlideSeq>)` `Radius(<STARmap>)` `Radius(<VisiumV1>)`	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`	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`	The TransferAnchorSet Class
`UpdateSCTAssays()`	Update pre-V4 Assays generated with SCTransform in the Seurat to the new SCTAssay class
`VisiumV1-class`	The VisiumV1 class
`as.CellDataSet()`	Convert objects to CellDataSet objects
`as.Seurat(<CellDataSet>)` `as.Seurat(<SingleCellExperiment>)`	Convert objects to objects`Seurat`
`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

預處理：單細胞數據的預處理

`CalculateBarcodeInflections()`	Calculate the Barcode Distribution Inflection
`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 object`Seurat`
`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_Image()`	Load a 10X Genomics Visium Image
`Read10X_h5()`	Read 10X hdf5 file
`ReadMtx()`	Load in data from remote or local mtx files
`ReadSlideSeq()`	Load Slide-seq spatial 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()`	Use regularized negative binomial regression to normalize UMI count data
`SampleUMI()`	Sample UMI
`ScaleData()`	Scale and center the data.
`SubsetByBarcodeInflections()`	Subset a Seurat Object based on the Barcode Distribution Inflection Points

差異分析

`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

降維分析

`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
`RunICA()`	Run Independent Component Analysis on gene expression
`RunPCA()`	Run Principal Component Analysis
`RunTSNE()`	Run t-distributed Stochastic Neighbor Embedding
`RunUMAP()`	Run UMAP
`ScoreJackStraw()`	Compute Jackstraw scores significance.

聚類

`FindClusters()`	Cluster Determination
`FindMultiModalNeighbors()`	Construct weighted nearest neighbor graph
`FindNeighbors()`	(Shared) Nearest-neighbor graph construction
`FindSubCluster()`	Find subclusters under one cluster

整合分析：與 Seurat v3 集成和標籤傳輸算法相關的函數

`AnnotateAnchors()`	Add info to anchor matrix
`FindIntegrationAnchors()`	Find integration anchors
`FindTransferAnchors()`	Find transfer anchors
`GetTransferPredictions()`	Get the predicted identity
`IntegrateData()`	Integrate data
`IntegrateEmbeddings()`	Integrate low dimensional embeddings
`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
`PredictAssay()`	Predict value from nearest neighbors
`PrepSCTIntegration()`	Prepare an object list normalized with sctransform for integration.
`SelectIntegrationFeatures()`	Select integration features
`TransferData()`	Transfer data

空間：與空間分辨單細胞數據分析相關的功能

`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>)`	Get Image Data
`GetTissueCoordinates(<SlideSeq>)` `GetTissueCoordinates(<STARmap>)` `GetTissueCoordinates(<VisiumV1>)`	Get Tissue Coordinates
`ISpatialDimPlot()`	Visualize clusters spatially and interactively
`ISpatialFeaturePlot()`	Visualize features spatially and interactively
`LinkedDimPlot()` `LinkedFeaturePlot()`	Visualize spatial and clustering (dimensional reduction) data in a linked, interactive framework
`PolyFeaturePlot()`	Polygon FeaturePlot
`Radius(<SlideSeq>)` `Radius(<STARmap>)` `Radius(<VisiumV1>)`	Get Spot Radius
`STARmap-class`	The STARmap class
`ScaleFactors()` `scalefactors()`	Get image scale factors
`SlideSeq-class`	The SlideSeq class
`SpatialPlot()` `SpatialDimPlot()` `SpatialFeaturePlot()`	Visualize spatial clustering and expression data.
`VisiumV1-class`	The VisiumV1 class

Mixscape：與mixscape算法相關的函數

`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

可視化

`AugmentPlot()`	Augments ggplot2-based plot with a PNG image.
`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 the pals package
`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
`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

構建進化樹

`BuildClusterTree()`	Phylogenetic Analysis of Identity Classes

實用功能

`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.
`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
`PercentageFeatureSet()`	Calculate the percentage of all counts that belong to a given set of features
`RegroupIdents()`	Regroup idents based on meta.data info
`SaveAnnoyIndex()`	Save the Annoy index
`GeneSymbolThesarus()` `UpdateSymbolList()`	Get updated synonyms for gene symbols
`as.sparse(<H5Group>)` `as.data.frame(<Matrix>)`	Cast to Sparse

數據：Seurat數據描述

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

便利功能：為方便用戶並保持向後兼容性而包含的功能

`DimHeatmap()` `PCHeatmap()`	Dimensional reduction heatmap
`DimPlot()` `PCAPlot()` `TSNEPlot()` `UMAPPlot()`	Dimensional reduction plot
`SpatialPlot()` `SpatialDimPlot()` `SpatialFeaturePlot()`	Visualize spatial clustering and expression data.