In vertesy/Seurat.utils: Seurat.utils - utility functions for Seurat

Vignette for 57 functions in Seurat.Utils.Visualization.R

Updated: 2024/03/25 15:51

Seurat.utils Is a collection of utility functions for Seurat single cell analysis. Functions allow 3D plotting, visualisation of statistics & QC, the automation / multiplexing of plotting, interaction with the Seurat object, etc. Some functionalities require functions from CodeAndRoll and MarkdownReports libraries.> For details, please use the help() function, or browse the source code.

1. PlotFilters: PlotFilters()

Plot filtering threshold and distributions, using four panels to highlight the relation between Gene- and UMI-count, ribosomal- and mitochondrial-content.

   PlotFilters(ls.Seurat)

2. Calculate the percent of variation explained by individual PC's: scCalcPCAVarExplained()

This function calculates the percentage of variation each principal component (PC)

   data("combined.obj") # Example Seurat object
   var_explained <- scCalcPCAVarExplained(combined.obj)
   print(var_explained)

3. Plot the percent of variation explained by individual PC's: scPlotPCAvarExplained()

This function plots the percentage of variation explained by each principal

   data("combined.obj") # Example Seurat object
   scPlotPCAvarExplained(combined.obj, use.MarkdownReports = TRUE)

4. Gene Expression as Fraction of Total UMI Counts: PercentInTranscriptome()

This function computes and visualizes gene expression levels as a fraction of total

 combined.obj <- PercentInTranscriptome(combined.obj)

5. Histogram of Gene Expression Levels: geneExpressionLevelPlots()

This function generates a histogram to visualize the expression level distribution

No examples found.

6. Proportion of Cells Expressing Given Genes: PrctCellExpringGene()

Calculates the proportion of cells expressing one or more specified genes.

 PrctCellExpringGene(genes = c("Gene1", "Gene2"), obj = seurat_object)

7. Helper to calculate Cell Expression Proportion for Gene: ww.calc_helper()

Computes the proportion of cells expressing a specific gene within a Seurat object.

 ww.calc_helper(obj = seurat_object, genes = "Gene1")

8. Cluster Composition Analysis: get.clustercomposition()

Analyzes and visualizes the composition of clusters in a Seurat object, indicating

 get.clustercomposition()

9. Generate Barplot of Cell Fractions: scBarplot.CellFractions()

This function generates a bar plot of cell fractions per cluster from a Seurat object.

   scBarplot.CellFractions(obj = combined.obj, group.by = "integrated_snn_res.0.1", fill.by = "Phase", downsample = TRUE)
   scBarplot.CellFractions(obj = combined.obj, group.by = "integrated_snn_res.0.1", fill.by = "Phase", downsample = FALSE)

10. Barplot of Fraction of Cells per Cluster: scBarplot.CellsPerCluster()

Visualizes the fraction of cells within each cluster through a barplot.

 scBarplot.CellsPerCluster()
 scBarplot.CellsPerCluster(sort = TRUE)

11. Barplot of Cells Per Seurat Object: scBarplot.CellsPerObject()

Visualizes the number of cells in each Seurat object within a list, showing the

No examples found.

12. Cluster Size Distribution Plot (Barplot or Histogram): plotClustSizeDistr()

Generates a bar plot or histogram to visualize the size distribution of clusters

 plotClustSizeDistr()

13. Barplot the Fraction of Cells Above Threshold per Cluster: scBarplot.FractionAboveThr()

Generates a bar plot depicting the percentage of cells within each cluster that

 scBarplot.FractionAboveThr(id.col = "cl.names.top.gene.res.0.3", value.col = "percent.ribo", thrX = 0.3)

14. Fraction of Cells Below Threshold per Cluster: scBarplot.FractionBelowThr()

Generates a bar plot to visualize the percentage of cells within each cluster that

 scBarplot.FractionBelowThr(id.col = "cl.names.top.gene.res.0.3", value.col = "percent.ribo", thrX = 0.01)

15. Stacked Barplot of Metadata Categories for List of Seurat Objects: scBarplotStackedMetaCateg_List()

Creates and saves a stacked barplot for a specified metadata category

No examples found.

16. Reproduce the ggplot2 default color palette: gg_color_hue()

Generates a vector of colors that emulates the default color palette used by ggplot2.

 # Generate a palette of 5 colors
 print(gg_color_hue(5))

17. Safely generate a discrete color palette (NA).: getDiscretePalette()

Safe wrapper around Seurat's DiscretePalette(), which returns NA's if too many

   getDiscretePalette()

18. Generate a Discrete Color Palette for Seurat Clusters: getDiscretePaletteObj()

Generates a discrete color palette for visualizing clusters in a Seurat object,

   ident.used <- "resolution_1"
   obj <- YourSeuratObject
   colors <- getDiscretePaletteObj(ident.used = ident.used, obj = obj)
   print(colors)

19. Safely generate a Discrete color palette.: DiscretePaletteSafe()

Generates a discrete color palette, ensuring no NA values are included, suitable

 colors <- DiscretePaletteSafe(n = 10)
 print(colors)

20. Regenerate Cluster Colors from a Seurat Object: getClusterColors()

Regenerate and optionally displays the color scheme associated with the clusters

   getClusterColors(obj = combined.obj, ident = GetClusteringRuns(combined.obj)[1])

21. Regenerate Color Scheme for Clusters in Seurat Object as a vector: SeuratColorVector()

Extracts and optionally displays the color scheme assigned to cluster identities

 # Display colors for the active identity
 SeuratColorVector()
 # Retrieve and plot colors for a specified clustering identity
 SeuratColorVector(ident = "RNA_snn_res.1", plot.colors = TRUE)

22. Plot and Save Heatmaps from Metadata Calculation Results: plotAndSaveHeatmaps()

Generates and saves heatmap visualizations for each metric in the results obtained

 # Assuming `results` is the output from `calculateAverageMetaData`:
 plotAndSaveHeatmaps(results, path = "path/to/save/heatmaps", file.prefix = "myData_")

23. Scatter Plot of Two Features in Seurat Object: qFeatureScatter()

Generates a scatter plot comparing two features (genes or metrics) from a Seurat

 # Generate and display a scatter plot for features TOP2A and ID2
 qFeatureScatter(feature1 = "TOP2A", feature2 = "ID2", obj = yourSeuratObject)

24. Create a Violin Plot for a Seurat Object Feature and save the file.: qSeuViolin()

Generates a violin plot for a specified feature in a Seurat object,

 # Assuming `seurat_obj` is a valid Seurat object
 qSeuViolin(object = seurat_obj, features = "nFeature_RNA")

25. Histogram of Gene Expression in Seurat Object: plotGeneExpHist()

Creates and optionally saves a histogram showing expression levels of specified genes

 plotGeneExpHist(obj = yourSeuratObject, genes = c("GeneA", "GeneB"))

26. Quick UMAP Visualization of Gene Expression and automatically save the plot: qUMAP()

Generates a UMAP visualization for a specific feature from a Seurat object, and

   qUMAP(feature = "nFeature_RNA", obj = yourSeuratObject)
   qUMAP(feature = "TOP2A", obj = yourSeuratObject, PNG = FALSE, save.plot = TRUE)

27. Quick Visualization of Clustering Results with UMAP and automatically save the plot: clUMAP()

Generates a UMAP visualization based on clustering results from a Seurat object,

 clUMAP(ident = "integrated_snn_res.0.5", obj = yourSeuratObj)
 clUMAP(ident = "integrated_snn_res.0.5", obj = yourSeuratObj, cols = RColorBrewer::brewer.pal(8, "Dark2"))

28. Highlight Selected Clusters on UMAP: umapHiLightSel()

Generates a UMAP plot from a Seurat object with specified clusters highlighted.

   umapHiLightSel(obj = combined.obj, COI = c("0", "1"), res.cl = "resolution_0.8")

29. DimPlot.ClusterNames: DimPlot.ClusterNames()

Plot UMAP with Cluster names.

   DimPlot.ClusterNames()

30. multiFeaturePlot.A4: multiFeaturePlot.A4()

Save multiple FeaturePlots, as jpeg, on A4 for each gene, which are stored as a list of gene names.

No examples found.

31. Generate Cluster Highlight UMAPs compiled into A4 pages: multiSingleClusterHighlightPlots.A4()

This function generates and saves cluster highlight plots for both single and multiple

 multiSingleClusterHighlightPlots.A4(ident = "cluster_id", obj = yourSeuratObject)

32. multiSeuratHeatmap.A4: multiSeuratHeatmap.A4()

Save multiple FeatureHeatmaps from a list of genes on A4 jpeg.

No examples found.

33. Plot multiple categorical variables in combined UMAPs: multi_clUMAP.A4()

Generates and saves multiple UMAP plots for clustering results, adjusting the

 multi_clUMAP.A4(idents = c("S1", "S2"), obj = YourSeuratObject)

34. Quick Clustering UMAPs on A4 Page: qClusteringUMAPS()

Generates and arranges UMAP plots for up to four specified clustering resolutions

 qClusteringUMAPS()

35. Plot qUMAPs for Genes in a Folder: plotQUMAPsInAFolder()

This function plots qUMAPs for a specified set of genes, storing the results in a

 plotQUMAPsInAFolder(
   genes = c("Gene1", "Gene2"), obj = combined.obj,
   foldername = "MyGenePlots", intersectionAssay = "RNA",
   plot.reduction = "umap"
 )

36. Plot Top N Differentially Expressed Genes Per Cluster: PlotTopGenesPerCluster()

Visualizes the top N differentially expressed (DE) genes for each cluster within a

   PlotTopGenesPerCluster(obj = combined.obj, cl_res = 0.5, nrGenes = 10)

37. Quickly Plot Key QC Markers in Brain Organoids: qQC.plots.BrainOrg()

Generates and arranges UMAP plots for specified QC features

 qQC.plots.BrainOrg()

38. Quickly Plot Key Markers in Brain Organoids: qMarkerCheck.BrainOrg()

Generates plots for a predefined or custom set of gene markers within brain organoids,

 qMarkerCheck.BrainOrg(combined.obj)
 qMarkerCheck.BrainOrg(combined.obj, custom.genes = c("Gene1", "Gene2"))

39. Plot Top Genes: PlotTopGenes()

This function plots the highest expressed genes on UMAPs, saving the plots in a

   PlotTopGenes()

40. Flip Reduction Coordinates: FlipReductionCoordinates()

Flips dimensionality reduction coordinates (such as UMAP or tSNE) vertically or

 # Before flipping UMAP coordinates
 clUMAP()
 # Flip UMAP coordinates and visualize again
 combined.obj <- FlipReductionCoordinates(combined.obj)
 clUMAP()

41. Relabel Cluster Numbers Along a UMAP (or tSNE) Axis: AutoNumber.by.UMAP()

Automatically renumbers clusters based on their position along a specified dimension

 combined.obj <- AutoNumber.by.UMAP(
   obj = combined.obj, dim = 1, reduction = "umap",
   res = "integrated_snn_res.0.5"
 )
 DimPlot.ClusterNames(combined.obj, ident = "integrated_snn_res.0.5.ordered")

42. Adjust Layout Parameters for multi* plotting fucntions: .adjustLayout()

Adjusts layout dimensions and properties based on the specified layout type.

 env <- new.env()
 .adjustLayout("tall", 1, 8.27, 11.69, env)
 print(env$w) # Should print the width based on "tall" layout scaling.

43. Save Two Plots on One A4 Page: save2plots.A4()

Arranges and saves two UMAP plots (or any plots) side-by-side or one above

 p1 <- ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
   geom_point()
 p2 <- ggplot(iris, aes(Petal.Length, Petal.Width, color = Species)) +
   geom_point()
 save2plots.A4(plot_list = list(p1, p2))

44. Save Four Plots on One A4 Page: save4plots.A4()

Arranges and saves four plots (e.g. UMAPs) onto a single A4 page, allowing for a

 p1 <- ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
   geom_point()
 p2 <- ggplot(mtcars, aes(mpg, disp, color = as.factor(cyl))) +
   geom_point()
 p3 <- ggplot(mpg, aes(displ, hwy, color = class)) +
   geom_point()
 p4 <- ggplot(diamonds, aes(carat, price, color = cut)) +
   geom_point()
 save4plots.A4(plot_list = list(p1, p2, p3, p4))

45. qqSaveGridA4: qqSaveGridA4()

Saves a grid of 2 or 4 ggplot objects onto an A4 page.

   qqSaveGridA4(plotlist = pl, plots = 1:2, fname = "Fractions.per.Cl.png")
   qqSaveGridA4(plotlist = pl, plots = 1:4, fname = "Fractions.per.Cl.4.png")

46. ww.check.if.3D.reduction.exist: ww.check.if.3D.reduction.exist()

ww.check.if.3D.reduction.exist in backup slot #

No examples found.

47. Check Quantile Cutoff and Clip Outliers: ww.check.quantile.cutoff.and.clip.outliers()

Checks a specified quantile cutoff and clips outliers from an expression vector,

 expr.vec <- c(...)
 quantileCutoff <- 0.99
 min.cells.expressing <- 10
 ww.check.quantile.cutoff.and.clip.outliers(expr.vec, quantileCutoff, min.cells.expressing)

48. plot3D.umap.gene: plot3D.umap.gene()

Plot a 3D umap with gene expression. Uses plotly. Based on github.com/Dragonmasterx87.

   plot3D.umap.gene(obj = combined.obj, gene = "DDIT4", quantileCutoff = .95)
   plot3D.umap.gene(obj = combined.obj, gene = "percent.mito", quantileCutoff = .95) # for continous meta variables
   plot3D.umap.gene(obj = combined.obj, gene = "nFeature_RNA", quantileCutoff = .95) # for continous meta variables

49. plot3D.umap: plot3D.umap()

Plot a 3D umap based on one of the metadata columns. Uses plotly. Based on github.com/Dragonmasterx87.

   plot3D.umap(combined.obj, category = "Phase")

50. SavePlotlyAsHtml: SavePlotlyAsHtml()

Save a Plotly 3D scatterplot as an HTML file.

No examples found.

51. Backup Dimensionality Reduction Data: BackupReduction()

Stores a backup of specified dimensionality reduction data (e.g., UMAP, tSNE, PCA)

   obj <- BackupReduction(obj = obj, dim = 2, reduction = "umap")

52. Compute and Backup Dimensionality Reductions: SetupReductionsNtoKdimensions()

Executes specified dimensionality reduction (UMAP, tSNE, or PCA) over a range of dimensions

   combined.obj <- SetupReductionsNtoKdimensions(
     obj = combined.obj, nPCs = 10,
     dimensions = 2:3, reduction = "umap"
   )

53. Recall Dimensionality Reduction from backup slot: RecallReduction()

Restores dimensionality reduction data (e.g., UMAP, tSNE, PCA) from a backup

   combined.obj <- RecallReduction(obj = combined.obj, dim = 2, reduction = "umap")
   qUMAP()
   combined.obj <- RecallReduction(obj = combined.obj, dim = 3, reduction = "umap")
   qUMAP()

54. Annotate4Plotly3D: Annotate4Plotly3D()

Create annotation labels for 3D plots. Source https://plot.ly/r/text-and-annotations/#3d-annotations.

No examples found.

55. Plot3D.ListOfGenes: Plot3D.ListOfGenes()

Plot and save list of 3D UMAP or tSNE plots using plotly.

   CellTypeMarkers <- c("PGK1", "CTIP2" = "BCL11B", "FEZF2", "EOMES", "DLX6-AS1", "HOPX", "DDIT4", "TOP2A", "PTGDS", "EDNRB", "EGFR", "SCGN", "NR2F2", "EMX2", "GAD2", "DLX2", "SATB2")
   Plot3D.ListOfGenes(obj = combined.obj, ListOfGenes = CellTypeMarkers)

56. Plot3D.ListOfCategories: Plot3D.ListOfCategories()

This function plots and saves a list of 3D UMAP or tSNE plots using plotly.

   categ3Dplots <- c("v.project", "experiment", "Phase", "integrated_snn_res.0.7", "Area", "Individual", "Type")
   Plot3D.ListOfCategories(obj = combined.obj, ListOfCategories = categ3Dplots)

57. Display Correlation Values in Pairs Plot: panelCorPearson()

This function displays the correlation coefficient and significance level within

 pairs(mtcars[, 1:4], panel = panelCorPearson)

58. suPlotVariableFeatures for Single Seurat Object: suPlotVariableFeatures()

Generates a Variable Feature Plot for a specified Seurat object, labels points with

 suPlotVariableFeatures(combined.obj)

vertesy/Seurat.utils documentation built on Dec. 4, 2024, 5:20 p.m.