Start the Shiny APP |
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Run the single cell analysis app |
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Importing scRNA-seq Data |
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Construct SCE object from Salmon-Alevin output |
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Create a SingleCellExperiment Object from Python AnnData .h5ad files |
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Construct SCE object from BUStools output |
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Construct SCE object from Cell Ranger output |
Construct SCE object from Cell Ranger V2 output for a single sample |
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Construct SCE object from Cell Ranger V3 output for a single sample |
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Create a SingleCellExperiment Object from DropEst output |
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Retrieve example datasets |
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Create a SingleCellExperiment object from files |
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Imports gene sets from a GeneSetCollection object |
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Imports gene sets from a GMT file |
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Imports gene sets from a list |
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Imports gene sets from MSigDB |
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Import mitochondrial gene sets |
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Imports samples from different sources and compiles them into a list of SCE objects |
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Construct SCE object from Optimus output |
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Construct SCE object from seqc output |
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Construct SCE object from STARsolo outputs |
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Read single cell expression matrix |
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Quality Control & Preprocessing |
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Perform comprehensive single cell QC |
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Perform comprehensive droplet QC |
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Wrapper for calculating QC metrics with scater. |
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Decontamination |
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Detecting contamination with DecontX. |
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Detecting and correct contamination with SoupX |
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Get or Set SoupX Result |
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Doublet/Empty Droplet Detection |
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Identify empty droplets using barcodeRanks. |
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Identify empty droplets using emptyDrops. |
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Find doublets/multiplets using bcds. |
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Find doublets/multiplets using cxds. |
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Find doublets/multiplets using cxds_bcds_hybrid. |
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Detect doublet cells using scDblFinder. |
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Generates a doublet score for each cell via doubletFinder |
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Find doublets using |
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Normalization |
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Wrapper function to run any of the integrated normalization/transformation methods in the singleCellTK. The available methods include 'LogNormalize', 'CLR', 'RC' and 'SCTransform' from Seurat, 'logNormCounts and 'CPM' from Scater. Additionally, users can 'scale' using Z.Score, 'transform' using log, log1p and sqrt, add 'pseudocounts' and trim the final matrices between a range of values. |
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scaterlogNormCounts Uses logNormCounts to log normalize input data |
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scaterCPM Uses CPM from scater library to compute counts-per-million. |
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runSeuratNormalizeData Wrapper for NormalizeData() function from seurat library Normalizes the sce object according to the input parameters |
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runSeuratScaleData Scales the input sce object according to the input parameters |
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runSeuratSCTransform Runs the SCTransform function to transform/normalize the input data |
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Compute Z-Score |
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Trim Counts |
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Batch Effect Correction |
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Apply ComBat-Seq batch effect correction method to SingleCellExperiment object |
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Apply BBKNN batch effect correction method to SingleCellExperiment object |
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Apply a fast version of the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object |
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Apply Limma's batch effect correction method to SingleCellExperiment object |
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Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object |
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Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object |
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Apply scMerge batch effect correction method to SingleCellExperiment object |
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runSeuratIntegration A wrapper function to Seurat Batch-Correction/Integration workflow. |
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Apply ZINBWaVE Batch effect correction method to SingleCellExperiment object |
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Plot the percent of the variation that is explained by batch and condition in the data |
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Feature Selection |
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Run Variable Feature Detection Methods |
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Calculate Variable Genes with Scran modelGeneVar |
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runSeuratFindHVG Find highly variable genes and store in the input sce object |
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Get or set top HVG after calculation |
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Plot highly variable genes |
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Dimensionality Reduction & Embedding |
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Generic Wrapper function for running dimensionality reduction |
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Perform scater PCA on a SingleCellExperiment Object |
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Run UMAP embedding with scater method |
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Run t-SNE embedding with Rtsne method |
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runSeuratICA Computes ICA on the input sce object and stores the calculated independent components within the sce object |
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runSeuratPCA Computes PCA on the input sce object and stores the calculated principal components within the sce object |
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runSeuratUMAP Computes UMAP from the given sce object and stores the UMAP computations back into the sce object |
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runSeuratTSNE Computes tSNE from the given sce object and stores the tSNE computations back into the sce object |
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Plot PCA run data from its components. |
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Plot UMAP results either on already run results or run first and then plot. |
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Plot t-SNE plot on dimensionality reduction data run from t-SNE method. |
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Plot dimensionality reduction from computed metrics including PCA, ICA, tSNE and UMAP |
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Dimension reduction plot tool for colData |
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Dimension reduction plot tool for assay data |
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Clustering |
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Get clustering with SNN graph |
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runSeuratFindClusters Computes the clusters from the input sce object and stores them back in sce object |
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Get clustering with KMeans |
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Differential Expression |
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Perform differential expression analysis on SCE object |
Get Top Table of a DEG analysis |
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Generate volcano plot for DEGs |
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Generate violin plot to show the expression of top DEGs |
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Create linear regression plot to show the expression the of top DEGs |
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Heatmap visualization of DEG result |
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MAST Identify adaptive thresholds |
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Find Marker |
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Find the marker gene set for each cluster |
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Fetch the table of top markers that pass the filtering |
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Plot a heatmap to visualize the result of |
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Differential Abundance |
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Calculate Differential Abundance with FET |
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Get/Set diffAbundanceFET result table |
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Plot the differential Abundance |
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Cell Type Labeling |
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Label cell types with SingleR |
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Enrichment & Pathway Analysis |
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Shows MSigDB categories |
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Run EnrichR on SCE object |
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Get or Set EnrichR Result |
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Run GSVA analysis on a SingleCellExperiment object |
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Run VAM to score gene sets in single cell data |
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List pathway analysis result names |
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Generate violin plots for pathway analysis results |
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Trajectory Analysis |
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Run TSCAN to obtain pseudotime values for cells |
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Find DE genes between all TSCAN paths rooted from given cluster |
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Test gene expression changes along a TSCAN trajectory path |
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Plot features identified by |
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Plot TSCAN pseudotime rooted from given cluster |
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Plot feature expression on cell 2D embedding with MST overlaid |
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Plot expression changes of top features along a TSCAN pseudotime path |
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Plot heatmap of genes with expression change along TSCAN pseudotime |
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Plot MST pseudotime values on cell 2D embedding |
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getTSCANResults accessor function |
Seurat Curated Workflow |
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runSeuratFindClusters Computes the clusters from the input sce object and stores them back in sce object |
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runSeuratFindHVG Find highly variable genes and store in the input sce object |
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runSeuratFindMarkers |
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runSeuratHeatmap Computes the heatmap plot object from the pca slot in the input sce object |
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runSeuratICA Computes ICA on the input sce object and stores the calculated independent components within the sce object |
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runSeuratIntegration A wrapper function to Seurat Batch-Correction/Integration workflow. |
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runSeuratJackStraw Compute jackstraw plot and store the computations in the input sce object |
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runSeuratNormalizeData Wrapper for NormalizeData() function from seurat library Normalizes the sce object according to the input parameters |
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runSeuratPCA Computes PCA on the input sce object and stores the calculated principal components within the sce object |
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runSeuratScaleData Scales the input sce object according to the input parameters |
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runSeuratSCTransform Runs the SCTransform function to transform/normalize the input data |
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runSeuratTSNE Computes tSNE from the given sce object and stores the tSNE computations back into the sce object |
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runSeuratUMAP Computes UMAP from the given sce object and stores the UMAP computations back into the sce object |
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computeHeatmap
The computeHeatmap method computes the heatmap visualization for a set
of features against a set of dimensionality reduction components. This
method uses the heatmap computation algorithm code from |
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Get variable feature names after running runSeuratFindHVG function |
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Visualization |
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Plots for runEmptyDrops outputs. |
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Plots for runBarcodeRankDrops outputs. |
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Plot comparison of batch corrected result against original assay |
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Plot the percent of the variation that is explained by batch and condition in the data |
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Plots for runBcds outputs. |
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Plot the differential Abundance |
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Plots for runCxds outputs. |
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Plots for runDecontX outputs. |
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Heatmap visualization of DEG result |
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Create linear regression plot to show the expression the of top DEGs |
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Generate violin plot to show the expression of top DEGs |
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Generate volcano plot for DEGs |
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Plot dimensionality reduction from computed metrics including PCA, ICA, tSNE and UMAP |
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Plots for runDoubletFinder outputs. |
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Plots for runEmptyDrops outputs. |
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Plots for runEmptyDrops outputs. |
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Plot a heatmap to visualize the result of |
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MAST Identify adaptive thresholds |
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Generate violin plots for pathway analysis results |
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Plot PCA run data from its components. |
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Plots for runPerCellQC outputs. |
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Plots for runScDblFinder outputs. |
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Plots for runCxdsBcdsHybrid outputs. |
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Bar plot of assay data. |
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Bar plot of colData. |
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Plot mean feature value in each batch of a SingleCellExperiment object |
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Density plot of any data stored in the SingleCellExperiment object. |
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Density plot of assay data. |
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Density plot of colData. |
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Dimension reduction plot tool for colData |
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Dimension reduction plot tool for assay data |
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Plot heatmap of using data stored in SingleCellExperiment Object |
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Dimension reduction plot tool for all types of data |
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Violin plot of any data stored in the SingleCellExperiment object. |
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Violin plot of assay data. |
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Violin plot of colData. |
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Plots for runScrublet outputs. |
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plotSeuratElbow Computes the plot object for elbow plot from the pca slot in the input sce object |
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Compute and plot visualizations for marker genes |
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plotSeuratHeatmap Modifies the heatmap plot object so it contains specified number of heatmaps in a single plot |
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plotSeuratHVG Plot highly variable genes from input sce object (must have highly variable genes computations stored) |
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plotSeuratJackStraw Computes the plot object for jackstraw plot from the pca slot in the input sce object |
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plotSeuratReduction Plots the selected dimensionality reduction method |
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Plot SoupX Result |
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Plot highly variable genes |
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Plot features identified by |
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Plot TSCAN pseudotime rooted from given cluster |
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Plot feature expression on cell 2D embedding with MST overlaid |
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Plot expression changes of top features along a TSCAN pseudotime path |
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Plot heatmap of genes with expression change along TSCAN pseudotime |
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Plot MST pseudotime values on cell 2D embedding |
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Plot t-SNE plot on dimensionality reduction data run from t-SNE method. |
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Plot UMAP results either on already run results or run first and then plot. |
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Report Generation |
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Get runCellQC .html report |
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Get plotClusterAbundance .html report |
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Get diffAbundanceFET .html report |
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Get runDEAnalysis .html report |
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Get runDropletQC .html report |
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Get findMarkerDiffExp .html report |
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Get .html report of the output of the selected QC algorithm |
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Generates an HTML report for the complete Seurat workflow and returns the SCE object with the results computed and stored inside the object. |
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Generates an HTML report for Seurat Clustering and returns the SCE object with the results computed and stored inside the object. |
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Generates an HTML report for Seurat Dimensionality Reduction and returns the SCE object with the results computed and stored inside the object. |
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Generates an HTML report for Seurat Feature Selection and returns the SCE object with the results computed and stored inside the object. |
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Generates an HTML report for Seurat Results (including Clustering & Marker Selection) and returns the SCE object with the results computed and stored inside the object. |
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Generates an HTML report for Seurat Normalization and returns the SCE object with the results computed and stored inside the object. |
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Generates an HTML report for Seurat Results (including Clustering & Marker Selection) and returns the SCE object with the results computed and stored inside the object. |
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Generates an HTML report for Seurat Run (including Normalization, Feature Selection, Dimensionality Reduction & Clustering) and returns the SCE object with the results computed and stored inside the object. |
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Generates an HTML report for Seurat Scaling and returns the SCE object with the results computed and stored inside the object. |
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Exporting Results |
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Export data in SingleCellExperiment object |
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Export a SingleCellExperiment R object as Python annData object |
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Export a SingleCellExperiment object to flat text files |
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Export data in Seurat object |
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Datasets |
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Example Single Cell RNA-Seq data in SingleCellExperiment Object, GSE60361 subset |
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Example Single Cell RNA-Seq data in SingleCellExperiment object, with different batches annotated |
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List of mitochondrial genes of multiple reference |
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MSigDB gene get Category table |
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Example Single Cell RNA-Seq data in SingleCellExperiment Object, subset of 10x public dataset https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k A subset of 390 barcodes and top 200 genes were included in this example. Within 390 barcodes, 195 barcodes are empty droplet, 150 barcodes are cell barcode and 45 barcodes are doublets predicted by scrublet and doubletFinder package. This example only serves as a proof of concept and a tutoriol on how to run the functions in this package. The results should not be used for drawing scientific conclusions. |
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Stably Expressed Gene (SEG) list obect, with SEG sets for human and mouse. |
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Other Data Processing |
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expData
Get data item from an input |
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expData Store data items using tags to identify the type of data item stored. To be used as a replacement for assay<- setter function but with additional parameter to set a tag to a data item. |
expData Store data items using tags to identify the type of data item stored. To be used as a replacement for assay<- setter function but with additional parameter to set a tag to a data item. |
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expData
Get data item from an input |
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expDataNames
Get names of all the data items in the input |
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expDataNames
Get names of all the data items in the input |
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expDeleteDataTag Remove tag against an input data from the stored tag information in the metadata of the input object. |
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expSetDataTag Set tag to an assay or a data item in the input SCE object. |
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expTaggedData
Returns a list of names of data items from the
input |
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Finds the effect sizes for all genes in the original dataset, regardless of significance. |
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Combine a list of SingleCellExperiment objects as one SingleCellExperiment object |
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convertSCEToSeurat Converts sce object to seurat while retaining all assays and metadata |
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convertSeuratToSCE Converts the input seurat object to a sce object |
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Create SingleCellExperiment object from csv or txt input |
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Deduplicate the rownames of a matrix or SingleCellExperiment object
Adds '-1', '-2', ... '-i' to multiple duplicated rownames, and in place
replace the unique rownames, store unique rownames in |
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Detecting outliers within the SingleCellExperiment object. |
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Generate given number of color codes |
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Generate a distinct palette for coloring different clusters |
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Estimate numbers of detected genes, significantly differentially expressed genes, and median significant effect size |
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Estimate numbers of detected genes, significantly differentially expressed genes, and median significant effect size |
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Retrieve row index for a set of features |
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Retrieve cell/feature index by giving identifiers saved in col/rowData |
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Generate table of SCTK QC outputs. |
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Stores and returns table of SCTK QC outputs to metadata. |
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Setter function which stores table of SCTK QC outputs to metadata. |
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Lists the table of SCTK QC outputs stored within the metadata. |
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Lists imported GeneSetCollections |
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List geneset names from geneSetCollection |
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Indicates which rowData to use for visualization |
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Subset a SingleCellExperiment object by columns |
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Subset a SingleCellExperiment object by rows |
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Generate HTAN manifest file for droplet and cell count data |
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Generate HTAN manifest file for droplet and cell count data |
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Generates a single simulated dataset, bootstrapping from the input counts matrix. |
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Given a list of genes and a SingleCellExperiment object, return the binary or continuous expression of the genes. |
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Extract QC parameters from the SingleCellExperiment object |
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Returns significance data from a snapshot. |
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Merging colData from two singleCellExperiment objects |
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Create SingleCellExperiment object from command line input arguments |
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Set rownames of SCE with a character vector or a rowData column |
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Passes the output of generateSimulatedData() to differential expression tests, picking either t-tests or ANOVA for data with only two conditions or multiple conditions, respectively. |
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Summarize an assay in a SingleCellExperiment |
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Python Environment Setting |
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Installs Python packages into a Conda environment |
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Selects a Conda environment |
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Installs Python packages into a virtual environment |
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Selects a virtual environment |