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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Get runDropletQC .html report |
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Get runCellQC .html report |
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Plots for runPerCellQC outputs. |
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Decontamination |
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Detecting contamination with DecontX. |
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Plots for runDecontX outputs. |
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Detecting and correct contamination with SoupX |
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Get or Set SoupX Result |
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Plot 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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Plots for runBarcodeRankDrops outputs. |
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Plots for runEmptyDrops outputs. |
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Plots for runBcds outputs. |
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Plots for runCxds outputs. |
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Plots for runCxdsBcdsHybrid outputs. |
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Plots for runScDblFinder outputs. |
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Plots for runDoubletFinder outputs. |
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Plots for runScrublet outputs. |
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Normalization |
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Run normalization/transformation with various methods |
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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 the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object |
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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 Harmony 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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Plot comparison of batch corrected result against original assay |
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Plot mean feature value in each batch of a SingleCellExperiment object |
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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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runSeuratSCTransform Runs the SCTransform function to transform/normalize the input data |
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runSeuratScaleData Scales the input sce object according to the input parameters |
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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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Computes heatmap for a set of features against dimensionality reduction components |
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Get variable feature names after running runSeuratFindHVG function |
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Scanpy Curated Workflow |
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runScanpyFindClusters Computes the clusters from the input sce object and stores them back in sce object |
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runScanpyFindHVG Find highly variable genes and store in the input sce object |
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runScanpyFindMarkers |
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runScanpyNormalizeData Wrapper for NormalizeData() function from scanpy library Normalizes the sce object according to the input parameters |
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runScanpyPCA Computes PCA on the input sce object and stores the calculated principal components within the sce object |
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runScanpyScaleData Scales the input sce object according to the input parameters |
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runScanpyTSNE Computes tSNE from the given sce object and stores the tSNE computations back into the sce object |
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runScanpyUMAP Computes UMAP from the given sce object and stores the UMAP computations back into the sce object |
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Visualization |
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Plots for runBarcodeRankDrops 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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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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Plots for runDecontX outputs. |
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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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Plot PCA run data from its components. |
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Generate violin plots for pathway analysis results |
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Plots for runPerCellQC 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 runScDblFinder outputs. |
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plotScanpyDotPlot |
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plotScanpyEmbedding |
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plotScanpyHVG |
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plotScanpyHeatmap |
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plotScanpyMarkerGenes |
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plotScanpyMarkerGenesDotPlot |
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plotScanpyMarkerGenesHeatmap |
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plotScanpyMarkerGenesMatrixPlot |
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plotScanpyMarkerGenesViolin |
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plotScanpyMatrixPlot |
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plotScanpyPCA |
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plotScanpyPCAGeneRanking |
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plotScanpyPCAVariance |
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plotScanpyViolin |
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Plots for runCxdsBcdsHybrid outputs. |
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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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plotSeuratHVG Plot highly variable genes from input sce object (must have highly variable genes computations stored) |
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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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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 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 highly variable genes |
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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 runFindMarker .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 data in Seurat 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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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 |
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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 |
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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. |
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 |