Package: scooter 0.0.0.9005

scooter: Streamlined scRNA-Seq Analysis Pipeline

Streamlined scRNA-Seq analysis pipeline.

Authors:Igor Dolgalev [cre, aut], Anna Yeaton [aut]

scooter_0.0.0.9005.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
scooter/json (API)

# Install 'scooter' in R:
install.packages('scooter', repos = c('https://igordot.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/igordot/scooter/issues

On CRAN:

Conda:

4.09 score 6 stars 103 scripts 57 exports 160 dependencies

Last updated from:5514bdff4c. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develNOTE527
source / vignettesOK287
linux-releaseNOTE512
macos-releaseNOTE383
macos-oldrelNOTE291
windows-develNOTE492
windows-releaseNOTE501
windows-oldrelNOTE371
wasm-releaseOK189

Exports:add_gene_class_percentadd_seurat_assayas_data_frame_seuratcalculate_cluster_expressioncalculate_cluster_markerscalculate_cluster_statscalculate_clusterscalculate_mito_pctcheck_identity_columncluster_seurat_objectcreate_color_vectcreate_seurat_objcreate_seurat_objectdifferential_expression_per_clusterdr_plot_widthfilter_cellsfilter_datageneset_scoreget_color_schemeget_dr_point_sizeget_test_counts_matriximport_mtxinitialize_seurat_objectintegrate_layersintegrate_seurat_objectload_sample_counts_matrixmerge_metadatamerge_seurat_objectsnormalize_countsnormalize_dataplot_cluster_markers_heatmapplot_cluster_markers_topplot_clustersplot_distributionplot_dr_featureplot_dr_groupplot_dr_umap_clustersplot_metrics_correlationsplot_metrics_distributionplot_var_genes_eulerplot_var_genes_upsetprofile_object_sizeread_counts_fileresolve_seurat_objectrun_drrun_pcarun_tsnerun_umapsave_countssave_dr_plotsave_metadatasave_seurat_counts_matrixset_identitysplit_layers_by_batchtransfer_labelsvariable_features_by_batchwrite_message

Dependencies:abindaskpassbase64encBHbitbit64bitopsbslibcachemcaToolsclicliprclustercodetoolscommonmarkcowplotcpp11crayoncrosstalkcurldata.tabledeldirdigestdotCall64dplyrdqrngeulerrevaluatefarverfastDummiesfastmapfitdistrplusFNNfontawesomefsfuturefuture.applygenericsggplot2ggrepelggridgesggsciglobalsgluegoftestgplotsgridExtragtablegtoolsherehighrhmshtmltoolshtmlwidgetshttpuvhttricaigraphirlbaisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevallifecyclelistenvlmtestmagrittrMASSMatrixmatrixStatsmemoisemimeminiUInlmeopensslotelparallellypatchworkpbapplypheatmappillarpkgconfigplotlyplyrpngpolyclipprettyunitsprogressprogressrpromisespurrrqs2R6RANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppParallelRcppProgressRcppThreadRcppTOMLreadrreshape2reticulaterlangrmarkdownROCRrprojrootRSpectraRtsneS7sassscalesscattermoresctransformSeuratSeuratObjectshinysitmosourcetoolsspspamspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstringfishstringistringrsurvivalsystensortibbletidyrtidyselecttinytextzdbUpSetRutf8uwotvctrsviridisLitevroomwithrxfunxtableyamlzoo

Readme and manuals

Help Manual

Help pageTopics
Add mitochondrial, ribosomal, and hemoglobin percentages to a Seurat object.add_gene_class_percent
Add assay to Seurat object.add_seurat_assay
Function to extract data from Seurat object.as_data_frame_seurat
Per-cluster average expression (non-log space).calculate_cluster_expression
Calculate cluster markers (versus all other clusters, or pairwise) and plot them.calculate_cluster_markers
Per-cluster cell counts and a joined metadata+embeddings table.calculate_cluster_stats
Identify clusters of cells by graph-based clustering.calculate_clusters calculate_clusters.Seurat
Check identity of the Seurat object.check_identity_column
Reduce dimensions, cluster, and plot the result.cluster_seurat_object
Function to create a color vector.create_color_vect
Create a usable Seurat object from a sample.create_seurat_object
Calculate differentially expressed genes within each subpopulation/clusterdifferential_expression_per_cluster
Width for a reduction plot, widened so many legend labels stay legible.dr_plot_width
Filter cells based on the number of genes, counts, and mitochondrial reads.filter_cells
Get geneset scores.geneset_score
Determine the color scheme.get_color_scheme
Determine the point size for reduced dimensions scatter plots (smaller for larger datasets).get_dr_point_size
Get an example counts matrix.get_test_counts_matrix
Read in 10x Genomics Cell Ranger Matrix Market format data.import_mtx
Create a new Seurat object from a matrix.initialize_seurat_object
Integrate the layers of a Seurat object.integrate_layers
Integrate a merged Seurat object across batches.integrate_seurat_object
Function to merge two metadata tables together.merge_metadata
Merge multiple Seurat objects into one.merge_seurat_objects
Normalize the counts, select variable features, and scale.normalize_counts
Heatmap of the top cluster markers, at several top-N cutoffs.plot_cluster_markers_heatmap
Plot a gene list in multiple ways: UMAP, dot plot, violin, and per-cluster bar plot.plot_cluster_markers_top
Plot a cluster resolution on tSNE and UMAP.plot_clusters
Plot the distribution of specified features/variables.plot_distribution
Plot a single feature overlaid on a dimensionality reduction.plot_dr_feature
Scatter plot of a reduction, colored by a grouping variable.plot_dr_group
Plot a UMAP colored by one or more cluster/grouping variables.plot_dr_umap_clusters
Scatter plots of the per-cell QC metrics against each other.plot_metrics_correlations
Violin plots of the per-cell QC metrics.plot_metrics_distribution
Euler diagram of the variable genes shared between batches.plot_var_genes_euler
UpSet plot of the variable genes shared between batches.plot_var_genes_upset
Report the size of an object's slots, largest first.profile_object_size
Read in Gene Expression and Antibody Capture data from a 10x Genomics Cell Ranger sparse matrix or from a text file.read_counts_file
Resolve a Seurat object, given either the object itself or a path to one on disk.resolve_seurat_object
Run dimensionality reduction: pca, tsne, or umaprun_dr
Run PCArun_pca run_pca.default run_pca.Seurat
Run TSNErun_tsne run_tsne.default run_tsne.Seurat
Run UMAPrun_umap run_umap.default run_umap.Seurat
Save the counts matrix as a single table.save_counts
Save a reduction plot, widened to fit its own legend.save_dr_plot
Save the cell metadata and the reduction embeddings as a single table.save_metadata
Set identity of the Seurat object.set_identity
Split a Seurat object into per-batch layers and prepare it for integration.split_layers_by_batch
Transfer labels from a reference Seurat objecttransfer_labels
Variable features of each batch of a layer-split object.variable_features_by_batch
Small function to write to message and to log file.write_message