All functions
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annotate_ambient_profile()
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Add average of ambient feature reads to rowData |
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annotate_cdr3()
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Annotate CDR3 sequences stored in colData(sce)$vdj based on a reference dataset (for example, Ag-specific data) |
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annotate_chain_count()
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Annotate number of TRA, TRB, IGL, IGK, and IGH reads per barcode |
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annotate_clonotype_count()
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Annotate number of occurrences of a given clonotype in a sample |
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annotate_n_cells_expr()
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Add number of cells with nonzero expression for each gene to rowData(sce)$num_cells_expr |
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annotate_n_genes_expr()
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Add number of genes expressed per barcode to colData(sce)$n_genes_expr |
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annotate_pct_gene_set()
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Add percent of gene set defined by a regular expression pattern to colData(sce) |
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annotate_pct_total_reads()
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Add percentage of total reads that each gene takes up across the whole dataset to rowData(sce)$pct_reads |
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annotate_total_umi_count()
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Add number of total umis per barcode to colData(sce)$total_umi |
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assign_clonotypes()
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Function to use for assigning clonotypes based on custom definition |
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cache()
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Cache and retrieve intermediate steps if path exists |
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compute_evenness_profile_long()
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Compute tidied data frame of evenness profiles per group based on clonotype frequency distributions |
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compute_evenness_profile_matrix()
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Compute matrix of evenness profiles per group based on clonotype frequency distributions |
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convert_identity_frequency_matrix_to_long()
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Convert an identity frequency matrix |
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.get_cell_annotations()
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Get cell annotations for feature_heatmap |
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.get_feature_annotations()
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Get feature annotations for feature_heatmap |
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encode_cell_identity_frequency_long()
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Encode frequencies of (combinations) of values in columns from colData into a long data frame |
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encode_cell_identity_frequency_matrix()
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Encode frequencies of (combinations) of values in columns from colData into a matrix with rows as groups and columns as features |
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encode_vdj_identity_frequency_long()
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Encode frequencies of (combinations) of values in columns from colData into long data frame |
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encode_vdj_identity_frequency_matrix()
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Encode frequencies of (combinations) of values in columns from colData into a matrix with rows as groups and columns as features |
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filter_ambient_barcode()
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Get filter to remove ambient RNA barcodes based on the combination of multiple methods: |
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filter_n_genes_expr()
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Get filter for number of genes expressed per barcode based on log transformed values |
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filter_pct_mito()
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Get filter for percentage of mitochondrial reads expressed per barcode |
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filter_total_umi()
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Get filter for total umis per barcode based on log transformed values outside (either above, below, or both based on type parameter) nmads median absolute deviations from the median |
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filter_vdj_chain_count()
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Get filter for barcodes based on the number of chains for TRA, TRB, IGL, IGK, and IGH present |
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get_assay_data()
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Get assay data from either the main experiment or altExps |
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get_cell_features()
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Get feature from assay data, colData, or reducedDims at once from main experiment or alternate experiments |
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get_multi_sample_pbmc_10k()
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Get modified version of PBMC 10k data from 10X |
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get_pbmc_5k_nextgem()
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Get PBMC 5k data using v3 chemistry |
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get_pbmc_5k_v3()
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Get PBMC 5k data using v3 chemistry |
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get_row_data()
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Get rowData from either the main experiment or altExps |
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plot_barcode_qc()
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QC plot of UMI rank vs total number of UMIs per barcode |
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plot_feature_distributions() plot_features()
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Plot feature distributions from SingleCellExperiment objects |
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plot_feature_heatmap()
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Plot heatmap of features across cells with annotations |
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plot_gex_bivariate_qc()
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Scatterplot of two features of interest from colData with annotated thresholds and counts based on filters |
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plot_gex_univariate_qc()
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Scatterplot of two features of interest from colData with annotated thresholds and counts based on filters |
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plot_pairwise_features()
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Plot pairwise scatterplot of cell-level data |
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plot_reduced_dimensions()
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Plot reduced dimensional plot with multiple features |
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plot_vdj_gex_univariate_qc()
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Scatterplot of two features of interest from colData with annotated thresholds and counts based on filters |
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plot_volcano()
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Plot volcano plot with annotations |
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read_10x()
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Read 10X gene expression and VDJ data into a SingleCellExperiment object |
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select_top_de_genes()
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Select the top DE genes, ranked on either fold change or -log10(p-value) |
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seurat_to_sce()
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Convert Seurat object to SingleCellExperiment and retain multi-modal data |
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unnest_vdj()
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Get VDJ data with cell-level metadata |