Package index

Command line interface

Install or check the command line interface.

SCDRAKE_CLI_VERSION: Matching CLI version for this package version.

install_cli() check_cli(): Install or check the command line interface scripts.

Project

Initialize or update a scdrake project.

init_project(): Initialize a new scdrake project.

update_project(): Update scdrake project files.

Config

Load or update config files

load_config() load_pipeline_config() load_single_sample_configs() load_integration_configs(): Load a YAML config file as scdrake_list.

update_config() update_pipeline_config() update_single_sample_configs() update_integration_configs() update_configs(): Update a local YAML config file using a default one.

scdrake_list() `$`(<scdrake_list>) `[`(<scdrake_list>) `[[`(<scdrake_list>): A scdrake's list with overloaded, strict access operators.

yq tool

Functions related to the yq tool.

check_yq(): Check the availability and version of the yq tool.

download_yq(): Download the binary of the yq tool (version 3.4.1).

get_yq_default_path(): Return a default download path for the yq tool's binary.

yq_merge_cmd(): Merge two YAML files using the yq tool.

Run pipeline

run_single_sample() run_integration() run_single_sample_r() run_integration_r(): Run the scdrake pipeline.

create_single_sample_dirs() create_integration_dirs(): Create a basic directory structure based on paths in config.

scdrake_make() scdrake_r_make(): Execute a scdrake's pipeline plan.

Check for scdrake dependencies

check_pandoc(): Check for pandoc's binary.

check_pkg_installed() check_qs_installed() check_future_installed() check_clustermq_installed() check_future.callr_installed() check_sc3_version() check_scdrake_packages(): Check if a package is installed and display an informative message.

check_scdrake(): Check for selected scdrake dependencies.

Get pipeline plans

Full plans of the single-sample and integration pipelines

get_single_sample_plan() get_integration_plan(): Get a drake plan for a specific analysis type.

Subplans common to the single-sample and integration pipelines

get_dimred_plots_other_vars_subplan(): Get a subplan for dimensionality reduction plots of selected variables.

get_common_subplan() get_cluster_markers_subplan() get_contrasts_subplan(): Get a drake subplan common to all pipelines.

get_clustering_graph_subplan(): Get a subplan for graph-based clustering.

get_clustering_kmeans_subplan(): Get a subplan for k-means clustering.

get_clustering_sc3_subplan(): Get a subplan for SC3 clustering.

get_clustering_subplan(): Get subplan for clustering.

get_cell_annotation_subplan(): Get a subplan for cell annotation via SingleR

Single-sample pipeline subplans

get_input_qc_subplan() get_norm_clustering_subplan(): Get a drake plan for a stage of single-sample pipeline.

Integration pipeline subplans

get_integration_subplan() get_int_clustering_subplan(): Get a drake plan for a stage of integration pipeline.

Custom plan

load_custom_plan(): Source a file returning a custom drake plan.

Download example data

download_pbmc1k() download_pbmc3k(): Download PBMC example data from 10x Genomics.

Options used by scdrake

get_scdrake_default_options() get_scdrake_options(): Options used by scdrake.

Those functions are mostly used for individual targets within pipeline plans. They are usually named as <target_name>_fn().

Single-sample pipeline / stage 01_input_qc

cell_qc_fn(): Return a SingleCellExperiment object with artifact spatial information

empty_droplets_fn(): Calculate empty droplet statistics for each cell.

get_gene_filter(): Get a logical filter for genes not passing a ratio of cells expressing a gene and a minimum number of UMI per gene.

get_used_qc_filters_operator_desc(): Return an informative message about the used operator to join cell QC filters

sce_final_input_qc_fn(): Finalize a SingleCellExperiment object which will proceed to the 02_norm_clustering stage.

sce_history_fn(): Create a tibble with history of cell and gene filtering.

sce_history_plot_fn(): Plot history of cell and gene filtering.

sce_raw_fn(): Import scRNA-seq data.

sce_selected_fn(): Select a SingleCellExperiment object which will proceed to the 02_norm_clustering stage.

sce_valid_cells_fn(): Subset cells in a SingleCellExperiment object to non-empty ones and add corresponding statistics.

Single-sample pipeline / stage 02_norm_clustering

cc_genes_fn(): Get dataframe of cell cycle genes.

pca_phase_plots_fn(): Make dimred plots of PCA colored by cell cycle phase.

sce_cc_fn(): Assign cell cycle phase to cells.

sce_norm_fn() scran_normalization() sctransform_normalization(): Normalize counts either by scran or sctransform.

sce_norm_hvg_fn(): Find highly variable genes (HVGs).

sce_rm_doublets_fn(): Remove cell doublets from a SingleCellExperiment object.

selected_markers_plots_fn(): Make a grid of feature plots for selected genes.

Integration pipeline / stage 01_integration

get_int_method_description(): Return description for an integration method.

hvg_int_list_fn(): Get a named list of HVG data (gene IDs, fit, metadata).

hvg_plot_int_fn(): Make a HVG plot for uncorrected SingleCellExperiment object (processed by batchelor::multiBatchNorm()).

int_diagnostics_df_fn(): Compute and make integration diagnostics and plots for each integration method.

integration_methods_df_fn(): Make a tibble with integration methods and their parameters.

sce_int_clustering_df_fn(): Compute a quick graph-based clustering for each integration method.

sce_int_combine_hvgs(): Combine HVG data from a list of SingleCellExperiment objects.

sce_int_df_fn(): Perform integration of single-samples.

sce_int_dimred_df_fn(): Calculate t-SNE and UMAP for each integration method result (SingleCellExperiment object).

sce_int_dimred_plots_df_fn(): Make dimred plot for each combination of integration method, dimred method, and coloring by batch (single-sample) and cell cycle phase.

sce_int_import_fn(): Import sce_final_norm_clustering targets from drake caches.

sce_int_pca_df_fn(): Compute PCA for each integration method result (SingleCellExperiment object).

sce_int_processed_fn(): Subset each object in a list of SingleCellExperiment objects to common data and their corresponding metadata (e.g. HVGs).

sce_int_raw_snn_clustering_fn(): Perform a fast shared nearest neighbor clustering of each sample.

selected_markers_int_df_fn(): Prepare parameters for expression plots of selected markers.

selected_markers_int_plots_df_fn(): Make expression plots of selected markers.

Annotation

collapse_ensembl_multivals(): Collapse a character vector by ",".

make_gene_annotation(): Create a dataframe with annotation of SCE object's genes using an AnnotationDbi package.

with_dbi(): Load a SQL database file and run a function from the AnnotationDbi package.

Cell type annotation

cell_annotation_diagnostic_plots_files_fn(): Save cell annotation diagnostic plots to PDF files.

cell_annotation_diagnostic_plots_fn(): Generate diagnostic plots for cell annotation.

cell_annotation_fn(): Perform cell annotation via SingleR::SingleR().

cell_annotation_labels_fn(): Create a named list of cell labels returned from SingleR::SingleR().

cell_annotation_params_fn(): Load a list of cell annotation references into a tibble.

Manual annotation

calculate_metadata(): Calculate metadata for manual cell/spot annotation for heatmap visualisation.

create_signature_matrix_fn(): Create signature matrix from provided file containing names with markers.

meta_heatmap_ploting(): Manual annotation heatmap plotting

run_page_man_annotation(): Calculate and run PAGE annotation.

Cluster markers

cluster_markers_fn(): Add LFC summaries for Wilcox tests, obtained from t-test results.

cluster_markers_heatmaps_df_fn(): Create a final tibble holding parameters for cluster markers heatmaps generation.

cluster_markers_out_fn(): Create a final dataframe of cluster markers.

cluster_markers_test_params_fn() cluster_markers_plot_params_fn() cluster_markers_heatmap_params_fn(): Extract columns with certain parameters from cluster markers sources tibble.

cluster_markers_params_fn(): Load a list of cluster markers sources into a tibble.

cluster_markers_processed_fn(): Add additional summary columns of cluster markers for comparisons with other group levels.

Clustering

calc_sc3(): Run SC3 clustering for a specific k.

cells_per_cluster_table(): Get a frequency table of cell-cluster assignments.

cluster_sc3_cluster_stability_plots_file_fn(): Save SC3 cluster stability plots to a single PDF.

cluster_sc3_df_fn(): Create a dataframe of SC3 clusters for a selected number of clusters.

get_pca_gene_var_pcs(): Get a number of PCs corresponding to biological variation.

get_pca_selected_pcs(): Get a final selection of number of PCs.

graph_snn_fn(): Compute shared nearest neighbors (SNN) graph.

make_kmeans_gaps_plot(): Make a plot of k-means gaps.

make_pca_selected_pcs_plot(): Make a plot showing numbers of selected PCs from all strategies.

run_graph_based_clustering(): Find clusters in SNN graph using a community detection algorithm and if possible, using a specified resolution.

run_kmeans_clustering(): Run k-means clustering for a specific k.

sce_calc_pca(): Calculate PCA of a SingleCellExperiment object.

sce_compute_dimreds(): Compute t-SNE and UMAP dimreds on a SingleCellExperiment object.

Contrasts

contrasts_fn(): Extract contrast statistics from cluster markers tests.

contrasts_heatmaps_df_fn(): Create a tibble holding contrast results and heatmap parameters.

contrasts_out_fn(): Format a tibble with contrast results.

contrasts_params_fn() contrasts_heatmap_params_fn() contrasts_plot_params_fn(): Prepare a tibble with parameters for contrasts tests.

Highly variable genes (HVGs)

get_top_hvgs(): Get top N highly variable genes (HVGs) by a specified metric.

plot_hvg(): Plot average expression vs. HVG metrics (total, bio, and technical variance) and highlight HVGs.

plot_hvg_fit(): Plot fit of a HVG metric and highlight HVGs.

sce_get_cc_genes(): Identify cell cycle-related genes by using variance explained threshold.

sce_remove_cc_genes(): Remove cell cycle-related genes from HVGs.

Markers

These functions are used in both cluster markers and contrasts stages.

add_marker_table_links(): Convert specified columns of a marker table to HTML links.

filter_markers(): Filter a dataframe with cluster markers.

generate_markers_results_section(): Generate a table with links to marker results.

marker_heatmap(): Make a heatmap of cell clusters.

marker_heatmaps_wrapper(): Make a heatmap of global or contrast markers.

marker_plot(): Make a marker plot.

markers_dimred_plots(): Make dimred plots for each marker source defined in CLUSTER_MARKERS_SOURCES (cluster_markers.yaml config) or CONTRASTS_SOURCES (contrasts.yaml config).

markers_dimred_plots_files(): Save dimred plots.

markers_for_tables(): Make a table used in HTML report of marker results.

markers_plots_files(): Make plots of top markers.

markers_plots_top(): Get a tibble of top markers extracted from each test result.

markers_table_files(): Prepare a table with markers and render a HTML report from RMarkdown template.

scran_markers(): Compute cell cluster markers.

additional_cell_data_fn(): Load additional cell data from a CSV or Rds (dataframe) file.

as_seurat(): Convert a SingleCellExperiment to Seurat object.

make_cell_groupings(): Create new cell groups based on existing ones.

cell_data_fn(): Merge all cell-related data to a single DataFrame.

cells_per_cluster_table(): Get a frequency table of cell-cluster assignments.

create_seu_for_heatmaps(): Create a Seurat object used for heatmap generation.

merge_sce_metadata(): Merge metadata() of multiple SingleCellExperiment object.

sce_add_cell_data(): Add columns to colData() of a SingleCellExperiment object.

sce_add_colData(): Append new columns to colData of a SingleCellExperiment object.

sce_add_metadata(): Append data to metadata() list of a SingleCellExperiment object.

sce_add_spatial_colData(): Append new columns to colData of a SingleCellExperiment or SpatialExperiment object.

seu_add_metadata(): Append data to metadata o a Seurat object's assay.

which_genes_regex(): Get indices of genes whose annotation matches a regex.

convertToSCE(): Rewrite SpatialExperiment object to a SingleCellExperiment object.

Visualization

dimred_plots_cell_annotation_params_df_fn(): Make a tibble with parameters for dimred plots of cell annotation labels.

dimred_plots_clustering_fn(): Make a dimred plot for each clustering and dimred method.

dimred_plots_clustering_united_files_fn(): Put clustering dimred plots for different parameters (resolution, k) into a single PDF.

dimred_plots_from_params_df(): Make dimred plots of selected variables.

dimred_plots_other_vars_params_df_fn(): Make a tibble with parameters for dimred plots of selected variables.

get_legend_35(): Extract legend from plot

highlight_points(): Highlight points belonging to certain levels.

plotReducedDim_mod(): A wrapper around scater::plotReducedDim().

plot_clustree(): Plot clustering tree.

plot_colData(): A wrapper for scater::plotColData().

plot_vln(): Make a combined violin-boxplot plot.

save_clustree(): Save a clustree plot into PDF.

save_plot_with_fallback(): Save a plot with a fallback for large legends

save_selected_markers_plots_files(): Save plots of selected markers.

selected_markers_dimplot(): Make a grid of feature plots for selected markers.

Spatial visualization

getDistinctColors(): A helper function for asigning colors in pseudotissue visualization

plot_spat_point_layer_ggplot(): A function for pseudotissue visualization

plot_spat_visuals(): A function for visualization selected qc matrices in image

plot_spat_visuals2(): A function for visualization selected qc matrices in pseudotissue

spatGenePlot2Dsce(): A function for visualization selected genes in pseudotissue visualization

visualized_spots(): A basic function for pseudotissue visualization

Spatial deconvolution

plot_deconv_results_fn(): Deconvolution annotation plotting.

spot_deconvolution_fn(): Deconvolution of spatial dataset.

Misc functions

HTML

cells_per_cluster_table_collapsed_html(): Print a HTML of table collapsible by button.

format_used_functions(): Use the downlit package to generate a Markdown list of autolinked functions wrapped inside <details> tags.

generate_cell_annotation_plots_section(): Generate a section with dimred plots used in some RMarkdown files.

generate_dimred_plots_clustering_section(): Generate a section of clustering dimensionality reduction plots in an RMarkdown document.

generate_dimred_plots_section(): Generate a section with dimred plots used in some RMarkdown files.

generate_stage_report(): Render RMarkdown document for a stage of the pipeline.

create_a_link() create_img_link(): Generate a HTML link as <a></a> or in the form of image as <a><img /></a>.

md_header(): Generate a Markdown header.

render_bootstrap_table(): Render a dataframe-like object using knitr::kable() and kableExtra::kable_styling().

Text

format_shell_command() wrap_code() format_shell_commands(): Format a shell command as a Markdown codeblock.

get_random_strings(): Generate random strings compounded from alphabetical characters by default.

glue0() catg0() gluec() glue0c() catn(): Various wrappers around glue::glue() and cat().

str_comma() str_line() str_space(): Various utils for joining of text.

Utils

add_item_to_list(): Add named item (including NULL) to a list.

assert_that_(): A wrapper around assertthat::assert_that().

create_dummy_plot(): Create a blank ggplot with label.

filter_nulls(): Return list with removed NULL elements.

get_sys_env(): Read an environment variable and if needed, assign a default value.

get_tmp_dir(): Get a path to the temporary directory according to the OS.

`%&&%`: A short-circuit evaluation-like function similar to bash's && operator.

lapply_rows(): Apply a function over rows of a data.frame-like object and concatenate the results back to tibble or data.frame.

list_names_to_values(): Append names of a list of lists as values in each sublist.

lists_to_tibble(): Convert list of lists to tibble.

na_empty(): Return NA if an object is empty.

replace_list_nas_with_nulls(): Replace NAs in a list by NULLs.

replace_list_nulls(): Replace NULL items in a defined depth of a list.

save_pdf(): Save a list of plots to multipage PDF.

save_print() save_object_info(): Capture output from print() of an object.

set_rstudio_drake_cache(): Set rstudio_drake_cache option.

with_plan(): Locally change a future plan.

.apply_config_patches(): Apply YAML config patches to package-bundled configs.

.check_integration_methods(): Check for valid INTEGRATION_SOURCES parameter.

.check_marker_sources(): Check for correct structure of CLUSTER_MARKERS_SOURCES or CONTRASTS_SOURCES.

.get_dict_param(): Unlist a list of length one containing a named list.

.hereize_paths(): Using the here package, contruct paths relative to project's root directory.

.load_config_group(): Load a group of YAML config files.

.load_configs(): Load a list of YAML config files.

.paths_to_base_dir(): Construct paths relative to base directory.

.update_configs_recursive(): Recursively update local configs using a file glob *.default.yaml.

.prepare_marker_source_params() .prepare_marker_sources_params(): Prepare a tibble with parameters for cluster marker tests.

.process_pipeline_config() .process_main_config() .process_cluster_markers_config() .process_contrasts_config() .prepare_cell_annotation_sources_params() .process_integration_config() .process_int_clustering_config() .process_input_qc_config() .process_norm_clustering_config(): Config processing.

update_config_group: Update a group of configs, i.e. pipeline, single-sample, or integration.