Package: scProkaR 0.99.2

Jinxin Zhao

scProkaR: Microbial Single-Cell RNA-Seq Analysis for Bacterial Systems

scProkaR provides a SingleCellExperiment-centred workflow for microbial single-cell RNA sequencing data. It standardises bacterial input objects from count matrices, 10x Genomics directories or Seurat objects, and computes prokaryote-specific quality-control metrics such as ribosomal RNA fraction. It performs batch integration with several backends and scores the resulting embeddings with scIB-inspired benchmarking metrics. It also supports pseudobulk differential expression with edgeR and Time Aware Trajectory Analysis (TATA), a time-informed trajectory abstraction for perturbation time courses.

Authors:Jinxin Zhao [aut, cre], Ning Liu [aut]

scProkaR_0.99.2.tar.gz
scProkaR_0.99.2.zip(r-4.7-any)scProkaR_0.99.2.zip(r-4.6-any)scProkaR_0.99.2.zip(r-4.5-any)
scProkaR_0.99.2.tgz(r-4.6-any)scProkaR_0.99.2.tgz(r-4.5-any)
scProkaR_0.99.2.tar.gz(r-4.7-any)scProkaR_0.99.2.tar.gz(r-4.6-any)
scProkaR_0.99.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
scProkaR/json (API)

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

Bug tracker:https://github.com/monash-li-lab/scprokar/issues

Datasets:
  • sce1 - Example bacterial single-cell dataset

On CRAN:

Conda:

softwaresinglecellrnaseqtranscriptomicsgeneexpressiondifferentialexpressionclusteringgraphandnetworkqualitycontrolmultiplecomparisonnormalizationbatcheffectdimensionreductiontimecoursemicrobiomesequencingvisualization

3.78 score 53 exports 56 dependencies

Last updated from:72678d162c. Checks:1 NOTE, 9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE178
linux-develOK248
source / vignettesOK329
linux-releaseOK207
macos-releaseOK223
macos-oldrelOK166
windows-develOK260
windows-releaseOK202
windows-oldrelOK201
wasm-releaseOK148

Exports:adjacent_timepoint_aucaggregate_pseudobulkanticausal_edge_massBenchmarkIntegrationbranch_onset_metricsbuild_knn_graphbuild_tata_graphcluster_graph_statescluster_tata_gene_trendscompute_tata_branch_probabilitiescompute_tata_gene_trendscompute_tata_pseudotimecompute_time_weightscompute_topology_weightsCreateBacObjectdirected_reachability_concordancefilter_pseudobulk_samplesFilterBacCellsIntegrateBacDatalocal_temporal_order_metricsMergeBacObjectsnormalize_pseudobulkorient_pseudotime_to_timeplot_pairwise_de_maplot_pairwise_de_volcanoplot_tata_branch_confusionplot_tata_branch_probabilitiesplot_tata_branch_selectionplot_tata_cluster_graphplot_tata_edge_causalityplot_tata_gene_trend_heatmapplot_tata_gene_trendsplot_tata_resultsplot_tata_terminal_probabilitiesplot_tata_time_calibrationplot_tata_trajectory_embeddingplot_time_series_deg_curvesPlotIntegrationOverviewPlotReductionRegisterExistingIntegrationEmbeddingsRegisterIntegrationEmbeddingrun_edger_pairwise_derun_edger_spline_derun_scproka_derun_tataRunBacQCRunIntegratedClusteringRunIntegratedUMAPselect_tata_branch_cellssimulate_tata_divergent_scesimulate_tata_multidrug_scesimulate_tata_scesimulate_tata_spiral_sce

Dependencies:abindassortheadbeachmatBHBiobaseBiocGenericsBiocNeighborsBiocParallelblustercliclustercodetoolscpp11DelayedArrayedgeRfarverformatRfutile.loggerfutile.optionsgenericsGenomicRangesggplot2gluegtableigraphIRangesisobandlabelinglambda.rlatticelifecyclelimmalocfitmagrittrMatrixMatrixGenericsmatrixStatspkgconfigR6RColorBrewerRcpprlangS4ArraysS4VectorsS7scalesSeqinfoSingleCellExperimentsnowSparseArraystatmodSummarizedExperimentvctrsviridisLitewithrXVector

scProkaR core workflow for microbial single-cell RNA-seq
Introduction | Installation | The example data | Subsetting for this vignette | Creating a scProkaR object | Other entry points | Quality control | Integration across treatment groups | MNN correction | Harmony correction | Registering your own embedding | UMAP and clustering on the integrated space | Visualisation | Benchmarking the integration | Pseudobulk differential expression | Defining replicates | Aggregating and normalising | Pairwise contrasts | Time-course testing with splines | Where to go next | Further reading | Session information
Rendered fromscprokar-workflow.Rmdusingknitr::rmarkdown

Last update: 2026-10-11
Started: 2026-03-23

Time Aware Trajectory Analysis (TATA) with scProkaR
Introduction | The problem TATA addresses | Relationship to Bioconductor single-cell infrastructure | Installation | Setting up | A simulated multi-drug branching time course | The TATA pipeline, step by step | Build the cell--cell graph | Cluster cells into coarse states | Score cluster pairs on topology and on time | Build the abstract graph | Compute pseudotime | The run_tata() wrapper | Comparing time-informed and topology-only runs | Benchmarking helpers | Temporal consistency plots | Branch probabilities and plasticity | Selecting cells for a branch | Branch-specific gene trends | Compact result overview | Practical guidance | Further reading | Session information
Rendered fromtata-workflow.Rmdusingknitr::rmarkdown

Last update: 2026-10-11
Started: 2026-03-23

Readme and manuals

Help Manual

Help pageTopics
Compute adjacent-timepoint AUC for a pseudotime orderingadjacent_timepoint_auc
Aggregate single-cell data into pseudobulk samplesaggregate_pseudobulk
Measure the weight of anti-causal edges in a directed graphanticausal_edge_mass
Benchmark integration quality with scIB-inspired metricsBenchmarkIntegration
Measure branch-emergence timing from pseudotimebranch_onset_metrics
Build a cell-cell kNN graphbuild_knn_graph
Build the TATA cluster graphbuild_tata_graph
Cluster graph-connected cell statescluster_graph_states
Cluster TATA gene trends by shapecluster_tata_gene_trends
Compute TATA branch probabilities and entropycompute_tata_branch_probabilities
Compute branch-specific TATA gene trendscompute_tata_gene_trends
Compute TATA pseudotimecompute_tata_pseudotime
Compute time consistency for cluster-cluster edgescompute_time_weights
Compute topology-based cluster connectivity weightscompute_topology_weights
Create a standardized bacterial single-cell objectCreateBacObject
Quantify how well a directed cluster graph follows real timedirected_reachability_concordance
Filter pseudobulk samples by sizefilter_pseudobulk_samples
Filter cells using bacterial QC thresholdsFilterBacCells
Integrate microbial single-cell batchesIntegrateBacData
Compute local temporal order metrics on a cell graphlocal_temporal_order_metrics
Merge multiple scProkaR or SingleCellExperiment objectsMergeBacObjects
Normalize pseudobulk samples for exploratory analysisnormalize_pseudobulk
Orient pseudotime to match increasing real timeorient_pseudotime_to_time
Plot an MA plot for a pairwise edgeR contrastplot_pairwise_de_ma
Plot a volcano plot for a pairwise edgeR contrastplot_pairwise_de_volcano
Plot truth versus inferred TATA branch assignmentsplot_tata_branch_confusion
Plot TATA branch probabilities on an embeddingplot_tata_branch_probabilities
Plot selected cells from one TATA branchplot_tata_branch_selection
Plot the cluster-level TATA graphplot_tata_cluster_graph
Plot TATA edge causality diagnosticsplot_tata_edge_causality
Plot a heatmap of TATA gene trendsplot_tata_gene_trend_heatmap
Plot fitted TATA gene trendsplot_tata_gene_trends
Plot an overview of TATA resultsplot_tata_results
Plot branch probabilities for representative cellsplot_tata_terminal_probabilities
Plot TATA time calibrationplot_tata_time_calibration
Plot TATA trajectories over an embeddingplot_tata_trajectory_embedding
Plot fitted spline expression curves from time-series DEplot_time_series_deg_curves
Plot Seurat-style integration result panelsPlotIntegrationOverview
Plot any stored reduced dimensionPlotReduction
Register existing reduced dimensions as integration embeddingsRegisterExistingIntegrationEmbeddings
Register an external embedding for benchmarking and downstream analysisRegisterIntegrationEmbedding
Run pairwise pseudobulk differential expression with edgeRrun_edger_pairwise_de
Run pseudobulk spline or time-series differential expression with edgeRrun_edger_spline_de
Run a full scProkaR differential expression workflowrun_scproka_de
Run the full TATA workflowrun_tata
Compute bacterial QC metricsRunBacQC
Cluster cells on an integrated embeddingRunIntegratedClustering
Run UMAP on an existing embeddingRunIntegratedUMAP
Example bacterial single-cell datasetsce1
Select cells from a TATA branchselect_tata_branch_cells
Simulate a multi-drug branching TATA datasetsimulate_tata_multidrug_sce
Simulate a divergent TATA datasetsimulate_tata_divergent_sce simulate_tata_sce
Simulate an extreme spiral-plus-divergent TATA datasetsimulate_tata_spiral_sce