Package: scPipe 2.5.0

Shian Su

scPipe: Pipeline for single cell multi-omic data pre-processing

A preprocessing pipeline for single cell RNA-seq/ATAC-seq data that starts from the fastq files and produces a feature count matrix with associated quality control information. It can process fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium 10x and SMART-seq protocols.

Authors:Luyi Tian [aut], Shian Su [aut, cre], Shalin Naik [ctb], Shani Amarasinghe [aut], Oliver Voogd [aut], Phil Yang [aut], Matthew Ritchie [ctb]

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scPipe.pdf |scPipe.html✨
scPipe/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/luyitian/scpipe/issues

Uses libs:
  • curl– Easy-to-use client-side URL transfer library
  • bzip2– High-quality block-sorting file compressor library
  • xz-utils– XZ-format compression library
  • zlib– Compression library
  • c++– GNU Standard C++ Library v3
Datasets:
  • UMI_duplication - UMI duplication statistics for a small sample scRNA-seq dataset to demonstrate capabilities of scPipe
  • cell_barcode_matching - Cell barcode demultiplex statistics for a small sample scRNA-seq dataset to demonstrate capabilities of scPipe
  • sc_sample_data - A small sample scRNA-seq counts dataset to demonstrate capabilities of scPipe
  • sc_sample_qc - Quality control information for a small sample scRNA-seq dataset to demonstrate capabilities of scPipe.

On BioConductor:scPipe-2.5.0(bioc 3.20)scPipe-2.4.0(bioc 3.19)

bioconductor-package

72 exports 1.64 score 173 dependencies 7 mentions

Last updated 2 months agofrom:54cf2d150a

Exports:anno_importanno_to_safcalculate_QC_metricsconvert_geneidcreate_processed_reportcreate_reportcreate_sce_by_dirdemultiplex_infodemultiplex_info.scedemultiplex_info<-detect_outlierfeature_infofeature_info.scefeature_info<-feature_typefeature_type.scefeature_type<-gene_id_typegene_id_type.scegene_id_type<-get_ercc_annoget_genes_by_GOget_read_strorganismorganism.sceorganism<-plot_demultiplexplot_mappingplot_QC_pairsplot_UMI_dupQC_metricsQC_metrics.sceQC_metrics<-remove_outlierssc_aligningsc_atac_bam_taggingsc_atac_cell_callingsc_atac_create_cell_qc_metricssc_atac_create_fragmentssc_atac_create_reportsc_atac_create_scesc_atac_emptydrops_cell_callingsc_atac_feature_countingsc_atac_filter_cell_callingsc_atac_peak_callingsc_atac_pipelinesc_atac_plot_cells_per_featuresc_atac_plot_features_per_cellsc_atac_plot_features_per_cell_orderedsc_atac_plot_fragments_cells_per_featuresc_atac_plot_fragments_features_per_cellsc_atac_plot_fragments_per_cellsc_atac_plot_fragments_per_featuresc_atac_remove_duplicatessc_atac_tfidfsc_atac_trim_barcodesc_correct_bam_bcsc_count_aligned_bamsc_demultiplexsc_demultiplex_and_countsc_detect_bcsc_exon_mappingsc_gene_countingsc_get_umap_datasc_integratesc_interactive_umap_plotsc_mae_plot_umapsc_trim_barcodeTF.IDF.customUMI_dup_infoUMI_dup_info.sceUMI_dup_info<-

Dependencies:abindAnnotationDbiaskpassbackportsbasiliskbasilisk.utilsbeachmatBHBiobaseBiocBaseUtilsBiocFileCacheBiocGenericsBiocIOBiocParallelbiomaRtBiostringsbitbit64bitopsblobbriobroombroom.helperscachemcallrclicliprcodetoolscolorspacecpp11crayoncurldata.tableDBIdbplyrDelayedArrayDelayedMatrixStatsDEoptimRdescdiffobjdigestdir.expirydplyrdqrngDropletUtilsedgeRevaluatefansifarverfastmapfilelockflexmixforcatsformatRfsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicRangesGGallyggplot2ggstatsgluegtablehashhavenHDF5Arrayherehmshttrhttr2IRangesisobandjsonliteKEGGRESTlabelinglabelledlambda.rlatticelifecyclelimmalocfitmagrittrMASSMatrixMatrixGenericsmatrixStatsmclustmemoisemgcvmimemodeltoolsMultiAssayExperimentmunsellnlmennetopensslorg.Hs.eg.dborg.Mm.eg.dbpatchworkpillarpkgbuildpkgconfigpkgloadplogrplyrpngpraiseprettyunitsprocessxprogresspspurrrR.methodsS3R.ooR.utilsR6rappdirsRColorBrewerRcppRcppTOMLRCurlreadrrematch2reshaperestfulrreticulaterhdf5rhdf5filtersRhdf5libRhtslibrjsonrlangrobustbaserprojrootRsamtoolsRSQLiteRsubreadrtracklayerS4ArraysS4VectorsscalesscuttleSingleCellExperimentsitmosnowSparseArraysparseMatrixStatsstatmodstringistringrSummarizedExperimentsystestthattibbletidyrtidyselecttzdbUCSC.utilsutf8vctrsviridisLitevroomwaldowithrXMLxml2XVectoryamlzlibbioc

scPipe: a flexible data preprocessing pipeline for 3' end scRNA-seq data

Luyi Tian

Rendered fromscPipe_tutorial.Rmdusingknitr::rmarkdownon Jun 30 2024.

Last update: 2023-10-18
Started: 2017-03-28

scPipe: a flexible data preprocessing pipeline for scATAC-seq data

Shani Amarasinghe, Phil Yang

Rendered fromscPipe_atac_tutorial.Rmdusingknitr::rmarkdownon Jun 30 2024.

Last update: 2023-10-05
Started: 2022-04-22

Readme and manuals

Help Manual

Help pageTopics
Detect outliers based on robust linear regression of QQ plot.qq_outliers_robust
Import gene annotationanno_import
Convert annotation from GenomicRanges to Simple Annotation Format (SAF)anno_to_saf
Calculate QC metrics from gene count matrixcalculate_QC_metrics
cell barcode demultiplex statistics for a small sample scRNA-seq dataset to demonstrate capabilities of scPipecell_barcode_matching
Check Valid Barcode Start Positioncheck_barcode_start_position
convert the gene ids of a SingleCellExperiment objectconvert_geneid
create_processed_reportcreate_processed_report
create_reportcreate_report
create a SingleCellExperiment object from data folder generated by preprocessing stepcreate_sce_by_dir
demultiplex_infodemultiplex_info demultiplex_info,SingleCellExperiment-method demultiplex_info.sce demultiplex_info<- demultiplex_info<-,SingleCellExperiment-method
Detect outliers based on QC metricsdetect_outlier
Get or set 'feature_info' from a SingleCellExperiment objectfeature_info feature_info,SingleCellExperiment-method feature_info.sce feature_info<- feature_info<-,SingleCellExperiment-method
Get or set 'feature_type' from a SingleCellExperiment objectfeature_type feature_type,SingleCellExperiment-method feature_type.sce feature_type<- feature_type<-,SingleCellExperiment-method
Get or set 'gene_id_type' from a SingleCellExperiment objectgene_id_type gene_id_type,SingleCellExperiment-method gene_id_type.sce gene_id_type<- gene_id_type<-,SingleCellExperiment-method
Get Chromosomesget_chromosomes
Get ERCC annotation tableget_ercc_anno
Get genes related to certain GO terms from biomart databaseget_genes_by_GO
Get read structure for particular scRNA-seq protocolget_read_str
Get or set 'organism' from a SingleCellExperiment objectorganism organism,SingleCellExperiment-method organism.sce organism<-,SingleCellExperiment-method
plot_demultiplexplot_demultiplex
Plot mapping statistics for 'SingleCellExperiment' object.plot_mapping
Plot GGAlly pairs plot of QC statistics from 'SingleCellExperiment' objectplot_QC_pairs
Plot UMI duplication frequencyplot_UMI_dup
Get or set quality control metrics in a SingleCellExperiment objectQC_metrics QC_metrics,SingleCellExperiment-method QC_metrics.sce QC_metrics<- QC_metrics<-,SingleCellExperiment-method
Read Cell barcode fileread_cells
Remove outliers in 'SingleCellExperiment'remove_outliers
aligning the demultiplexed FASTQ reads using the Rsubread:align()sc_aligning
BAM taggingsc_atac_bam_tagging
identifying true vs empty cellssc_atac_cell_calling
generating a file useful for producing the qc plotssc_atac_create_cell_qc_metrics
Generating the popular fragments for scATAC-Seq datasc_atac_create_fragments
HTML report generationsc_atac_create_report
sc_atac_create_sce()sc_atac_create_sce
empty drops cell callingsc_atac_emptydrops_cell_calling
generating the feature by cell matrixsc_atac_feature_counting
filter cell callingsc_atac_filter_cell_calling
sc_atac_peak_calling()sc_atac_peak_calling
A convenient function for running the entire pipelinesc_atac_pipeline
A function that tests the pipeline on a small test sample (without duplicate removal)sc_atac_pipeline_quick_test
A histogram of the log-number of cells per featuresc_atac_plot_cells_per_feature
A histogram of the log-number of features per cellsc_atac_plot_features_per_cell
Plot showing the number of features per cell in ascending ordersc_atac_plot_features_per_cell_ordered
A scatter plot of the log-number of fragments and log-number of cells per featuresc_atac_plot_fragments_cells_per_feature
A scatter plot of the log-number of fragments and log-number of features per cellsc_atac_plot_fragments_features_per_cell
A histogram of the log-number of fragments per cellsc_atac_plot_fragments_per_cell
A histogram of the log-number of fragments per featuresc_atac_plot_fragments_per_feature
Removing PCR duplicates using samtoolssc_atac_remove_duplicates
generating the UMAPs for sc-ATAC-Seq preprocessed datasc_atac_tfidf
demultiplex raw single-cell ATAC-Seq fastq readssc_atac_trim_barcode
sc_correct_bam_bcsc_correct_bam_bc
sc_count_aligned_bamsc_count_aligned_bam
sc_demultiplexsc_demultiplex
sc_demultiplex_and_countsc_demultiplex_and_count
sc_detect_bcsc_detect_bc
sc_exon_mappingsc_exon_mapping
sc_gene_countingsc_gene_counting
Generates UMAP data from sce objectsc_get_umap_data
Integrate multi-omic scRNA-Seq and scATAC-Seq data into a MultiAssayExperimentsc_integrate
Produces an interactive UMAP plot via Shinysc_interactive_umap_plot
Generates UMAP of multiomic datasc_mae_plot_umap
a small sample scRNA-seq counts dataset to demonstrate capabilities of scPipesc_sample_data
quality control information for a small sample scRNA-seq dataset to demonstrate capabilities of scPipe.sc_sample_qc
sc_trim_barcodesc_trim_barcode
scPipe - single cell RNA-seq pipelinescPipe-package scPipe
Returns the TF-IDF normalised version of a binary matrixTF.IDF.custom
Get or set UMI duplication results in a SingleCellExperiment objectUMI_dup_info UMI_dup_info,SingleCellExperiment-method UMI_dup_info.sce UMI_dup_info<- UMI_dup_info<-,SingleCellExperiment-method
UMI duplication statistics for a small sample scRNA-seq dataset to demonstrate capabilities of scPipeUMI_duplication