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General

  • Installation
  • API
    • Preprocessing: pp
      • dotools_py.pp.run_cellbender
      • dotools_py.pp.importer_py
      • dotools_py.pp.quality_control
      • dotools_py.pp.log_normalize
      • dotools_py.pp.pearson_residuals_normalize
      • dotools_py.pp.find_doublets
    • Tools: tl
      • dotools_py.tl.integrate_data
      • dotools_py.tl.run_scvi
      • dotools_py.tl.run_scanvi
      • dotools_py.tl.run_harmony
      • dotools_py.tl.run_seurat_integration
      • dotools_py.tl.auto_annot
      • dotools_py.tl.reclustering
      • dotools_py.tl.full_recluster
      • dotools_py.tl.go_analysis
      • dotools_py.tl.DGEAnalysis
      • dotools_py.tl.rank_genes_groups
      • dotools_py.tl.rank_genes_condition
      • dotools_py.tl.rank_genes_pseudobulk
      • dotools_py.tl.rank_genes_consensus
      • dotools_py.tl.grouped_ttest
      • dotools_py.tl.run_mast
    • Plotting: pl
      • dotools_py.pl.barplot
      • dotools_py.pl.violinplot
      • dotools_py.pl.boxplot
      • dotools_py.pl.lineplot
      • dotools_py.pl.ridgeplot
      • dotools_py.pl.embedding
      • dotools_py.pl.split_embedding
      • dotools_py.pl.umap
      • dotools_py.pl.density
      • dotools_py.pl.dotplot
      • dotools_py.pl.heatmap
      • dotools_py.pl.heatmap_foldchange
      • dotools_py.pl.cell_composition
      • dotools_py.pl.split_bar_gsea
      • dotools_py.pl.correlation
      • dotools_py.pl.volcano_plot
      • dotools_py.pl.slides
      • dotools_py.pl.layers
      • dotools_py.pl.TestData
      • dotools_py.pl.StatsPlotter
    • Get: get
      • dotools_py.get.expr
      • dotools_py.get.mean_expr
      • dotools_py.get.log2fc
      • dotools_py.get.pcts_cells
      • dotools_py.get.dge_results
      • dotools_py.get.subset
      • dotools_py.get.subset_df
      • dotools_py.get.pseudobulk
      • dotools_py.get.layer_swap
    • Input/Output: io
      • dotools_py.io.read_h5ad
      • dotools_py.io.read_visium
      • dotools_py.io.read_zarr
      • dotools_py.io.read_rds
      • dotools_py.io.save_rds
      • dotools_py.io.read_10x_mtx
      • dotools_py.io.read_10x_h5
      • dotools_py.io.read_mtx
      • dotools_py.io.read_excel
      • dotools_py.io.read_csv
      • dotools_py.io.read_parquet
    • Utility
      • dotools_py.utility.free_memory
      • dotools_py.utility.create_report
      • dotools_py.utility.transfer_labels
      • dotools_py.utility.add_gene_metadata
      • dotools_py.utility.live_display
      • dotools_py.utility.generate_cmap
      • dotools_py.utility.extended_tab20
      • dotools_py.utility.get_hex_colormaps
      • dotools_py.utility.spine_format
      • dotools_py.utility.tab30
      • dotools_py.utility.select_slide
      • dotools_py.utility.add_smooth_kernel
    • Data: dt
      • dotools_py.dt.example_10x
      • dotools_py.dt.example_10x_processed
      • dotools_py.dt.example_visium
      • dotools_py.dt.example_visium_processed
      • dotools_py.dt.heart_markers
      • dotools_py.dt.standard_ct_labels_heart
    • Benchmarking: bm
      • dotools_py.bm.eval_integration
      • dotools_py.bm.kbet
      • dotools_py.bm.silhouette_batch
      • dotools_py.bm.pcr_comparison
      • dotools_py.bm.graph_connectivity
    • Settings
      • dotools_py.settings.session_settings
      • dotools_py.settings.interactive_session
      • dotools_py.settings.matplotlib_backend
      • dotools_py.settings.set_kernel_logger
      • dotools_py.settings.toogle_kernel_logger
      • dotools_py.settings.set_random_state
  • Changelog
  • References

Use cases

  • Tutorials
    • Remove ambient RNA with CellBender
    • Quality control of sc/snRNA-seq
    • Integration of scRNA-seq
    • Quality control of Visium
    • Differential gene expression analysis
    • Usage of functions

About

  • Citing DoTools
  • GitHub
  • R version
  • .md

Tutorials

Contents

  • Quality Control
  • Visualisation and Usage Examples

Tutorials#

Quality Control#

In these tutorials we show the complete workflow to perform quality control and downstream analysis using a public dataset from 10X.

  • Remove ambient RNA with CellBender
  • Quality control of sc/snRNA-seq
  • Integration of scRNA-seq
  • Quality control of Visium

Visualisation and Usage Examples#

In these tutorials we compile case examples for the visualisation and differential expression analysis of data.

  • Differential gene expression analysis
    • Environment setup
    • DEA at the single-cell level
    • DEA at the pseudobulk level
    • DEA consensus
  • Usage of functions
    • Environment setup
    • Dotplot
    • Heatmap
    • UMAP
    • Split embeddings
    • Changes in cell proportion
    • Expression of genes and continuous metadata
    • Correlation between condition

previous

References

next

Remove ambient RNA with CellBender

Contents
  • Quality Control
  • Visualisation and Usage Examples

By David Rodriguez Morales, Mariano Ruz Jurado, David John

© Copyright 2026, David Rodriguez Morales, Mariano Ruz Jurado, David John..