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omicverse
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  • OmicVerse Installation Guide
  • Tutorials
    • Bulk RNA-seq
      • Upstream
        • Bulk RNA-seq mapping with STAR
        • Bulk RNA-seq mapping with kb-python
      • Preprocessing
        • Batch correction in Bulk RNA-seq or microarray data
      • Downstream
        • Different Expression Analysis
        • Different Expression Analysis with DEseq2
        • Protein-Protein interaction (PPI) analysis by String-db
        • WGCNA (Weighted gene co-expression network analysis) analysis
      • Deconvolution
        • Bulk deconvolution with reference scRNA-seq
      • Others
        • TCGA database preprocess
    • Single-Cell RNA-seq
      • Alignment
        • Alignment and analysis of single-cell RNA-seq data
        • Alignment and RNA velocity analysis of single-cell RNA-seq data.
      • Preprocessing
        • Preprocessing the data of scRNA-seq with omicverse[CPU-GPU-mixed]
        • Preprocessing the data of scRNA-seq with omicverse[GPU]
        • Clustering space
        • Data integration and batch correction
        • GeneModule Identified
        • Lazy analysis of scRNA-seq
      • Annotation
        • Reference-free automated single-cell cell type annotation
        • Reference automated single-cell cell type annotation
        • Automatic cell type annotation with GPT/Other
        • Mapping Cell Names to the Cell Ontology/Taxonomy
        • Celltype auto annotation with SCSA
        • Celltype auto annotation with MetaTiME
        • Celltype annotation migration(mapping) with TOSICA
        • Using scMulan to annotate cell types in Heart, Lung, Liver, Bone marrow, Blood, Brain, and Thymus
        • 1. load h5ad
        • 2. transform original h5ad with uniformed genes (42117 genes)
        • 3. process uniformed data (simply norm and log1p)
        • 4. load scMulan
        • 5. visualization
        • Consensus annotation with CellVote
      • Trajectory
        • Prediction of absolute developmental potential using CytoTrace2
        • Basic Trajectory Inference
        • Trajectory Inference with StaVIA
        • Timing-associated genes analysis with TimeFateKernel
        • Identify the driver regulators of cell fate decisions
        • Data loading and processing
        • Training CEFCON model
        • Downstream analysis
      • Cell Structure
        • Inference of MetaCell from Single-Cell RNA-seq
        • Differential expression and celltype analysis [All Cell]
        • Differential expression analysis [Meta Cell]
        • Gene Regulatory Network Analysis with SCENIC
        • Pathway analysis with AUCell
        • Cell-cell communication using CellPhoneDBViz
        • Drug response predict with scDrug
        • Data integration and batch correction with SIMBA
      • Velocity
        • Velocity Basic Calculation
        • Velocity Optimization
      • Multi-omics
        • Multi omics analysis by MOFA
        • Multi omics analysis by MOFA and GLUE
        • Celltype annotation transfer in multi-omics
    • Bulk-to-Single & Cross-Platform
      • Bulk RNA-seq generate ‘interrupted’ cells to interpolate scRNA-seq
      • Bulk RNA-seq to Single RNA-seq
      • Single RNA-seq to Spatial RNA-seq
    • Spatial Transcriptomics
      • Preprocessing
        • Crop and Rotation of spatial transcriptomic data
        • Cell Segmemtation (10x HD)
        • Analyze Nanostring data
        • Analyze Visium HD data
        • Spatial clustering and denoising expressions
        • Spatial integration and clustering
        • Preprocess data
        • Training STAligner model
        • Clustering the space
      • Deconvolution
        • Identifying Pseudo-Spatial Map
        • Spatial deconvolution with reference scRNA-seq
        • FlashDeconv: Fast Spatial Deconvolution via Structure-Preserving Sketching
        • Spatial deconvolution without reference scRNA-seq
      • Downstream
        • Spatial transition tensor of single cells
        • Spatial Communication
        • Spatial IsoDepth Calculation
        • Single cell spatial alignment tools
    • Foundation Models
      • Overview
        • ov.fm — Foundation Model Module
        • ov.fm — Unified Foundation Model API
      • Skill-Ready Models
        • scGPT
        • scGPT
        • Geneformer
        • GeneFormer
        • UCE
        • UCE
        • scFoundation
        • scFoundation
        • CellPLM
        • CellPLM
      • Core Models
        • scBERT
        • GeneCompass
        • Nicheformer
        • scMulan
      • Specialized Models
        • tGPT
        • CellFM
        • scCello
        • scPRINT
        • AIDO.Cell
        • PULSAR
        • Tabula
      • Domain-Specific Models
        • ATACformer
        • scPlantLLM
        • LangCell
        • Cell2Sentence
        • GenePT
        • ChatCell
    • Visualization & Plotting
      • Visualization of single cell RNA-seq
      • Visualization of Bulk RNA-seq
      • Palette optimization for publication-quality single-cell & spatial plots
      • Scientific plotting for publication with OmicVerse
      • Color system
  • OmicClaw
    • Gateway and Channels
      • OmicClaw Gateway Overview
      • OmicClaw Setup and Auth
      • OmicClaw Telegram Tutorial
      • OmicClaw Feishu 教程
      • OmicClaw iMessage Tutorial
      • OmicClaw QQ Tutorial
      • OmicClaw Session Workflow
      • OmicClaw Troubleshooting
    • MCP Server
      • OmicVerse MCP Server
      • OmicVerse MCP Quick Start
      • OmicVerse MCP Full Start
      • OmicVerse MCP Tool Catalog
      • OmicVerse MCP Clients and Deployment
      • OmicVerse MCP Runtime and Troubleshooting
      • OmicVerse MCP Reference
      • Using OmicVerse MCP with Claude Code — Step by Step
    • General Notebooks
      • J.A.R.V.I.S. with PBMC3k
      • J.A.R.V.I.S. with Ten-Task Suite
  • API Reference
    • User API
      • omicverse.read
      • omicverse.io.read_h5ad
      • omicverse.io.read_h5ad
      • omicverse.io.read_10x_h5
      • omicverse.io.read_10x_mtx
      • omicverse.io.read_nanostring
      • omicverse.io.read_visium_hd
      • omicverse.pp.qc
      • omicverse.pp.filter_cells
      • omicverse.pp.filter_genes
      • omicverse.pp.scrublet
      • omicverse.pp.normalize_total
      • omicverse.pp.log1p
      • omicverse.pp.highly_variable_genes
      • omicverse.pp.highly_variable_features
      • omicverse.pp.normalize_pearson_residuals
      • omicverse.pp.recover_counts
      • omicverse.pp.pca
      • omicverse.pp.neighbors
      • omicverse.pp.umap
      • omicverse.pp.tsne
      • omicverse.pp.mde
      • omicverse.pp.leiden
      • omicverse.pp.louvain
      • omicverse.pp.scale
      • omicverse.pp.regress
      • omicverse.pp.regress_and_scale
      • omicverse.pp.remove_cc_genes
      • omicverse.pp.score_genes_cell_cycle
      • omicverse.single.pySCSA
      • omicverse.single.MetaTiME
      • omicverse.single.CellVote
      • omicverse.single.gptcelltype
      • omicverse.single.gptcelltype_local
      • omicverse.single.CellOntologyMapper
      • omicverse.single.Annotation
      • omicverse.single.AnnotationRef
      • omicverse.single.TrajInfer
      • omicverse.single.Velo
      • omicverse.single.Fate
      • omicverse.single.cytotrace2
      • omicverse.single.MetaCell
      • omicverse.single.DEG
      • omicverse.single.SCENIC
      • omicverse.single.aucell
      • omicverse.single.geneset_aucell
      • omicverse.single.cellphonedb_v5
      • omicverse.single.Drug_Response
      • omicverse.single.Batch
      • omicverse.single.pySIMBA
      • omicverse.single.Integration
      • omicverse.single.pyMOFA
      • omicverse.single.pyMOFAART
      • omicverse.single.GLUE_pair
      • omicverse.single.pyTOSICA
      • omicverse.single.cNMF
      • omicverse.bulk.pyDEG
      • omicverse.bulk.pyGSEA
      • omicverse.bulk.pyPPI
      • omicverse.bulk.pyTCGA
      • omicverse.bulk.Deconvolution
      • omicverse.bulk.Matrix_ID_mapping
      • omicverse.bulk.batch_correction
      • omicverse.bulk.geneset_enrichment
      • omicverse.space.clusters
      • omicverse.space.Deconvolution
      • omicverse.space.pySTAGATE
      • omicverse.space.pySTAligner
      • omicverse.space.pySpaceFlow
      • omicverse.space.Tangram
      • omicverse.space.STT
      • omicverse.space.GASTON
      • omicverse.space.Cal_Spatial_Net
      • omicverse.space.spatial_neighbors
      • omicverse.space.moranI
      • omicverse.bulk2single.BulkTrajBlend
      • omicverse.bulk2single.Bulk2Single
      • omicverse.bulk2single.Single2Spatial
      • omicverse.fm.run
      • omicverse.fm.list_models
      • omicverse.fm.get_registry
      • omicverse.fm.describe_model
      • omicverse.fm.select_model
      • omicverse.fm.preprocess_validate
      • omicverse.fm.profile_data
      • omicverse.fm.interpret_results
      • omicverse.fm.ModelSpec
      • omicverse.fm.ModelRegistry
      • omicverse.pl.embedding
      • omicverse.pl.embedding_celltype
      • omicverse.pl.embedding_density
      • omicverse.pl.embedding_multi
      • omicverse.pl.embedding_atlas
      • omicverse.pl.pca
      • omicverse.pl.umap
      • omicverse.pl.tsne
      • omicverse.pl.volcano
      • omicverse.pl.marker_heatmap
      • omicverse.pl.rank_genes_groups_dotplot
      • omicverse.pl.dotplot
      • omicverse.pl.markers_dotplot
      • omicverse.pl.cellproportion
      • omicverse.pl.cellstackarea
      • omicverse.pl.venn
      • omicverse.pl.bardotplot
      • omicverse.pl.violin
      • omicverse.pl.violin_box
      • omicverse.pl.boxplot
      • omicverse.pl.plot_boxplots
      • omicverse.pl.spatial
      • omicverse.pl.plot_spatial
      • omicverse.pl.highlight_spatial_region
      • omicverse.pl.cpdb_heatmap
      • omicverse.pl.cpdb_network
      • omicverse.pl.cpdb_chord
      • omicverse.pl.CellChatViz
      • omicverse.pl.palette_112
      • omicverse.pl.palette_28
      • omicverse.pl.sc_color
      • omicverse.pl.ForbiddenCity
      • omicverse.pl.optim_palette
      • omicverse.pl.colormaps_palette
      • omicverse.datasets.pbmc3k
      • omicverse.datasets.zebrafish
      • omicverse.datasets.pancreatic_endocrinogenesis
      • omicverse.datasets.dentate_gyrus
      • omicverse.datasets.create_mock_dataset
      • omicverse.datasets.predefined_signatures
  • Release Notes
  • Developer guild
  • Registered Functions — GPU Support Overview
  • Discussion
  • GitHub
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Upstream¶

Tutorials for bulk RNA-seq read alignment.

  • Bulk RNA-seq mapping with STAR
  • Bulk RNA-seq mapping with kb-python
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Bulk RNA-seq mapping with STAR
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Bulk RNA-seq
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