Tutorials

The easiest way to get familiar with OmicVerse is to follow along with our tutorials. Most notebooks are designed to work in Google Colab with minimal setup.

• Genomics
  • GWAS pipeline 1 — From genotypes to a fine-mapped locus
  • GWAS pipeline 2 — From a GWAS hit to a mechanism
  • GWAS pipeline 3 — Spatially Resolved GWAS Mapping
  • References
• Bulk Transcriptomics
  • Upstream
  • Preprocessing
  • Downstream
  • Deconvolution
  • Others
• Single-Cell Transcriptomics
  • Alignment
  • Preprocessing
  • Batch correction
  • Annotation
  • MetaCell
  • Trajectory
  • In-silico Perturbation
  • Cell-Cell Communication
  • Cell Structure
  • Copy-Number Variation
  • Metabolism
  • Enrichment
  • Velocity
  • Multi-omics
  • Single-EV proteomics
• Spatial Transcriptomics
  • SPATA2-inspired utilities
  • Preprocessing
  • Clustering
  • Deconvolution
  • H&E → spatial transcriptomics prediction (HE-zoo)
  • Downstream
• Immune Repertoire
  • Single-cell TCR + transcriptome — the immune-repertoire pipeline
  • Bulk TCR immune-repertoire analysis with ov.airr
  • B-cell receptor repertoire analysis with ov.airr
  • Single-cell BCR + transcriptome — clonal expansion, isotypes and somatic hypermutation
  • TCR specificity analysis — grouping receptors by their antigen
  • Joint single-cell TCR + gene-expression analysis — a CoNGA-style workflow
• Flow Cytometry
  • Reading FCS, compensation and display scales
  • Gates, the gating strategy, and reading the numbers
  • Saving a strategy and applying it to a batch (Gating-ML 2.0)
  • FlowSOM — and what clustering does not replace
  • The whole module on real data
  • Case study — immunophenotyping PBMC after cardiac arrest
  • Installation
• Proteomics
  • Bulk LC-MS/MS proteomics — the best-practice pipeline
  • Missing values in proteomics — diagnosis & imputation
  • Peptide → protein summarization & the DIA workflow
  • Differential expression in proteomics: a benchmark with ground truth
  • Olink NPX — affinity proteomics analysis
• Structure, Docking & Dynamics
  • Protein structure of an omics target — fetch, visualize, and assess before you trust
  • Is the target druggable? — binding pockets and known drugs
  • Molecular docking — validate the protocol, then dock the candidate
  • Molecular dynamics — from a static model to motion
  • Why add dynamics
  • Installation
• Synthetic Biology
  • Synthetic biology with ov.synbio — from metabolism to enzyme to DNA
  • ov.synbio II — circuits, CRISPR, assembly & pathway design
  • ov.synbio III — CRISPR editing & directed-evolution libraries
  • ov.synbio IV — advanced SOTA: mRNA design, de-novo binders, prime editing
  • Case study I — an E. coli succinate cell factory
  • Case study II — engineering a more thermostable DHFR
  • Case study III — evaluating a protein design (does it get better?)
  • Installation
• Metabolomics
  • Metabolomics preprocessing and univariate statistics
  • Multivariate discrimination with PLS-DA and OPLS-DA
  • Metabolite-set enrichment analysis (MSEA)
  • Untargeted LC-MS and mummichog pathway inference
  • Lipidomics — the lipidr workflow in omicverse
  • Batch effect and drift correction for LC-MS
  • Multi-factor designs — ASCA and linear mixed models
  • Biomarker discovery — univariate AUC + multivariate panel
  • Differential correlation — DGCA
  • Multi-omics integration — metabolomics + RNA-seq with MOFA
  • Real-data case study — MTBLS1 (urine NMR, Type 2 Diabetes)
• Epigenetics
  • scATAC-seq preprocessing and quality control
  • scATAC clustering, annotation and gene activity
  • Transcription-factor motif activity with chromVAR
  • Peak-to-gene linkage (multiome)
  • Marker peaks and differential accessibility
  • scRNA–scATAC integration and label transfer
  • Bulk Hi-C — contact maps, compartments and TADs
  • Single-cell Hi-C — imputation and cell-cycle embedding
  • Bulk ChIP-seq upstream: FASTQ → peaks
  • Bulk ATAC footprinting — TF activity from Tn5 cut profiles
• Microbiome
  • 16S rRNA amplicon analysis with OmicVerse
  • 16S phylogeny: MAFFT → FastTree → Faith PD + UniFrac
  • DADA2 backend: pure-Python ASV inference
  • Differential abundance: Wilcoxon vs DESeq2 vs ANCOM-BC
  • Cross-cohort 16S meta-analysis
• Multi-Omics
  • Bulk RNA-seq generate ‘interrupted’ cells to interpolate scRNA-seq
  • Bulk RNA-seq to Single RNA-seq
  • Single RNA-seq to Spatial RNA-seq
  • Paired microbe ↔ metabolite integration (Franzosa 2019 IBD)
• Foundation Models
  • Overview
  • Skill-Ready Models
  • Core Models
  • Specialized Models
  • Domain-Specific Models
• Visualization
  • 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
  • Circular UMAP with plot1cell
  • Funky heatmaps for benchmark / multi-metric tables
  • Clinical statistics and figure assembly
  • Statistical plots from a table