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
- Bulk Transcriptomics
- Single-Cell Transcriptomics
- Spatial Transcriptomics
- 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
- Proteomics
- Structure & Docking
- 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
- Multi-Omics
- Foundation Models
- Visualization