We're a bioengineering lab at UCLA developing tensor factorization and computational modeling methods, applied to immunology and cancer biology, to study cell communication and decision making. Read more about our research at asmlab.org.
| Repo | Description | Status |
|---|---|---|
| RISE | PARAFAC2 tensor factorization for multi-sample scRNA-seq |  |
| tHMM | Hidden Markov models for cell lineage trees |  |
| DDMC | Clusters phosphoproteomics data by sequence and abundance dynamics |  |
| vsparse | Store and analyze scRNAseq data with extreme memory efficiency |  |
Core methods and libraries underpinning most of our analysis pipelines.
- RISE — PARAFAC2 tensor factorization for multi-sample scRNA-seq
- parafac2 — Scalable PARAFAC2 implementation with line search
- tensorpack — Collection of tensor factorization methods from the Meyer lab
- cmtf-pls — Partial least squares implementation within CMTF
- FastPIDC.jl — Infers undirected networks from data
- Pf2-scRNAseq — Pf2 tensor factorization applied to single-cell RNA-seq
- valentBind — Multivalent binding model implemented in Python
- bi-cytok — Models bispecific cytokine fusion binding and signaling
- tHMM — Hidden Markov models for cell lineage trees
- DDMC — Clusters phosphoproteomics data by sequence and abundance dynamics
- tensordata — Shared repository of tensor-structured datasets
- vsparse — Store and analyze scRNAseq data with extreme memory efficiency
- bootstraptools — Bootstrap resampling tools for model uncertainty analysis
- asmlab.org — Source for the Meyer lab website
Find us at asmlab.org or reach out via github-public@asmlab.org.
