Software

This page contains the tools and software packages we have developed @ Maize Zhou Lab or previously @ Stanford.

Please visit our Lab GitHub page for the latest releases.

• BenchmarkST: Benchmarking clustering, alignment, and integration methods for spatial transcriptomics. 
• CNVeil: accurate and robust tumor subclone identification and copy number estimation from single-cell DNA sequencing data.
• VolcanoSV: accurate and robust structural variant calling in diploid genomes from single-molecule long read sequencing.
• MaskGraphene: advancing joint embedding, clustering, and batch correction for spatial transcriptomics using graph-based self-supervised learning.
• RegionIndel: Large indel detection in region-based phased diploid assemblies from linked-reads.
• ADEPT: autoencoder with differentially expressed genes and imputation for a robust spatial transcriptomics clustering.
• LRSV_combo: long read based SV calling tools analysis.
• embSV: haplotype-phasing of long-read HiFi data to enhance structural variant detection through a Skip-Gram model.
• AquilaDeepFilter: automated filtering of genome-wide large deletions through an ensemble deep learning framework.
• Bfimpute: a Bayesian factorization method to recover single-cell RNA sequencing data.
• RNN_workingmemoryaccuracy: neural mechanisms of working memory accuracy revealed by recurrent neural networks.
• RNN_BrainMaturation: emergence of prefrontal neuron maturation properties by training recurrent neural networks in cognitive tasks.
• Autism_genepheno: text mining of gene-phenotype associations reveals new phenotypic profiles of autism-associated genes.
• MARS: a package for multiple samples alignment-base structural variant calling and refinement.
• Aquila_stLFR: diploid genome assembly based structural variant calling package for stLFR linked-read.
• Aquila: diploid personal genome assembly and comprehensive variant detection based on linked-reads.
• HAPDeNovo: a haplotype-based approach for filtering and phasing de novo mutations in linked read sequencing data.