TianGzlab
Official@tiangzlab
Offers specialized computational processing for multi-omics datasets, including single-cell, spatial transcriptomics, genomics, metabolomics, and mass spectrometry proteomics analysis.
Agent Skills by TianGzlab
Showing 56 vetted skills indexed across 1 GitHub repositories.
TianGzlabLiterature Parsing Skill
Parse scientific literature to extract GEO dataset metadata and download associated data.
TianGzlaborchestrator
Route natural language queries to omics analysis skills across multiple domains.
TianGzlabmetabolomics-annotation
Annotate metabolomics features by matching MS2 spectra against SIRIUS, GNPS, and MetFrag databases.
TianGzlabmetabolomics-normalization
Normalize metabolomics data using median, quantile, total-ion-count, PQN, and log2 transformations.
TianGzlabmetabolomics-de
Perform univariate and multivariate statistical analysis on metabolomics data to identify biomarkers.
TianGzlabmetabolomics-quantification
Impute missing values and normalize metabolomics feature data.
TianGzlabmetabolomics-statistics
Perform univariate statistical tests and FDR correction on metabolomics data.
TianGzlabmetabolomics-xcms-preprocessing
Preprocess LC-MS/GC-MS metabolomics data with XCMS3 peak detection and alignment.
TianGzlabmetabolomics-peak-detection
Detect metabolite peaks from LC-MS data using XCMS, MZmine 3 or MS-DIAL and extract features for downstream analysis.
TianGzlabmetabolomics-pathway-enrichment
Perform metabolomics pathway enrichment with hypergeometric tests and FDR correction.
TianGzlabsc-grn
Infers gene regulatory networks from single-cell expression data using pySCENIC or correlation-based fallback.
TianGzlabsc-batch-integration
Integrate multi-sample single-cell RNA sequencing data with Harmony, scVI, Seurat CCA/RPCA, BBKNN, and fastMNN.
TianGzlabsc-communication
Score ligand-receptor interactions from single-cell RNA sequencing data.
TianGzlabspatial-trajectory
Infer single-cell trajectories and pseudotime from AnnData or Loom files.
TianGzlabsc-preprocessing
Automate single-cell RNA sequencing preprocessing with Scanpy and Seurat workflows.
TianGzlabsc-cell-annotation
Automate cell type annotation for single-cell omics data using marker genes, CellTypist, SingleR, or scmap.
TianGzlabsc-doublet-detection
Detect and remove doublets from single-cell RNA sequencing data using Scrublet, DoubletFinder, or scDblFinder.
TianGzlabspatial-de
Perform differential expression analysis on AnnData single-cell data to discover marker genes.
TianGzlabsc-multiome
Integrate single-cell RNA, protein, and ATAC data with WNN and MOFA+ workflows.
TianGzlabgenomics-vcf-operations
Parse, classify, and filter VCF variants with QUAL and DP metrics.
TianGzlabgenomics-epigenomics
Perform ATAC-seq and ChIP-seq peak calling with MACS3 and motif enrichment using Homer.
TianGzlabgenomics-qc
Perform quality control on genomic sequencing data with FastQC and fastp.
TianGzlabgenomics-sv-detection
Detect and classify structural variants from genomic VCF files.
TianGzlabgenomics-alignment
Align FASTQ reads to reference genomes with BWA-MEM, Bowtie2, and Minimap2.
Frequently Asked Questions About TianGzlab
FAQPage SchemaWhat specific biological data types can be processed?▼
These capabilities support single-cell RNA sequencing, spatial transcriptomics, bulk RNA-seq, genomic VCF files, metabolomics MS2 spectra, and mass spectrometry proteomics data.
Who is the target user for these computational modules?▼
Bioinformaticians, computational biologists, and research scientists requiring standardized processing for high-throughput omics data benefit from these modular analysis components.
How are these analysis modules executed?▼
Modules are invoked via an orchestrator that routes natural language queries to specific domain-focused functions, enabling standardized processing of AnnData, VCF, and mass spectrometry formats.