TianGzlab

TianGzlab/OmicsClaw

Skill Claude CodeCodex

For de novo PRS from raw GWAS summary statistics, use the polygenic-risk-score skill (LDpred2-auto) instead.

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Analyze pooled CRISPR screens with single-cell RNA-seq readout using a tiered workflow: fast screening → target validation → rigorous differential expression.

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Differential protein expression analysis for TMT/LFQ mass spectrometry proteomics data using limma linear models with DEqMS PSM-count-aware variance correction.

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Use when you have preprocessed scRNA-seq data and want to.

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Complete workflow for single-cell RNA-seq analysis using Scanpy and the scverse ecosystem. Process raw data through quality control, normalization, clustering, and cell type annotation with publication-ready visualizations.

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Complete workflow for single-cell RNA-seq analysis using Seurat v5. Process raw data through quality control, normalization, clustering, and cell type annotation with publication-ready visualizations.

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Not for: Single-molecule FISH (MERFISH/Xenium), Slide-seq, or single-cell RNA-seq without spatial coordinates. For scRNA-seq, use scrnaseq-scanpy-core-analysis.

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Kaplan-Meier survival estimation, Cox proportional hazards regression, and risk stratification for clinical and real-world evidence (RWE) datasets.

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Identify transcription factors (TFs) driving observed differential expression by integrating ChIP-Atlas TF binding data (epigenomics) with RNA-seq DE results (transcriptomics). Ranks TFs by a combined regulatory score incorporating binding enrichment, target-DE overlap (Fisher's exact test), and directional…

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Load when removing batch effects from a multi-cohort bulk RNA-seq dataset using ComBat (R or Python implementation). Skip when there is only one batch; single-cell batch integration (use sc-batch-integration); spatial multi-slice integration (use spatial-integrate).

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Load when discovering gene co-expression modules and hub genes in a bulk RNA-seq cohort via WGCNA-style soft-thresholded networks. Skip when direct DE comparison (use bulkrna-de); PPI lookup of an existing gene list (use bulkrna-ppi-network); single-cell co-expression (use sc-grn).

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Load when comparing gene expression between two conditions in bulk RNA-seq count data. Skip when the data is single-cell (use sc-de); spatial (use spatial-de); you need exon-level alternative splicing (use bulkrna-splicing).

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Load when estimating cell-type proportions in bulk RNA-seq samples from a single-cell or signature-matrix reference. Skip when the data is already single-cell (no deconvolution needed); spatial deconvolution (use spatial-deconv).

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Load when running pathway / GO term enrichment on a bulk RNA-seq DE result list. Skip when the input is single-cell (use sc-enrichment); the input is spatial (use spatial-enrichment); metabolite pathways (use metabolomics-pathway-enrichment).

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Load when converting gene identifiers between Ensembl, Entrez, and HGNC symbol in a bulk RNA-seq count matrix. Skip when the input is already in the desired identifier system; organisms outside human/mouse; non-bulk-counts inputs.

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Load when querying STRING for the protein-protein interaction subgraph induced by a bulk RNA-seq DEG list and finding hub genes. Skip when pathway enrichment of the same list (use bulkrna-enrichment); de novo co-expression network discovery (use bulkrna-coexpression).

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Load when checking a bulk RNA-seq count matrix for library-size outliers, gene detection rates, and sample-sample correlation before DE. Skip when data is raw FASTQ (use bulkrna-read-qc); aligner logs (use bulkrna-read-alignment); single-cell counts (use sc-qc).

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Load when summarising STAR / HISAT2 / Salmon alignment-rate logs in bulk RNA-seq. Skip when data is raw FASTQ (use bulkrna-read-qc); already counted (use bulkrna-qc); genome-DNA alignment (use genomics-alignment).

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Load when checking raw FASTQ quality (Phred / GC / adapter / Q20-Q30) before alignment in bulk RNA-seq. Skip when reads are already aligned (use bulkrna-read-alignment); counted (use bulkrna-qc); single-cell FASTQ (use sc-fastq-qc).

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Load when summarising rMATS / SUPPA2 alternative-splicing output and identifying significant differential splicing events. Skip when you only have count-level DE (use bulkrna-de); splicing in single-cell; spatial data (currently unsupported).

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Load when stratifying patients by gene expression and testing for survival differences (Kaplan-Meier + Cox) in bulk RNA-seq. Skip when no time-to-event clinical data exists; non-bulk cohorts (single-cell / spatial survival is not supported).

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Load when placing bulk RNA-seq samples on a single-cell reference's pseudotime axis (NNLS deconvolution + nearest-neighbour mapping). Skip when plain cell-type proportions (use bulkrna-deconvolution); native single-cell trajectory inference (use sc-pseudotime).

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Load when computing alignment QC metrics (mapping rate, MAPQ distribution, insert size, duplicate rate, proper-pair rate) from a SAM or BAM file produced by any short-/long-read aligner (BWA / Bowtie2 / Minimap2). Skip when running the alignment step itself; only FASTQ-level QC is needed (use genomics-qc).

TianGzlab/OmicsClaw

Skill Claude CodeCodex

Load when computing genome-assembly QC metrics — N50/N90, L50/L90, total length, contig count, GC content, longest-contig — from a FASTA produced by any assembler (SPAdes / Megahit / Flye / Canu). Skip when running the assembly itself; assessing alignment quality (use genomics-alignment).