Best bioinformatics skills, page 20

Load when computing cell-cell ligand-receptor communication on an annotated scRNA AnnData via builtin scorer, LIANA, CellPhoneDB, CellChat (R), or NicheNet (R).

TianGzlab/OmicsClaw

Load when building the neighbour graph, embedding (UMAP/t-SNE/diffmap/PHATE), and clustering (Leiden/Louvain) on a normalised single-cell AnnData.

TianGzlab/OmicsClaw

Load when turning scRNA FASTQ (or existing CellRanger/STARsolo/SimpleAF/kb-python output) into a downstream-ready AnnData.

TianGzlab/OmicsClaw

Load when computing per-cell differentiation potency / stemness scores from gene-expression complexity on a scRNA AnnData via the CytoTRACE-simple method.

TianGzlab/OmicsClaw

Load when finding marker genes per cluster or comparing condition expression in single-cell RNA-seq.

TianGzlab/OmicsClaw

Load when testing whether cell-type / cluster proportions or neighbourhood densities differ between conditions in a multi-sample scRNA AnnData via Milo, scCODA, simple proportion screen, or R…

TianGzlab/OmicsClaw

Load when annotating putative doublets in single-cell RNA-seq using Scrublet, DoubletDetection, DoubletFinder, scDblFinder, or scds.

TianGzlab/OmicsClaw

Load when scoring drug sensitivity per cluster on an annotated scRNA AnnData via simple-correlation against drug-target signatures or via CaDRReS-Sc pretrained models (GDSC / PRISM).

TianGzlab/OmicsClaw

Load when running bulk-style pathway enrichment (ORA / GSEA / GSEA-R / GSVA-R) on a per-group ranked DE / marker list against a gene-set library.

TianGzlab/OmicsClaw

Load when checking raw single-cell FASTQ read quality (Phred / GC / adapter / length) before counting.

TianGzlab/OmicsClaw

Load when removing low-quality cells and lowly-detected genes from a single-cell AnnData using QC-derived thresholds or tissue presets.

TianGzlab/OmicsClaw

Load when extracting gene programs (NMF / cNMF factorisation) and per-cell program usage scores from a non-negative scRNA AnnData.

TianGzlab/OmicsClaw

Load when inferring TF → target gene regulatory networks on a normalised scRNA AnnData via pySCENIC (GRNBoost2 + cisTarget + AUCell) or correlation-based GRN fallback (when arboreto is unavailable…

TianGzlab/OmicsClaw

Load when predicting in-silico gene knockout effects on a normalised scRNA AnnData via GRN-based propagation (Python) or scTenifoldKnk (R).

TianGzlab/OmicsClaw

Load when ranking cluster-level marker genes from a clustered single-cell AnnData via Scanpy Wilcoxon / t-test / logreg or COSG specificity.

TianGzlab/OmicsClaw

Load when aggregating single cells into metacells (sample-aware coarse-grained pseudo-cells) on a normalised scRNA AnnData via SEACells or KMeans on a low-D embedding.

TianGzlab/OmicsClaw

Load when merging multiple single-sample scRNA-seq count matrices (one per sample-from-sc-count) into a single downstream-ready AnnData with sample labels.

TianGzlab/OmicsClaw

Load when computing per-cell pathway / gene-set scores on a normalised scRNA AnnData via AUCell (R or Python) or Scanpy scoregenes.

TianGzlab/OmicsClaw

Load when classifying perturbed vs non-perturbed cells in a Perturb-seq / CRISPR-screen scRNA AnnData via the pertpy Mixscape workflow.

TianGzlab/OmicsClaw

Load when attaching cell-barcode → sgRNA assignments from a mapping TSV/CSV onto a Perturb-seq expression AnnData, producing standardised perturbation / sgRNA / target-gene obs columns.

TianGzlab/OmicsClaw

Load when normalising QC'd scRNA into a PCA-ready AnnData via scanpy / Seurat / SCTransform / Pearson residuals.

TianGzlab/OmicsClaw

Load when ordering cells along a developmental trajectory in a normalised scRNA AnnData via DPT, Palantir, VIA, CellRank, Slingshot (R), or Monocle3 (R).

TianGzlab/OmicsClaw

Load when computing per-cell QC metrics (ngenes, total counts, mt%, ribo%) on a single-cell AnnData before filtering.

TianGzlab/OmicsClaw

Load when an external single-cell h5ad/h5/loom/mtx needs to be canonicalised onto the OmicsClaw AnnData contract before downstream scRNA skills run.

TianGzlab/OmicsClaw

Load when computing RNA velocity vectors on a scRNA AnnData with spliced / unspliced layers via scVelo (stochastic / dynamical / steady-state); dynamical mode additionally exports latent time.

TianGzlab/OmicsClaw

Load when generating spliced / unspliced layers from Cell Ranger BAM, FASTQ, STARsolo output, or velocyto loom — the prerequisite for sc-velocity.

TianGzlab/OmicsClaw

Load when preprocessing a single-cell ATAC peak × cell AnnData via Signac-style TF-IDF + LSI + Leiden, producing a clustered UMAP-ready object.

TianGzlab/OmicsClaw

Load when assigning per-spot cell-type labels on a spatial AnnData via marker-gene scoring or scRNA-reference mapping (Tangram / scANVI / CellAssign).

TianGzlab/OmicsClaw

Load when computing ligand-receptor communication on labelled spatial AnnData with LIANA, CellPhoneDB, FastCCC or CellChat.

TianGzlab/OmicsClaw

Load when ranking spatial cluster markers or comparing two spatial groups.

TianGzlab/OmicsClaw

Load when deconvolving spot-level cell-type proportions on a Visium-style spatial AnnData using a labelled scRNA reference (FlashDeconv / Cell2location / RCTD / DestVI / Tangram / others).

TianGzlab/OmicsClaw

Load when running the foundational spatial transcriptomics QC + filtering + normalisation + HVG + PCA + neighbour-graph + Leiden pipeline on a Visium / Xenium / generic spatial AnnData.

TianGzlab/OmicsClaw

Load when converting spatial transcriptomics raw FASTQ pairs through ST-Pipeline into a rawcounts.h5ad ready for spatial-preprocess.

TianGzlab/OmicsClaw

Load when inferring pseudotime / lineage trajectories on a preprocessed spatial AnnData via DPT (default — diffusion pseudotime), CellRank (terminal-state + fate-probability), or Palantir (waypoint…

TianGzlab/OmicsClaw

Load when estimating RNA velocity on a spatial AnnData with layers["spliced"] + layers["unspliced"] via scVelo (stochastic / deterministic / dynamical) or veloVI (deep generative).

TianGzlab/OmicsClaw

API + Python SDK for ordering cell-free protein expression and binding assays.

jaechang-hits/SciAgent-Skills

CLI for VCF/BCF: filter, merge, annotate, query, normalize, compute stats.

jaechang-hits/SciAgent-Skills

Genomic interval ops on BED/BAM/GFF/VCF. An agent skill from jaechang-hits/SciAgent-Skills.

jaechang-hits/SciAgent-Skills

Unified Python interface to 40+ bioinformatics web services: UniProt proteins, KEGG pathways, ChEMBL/ChEBI/PubChem, BLAST, cross-database ID mapping, GO annotations, PPI.

jaechang-hits/SciAgent-Skills

Fast short-read DNA aligner for WGS/WES/ChIP-seq. An agent skill from jaechang-hits/SciAgent-Skills.

jaechang-hits/SciAgent-Skills

Query NCBI ClinVar via E-utilities for variant clinical significance, pathogenicity, disease associations.

jaechang-hits/SciAgent-Skills

Query COSMIC for cancer somatic mutations, gene census, mutational signatures, drug resistance variants.

jaechang-hits/SciAgent-Skills

NGS CLI for ChIP/RNA/ATAC-seq. An agent skill from jaechang-hits/SciAgent-Skills.

jaechang-hits/SciAgent-Skills

DepMap CRISPR gene effect (Chronos) analysis: sign convention for essentiality, per-gene NaN-safe Spearman correlation, data loading/alignment.

jaechang-hits/SciAgent-Skills

Ensembl REST API for gene/transcript/variant annotations in 300+ species.

jaechang-hits/SciAgent-Skills

All-in-one FASTQ QC and adapter trimming. An agent skill from jaechang-hits/SciAgent-Skills.

jaechang-hits/SciAgent-Skills

Counts RNA-seq reads overlapping GTF gene features. An agent skill from jaechang-hits/SciAgent-Skills.

jaechang-hits/SciAgent-Skills

GATK Best Practices for germline SNP/indel calling from WGS/WES BAMs.

jaechang-hits/SciAgent-Skills