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このリポジトリの skills
SkillsMP は mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- から 810 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-収集済み skill 810 件中 40 件を表示しています。
Load when running over-representation analysis (ORA) on a metabolite list via Fisher's exact test against a built-in 9-pathway DEMO dictionary, BH-FDR adjusted. Skip when needing real KEGG / Reactome (this skill is demo-only) or `mummichog` / `fella` topology…
原文の言語: 英語
Load when running per-sample peak picking on a feature × intensity table via `scipy.signal.find_peaks` — emits per-(sample, feature) detected peaks with prominence and width. Skip when working with mz / RT raw scans (use `metabolomics-xcms-preprocessing`…
原文の言語: 英語
Load when imputing missing values (min / median / KNN) and normalising (TIC / median / log) a feature × sample metabolomics CSV. Skip when only normalisation is needed (use `metabolomics-normalization`) or when the input is raw spectra (run…
原文の言語: 英語
Load when running univariate two-group testing (t-test / Wilcoxon / ANOVA / Kruskal-Wallis) on a feature × sample metabolomics CSV with `--group1-prefix` / `--group2-prefix` column matching, BH-FDR adjusted. Skip when working with raw spectra (run…
原文の言語: 英語
Load when running an XCMS-style preprocessing summary on LC-MS metabolomics raw / vendor-converted files — emits a peak table with m/z, retention time, and per-sample intensities. Skip when working with an already-built peak table (use…
原文の言語: 英語
Load when scaffolding a NEW OmicsClaw skill from a natural-language request — generates the skill directory layout (SKILL.md, parameters.yaml, references/, tests/) under the chosen domain. Skip when modifying an existing skill (edit its files directly) or…
原文の言語: 英語
Load when routing a natural-language omics query to the correct domain skill across spatial / singlecell / genomics / proteomics / metabolomics / bulkrna domains via keyword / LLM / hybrid matching. Skip when the target skill is already known — invoke that…
原文の言語: 英語
Load when ingesting a MaxQuant `proteinGroups.txt`, FragPipe `combined_protein.tsv`, DIA-NN report, or generic CSV / TSV protein-quantification table — normalises columns to a standard schema, emits `tables/proteins.csv`. Skip when raw spectra are the input…
原文の言語: 英語
Load when computing two-group differential protein abundance (group2 vs group1, log2FC + p-value + BH-adjusted FDR) via Welch t-test, equal-variance t-test, or Mann-Whitney on a wide protein × sample CSV. Skip when you need multi-condition DE (run pairwise…
原文の言語: 英語
Load when running over-representation analysis (ORA) on a list of proteins via Fisher's exact test against a built-in 8-pathway DEMO dictionary, with BH-FDR correction. Skip when needing a real pathway database (this skill is demo-only — use…
原文の言語: 英語
Load when summarising peptide identifications (PSM count, unique peptide count, distinct protein count, score / charge distributions) from a peptide-level CSV produced by MaxQuant / FragPipe / DIA-NN. Skip when raw spectra are the input (run a search engine…
原文の言語: 英語
Load when computing protein-table QC — proteins × samples count, missing-value rate, intensity CV (median + mean) — from a MaxQuant / FragPipe / DIA-NN protein-quantification CSV. Skip when raw mzML / RAW spectra are the input (run a search engine first) or…
原文の言語: 英語
Load when summarising PTM sites (phosphorylation, acetylation, ubiquitination, etc.) from a per-site CSV — site-class assignment (Olsen et al. Class I/II/III by `localization_probability`), per-PTM-type counts, amino-acid distribution, sites-per-protein. Skip…
原文の言語: 英語
Load when computing per-protein abundance from a peptide / PSM table via LFQ (intensity summation), iBAQ (intensity / tryptic peptide count), or spectral counting (PSMs per protein). Skip when the input is already protein-level (use `proteomics-ms-qc` for QC)…
原文の言語: 英語
Load when summarising cross-linking MS (XL-MS) results — intra/inter-protein link split, optional FDR filtering, distance-constraint validation against a per-crosslinker (DSS / BS3 / EDC / DSSO / DSBU) max distance. Skip when raw spectra are the input (run…
原文の言語: 英語
Load when preprocessing a single-cell ATAC peak × cell AnnData via Signac-style TF-IDF + LSI + Leiden, producing a clustered UMAP-ready object. Skip when input is fragments or BAM (peak calling not implemented here) or for scRNA preprocessing (use…
原文の言語: 英語
Load when removing ambient RNA contamination from droplet-based scRNA-seq using a simple subtraction path, CellBender, or SoupX. Skip when the contamination is multiplet barcodes (use sc-doublet-detection) or before counts exist (use sc-count).
原文の言語: 英語
Load when integrating multi-sample scRNA-seq with Harmony, scVI, scANVI, BBKNN, Scanorama, SIMBA, or supported R-backed methods to remove batch effects. Skip when the data is one sample (no batch effect to integrate) or for upstream merging only (use…
原文の言語: 英語
Load when assigning cell-type labels to a clustered scRNA AnnData via marker dictionaries, CellTypist, PopV, KNNPredict, SingleR, scmap, SCSA, or a manual cluster-to-label map. Skip when ranking marker genes per cluster (use sc-markers) or for…
原文の言語: 英語
Load when computing cell-cell ligand-receptor communication on an annotated scRNA AnnData via builtin scorer, LIANA, CellPhoneDB, CellChat (R), or NicheNet (R). Skip when assigning cell-type labels (use sc-cell-annotation) or for transcription factor → target…
原文の言語: 英語
Load when building the neighbour graph, embedding (UMAP/t-SNE/diffmap/PHATE), and clustering (Leiden/Louvain) on a normalised single-cell AnnData. Skip when QC/normalisation/HVG/PCA have not run yet (use sc-preprocessing) or for marker ranking after…
原文の言語: 英語
Multi-resolution typed consensus over sc-clustering. Fans out leiden / louvain at several resolutions in parallel, scores members by silhouette + cross-method NMI, runs kmode / weighted / LCA consensus on the surviving base clusterings, and emits a verified…
原文の言語: 英語
Load when turning scRNA FASTQ (or existing CellRanger/STARsolo/SimpleAF/kb-python output) into a downstream-ready AnnData. Skip when reads are already counted into AnnData (use sc-standardize-input) or for raw quality assessment only (use sc-fastq-qc).
原文の言語: 英語
Load when computing per-cell differentiation potency / stemness scores from gene-expression complexity on a scRNA AnnData via the CytoTRACE-simple method. Skip when ordering cells along a trajectory (use sc-pseudotime) or for marker-based cell-type labelling…
原文の言語: 英語
Load when finding marker genes per cluster or comparing condition expression in single-cell RNA-seq. Skip if the data is bulk (use bulkrna-de) or spatial (use spatial-de), or for cluster-only markers without conditions (use sc-markers).
原文の言語: 英語
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 Monte-Carlo permutation. Skip when ranking marker genes (use…
原文の言語: 英語
Load when annotating putative doublets in single-cell RNA-seq using Scrublet, DoubletDetection, DoubletFinder, scDblFinder, or scds. Skip when ambient RNA is the contamination problem (use sc-ambient-removal) or before counts exist (use sc-fastq-qc /…
原文の言語: 英語
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). Skip when the AnnData has no cluster labels yet (run sc-clustering first) or…
原文の言語: 英語
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. Skip when computing per-cell pathway scores in-place (use sc-pathway-scoring) or for de-novo gene-program…
原文の言語: 英語
Load when checking raw single-cell FASTQ read quality (Phred / GC / adapter / length) before counting. Skip when reads are already counted (use sc-qc) or for bulk FASTQ (use bulkrna-read-qc).
原文の言語: 英語
Load when removing low-quality cells and lowly-detected genes from a single-cell AnnData using QC-derived thresholds or tissue presets. Skip for the full normalize→HVG→PCA→cluster pipeline (use sc-preprocessing) or when reads are still raw FASTQ (use…
原文の言語: 英語
Load when extracting gene programs (NMF / cNMF factorisation) and per-cell program usage scores from a non-negative scRNA AnnData. Skip when ranking marker genes per cluster (use sc-markers) or for inferring TF → target regulons (use sc-grn).
原文の言語: 英語
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, in --demo, or with --allow-simplified-grn). Skip when…
原文の言語: 英語
Load when predicting in-silico gene knockout effects on a normalised scRNA AnnData via GRN-based propagation (Python) or scTenifoldKnk (R). Skip when you have a real Perturb-seq / CRISPR screen (use sc-perturb / sc-perturb-prep) or for predicting drug…
原文の言語: 英語
Load when ranking cluster-level marker genes from a clustered single-cell AnnData via Scanpy Wilcoxon / t-test / logreg or COSG specificity. Skip when comparing condition-vs-control with replicates (use sc-de) or for assigning cell-type labels (use…
原文の言語: 英語
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. Skip when ranking marker genes per cluster (use sc-markers) or for trajectory pseudotime…
原文の言語: 英語
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. Skip when input is one already-merged AnnData (use sc-standardize-input) or for FASTQ→counts on each…
原文の言語: 英語
Load when computing per-cell pathway / gene-set scores on a normalised scRNA AnnData via AUCell (R or Python) or Scanpy score_genes. Skip when running condition-vs-control bulk-style enrichment on top of a DE table (use sc-enrichment) or for de-novo…
原文の言語: 英語
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. Skip when the AnnData already has perturbation labels (go straight to…
原文の言語: 英語
Load when classifying perturbed vs non-perturbed cells in a Perturb-seq / CRISPR-screen scRNA AnnData via the pertpy Mixscape workflow. Skip when guide labels are not yet attached to the expression object (run sc-perturb-prep first) or for in-silico KO…
原文の言語: 英語