3,283 件 ・ 関連度順
概要と使いどころ
Apply statsmodels in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses statsmodels or its strengths fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
Apply polars in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses polars or its strengths fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
Apply spark optimization in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses spark optimization or its strengths fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
Apply stable-baselines3 in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses stable-baselines3 or its strengths fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
Apply networkx in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses networkx or its strengths fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
Apply sql queries in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses sql queries or its strengths fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
Apply hugging face datasets in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses hugging face datasets or its strengths fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
Apply matplotlib in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses matplotlib or its strengths fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
Apply data context extractor in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses data context extractor or its strengths fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
Apply data visualization in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses data visualization or its strengths fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
Apply experimental design in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses experimental design or its strengths fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
Apply dask in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses dask or its strengths fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
Apply airflow dag patterns in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses airflow dag patterns or its strengths fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
Apply backtesting frameworks in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses backtesting frameworks or its strengths fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
Apply dbt transformation patterns in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses dbt transformation patterns or its strengths fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end eDNA metabarcoding from raw amplicons to community ecology. Covers QC, primer removal, denoising with OBITools3 or DADA2, contamination filtering, taxonomy assignment, Hill number diversity, and constrained ordination. Use when processing environmental DNA samples for biodiversity assessment or ecological surveys.
日本語の概要は準備中です。原文の説明を表示しています。
Workflow from differential expression results to functional enrichment analysis. Covers GO, KEGG, Reactome enrichment with clusterProfiler and visualization. Use when taking DE results to pathway enrichment.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end DNA sequencing workflow from FASTQ files to variant calls. Covers QC, alignment with BWA, BAM processing, and variant calling with bcftools or GATK HaplotypeCaller. Use when calling variants from raw sequencing reads.
日本語の概要は準備中です。原文の説明を表示しています。
Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.
日本語の概要は準備中です。原文の説明を表示しています。
Data-independent acquisition (DIA) proteomics analysis with DIA-NN and other tools. Use when analyzing DIA mass spectrometry data with library-free or library-based workflows for deep proteome profiling.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end CRISPR experiment design from target selection to delivery-ready constructs. Covers guide RNA design, off-target assessment, and specialized editing strategies including knockouts, base editing, and HDR knockins. Use when designing complete CRISPR editing experiments for gene knockout, correction, or tagging.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end flow cytometry workflow from FCS files to differential analysis. Orchestrates compensation, transformation, gating/clustering, and statistical testing with CATALYST/diffcyt. Use when processing flow or mass cytometry data end-to-end.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end pooled and single-cell CRISPR screen analysis from FASTQ to hit genes. Orchestrates library design QC, guide counting, six-stage screen QC (plasmid Gini, replicate Pearson, CEGv2 PR-AUC, copy-number artifact), method-appropriate hit calling across MAGeCK RRA/MLE, BAGEL2, drugZ, JACKS, and Chronos, cancer-cell-line copy-number correction (CRISPRcleanR / Chronos), batch correction for multi-batch screens, and the specialized branches for combinatorial paralog screens, single-cell Perturb-seq, base-editor variant-function screens, prime-editor screens, and in vivo bottleneck-aware screens. Use when analyzing any pooled CRISPR screen end-to-end, choosing the correct hit-calling method by experimental design, integrating copy-number correction into the pipeline, or branching the workflow for single-cell, combinatorial, base-editor, prime-editor, or in vivo variants.
日本語の概要は準備中です。原文の説明を表示しています。
Unsupervised clustering and cell type identification for flow/mass cytometry. Covers FlowSOM, Phenograph, and CATALYST workflows. Use when discovering cell populations in high-dimensional cytometry data without predefined gates.
日本語の概要は準備中です。原文の説明を表示しています。