Skill UI

PyDESeq2 brings the DESeq2 differential expression workflow to Python, letting users load bulk RNA-seq counts, specify design formulas, fit DESeq2 models, run Wald tests with FDR correction, optionally apply apeGLM shrinkage, and export gene-level statistics within pandas/AnnData pipelines.

FlowIO is a specialized, low-level library designed for reading, inspecting, and writing Flow Cytometry Standard (FCS) files (versions 2.0, 3.0, and 3.1). It allows users to extract event data as NumPy arrays, inspect detailed metadata (HEADER, TEXT, ANALYSIS), and handle multi-dataset structures. It is crucial for data preparation, format conversion, and foundational metadata management in bioinformatics pipelines, but does not perform complex analysis like gating or compensation.

PySam provides a powerful Python interface for low-level, streaming access to HTSlib-supported genomic data formats. It enables reading, querying, filtering, and writing complex formats such as SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, and tabix-indexed tables. It is essential for bioinformatics pipelines requiring precise control over genomic data manipulation, including pileup and coverage calculations.

Implement robust data governance and security measures within Palantir Foundry. This skill guides users through PII redaction, applying data markings (e.g., CONFIDENTIAL, RESTRICTED), ensuring GDPR/CCPA compliance, and establishing automated data retention policies. It provides methods for column-level security and auditable deletion workflows for sensitive information.

This skill orchestrates a complete, reproducible pipeline for bulk RNA-seq differential expression analysis. It guides users from raw FASTQ reads through QC, trimming, alignment (STAR/Salmon), quantification, generating a gene-level counts matrix, and concluding with differential expression analysis (DESeq2) and pathway enrichment. It ensures statistical soundness and reproducibility by connecting dedicated tools and enforcing proper experimental design.

This skill allows users to save structured marketing insights and learnings into a persistent intelligence graph. By capturing observations, context conditions (channel, audience, objective), and confidence levels, it builds compound intelligence. This process transforms one-off campaign findings into a consistent, queryable knowledge base that enhances all future marketing strategies and decision-making.