v0.1.0 — Initial release

ncountr v0.1.0

First public release of ncountr — the first Python package for end-to-end Nanostring nCounter gene expression analysis.

Features

• RCC parsing — read .RCC files from one or more directories into a structured experiment object
• Quality control — FOV ratio, positive control linearity, housekeeping stability, negative background
• Normalization — positive control, housekeeping, and background subtraction methods
• Differential expression — Mann-Whitney U or t-test with FDR correction
• Gene set scoring — built-in IFN/JAK-STAT pathway (48 genes) + custom gene sets
• Publication-ready plots — QC summary, volcano, heatmap, pathway score box plots
• Cross-platform validation — correlation, DE concordance, and composition analysis vs RNA-seq
• AnnData export — to_anndata() for scverse ecosystem integration
• GEO downloader — ncountr fetch-geo GSE275334 downloads RCC files directly
• Config-driven pipeline — single YAML file runs the entire analysis

Validated on 5 published datasets (1,458 samples)

Dataset	Panel	Samples	Key result
GSE275334	Immune Exhaustion	47	Long COVID / ME/CFS 3-group design
GSE140901	PanCancer Immune	24	ICI responder vs non-responder
GSE117751	Human Immunology	42	2 FDR-significant genes (AIR vs Control)
GSE268012	Human Metabolism	24	232 DE genes for IFNβ vs Control
GSE74821	PAM50 Custom	1,321	Parsed in 3s, 99.5% QC pass rate

Install

pip install ncountr