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Related Experiment Video

Updated: Oct 13, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

RCRdiff: A fully integrated Bayesian method for differential expression analysis using raw NanoString nCounter data.

Can Xu¹, Xinlei Wang¹, Johan Lim²

¹Department of Statistical Science, Southern Methodist University, Dallas, Texas, USA.

Statistics in Medicine

|November 13, 2021

Summary

This summary is machine-generated.

We developed RCRdiff, a Bayesian method for analyzing NanoString nCounter data to find differentially expressed (DE) genes in FFPE tissues. This approach improves statistical inference by directly modeling raw counts and internal controls.

Keywords:

Bayesian LASSO FFPE gene expression gene selection normalization random-coefficient regression

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Area of Science:

Genomics
Bioinformatics
Statistical Modeling

Background:

NanoString nCounter is a popular medium-throughput platform for mRNA abundance analysis.
Its high sensitivity, reproducibility, and applicability to FFPE tissues make it valuable.
Existing methods often require pre-normalization, potentially leading to information loss.

Purpose of the Study:

To develop an integrated Bayesian method for detecting differentially expressed (DE) genes using NanoString nCounter data.
To address limitations of sequential normalization in existing methods.
To offer more reliable statistical inference for gene expression analysis.

Main Methods:

Proposed RCRdiff, a fully integrated Bayesian method based on RCRnorm and Bayesian LASSO.
RCRdiff directly models raw read counts, bypassing separate normalization steps.
Incorporates joint modeling of internal controls, DE, and non-DE gene patterns.
Introduced clustering-based strategies for DE gene selection without external datasets or arbitrary cutoffs.

Main Results:

RCRdiff offers improved statistical inference by avoiding efficiency loss from sequential normalization.
The method directly handles raw counts and internal controls for more robust analysis.
Clustering strategies provide DE gene selection free from external data or arbitrary cutoffs.
Extensive simulations and data examples demonstrate the method's effectiveness.

Conclusions:

RCRdiff provides a more reliable and integrated approach for DE gene detection from NanoString nCounter data.
The method enhances statistical power and accuracy in gene expression studies.
It offers a robust alternative for analyzing FFPE tissue samples.