Related Experiment Video
Updated: Jan 24, 2026

MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data
Published on: February 7, 2025
A meta-analysis of multiple matched copy number and transcriptomics data sets for inferring gene regulatory
Richard Newton1, Lorenz Wernisch1
1Biostatistics Unit, Medical Research Council, Cambridge, United Kingdom.
None:
Inferring gene regulatory relationships from observational data is challenging. Manipulation and intervention is often required to unravel causal relationships unambiguously. However, gene copy number changes, as they frequently occur in cancer cells, might be considered natural manipulation experiments on gene expression. An increasing number of data sets on matched array comparative genomic hybridisation and transcriptomics experiments from a variety of cancer pathologies are becoming publicly available. Here we explore the potential of a meta-analysis of thirty such data sets. The aim of our analysis was to assess the potential of in silico inference of trans-acting gene regulatory relationships from this type of data. We found sufficient correlation signal in the data to infer gene regulatory relationships, with interesting similarities between data sets. A number of genes had highly correlated copy number and expression changes in many of the data sets and we present predicted potential trans-acted regulatory relationships for each of these genes. The study also investigates to what extent heterogeneity between cell types and between pathologies determines the number of statistically significant predictions available from a meta-analysis of experiments.
More Related Videos
10:44Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
09:58Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
Published on: June 27, 2020
Related Concept Videos
Design Example: Setting a Curve Using Design Data
Cis-regulatory Sequences
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Sign Test for Matched Pairs
To conduct the sign test, we first calculate the differences in...
Relationship Formation
Gene Evolution - Fast or Slow?
In contrast, regions which code...