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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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JUMP: replicability analysis of high-throughput experiments with applications to spatial transcriptomic studies.

Pengfei Lyu¹, Yan Li², Xiaoquan Wen³

¹Department of Statistics, Florida State University, 600 W College AVE, Tallahassee, FL 32306, United States.

Bioinformatics (Oxford, England)

|June 6, 2023

Summary

This summary is machine-generated.

We developed JUMP, a new statistical method for high-dimensional replicability analysis. JUMP enhances power and controls the false discovery rate (FDR) for more reliable scientific discoveries.

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Area of Science:

Biostatistics
Genomics
Computational Biology

Background:

Replicability is crucial in scientific research.
Existing statistical methods for high-dimensional replicability analysis struggle with controlling the false discovery rate (FDR) or are overly conservative.

Purpose of the Study:

To propose a novel statistical method, JUMP, for high-dimensional replicability analysis in two studies.
To improve upon existing methods by enhancing statistical power while maintaining FDR control.

Main Methods:

JUMP utilizes a high-dimensional paired sequence of p-values from two studies.
It employs a test statistic based on the maximum of paired p-values and considers four hidden states for p-value pairs.
The method approximates the probability of rejection under a composite null of replicability and uses a step-up procedure for FDR control.

Main Results:

JUMP achieves substantial power gains compared to existing methods.
It effectively controls the false discovery rate (FDR).
Application to spatially resolved transcriptomic datasets yielded novel biological discoveries.

Conclusions:

JUMP offers a powerful and reliable approach for high-dimensional replicability analysis.
The method facilitates new biological insights from complex datasets.
An R package is available for implementing the JUMP method.