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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Improving massive experiments with threshold blocking.

Michael J Higgins¹, Fredrik Sävje², Jasjeet S Sekhon³

¹Department of Statistics, Kansas State University, Manhattan, KS 66506;

Proceedings of the National Academy of Sciences of the United States of America

|July 7, 2016

Summary

This summary is machine-generated.

We developed an efficient algorithm for threshold blocking in randomized experiments, improving data analysis. This method effectively groups experimental units, even with large datasets and complex structures, ensuring reliable inferences.

Keywords:

big data blocking causal inference experimental design

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Area of Science:

Statistics
Experimental Design
Computer Science

Background:

Randomized experiments benefit from blocking, which assigns treatments in fixed proportions within similar unit groups.
Existing blocking methods are computationally expensive, limited in scope, or heuristic, failing to adequately address clustered data or provide guaranteed performance.
The difficulty in deriving effective blocking groups hinders the widespread application of this powerful statistical technique.

Purpose of the Study:

To present a novel algorithm for threshold blocking that overcomes limitations of current methods.
To address the problem of minimizing maximum intra-group distance in blocking with a minimum group size requirement.
To provide a computationally efficient and scalable solution for blocking in large-scale randomized experiments.

Main Methods:

Developed an approximation algorithm for a class of blocking problems, specifically minimizing the maximum distance within groups.
Proved the underlying blocking problem to be nondeterministic polynomial-time hard.
The algorithm achieves O(n log n) time and O(n) space complexity, offering a guaranteed performance bound (at most four times the optimal maximum distance).

Main Results:

The proposed algorithm efficiently implements threshold blocking for large datasets (tens of millions of units) on standard hardware.
It provides a guaranteed performance bound, ensuring a maximum intra-group distance within a factor of four of the optimal.
The method flexibly constructs groups of any minimum size, accommodating complex experimental designs and clustered data.

Conclusions:

This is the first blocking method with guaranteed performance suitable for massive experiments.
The algorithm offers a practical and efficient solution for complex experimental designs, including those with multiple treatment arms and clustered data.
Simulation studies confirm the algorithm's efficiency and efficacy, enabling rapid blocking of large-scale experimental data.