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Analysis of Histone Antibody Specificity with Peptide Microarrays

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Published on: August 1, 2017

Challenges and Future Trends for Microarray Analysis.

Verónica Bolón-Canedo¹, Amparo Alonso-Betanzos², Ignacio López-de-Ullibarri³

¹Research Group LIDIA, Departamento de Computación, CITIC, Universidade da Coruña, A Coruña, Spain. vbolon@udc.es.

Methods in Molecular Biology (Clifton, N.J.)

|May 23, 2019

Summary

This chapter reviews microarray data analysis, comparing it with high-throughput technologies. It highlights current limitations and future trends, including advanced analysis techniques for better interpretability.

Keywords:

Big data Deep learning False discovery rate Feature selection Microarray data Significance testing

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Area of Science:

Bioinformatics and Computational Biology
Genomics and Molecular Biology

Background:

Microarray technology has been a cornerstone of genomic research, but faces increasing competition from newer high-throughput sequencing methods.
Data analysis remains a critical bottleneck, impacting the interpretation and utility of microarray experiments.

Purpose of the Study:

To provide an overview of the current landscape of microarray data analysis.
To forecast future challenges and trends in the field, considering technological advancements.
To examine the competitive dynamics between microarray and high-throughput technologies from a data analysis perspective.

Main Methods:

Comparative analysis of microarray and high-throughput technology data analysis approaches.
Review of current limitations in DNA microarray data processing and interpretation.
Exploration of emerging data analysis techniques relevant to large-scale biological data.

Main Results:

Identified key limitations in current DNA microarray data analysis, such as handling increasing sample sizes.
Highlighted the need for advanced methods like new feature selection, deep learning, and improved statistical testing.
Emphasized the importance of false discovery rate control and interpretability in complex biological data.

Conclusions:

Future trends in microarray data analysis will focus on overcoming current limitations through sophisticated computational and statistical methods.
Integrating advanced techniques like deep learning and robust significance testing is crucial for maximizing the value of genomic data.
The ongoing evolution necessitates continuous adaptation of data analysis strategies to maintain the relevance of microarray technologies.