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Shaped 3D singular spectrum analysis for quantifying gene expression, with application to the early zebrafish embryo
Alex Shlemov1, Nina Golyandina1, David Holloway2
1Faculty of Mathematics and Mechanics, St. Petersburg State University, Universitetsky Pr. 28, St. Peterhof, St. Petersburg 198504, Russia.
Biomed Research International
|October 24, 2015
Summary
This study introduces a novel computational method to analyze noisy gene expression data from whole embryos. The approach effectively processes 3D cellular data, improving the accuracy of gene expression atlases.
Area of Science:
- Developmental Biology
- Computational Biology
- Genomics
Background:
- Advancements in microscopy and biological markers enable cellular-level gene expression atlases.
- Gene expression data is inherently noisy due to experimental and biological factors.
- Irregular 3D arrangement of cells poses challenges for data processing.
Purpose of the Study:
- To develop a computational approach for analyzing gene expression in 3D cellular data.
- To address challenges posed by noise and irregular data geometry in gene expression atlases.
- To extract meaningful biological signals from complex developmental datasets.
Main Methods:
- Depth equalization on a spherical surface.
- Flattening and interpolation to a regular grid.
- Pattern extraction using Shaped 3D Singular Spectrum Analysis (SSA).
- Interpolation back to original nuclear positions.
Main Results:
- Demonstrated the method's effectiveness on zebrafish embryonic gene expression data.
- Validated the approach across various data geometries and gene expression patterns.
- Successfully extracted gene expression signals from noisy 3D cellular data.
Conclusions:
- The developed computational method enhances the analysis of 3D gene expression data.
- Shaped 3D SSA offers a promising tool for processing and analyzing developmental gene expression datasets.
- This approach facilitates the creation of more accurate and detailed gene expression atlases.
