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Shaped singular spectrum analysis for quantifying gene expression, with application to the early Drosophila embryo.

Shlemov A, Golyandina N, Holloway D, Spirov A - Biomed Res Int (2015)

Bottom Line: We consider the commonly used cylindrical projection of the ellipsoidal Drosophila embryo.We demonstrate how circular and shaped versions of 2D-SSA help to decompose expression data into identifiable components (such as trend and noise), as well as separating signals from different genes.Detection and improvement of under- and overcorrection in multichannel imaging is addressed, as well as the extraction and analysis of 3D features in 3D gene expression patterns.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Mathematics and Mechanics, St. Petersburg State University, Universitetsky Pr. 28, Peterhof, St. Petersburg 198504, Russia.

ABSTRACT
In recent years, with the development of automated microscopy technologies, the volume and complexity of image data on gene expression have increased tremendously. The only way to analyze quantitatively and comprehensively such biological data is by developing and applying new sophisticated mathematical approaches. Here, we present extensions of 2D singular spectrum analysis (2D-SSA) for application to 2D and 3D datasets of embryo images. These extensions, circular and shaped 2D-SSA, are applied to gene expression in the nuclear layer just under the surface of the Drosophila (fruit fly) embryo. We consider the commonly used cylindrical projection of the ellipsoidal Drosophila embryo. We demonstrate how circular and shaped versions of 2D-SSA help to decompose expression data into identifiable components (such as trend and noise), as well as separating signals from different genes. Detection and improvement of under- and overcorrection in multichannel imaging is addressed, as well as the extraction and analysis of 3D features in 3D gene expression patterns.

No MeSH data available.


sna, combined image (both zones from Figures 14 and 15). ((a) to (d)): original image, reconstruction without stripes, and the difference. BDTNP embryo  v5-s10531-28fe05-07.pce.
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fig16: sna, combined image (both zones from Figures 14 and 15). ((a) to (d)): original image, reconstruction without stripes, and the difference. BDTNP embryo  v5-s10531-28fe05-07.pce.

Mentions: A number of genes express in patterns which are more complex than the general AP variation seen with gap genes such as hb and Kr. To analyze crosstalk for such data, we introduce the shaped version of 2D-SSA. As an example, snail (sna) is expressed in a broad band along the ventral midline of the embryo (Figure 16, v5-s10531-28fe05-07, cy3_apical). Since sna shows a very sharp transition from expressing to nonexpressing regions, we analyzed these separately (Figure 14, expressing; Figure 15, nonexpressing). Analysis was conducted on a regular grid (step 0.5%), clipped 15% from left and right (as for Figure 9). For the central expressing zone (Figure 14), we used a window of 40 × 10; for the lateral nonexpressing zone (Figure 15), we used a window of 30 × 10.


Shaped singular spectrum analysis for quantifying gene expression, with application to the early Drosophila embryo.

Shlemov A, Golyandina N, Holloway D, Spirov A - Biomed Res Int (2015)

sna, combined image (both zones from Figures 14 and 15). ((a) to (d)): original image, reconstruction without stripes, and the difference. BDTNP embryo  v5-s10531-28fe05-07.pce.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4402483&req=5

fig16: sna, combined image (both zones from Figures 14 and 15). ((a) to (d)): original image, reconstruction without stripes, and the difference. BDTNP embryo  v5-s10531-28fe05-07.pce.
Mentions: A number of genes express in patterns which are more complex than the general AP variation seen with gap genes such as hb and Kr. To analyze crosstalk for such data, we introduce the shaped version of 2D-SSA. As an example, snail (sna) is expressed in a broad band along the ventral midline of the embryo (Figure 16, v5-s10531-28fe05-07, cy3_apical). Since sna shows a very sharp transition from expressing to nonexpressing regions, we analyzed these separately (Figure 14, expressing; Figure 15, nonexpressing). Analysis was conducted on a regular grid (step 0.5%), clipped 15% from left and right (as for Figure 9). For the central expressing zone (Figure 14), we used a window of 40 × 10; for the lateral nonexpressing zone (Figure 15), we used a window of 30 × 10.

Bottom Line: We consider the commonly used cylindrical projection of the ellipsoidal Drosophila embryo.We demonstrate how circular and shaped versions of 2D-SSA help to decompose expression data into identifiable components (such as trend and noise), as well as separating signals from different genes.Detection and improvement of under- and overcorrection in multichannel imaging is addressed, as well as the extraction and analysis of 3D features in 3D gene expression patterns.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Mathematics and Mechanics, St. Petersburg State University, Universitetsky Pr. 28, Peterhof, St. Petersburg 198504, Russia.

ABSTRACT
In recent years, with the development of automated microscopy technologies, the volume and complexity of image data on gene expression have increased tremendously. The only way to analyze quantitatively and comprehensively such biological data is by developing and applying new sophisticated mathematical approaches. Here, we present extensions of 2D singular spectrum analysis (2D-SSA) for application to 2D and 3D datasets of embryo images. These extensions, circular and shaped 2D-SSA, are applied to gene expression in the nuclear layer just under the surface of the Drosophila (fruit fly) embryo. We consider the commonly used cylindrical projection of the ellipsoidal Drosophila embryo. We demonstrate how circular and shaped versions of 2D-SSA help to decompose expression data into identifiable components (such as trend and noise), as well as separating signals from different genes. Detection and improvement of under- and overcorrection in multichannel imaging is addressed, as well as the extraction and analysis of 3D features in 3D gene expression patterns.

No MeSH data available.