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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.


Four cases of the 3D geometry of eve expression stripes. Stripe 4 can be a forward “C”-shape (a), straight (b), a negative “C”-shape (c), or “S”-shaped (d). BDTNP embryo IDs are given on the images.
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fig19: Four cases of the 3D geometry of eve expression stripes. Stripe 4 can be a forward “C”-shape (a), straight (b), a negative “C”-shape (c), or “S”-shaped (d). BDTNP embryo IDs are given on the images.

Mentions: Using this boundary identification procedure, let us focus on the shape of the central (4th) eve stripe (Figure 19; the 4th stripe has minimal effect from the ellipsoidal to cylindrical projection). Preprocessing included interpolation of the cylindrical projection to a regular grid, clipping 25% of the image on the left and 15% on the right and using a 15 × 10 window. Applying circular 2D-SSA, we use components 2 and 3 to represent the striped expression. The 4th stripe is frequently straight across the ventral midline (Figure 19(b)) but can often show curvature as well. Curvature can be “C”-shaped, both forwards (Figure 19(a)) and backwards (Figure 19(c)), or “S”-shaped (Figure 19(d)). For clarity, Figure 20 shows the “C” and straight shapes in black and white and in the original aspect ratio.


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)

Four cases of the 3D geometry of eve expression stripes. Stripe 4 can be a forward “C”-shape (a), straight (b), a negative “C”-shape (c), or “S”-shaped (d). BDTNP embryo IDs are given on the images.
© Copyright Policy
Related In: Results  -  Collection

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

fig19: Four cases of the 3D geometry of eve expression stripes. Stripe 4 can be a forward “C”-shape (a), straight (b), a negative “C”-shape (c), or “S”-shaped (d). BDTNP embryo IDs are given on the images.
Mentions: Using this boundary identification procedure, let us focus on the shape of the central (4th) eve stripe (Figure 19; the 4th stripe has minimal effect from the ellipsoidal to cylindrical projection). Preprocessing included interpolation of the cylindrical projection to a regular grid, clipping 25% of the image on the left and 15% on the right and using a 15 × 10 window. Applying circular 2D-SSA, we use components 2 and 3 to represent the striped expression. The 4th stripe is frequently straight across the ventral midline (Figure 19(b)) but can often show curvature as well. Curvature can be “C”-shaped, both forwards (Figure 19(a)) and backwards (Figure 19(c)), or “S”-shaped (Figure 19(d)). For clarity, Figure 20 shows the “C” and straight shapes in black and white and in the original aspect ratio.

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.