scholarly journals Singular Spectrum Analysis of Gene Expression Profiles of EarlyDrosophila embryo: Exponential-in-Distance Patterns

2008 ◽  
Vol 2008 ◽  
pp. 1-5 ◽  
Author(s):  
T. Alexandrov ◽  
N. Golyandina ◽  
A. Spirov
Keyword(s):  
Gene Expression ◽  
Spectrum Analysis ◽  
Expression Profiles ◽  
Singular Spectrum Analysis ◽  
Gene Expression Profiles ◽  
Analytical Approximation ◽  
Similar Data ◽  
Singular Spectrum ◽  
Trend Extraction ◽  
The Given

We present investigation of gene expression profiles by means of singular spectrum analysis (SSA). The biological problem under investigation is the decomposition ofbicoidprotein profiles ofDrosophila melanogasterinto the sum of a signal and noise, where the former consists of an exponential-in-distance pattern and is close to constant nonspecific component, or “background.” The signal processing problems addressed are (i) trend extraction from a noisy signal, (ii) batch processing of similar data, and (iii) analytical approximation of the signal components by the sum of exponential and constant-like functions. The proposed methods are evaluated on the given 17 series.

scholarly journals Shaped 3D Singular Spectrum Analysis for Quantifying Gene Expression, with Application to the Early Zebrafish Embryo

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Alex Shlemov ◽  
Nina Golyandina ◽  
David Holloway ◽  
Alexander Spirov
Keyword(s):  
Gene Expression ◽  
Spectrum Analysis ◽  
Spherical Surface ◽  
Expression Patterns ◽  
Singular Spectrum Analysis ◽  
Thick Layer ◽  
Cellular Level ◽  
Grid Pattern ◽  
Developmental Gene Expression ◽  
Singular Spectrum

Recent progress in microscopy technologies, biological markers, and automated processing methods is making possible the development of gene expression atlases at cellular-level resolution over whole embryos. Raw data on gene expression is usually very noisy. This noise comes from both experimental (technical/methodological) and true biological sources (from stochastic biochemical processes). In addition, the cells or nuclei being imaged are irregularly arranged in 3D space. This makes the processing, extraction, and study of expression signals and intrinsic biological noise a serious challenge for 3D data, requiring new computational approaches. Here, we present a new approach for studying gene expression in nuclei located in a thick layer around a spherical surface. The method includes depth equalization on the sphere, flattening, interpolation to a regular grid, pattern extraction by Shaped 3D singular spectrum analysis (SSA), and interpolation back to original nuclear positions. The approach is demonstrated on several examples of gene expression in the zebrafish egg (a model system in vertebrate development). The method is tested on several different data geometries (e.g., nuclear positions) and different forms of gene expression patterns. Fully 3D datasets for developmental gene expression are becoming increasingly available; we discuss the prospects of applying 3D-SSA to data processing and analysis in this growing field.

scholarly journals Differences in the Gene Expression Profiles of Slow- and Fast-Forming Preinduced Pluripotent Stem Cell Colonies

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Sujin Kwon ◽  
Jung Sun Park ◽  
Byungkuk Min ◽  
Yong-Kook Kang
Keyword(s):  
Gene Expression ◽  
Mode Of Action ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Total Activity ◽  
Gradual Process ◽  
Stochastic Events ◽  
Induced Pluripotent ◽  
The Given ◽  
Pluripotency Genes

Induced pluripotent stem cells (iPSCs) are generated through a gradual process in which somatic cells undergo a number of stochastic events. In this study, we examined whether two different doxycycline-inducible iPSCs, slow-forming 4F2A-iPSCs and fast-forming NGFP-iPSCs, have equivalent levels of pluripotency. Multiplex reverse-transcriptase PCR generated gene expression profiles (GEPs) of 13 pluripotency genes in single initially formed-iPSC (if-iPSC) colonies of NGFP and 4F2A group. Assessment of GEP difference using a weighted root mean square deviation (wRMSD) indicates that 4F2A if-iPSCs are more closely related to mESCs than NGFP if-iPSCs. Consistently,NanogandSox2genes were more frequently derepressed in 4F2A if-iPSC group. We further examined 20 genes that are implicated in reprogramming. They were, overall, more highly expressed in NGFP if-iPSCs, differing from the pluripotency genes being more expressed in 4F2A if-iPSCs. wRMSD analysis for these reprogramming-related genes confirmed that the 4F2A if-iPSC colonies were less deviated from mESCs than the NGFP if-iPSC colonies. Our findings suggest that more important in attaining a better reprogramming is the mode of action by the given reprogramming factors, rather than the total activity of them exerting to the cells, as the thin-but-long-lasting mode of action in 4F2A if-iPSCs is shown to be more effective than its full-but-short-lasting mode in NGFP if-iPSCs.

scholarly journals Electricity consumption forecasting using singular spectrum analysis

DYNA ◽  
2015 ◽  
Vol 82 (190) ◽  
pp. 138-146 ◽  
Author(s):  
Moises Lima de Menezes ◽  
Reinaldo Castro Souza ◽  
José Francisco Moreira Pessanha
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Computational Experiment ◽  
Singular Spectrum Analysis ◽  
Electricity Consumption ◽  
Principal Component ◽  
Graphical Analysis ◽  
Singular Spectrum ◽  
Trend Extraction ◽  
Residential Consumption

Singular Spectrum Analysis (SSA) is a non-parametric technique that allows the decomposition of a time series into signal and noise. Thus, it is a useful technique to trend extraction, smooth and filter a time series. The effect on performance of both Box and Jenkins' and Holt-Winters models when applied to the time series filtered by SSA is investigated in this paper. Three different methodologies are evaluated in the SSA approach: Principal Component Analysis (PCA), Cluster Analysis and Graphical Analysis of Singular Vectors. In order to illustrate and compare the methodologies, in this paper, we also present the main results of a computational experiment with the monthly residential consumption of electricity in Brazil.

scholarly journals Shaped Singular Spectrum Analysis for Quantifying Gene Expression, with Application to the EarlyDrosophilaEmbryo

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Alex Shlemov ◽  
Nina Golyandina ◽  
David Holloway ◽  
Alexander Spirov
Keyword(s):  
Gene Expression ◽  
Spectrum Analysis ◽  
Expression Patterns ◽  
Singular Spectrum Analysis ◽  
Image Data ◽  
Fruit Fly ◽  
Biological Data ◽  
Singular Spectrum ◽  
Cylindrical Projection ◽  
2D And 3D

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 theDrosophila(fruit fly) embryo. We consider the commonly used cylindrical projection of the ellipsoidalDrosophilaembryo. 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.

1327: Gene Expression Profiles in Benign Prostatic Hyperplasia

2004 ◽  
Vol 171 (4S) ◽  
pp. 349-350
Author(s):  
Gaelle Fromont ◽  
Michel Vidaud ◽  
Alain Latil ◽  
Guy Vallancien ◽  
Pierre Validire ◽  
...  
Keyword(s):  
Gene Expression ◽  
Benign Prostatic Hyperplasia ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Prostatic Hyperplasia
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