scholarly journals Integration of Transcriptome and Proteome Data from Human-Pathogenic Fungi by Using a Data Warehouse

2007 ◽  
Vol 4 (1) ◽  
pp. 51-63 ◽  
Author(s):  
Daniela Albrecht ◽  
Olaf Kniemeyer ◽  
Axel A. Brakhage ◽  
Matthias Berth ◽  
Reinhard Guthke
Keyword(s):  
Data Warehouse ◽  
Time Series Data ◽  
Temperature Shift ◽  
Pathogenic Fungi ◽  
Differential Regulation ◽  
Series Data ◽  
Integrated Analysis ◽  
Two Dimensional ◽  
Microarray Experiments ◽  
2D Gel

Summary A data warehouse for the integrated storage and visualisation of genome and experimental transcriptome and proteome data of human-pathogenic fungi was established. It provides tools for uploading images and corresponding data from microarray experiments, two-dimensional (2D) gel experiments and mass spectrometry (MS) analyses. All data are cross-linked. A user can find out, on which gels in the database an interesting protein was detected. Additionally, he can see on which microarrays the corresponding mRNA had been spotted and whether these spots show interesting intensity values. So the data warehouse enables an integrated analysis of both transcriptome and proteome data. Some of the uploaded data were transcriptome and proteome time series data of temperature shift experiments obtained from Aspergillus fumigatus. Several proteins were differentially regulated at different times after the temperature shift. For a couple of them also the respective transcripts were found to be differentially expressed. For even more of those proteins the transcripts did not show differential regulation and vice versa. So both kinds of data clearly complement each other and should be analysed together.

scholarly journals HTRgene: a computational method to perform the integrated analysis of multiple heterogeneous time-series data: case analysis of cold and heat stress response signaling genes in Arabidopsis

2019 ◽  
Vol 20 (S16) ◽  
Author(s):  
Hongryul Ahn ◽  
Inuk Jung ◽  
Heejoon Chae ◽  
Dongwon Kang ◽  
Woosuk Jung ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Heat Stress ◽  
Stress Response ◽  
Time Series Data ◽  
Computational Method ◽  
Series Data ◽  
Integrated Analysis ◽  
Stress Response Genes ◽  
Multiple Samples

Abstract Background Integrated analysis that uses multiple sample gene expression data measured under the same stress can detect stress response genes more accurately than analysis of individual sample data. However, the integrated analysis is challenging since experimental conditions (strength of stress and the number of time points) are heterogeneous across multiple samples. Results HTRgene is a computational method to perform the integrated analysis of multiple heterogeneous time-series data measured under the same stress condition. The goal of HTRgene is to identify “response order preserving DEGs” that are defined as genes not only which are differentially expressed but also whose response order is preserved across multiple samples. The utility of HTRgene was demonstrated using 28 and 24 time-series sample gene expression data measured under cold and heat stress in Arabidopsis. HTRgene analysis successfully reproduced known biological mechanisms of cold and heat stress in Arabidopsis. Also, HTRgene showed higher accuracy in detecting the documented stress response genes than existing tools. Conclusions HTRgene, a method to find the ordering of response time of genes that are commonly observed among multiple time-series samples, successfully integrated multiple heterogeneous time-series gene expression datasets. It can be applied to many research problems related to the integration of time series data analysis.

scholarly journals Multi-scale analysis of linear data in a two-dimensional space

2013 ◽  
Vol 13 (3) ◽  
pp. 248-265 ◽  
Author(s):  
Yi Qiang ◽  
Seyed H Chavoshi ◽  
Steven Logghe ◽  
Philippe De Maeyer ◽  
Nico Van de Weghe
Keyword(s):  
Time Series ◽  
Visual Analysis ◽  
Time Series Data ◽  
Dimensional Space ◽  
Series Data ◽  
Multiple Time ◽  
Two Dimensional ◽  
Map Algebra ◽  
Real World Datasets ◽  
Triangular Model

Many disciplines are faced with the problem of handling time-series data. This study introduces an innovative visual representation for time series, namely the continuous triangular model. In the continuous triangular model, all subintervals of a time series can be represented in a two-dimensional continuous field, where every point represents a subinterval of the time series, and the value at the point is derived through a certain function (e.g. average or summation) of the time series within the subinterval. The continuous triangular model thus provides an explicit overview of time series at all different scales. In addition to time series, the continuous triangular model can be applied to a broader sense of linear data, such as traffic along a road. This study shows how the continuous triangular model can facilitate the visual analysis of different types of linear data. We also show how the coordinate interval space in the continuous triangular model can support the analysis of multiple time series through spatial analysis methods, including map algebra and cartographic modelling. Real-world datasets and scenarios are employed to demonstrate the usefulness of this approach.

Band Centers and Track Finding for Large Two-Dimensional Infrared Spectroscopic Arrays

2003 ◽  
Vol 57 (3) ◽  
pp. 323-330 ◽  
Author(s):  
Li Chen ◽  
Marc Garland
Keyword(s):  
Time Series ◽  
Spectral Data ◽  
Time Series Data ◽  
Series Data ◽  
Second Derivative ◽  
Two Dimensional ◽  
Peak Finding ◽  
Derivative Method ◽  
Computationally Intensive ◽  
Second Derivative Method

An efficient two-dimensional (2D) peak-finding algorithm is proposed to find peak maps that specify the peak centers of all bands in two-dimensional arrays of time-series infrared spectral data. The algorithm combines the second-derivative method with the intrinsic characteristics of 2D infrared reaction spectral data. Initially, the second-derivative method is used to detect all possible peak center positions, and then three criteria drawn from characteristics of 2D continuous spectral data are employed to filter peak positions. Four 2D peak maps are generated in a sequential order, with better and better approximations to the peak center positions being obtained in each. The 2D peak-finding algorithm has been successfully applied to both simulated spectra (to initially evaluate the algorithm) and then real 2D experimental spectra. The resulting peak maps exhibit very good estimates of the peak center positions. An ordering from the most significant to the least significant bands is obtained. The final peak maps can be used as starting parameters for various applications including the computationally intensive curve-fitting of time-series data.

Wave-SOM

2010 ◽  
Vol 1 (2) ◽  
pp. 50-73 ◽  
Author(s):  
Andrew Blanchard ◽  
Christopher Wolter ◽  
David S. McNabb ◽  
Eitan Gross
Keyword(s):  
Oxidative Stress ◽  
Time Series ◽  
Ribosomal Proteins ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Expression Patterns ◽  
Series Data ◽  
Self Organizing Map ◽  
Two Dimensional ◽  
Yeast Genes

In this paper, the authors present a wavelet-based algorithm (Wave-SOM) to help visualize and cluster oscillatory time-series data in two-dimensional gene expression micro-arrays. Using various wavelet transformations, raw data are first de-noised by decomposing the time-series into low and high frequency wavelet coefficients. Following thresholding, the coefficients are fed as an input vector into a two-dimensional Self-Organizing-Map clustering algorithm. Transformed data are then clustered by minimizing the Euclidean (L2) distance between their corresponding fluctuation patterns. A multi-resolution analysis by Wave-SOM of expression data from the yeast Saccharomyces cerevisiae, exposed to oxidative stress and glucose-limited growth, identified 29 genes with correlated expression patterns that were mapped into 5 different nodes. The ordered clustering of yeast genes by Wave-SOM illustrates that the same set of genes (encoding ribosomal proteins) can be regulated by two different environmental stresses, oxidative stress and starvation. The algorithm provides heuristic information regarding the similarity of different genes. Using previously studied expression patterns of yeast cell-cycle and functional genes as test data sets, the authors’ algorithm outperformed five other competing programs.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data
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