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Time-Series Graph

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...

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A platform for processing expression of short time series (PESTS).

Anshu Sinha¹, Marianthi Markatou

¹Department of Biostatistics, Columbia University, New York, NY, USA.

BMC Bioinformatics

|January 13, 2011

Summary

Researchers can now analyze short gene expression time series data more effectively with the new Processing Expression of Short Time Series (PESTS) platform. PESTS offers unique methods for significance analysis, multiple test correction, and clustering, improving biological insights.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Time course microarray profiles are crucial for understanding gene expression dynamics and interactions.
Existing tools are limited for analyzing short time series datasets (typically <9 points) due to cost and resource constraints.

Purpose of the Study:

To introduce a user-friendly platform, Processing Expression of Short Time Series (PESTS), for analyzing short gene expression time series data.
To provide researchers with tools for significance analysis, multiple test correction, and clustering tailored for short time series.

Main Methods:

PESTS implements novel methods focusing on biologically relevant features to summarize gene expression profiles.
Features inherently account for temporal dependence and handle missing data points.
The platform integrates standard techniques for comparability and visualization.

Main Results:

PESTS enables effective significance analysis, multiple test correction, and clustering of short time series data.
The use of biologically relevant features enhances the interpretability and completeness of the analysis.
The platform supports both standard and novel analytical approaches.

Conclusions:

PESTS is a valuable, generalizable resource for researchers analyzing short time series data in genomics and bioinformatics.
The platform offers a unique combination of novel methods and standard techniques for enhanced data interpretation.
PESTS is freely available for download to academic and non-profit users.