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Related Concept Videos

P-value01:10

P-value

P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more unlikely...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...

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Oncogene Expression Analysis with Alterations in pH in a Pancreatic Ductal Cell Line
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Oncogene Expression Analysis with Alterations in pH in a Pancreatic Ductal Cell Line

Published on: April 11, 2025

Fold change and p-value cutoffs significantly alter microarray interpretations.

Mark R Dalman1, Anthony Deeter, Gayathri Nimishakavi

  • 1Department of Biology, University of Akron, Akron, OH, USA. mrd31@zips.uakron.edu

BMC Bioinformatics
|April 28, 2012
PubMed
Summary
This summary is machine-generated.

Microarray data analysis is sensitive to statistical cut-offs, impacting gene expression interpretation. Different cut-offs yield varied results, highlighting the need for careful analysis and updated gene chip technology.

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Published on: September 18, 2021

Area of Science:

  • Transcriptomics
  • Gene expression analysis
  • Bioinformatics

Background:

  • Microarray data preprocessing is crucial for transcriptomics.
  • Arbitrary fold change and p-value cut-offs can bias gene expression analysis.
  • Current microarray technology has limitations in gene coverage, missing key genes like leptin.

Purpose of the Study:

  • To reanalyze zebrafish microarray data using different cut-offs.
  • To investigate the impact of statistical cut-offs on gene expression interpretation.
  • To highlight limitations in current microarray technology and annotation.

Main Methods:

  • Reanalysis of a zebrafish (D. rerio) microarray dataset.
  • Utilized GeneSpring software for data analysis.
  • Applied various differential gene expression cut-offs.

Main Results:

  • Data interpretation varied significantly with different cut-offs.
  • More genes were found to be up-regulated than down-regulated.
  • Some genes showed unexpected signaling, possibly due to tissue specificity or transient responses.

Conclusions:

  • Microarray analysis can yield multiple interpretations based on chosen cut-offs, emphasizing interpretation as an art.
  • Follow-up gene expression studies are essential for validation.
  • Gene chip annotation and development must advance with new genomes and novel genes.