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Related Concept Videos

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...
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used; instead...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
The Anderson-Darling Test01:16

The Anderson-Darling Test

The Anderson-Darling test is a statistical method used to determine whether a data sample is likely drawn from a specific theoretical distribution. Unlike parametric tests, it does not require assumptions about specific parameters of the distribution. Instead, it compares the sample's empirical cumulative distribution function (ECDF) with the cumulative distribution function (CDF) of the hypothesized distribution. Critical values for the test are specific to the chosen distribution rather than...
Testing a Claim about Mean: Known Population SD01:11

Testing a Claim about Mean: Known Population SD

A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...

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Updated: Jun 23, 2026

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
16:24

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells

Published on: February 21, 2014

Estimating the false discovery rate using mixed normal distribution for identifying differentially expressed genes in

Akihiro Hirakawa1, Yasunori Sato, Takashi Sozu

  • 1Genetics Division, National Cancer Center Research Institute, Chuo-ku, Tokyo, Japan. ahirakaw@ncc.go.jp

Cancer Informatics
|May 21, 2009
PubMed
Summary
This summary is machine-generated.

A new method improves false discovery rate (FDR) estimation for identifying genes linked to anticancer drug response. This approach enhances accuracy in analyzing gene expression data from DNA microarrays, crucial for personalized cancer treatments.

Keywords:
differentially expressed genesfalse discovery ratemicroarraymixed normal distributionsignificance analysis of microarray

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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Last Updated: Jun 23, 2026

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
16:24

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Published on: February 21, 2014

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • DNA microarray technology enables simultaneous measurement of thousands of gene expression levels.
  • Identifying genes correlated with anticancer drug response is vital for personalized medicine.
  • Significance Analysis of Microarray (SAM) is commonly used for False Discovery Rate (FDR) estimation, but its accuracy is not always guaranteed.

Purpose of the Study:

  • To propose and evaluate a novel method for estimating FDR in gene expression analysis.
  • To compare the performance of the proposed FDR estimation method against SAM using simulated and real-world data.
  • To identify differentially expressed genes associated with docetaxel response in breast cancer patients.

Main Methods:

  • Development of a new FDR estimation method based on a mixed normal distribution model for the test statistic.
  • Performance evaluation using simulated gene expression datasets under various experimental conditions.
  • Application of the proposed method and SAM to analyze gene expression data from breast cancer patients treated with docetaxel.

Main Results:

  • The accuracy of FDR estimation by both the proposed method and SAM varied depending on simulation conditions.
  • The proposed method identified 280 differentially expressed genes correlated with docetaxel response at an FDR <0.01 threshold.
  • This finding contrasts with previous studies that identified only 92 correlated genes, suggesting improved sensitivity or specificity.

Conclusions:

  • The proposed mixed normal distribution-based FDR estimation method offers a valuable alternative to SAM for analyzing gene expression data.
  • Accurate FDR estimation is critical for reliable identification of genes predictive of drug response.
  • This research contributes to a more precise understanding of gene expression patterns in docetaxel-treated breast cancer.