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SAM Significance Analysis of Microarrays Users guide and technical document Gil Chu Michael SEO Jun Li Balasubramanian Narasimhan Robert Tibshirani Virginia TusherAcknowledgments: We would like to

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How to fill out significance analysis of microarrays:

1. Identify the null hypothesis: Determine the hypothesis you want to test by comparing the expression levels of genes on a microarray. The null hypothesis assumes that there is no significant difference in gene expression between the compared conditions.
2. Select appropriate statistical tests: Choose the appropriate statistical tests based on the experimental design and the nature of your data. Common tests for significance analysis of microarrays include t-tests, ANOVA, or fold-change analysis.
3. Preprocess the data: Preprocess the microarray data to remove any noise or bias that could affect the analysis. This may involve background correction, normalization, and filtering of low-quality data points.
4. Perform statistical analysis: Apply the selected statistical tests to the preprocessed data to determine the significance of the observed gene expression differences. This analysis will generate p-values or other statistical measures that indicate the level of confidence in rejecting the null hypothesis.
5. Correct for multiple hypothesis testing: Adjust the p-values obtained from the statistical analysis to account for the multiple comparisons made during significance analysis. Common methods for adjusting p-values include Bonferroni correction, false discovery rate (FDR) control, or q-value estimation.

Who needs significance analysis of microarrays?

1. Researchers studying gene expression: Significance analysis of microarrays is commonly used by researchers studying gene expression to identify genes that are differentially expressed between experimental conditions. This analysis helps in understanding the underlying biological processes and identifying potential biomarkers.
2. Pharmaceutical companies: Pharmaceutical companies often use significance analysis of microarrays during drug development to identify genes or pathways that are affected by potential therapeutics. This analysis helps in understanding the drug mechanism of action and predicting potential side effects.
3. Clinicians and medical researchers: Significance analysis of microarrays is also valuable in clinical research and personalized medicine. It can help identify potential diagnostic markers or therapeutic targets for various diseases, allowing for more targeted treatment approaches.

Overall, significance analysis of microarrays is essential for anyone working with gene expression data to uncover meaningful insights and understand the biological significance of observed differences in gene expression.

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What is significance analysis of microarrays?

Significance analysis of microarrays is a statistical method used to determine which genes are significantly differentially expressed between samples.

Who is required to file significance analysis of microarrays?

Researchers and scientists conducting gene expression studies using microarrays are required to perform and report significance analysis of microarrays.

How to fill out significance analysis of microarrays?

Significance analysis of microarrays is typically carried out using specialized software or programming languages specifically designed for this purpose, such as R or Bioconductor packages.

What is the purpose of significance analysis of microarrays?

The purpose of significance analysis of microarrays is to identify genes that are differentially expressed between different experimental conditions or groups, helping researchers to understand the underlying biological processes.

What information must be reported on significance analysis of microarrays?

The results of significance analysis of microarrays typically include a list of significantly differentially expressed genes, along with statistical measures such as p-values and fold changes.