Difference between revisions of "MSymond1 Week 9"
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| + | ==Purpose== | ||
| + | This lab was conducted in Microsoft Excel to analyze a microarray dataset. Each group in class was given a different dataset for a different strain of data. The strain used in the present study was the wild type. The ANOVA tests done on the dataset determined which genes had a p value of less than .05, and they were then further analyzed further to discover that an ANOVA test alone is not enough to determine if there is a statistically significant change in the genes over time. | ||
| + | ==Methods & Results== | ||
| + | ===Statistical Significance Part 1: ANOVA=== | ||
| + | The data was imported into Microsoft Excel, an ANOVA test was run on every gene (6189) in the data set by first calculating the average of each data point for each time interval (15, 30, 60, 90, 120). Then the sum of squares for each time interval was also calculated using the syntax provided by the lab protocol. The Fstat was then calculated for the full dataset, which then allowed to calculate the p-value for each gene. Once the p-values were all calculated, they were then modified to correct for the multiple testing problem. First, to calculate the Bonferroni p-value, the original p values were all multiplied by 6189. Then, in another column, the Bonferroni p values were all either changed to 1 if they were greater than 1, or they were reported as their Bonferroni p-values if they were less than 1. | ||
| + | |||
Strain: Wild type | Strain: Wild type | ||
File name: Symonds_BIOL367_S24_microarrary-data_wt.xlsx | File name: Symonds_BIOL367_S24_microarrary-data_wt.xlsx | ||
Revision as of 14:37, 20 March 2024
Contents
Purpose
This lab was conducted in Microsoft Excel to analyze a microarray dataset. Each group in class was given a different dataset for a different strain of data. The strain used in the present study was the wild type. The ANOVA tests done on the dataset determined which genes had a p value of less than .05, and they were then further analyzed further to discover that an ANOVA test alone is not enough to determine if there is a statistically significant change in the genes over time.
Methods & Results
Statistical Significance Part 1: ANOVA
The data was imported into Microsoft Excel, an ANOVA test was run on every gene (6189) in the data set by first calculating the average of each data point for each time interval (15, 30, 60, 90, 120). Then the sum of squares for each time interval was also calculated using the syntax provided by the lab protocol. The Fstat was then calculated for the full dataset, which then allowed to calculate the p-value for each gene. Once the p-values were all calculated, they were then modified to correct for the multiple testing problem. First, to calculate the Bonferroni p-value, the original p values were all multiplied by 6189. Then, in another column, the Bonferroni p values were all either changed to 1 if they were greater than 1, or they were reported as their Bonferroni p-values if they were less than 1.
Strain: Wild type File name: Symonds_BIOL367_S24_microarrary-data_wt.xlsx number of replicates: 4 times 15, 30, 60, 90, 120
- P value: probability that you would have seen a change of that size due to chance
- P value of >.05 is significant, 5%, 1/20
- 5% of 6189, roughly 300
- multiple hypothesis problem, the more tests you do, the more likely you'll find significance by chance
- Bon Feronni correction, multiply p value by # hypothesis test
- multiply p value by 6189
Media:Symonds_BIOL367_S24_microarray-data_wt3-19-24.xlsx.zip
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