Comprehensive Guide to Differential Analysis and FDR Correction Steps in Excel
This detailed guide walks you through the essential steps for conducting differential analysis and applying false discovery rate (FDR) correction using Excel and statistical tests. Learn how to construct your input data table, save it as a tab-delimited text file, and upload it for analysis. Understand the distinction between T-tests and Mann-Whitney U tests, and how to interpret results, including global and local FDR plots. This tutorial provides insights on analyzing your data effectively, ensuring you identify genuinely statistically significant features.
Comprehensive Guide to Differential Analysis and FDR Correction Steps in Excel
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Presentation Transcript
Differential Analysis Steps Step 1: Construction of input data table in EXCEL Step 2: Save EXCEL file into tab delimited txt file Step 3: Upload data - tab delimited txt file Step 4: Choose T or U Test Step 5: Enter your email and submit Step 6: Result interpretation: global FDR Step 7: Result interpretation: local FDR
Step 1: Construction of input data table in EXCEL
Step 1: • Input data format: • Cell A1: “CLASS” • 1st Column: feature names • 1st Row: sample categories. • It has to be binary, either 1 or 0 • e.g. 1 is disease, 0 is control • All other cells should be data, one sample per one column • e.g. array intensity or protein quantities
Step 3: Upload data - tab delimited txt file Input data “input.txt” selected
Step 4: Choose T test or U test Choose either T or U test for analysis
Step 4: T test or U test, which one to choose? • The U test is useful in the same situations as t test • U test should be used if the data are ordinal • U test is more robust to outliers • U test is more efficient • For distribution far from normal • and for sufficiently large samples
To Discover Differential Features:Student’s T test or Mann Whitney U test? Student’s T test: Student’s T test is a parametric test of the null hypothesis, where the means of 2 normally distributed populations are equal.It is used when you have a nominal variable, which must only have 2 values, such as “male” and “female,” and measurement variable, and you want to compare the mean values of the measurement variable. It is a test of the null hypothesis, where the means of 2 normally distributed populations are equal. Mann-Whitney U Test: Mann-Whitney U Test is a non-parametric test that examines whether 2 sites of data could have come from the same population. It requires 2 data sets that do not need to be paired, normally distributed, or have equal numbers in each set.
Step 5: Enter your email and submit Enter your email Submit
Step 6: Result interpretationGlobal FDR FDR plot red line: Total Discoveries (TD) or Total Discovery rate = 1 FDR plot green line: False Discoveries (MEAN) or False Discovery Rate FDR (MEAN) FDR plot black bar line: False Discoveries (MEDIAN) or False Discovery Rate FDR (MEDIAN) FDR plot blue line: False Discoveries (95%) or False Discovery Rate FDR (95%) FDR plot dotted black line: FDR=0.05
Step 6: How to read the gFDR plots • Commonly used global FDR cut off • 0.05 • If there are no significant features • No data points will show up below • the 0.05 dotted horizontal line
Step 6: Result interpretationGlobal FDR Commonly used gFDR cutoff: 0.05 Features which satisfy global FDR < 0.05
Step 6: Result interpretationGlobal FDR Commonly used gFDR cutoff: 0.05 Features which satisfy global FDR < 0.05
Step 7: How to read the lFDR plots • It has been suggested (Aubert, et al., 2004) that the first abrupt change of the local FDR can be an indication for the determination of a good threshold to choose genuinely statistically significant features.
Step 7: Result interpretationlocal FDR 1st abrupt change of lFDR
Step 7: Result interpretationlocal FDR Click to download result file
Step 7: Result interpretationlocal FDR • Local FDR results: • 1st column: feature name • 2nd column: t or U test P value • 3rd column: local FDR results
