Differential Analysis& FDR Correction

Differential Analysis Steps

Step 1: Construction of input data table in EXCELStep 2: Save EXCEL file into tab delimited txt fileStep 3: Upload data - tab delimited txt fileStep 4: Choose T or U Test Step 5: Enter your email and submitStep 6: Result interpretation: global FDRStep 7: Result interpretation: local FDR

Step 1:

CLASS 1 1 0 0

Gene.1.name … … … …

Gene.2.name … … … …

… … … … …

… … … … …

Construction of input data table in EXCEL

Step 1:

CLASS 1 1 0 0

Gene.1.name … … … …

Gene.2.name … … … …

… … … … …

… … … … …

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

EXCEL file example

Step 2: Save EXCEL file into tab delimited txt file

Step 3: Upload data - tab delimited txt file

1

2

3

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

95% FD/TD .05 FDR1 = TD/TD

Single hypothesis test P-value thresholds

Mean FD/TD Median FD/TDA

Glo

bal

FD

R0.

00.

20.

40.

60.

81.

0

0.0

0.2

0.4

0.6

10-9 0.01 0.02 0.03 0.0410-9 0.05 1.0

Step 6: Result interpretationGlobal FDR

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

95% FD/TD .05 FDR1 = TD/TD

Single hypothesis test P-value thresholds

Mean FD/TD Median FD/TDA

Glo

bal

FD

R0.

00.

20.

40.

60.

81.

0

0.0

0.2

0.4

0.6

10-9 0.01 0.02 0.03 0.0410-9 0.05 1.0

Step 6: Result interpretationGlobal FDR

Features which satisfy global FDR < 0.05

Commonly used gFDR cutoff: 0.05

95% FD/TD .05 FDR1 = TD/TD

Single hypothesis test P-value thresholds

Mean FD/TD Median FD/TDAG

lob

al

FD

R0.

00.

20.

40.

60.

81.

0

0.0

0.2

0.4

0.6

10-9 0.01 0.02 0.03 0.0410-9 0.05 1.0

Step 6: Result interpretationGlobal FDR

95% FD/TD .05 FDR1 = TD/TD

Single hypothesis test P-value thresholds

Mean FD/TD Median FD/TDA

Glo

bal

FD

R0.

00.

20.

40.

60.

81.

0

0.0

0.2

0.4

0.6

10-9 0.01 0.02 0.03 0.0410-9 0.05 1.0

Features which satisfy global FDR < 0.05

Commonly used gFDR cutoff: 0.05

Step 7: Result interpretationlocal FDR

Lo

cal F

DR

Single hypothesis test P-value

0.0 0.01 0.02 0.03 0.04 0.050.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

0.0

0.0

50

.10

0.1

50

.20

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

Lo

cal F

DR

Single hypothesis test P-value

0.0 0.01 0.02 0.03 0.04 0.050.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

0.0

0.0

50

.10

0.1

50

.20

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