Here are some example datasets that could be used with Cyber-T. Click on the links to bring you to the relevant analysis pages with all the parameters filled in.

## Unpaired Two Conditions Datasets

These are all control vs. experiment data sets. Analyze one!

### DNA Microarray Control + Experimental Data

#### Basic file input parameters

This is a toy data set of a small amount of control and experimental data from a DNA Microarray . The data file is comma-delimited. The data has been ln-transformed. The first row has a '#' so is considered a comment line. The data starts on the first line not beginning with a '#'. There is one label column, and then there are four control columns and then four experimental columns.

- Delimiter: Commas
- Row Start: 1
- Number of label columns: 1
- Number of first condition columns: 4
- Number of second condition columns: 4

#### Bayes-regularized Analysis Parameters

This performs a two-sample t-test using the Bayes-regularized variance estimates. No normalization is performed. PPDE analysis and multiple hypothesis testing correction are performed on the p-values.

- Normalization: None
- Bayes Sliding Window: 101
- Bayes Confidence: 4
- Run PPDE: checked
- Run Multiple Hypothesis Testing Correction: checked

#### Non Bayes-regularized Analysis Parameters

This is equivalent to a standard t-test using empirical variances. No normalization is performed. PPDE analysis and multiple hypothesis testing correction are performed on the p-values.

- Bayes Confidence: BLANK (Default)
- Run PPDE: checked
- Run Multiple Hypothesis Testing Correction: checked

### Complete P. falciparum Protein Microarray Data

#### Basic file input parameters

This data comes from a previously published protein microrray study of the natural
immune response to *Plasmodium falciparum* [10].
The array consists of 2,320 probes.
The specific analysis is looking at the sera of children who did (n=29) and did not (n=12)
have a clinical malaria episode in the following malaria season.

It has been shown that DNA microarray analysis techniques, like Cyber-T, work well on this type of data [13]. The data file is comma-delimited. The data has not been transformed. It is common to use a VSN normalization for this type of data. There is one header row. There is one label column, and then there are 29 columns for the malaria positive children and then 12 columns for the malaria negative children.

- Delimiter: Commas
- Row Start: 2
- Number of label columns: 1
- Number of first condition columns: 29
- Number of second condition columns: 12

#### Bayes-regularized Analysis Parameters

This performs a two-sample t-test using the Bayes-regularized variance estimates. The data is normalized using VSN. PPDE analysis and multiple hypothesis testing correction are performed on the p-values.

- Normalization: VSN
- Bayes Sliding Window: 101
- Bayes Confidence: 1 (We have a lot of data, no need for a lot of prior!)
- Run PPDE: checked
- Run Multiple Hypothesis Testing Correction: checked

#### Non Bayes-regularized Analysis Parameters

This is equivalent to a standard t-test using empirical variances. The data is normalized using VSN. PPDE analysis and multiple hypothesis testing correction are performed on the p-values.

- Normalization: VSN
- Bayes Confidence: BLANK (Default)
- Run PPDE: checked
- Run Multiple Hypothesis Testing Correction: checked

### Quantitative Mass Spec Control + Experimental Data

#### Basic file input parameters

This data comes from a real unpublished shotgun proteomics experiment with two control and three bait (experimental) samples. It has been shown that such quantative mass spectrometry data follows a similar error model as DNA microarray data [15], thus Cyber-T is an appropriate approach for analysis. The data file is tab-delimited. The data has been pre-processed with dNSAF normalization. NOTE: The Cyber-T webserver does not currently offer this as a normalization option. There is one header row. There is one label column, and then there are two control columns and then three experimental columns.

- Delimiter: Commas
- Row Start: 2
- Number of label columns: 1
- Number of first condition columns: 2
- Number of second condition columns: 3

#### Bayes-regularized Analysis Parameters

This performs a two-sample t-test using the Bayes-regularized variance estimates. No normalization is performed. PPDE analysis and multiple hypothesis testing correction are performed on the p-values.

- Normalization: None
- Bayes Sliding Window: 101
- Bayes Confidence: 6
- Run PPDE: checked
- Run Multiple Hypothesis Testing Correction: checked

#### Non Bayes-regularized Analysis Parameters

This is equivalent to a standard t-test using empirical variances. No normalization is performed. PPDE analysis and multiple hypothesis testing correction are performed on the p-values.

- Normalization: None
- Bayes Confidence: BLANK (Default)
- Run PPDE: checked
- Run Multiple Hypothesis Testing Correction: checked

## Paired Two Conditions Datasets

All datasets have paired samples. Analyze one!

### DNA Microarray Paired Data Raw Measures

#### Basic file input parameters

This data comes from a toy DNA microarray data set of paired raw measurements. The data file is comma-delimited. The data has not been transformed. We want to use VSN normalization for this data. There is one header row. There is one label column, and then there are five condition 1 columns and then five condition 2 columns.

- Delimiter: Commas
- Row Start: 2
- Number of label columns: 1
- Number of ratio columns: 5

#### Bayes-regularized Analysis Parameters

This performs a two-sample t-test using the Bayes-regularized variance estimates. The data is normalized using VSN. PPDE analysis and multiple hypothesis testing correction are performed on the p-values.

- Normalization: VSN
- Bayes Sliding Window: 101
- Bayes Confidence: 3
- Run PPDE: checked
- Run Multiple Hypothesis Testing Correction: checked

#### Non Bayes-regularized Analysis Parameters

This is equivalent to a standard t-test using empirical variances. The data is normalized using VSN. PPDE analysis and multiple hypothesis testing correction are performed on the p-values.

- Normalization: VSN
- Bayes Confidence: BLANK (Default)
- Run PPDE: checked
- Run Multiple Hypothesis Testing Correction: checked

## Multiple Conditions Datasets

These all have more than two conditions. Analyze one!

### DNA Microarray ANOVA Data

#### Basic file input parameters

This is a toy data set of a small amount of three condition data. The data file is comma-delimited. The data has been ln-transformed, so no normalization is needed. There is one comment line (beginning with '#') at the top, but then no header row besides. There is one label column, followed by three columns for condition 1, then three columns for condition 2, and finally two columns for condition 3.

- Delimiter: Commas
- Row Start: 1
- Number of label columns: 1
- Data columns for each condition: 3 3 2

#### Bayes-regularized Analysis Parameters

This performs a one-way ANOVA using the Bayes-regularized variance estimates. No normalization is performed. PPDE analysis and multiple hypothesis testing correction are performed on the p-values. TukeyHSD pairwise post-hoc tests are performed between each condition using the Bayes-regularized variance estimates.

- Normalization: None
- Bayes Sliding Window: 101
- Bayes Confidence: 6
- Run Pairwise Post-hoc: checked
- Pairwise Post-hoc Type: TukeyHSD
- Run PPDE: checked
- Run Multiple Hypothesis Testing Correction: checked

#### Non Bayes-regularized Analysis Parameters

This is equivalent to a standard one-way ANOVA using empirical variances. No normalization is performed. PPDE analysis and multiple hypothesis testing correction are performed on the p-values. TukeyHSD pairwise post-hoc tests are performed between each condition using the empirical variances.

- Normalization: None
- Bayes Confidence: BLANK (Default)
- Run Pairwise Post-hoc: checked
- Pairwise Post-hoc Type: TukeyHSD
- Run PPDE: checked
- Run Multiple Hypothesis Testing Correction: checked

### Protein Array ANOVA Data

#### Basic file input parameters

This data comes from a real *Plasmodium falciparum* protein microarray data set
measuring antibody presence from sera samples of Younger children at three different time points
(C1-C3, n=5 for each conditions) from Kambila, Ghana during the malaria season.
It has been shown that DNA microarray analysis techniques, like CyberT, work well on this type of data
[13].
The data file is comma-delimited.
The data has not been transformed.
It is common to use a VSN normalization
for this type of data.
There is one header row.
There is one label column, then five columns for condition one, then five columns for condition two,
and finally five columns for condition three.

- Delimiter: Commas
- Row Start: 1
- Number of label columns: 1
- Data columns for each condition: 5 5 5

#### Bayes-regularized Analysis Parameters

This performs a one-way ANOVA using the Bayes-regularized variance estimates. VSN normalization is performed. PPDE analysis and multiple hypothesis testing correction are performed on the p-values. TukeyHSD pairwise post-hoc tests are performed between each condition using the Bayes-regularized variance estimates.

- Normalization: VSN
- Bayes Sliding Window: 101
- Bayes Confidence: 3
- Run Pairwise Post-hoc: checked
- Pairwise Post-hoc Type: TukeyHSD
- Run PPDE: checked
- Run Multiple Hypothesis Testing Correction: checked

#### Non Bayes-regularized Analysis Parameters

This is equivalent to a standard one-way ANOVA using empirical variances. VSN normalization is performed. PPDE analysis and multiple hypothesis testing correction are performed on the p-values. TukeyHSD pairwise post-hoc tests are performed between each condition using the empirical variances.

- Normalization: VSN
- Bayes Confidence: BLANK (Default)
- Run Pairwise Post-hoc: checked
- Pairwise Post-hoc Type: TukeyHSD
- Run PPDE: checked
- Run Multiple Hypothesis Testing Correction: checked