To begin, upload or restore a dataset from the file management tab.

Alternatively, load and automatically analyse an example dataset to see the Differential Expression Analysis pipeline in action.
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Search by metadata

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Search by protein/gene ID

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Separate IDs or ID substrings by commas or semicolons; spaces are ignored. e.g. 'akap1; mtor'

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The Differential Expression Analysis Pipeline allows you explore your data and to perform DE analysis on experimental groups within a dataset.

To begin, upload or restore a dataset from the file management tab.

Alternatively, load and automatically analyse an example dataset to see the pipeline in action.

Experimental design

Thunderbolt needs to know about the experimental design for this dataset to properly group sample data. This design is comprised of experimental variables and conditions .

Experimental variables

Aspects of the experiment which were varied. Examples may include:

  • Diet
  • Drug treatment
  • IP bait

Conditions

Different perturbations used for each experimental variables, including controls. Examples may include:

  • For diet : chow, HFD (high-fat diet)
  • For treatment : vehicle, drugX1nM, drugX10nM, drugX100nM, drugY1nM, drugY10nM, drugY100nM
  • For IP bait : control, AKAP1

To begin inputting conditions:

  1. To the right, enter the name of the ONE variable associated with the condition[s] to be input. If there are multiple variables, you can add conditions for those later.
  2. Enter the name of a condition. Conditions can be entered one at a time or simultaneously. To enter multiple conditions simultaneously, separate with a comma or semi-colon.
  3. Optionally, enter a string of characters (or one per condition, separated by commas/semi-colons) in the “autofill” text box which appears in the column names in your data associated with the condition.
  4. Hit 'Add condition'. The variable and condition[s] will appear in a table.
  5. Repeat steps 1-4 as needed to add to this table. To add a new condition to an existing variable, leave the same variable name in the variable input box and enter your new condition. When satisfied, scroll down.


List of conditions and list of autofill strings must be same length!

Annotate columns

Mark columns from your dataset (listed below) as descriptive data (such as Protein ID, Gene name, description etc.) or numeric data (protein abundance). You only need to mark columns you want included in your analysis. If every numeric data column has a common string of characters in its column name, you may automatically mark them by entering them in the text box to the right.

Next, assign a set of conditions to each numeric column. When finished, hit 'Apply design' below the table. Thunderbolt will assign columns to unique groups for the analysis. Then, proceed to the next page.

Pipeline navigation

1. Experimental Design

Individual protein expression

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Missingness per sample

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Sample boxplots

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Hierarchical clustering

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Mean row variance by group

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Variance by sample

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PCA

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Data heatmap

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Intensity vs missingness

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SVD plot

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Pipeline navigation

1. Experimental Design
2. Exploration and preprocessing
3. Define and Run Analysis
Please select 'Open dataset' to begin differential expression analysis.

Enter contrasts


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e.g. 'HFD.Basal - Chow.Basal'
e.g. '(HFD.Insulin - Chow.Insulin) - (HFD.Basal - Chow.Basal)'


Current contrasts



To exclude groups from a contrast, select a contrast and deselect the groups you want to exclude.

Groups:




Pipeline navigation

1. Experimental Design
2. Exploration and preprocessing
3. Define and Run Analysis

Results

Select contrast

Summary

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Both

Significant

Excluded

Significance criteria

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Remember

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Excluding hits from:


Global results plots

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Pipeline navigation

1. Experimental Design
2. Exploration and preprocessing
3. Define and Run Analysis
4. DE Results
Please select 'Open dataset' to begin differential expression analysis.
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Save pathway list to server

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