CVRG Galaxy

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This document describes in detail on how one can create a log in to CVRG Galaxy Instance and how to execute CRData tools in Galaxy.

The CVRG Galaxy instance can be found here: http://ec2-50-16-130-168.compute-1.amazonaws.com:8080/


Contents

How to create a log in to Galaxy?

Galaxy supports user logins. Although you can use Galaxy without creating a user account, we highly recommend you to do so.

  • First, having an account will allow you to see the data from any machine connected to the Internet (not only the one you are using right now).
  • Second, account will safeguard data stored in your history -- accountless histories are cleared on a systematic basis.

To register as a new user, click the top menu “User” and then “Register”, as shown in Figure 1.

Figure 1. Register a new user
Figure 1. Register a new user

In the middle “detail panel”, input your Email address, Password and Public name. Your public name is an identifier that will be used to generate addresses for information you share publicly. Public names must be at least four characters in length and contain only lower-case letters, numbers, and the '-' character.

After filling the above information, click “Submit” button to complete your registration.

Then you can log in by clicking the top menu “User” and then “Login”, as shown in Figure 2.

Figure 2. Login to Galaxy
Figure 2. Login to Galaxy


In this page, input your Email and Password and then click “Login” button.

After logging in, the “User” menu will show “Preferences”, “Logout”, “Saved Histories” and “Saved Datasets”. In “Preferences” menu, you can:

  • Manage your information
  • Change default permissions for new histories

“Saved Histories” and “Saved Datasets” menus will show the histories and datasets that you have saved respectively.


How to use CRData tools in Galaxy?

In the left “Tool” panel, all the Galaxy tools are listed.

For example, by clicking “Globus” tool, you can see 5 tools including “GO Transfer”, “Get Data via Globus Online”, “Send Data via Globus Online”, etc. (see Figure 3)

Figure 3. “Globus” tools
Figure 3. “Globus” tools


Clicking “CRData” tool, you can see a list of tools, which realize the function of R scripts in crdata.org website. (see Figure 4)

Figure 4. “CRData” tools
Figure 4. “CRData” tools


All the R scripts could be viewed from http://crdata.org/r_scripts .

Each R script corresponds to a Galaxy tool.

To execute these R scripts, just simply click the R script in Galaxy tool, configure input parameters and input files if needed, and then click “Execute” button. For users’ convenience, each input parameter and input file have default value, so directly clicking “Execute” button could get output results.

Here we give some examples to illustrate how to execute CRData tools.


heatmap_plot_demo.R

This demo script performs hierarchical clustering by genes or samples, then plots a heatmap.

In either case, each data column is transformed to Z-scores (i.e. normalized by the column mean and standard deviation) before clustering.

The input file is a comma-separated values (CSV) file (e.g. from Excel) with row and column labels: one sample per row, one gene per column.

Click the “heatmap_plot_demo.R” in the left “Tools” panel. (see Figure 5)

Figure 5. “heatmap_plot_demo.R” tool
Figure 5. “heatmap_plot_demo.R” tool


This tool needs an input file with the format CSV. You may choose from “default input” or “uploaded input”.

If you choose “default input”, there shows three input files:

  • ExampleHeatMapData1.csv
  • ExampleHeatMapData2.csv
  • ExampleHeatMapData3.csv

E.g., we select the first file “ExampleHeatMapData1.csv”, and then click “Execute”.

The R script will be run as shown in Figure 6.

Figure 6. Run “heatmap_plot_demo.R” tool
Figure 6. Run “heatmap_plot_demo.R” tool


The execution results are shown in the right “History” panel.

One is “12: output of heatmap_plot_demo.R”. It records the text output when running this R script, including some notes, tables and matrix. (see Figure 7)

The other is “13: heatmap”. It shows a heatmap generated by this R script. (see Figure 8)

Figure 7. output of heatmap_plot_demo.R
Figure 7. output of heatmap_plot_demo.R
Figure 8. heatmap
Figure 8. heatmap


You can also download the result to your local computer by clicking the “download” button. A window will be opened for selecting a location for storing the output file. Figure 9 shows the downloading of “13:heatmap” (a jpg file).

Figure 9. download of “heatmap”
Figure 9. download of “heatmap”


If you want to use your own input file, you can upload a file first through “Upload File” tool.

Click “Get Data” Tool and then click “Upload File” tool. (see Figure 10)

Figure 10. Upload File tool
Figure 10. Upload File tool


In “File Format” blank, select “Auto-detect”. In “File” blank, click “Browse” and select your local file for uploading. After that, click “Execute” (see Figure 11). The file will be uploaded to Galaxy server and shown in “History” panel, e.g. “14: HeatMapData.csv”. (see Figure 12)

Figure 11. Upload a file
Figure 11. Upload a file
Figure 12. File “HeatMapData.csv” is uploaded
Figure 12. File “HeatMapData.csv” is uploaded


Then you can use your uploaded file as input of the tools.

For the tool “heatmap_plot_demo.R”, select “Uploaded input” in the first blank, and then select a file from your history as input file (e.g. “14: HeatMapData.csv”). (see Figure 13)

Figure 13. Select uploaded file as input
Figure 13. Select uploaded file as input


After execution, the output results are shown in “History” panel.

  • 15: output of heatmap_plot_demo.R (see Figure 14)
  • 16: heatmap (see Figure 15)
Figure 14. “output of heatmap_plot_demo.R”
Figure 14. “output of heatmap_plot_demo.R”
Figure 15. “heatmap”
Figure 15. “heatmap”


sequenceDifferentialExperssion.R

This script performs a two-sample test for RNA-seq differential expression. It is based on the workflow and assumptions of the DESeq package, using specific methods for between-sample normalization, variance stabilization, and differential expression. Details are available in the DESeq package vignette, http://bioconductor.org/packages/release/bioc/html/DESeq.html.

This R script needs one input file and two input parameters. (see Figure 16 and 17)

If you select default input, the file “SequenceDifferentialExpressionInput.tab” will be served as the input. Otherwise, you may select uploaded file as input.

The two input parameters are:

  • condition1: default value is 'Normal' .
  • condition2: default value is 'Treatment' .
Figure 16. sequenceDifferentialExperssion.R tool
Figure 16. sequenceDifferentialExperssion.R tool
Figure 17. execution of sequenceDifferentialExperssion.R tool
Figure 17. execution of sequenceDifferentialExperssion.R tool

After execution, the output results contain:

  • 17: output of sequenceDifferentialExperssion.R
  • 18: relationship between log fold change and P values
  • 19: SequenceDifferentialExpression-TopTable.txt

“17: output of sequenceDifferentialExperssion.R” shows the text output when executing this R script, including loaded packages, matrix and notes. (see Figure 18)

Figure 18. “output of sequenceDifferentialExperssion.R”
Figure 18. “output of sequenceDifferentialExperssion.R”


“18: relationship between log fold change and P values” shows a plot that describes the relationship between log fold change and P values. (see Figure 19)

Figure 19. “relationship between log fold change and P values”
Figure 19. “relationship between log fold change and P values”


“19: SequenceDifferentialExpression-TopTable.txt” shows a table generated by this R script, named “SequenceDifferentialExpression-TopTable.txt”. This file could be downloaded by clicking “download” button, and also processed by later steps in Galaxy. (see Figure 20)

Figure 20. “SequenceDifferentialExpression-TopTable.txt”
Figure 20. “SequenceDifferentialExpression-TopTable.txt”


SakaSmith_model_plot_trajectories_nullclines.R

This simulation plots the phase portrait of the Saka-Smith mutual-repression model for the given parameter set. See BMC Developmental Biology 2007, 7:47 for details of the model.

The R script needs 6 input parameters, their default values are listed below: (see Figure 21)

  • alpha: 6
  • beta: 3
  • ka: 5.5
  • kb: 5.4
  • mu: 3
  • M: 2.0
Figure 21. SakaSmith_model_plot_trajectories_nullclines.R tool
Figure 21. SakaSmith_model_plot_trajectories_nullclines.R tool


After execution, there are three outputs:

  • 34: output of SakaSmith_model_plot_trajectories_nullclines.R (see Figure 22)
  • 35: portrait of the Saka and Smith mutual repression model (see Figure 23)
  • 36: Plot steady state loci (nullclines) of A and B (see Figure 24)
Figure 22. output of SakaSmith_model_plot_trajectories_nullclines.R
Figure 22. output of SakaSmith_model_plot_trajectories_nullclines.R
Figure 23. portrait of the Saka and Smith mutual repression model
Figure 23. portrait of the Saka and Smith mutual repression model
Figure 24. Plot steady state loci (nullclines) of A and B
Figure 24. Plot steady state loci (nullclines) of A and B



Most CRData tools have help information and default input parameters/files, so it’s not hard for users to execute these tools. The source code of R scripts could be seen from http://crdata.org/r_scripts .

If you have any doubts or questions, please contact Bo Liu (Email: boliu at uchicago dot edu).

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