Description

This tutorial is made for those who have never used Xena but who have completed Section 1 of the Basic Tutorial. We will cover how to filter to just the samples you are interested in, how to create subgroups, and how to run a Kaplan Meier survival analysis.

Prerequisites

This tutorial assumes completion of the Basic Tutorial: Section 1. This tutorial begins where the Basic Tutorial: Section 1 ends.

Estimated time needed

Part A: 7 min

Part B: 15 min

Part C: 5 min

Learning goals

Part A

• Search for samples of interest

• Remove samples with no data

Part B

• Make subgroups

• Rename subgroups

Part C

• Run a Kaplan Meier survival analysis

• Use a custom time endpoint

Tutorial

In the Basic Tutorial Section 1 we found that we found that samples from patients that have aberrations in EGFR have relatively higher expression. These aberrations could be mutations or copy number amplifications.

Now we are going to look at whether those patient with aberrations in their samples also have a worse survival prognosis.

Part A

Our goal is to remove patient's samples with no data (i.e. null) from the view. This will make the view look cleaner and remove irrelevant samples from our Kaplan Meier survival analysis.

Ending Screenshot

Steps

1. Type 'null' into the samples search bar. This will highlight samples that have 'null' values in any column on the screen. Null means that there is no data for that sample for that column.

2. Click the filter menu and select 'Remove samples'.

3. Delete the search term.

Video of steps

Shortcut for Part A

Instead of typing 'null' and removing those samples from the view, you can also use the 'Remove samples with nulls' shortcut in the filter menu.

Part B

Our goal is to create two subgroups, those patient's with samples with aberrations in EGFR and those patient's samples without aberrations in EGFR. We will then name the subgroups.

Ending Screenshot

Steps

1. Type '(mis OR inframe) OR B:>0.5' into the samples search bar. This will select samples that either have a missense or inframe deletion '(mis OR inframe)', or where copy number variation (column B) is greater than 0.5. Note that I arbitrarily choose a cutoff of 0.5.

1. Click the filter menu and select 'New subgroup column'. This will create a new column that has samples that met our search term marked as 'true' (ie. those that have an EGFR aberration) and those that did not meet our search term as 'false' (ie. those that do not have an EGFR aberration).

2. Click the column menu for the column we just created (column B) and chose 'Display'.

3. Rename the display so that samples that are 'true' are instead labeled as 'EGFR Aberrations' and the samples that are 'false' are instead labeled as 'No EGFR Aberrations'. Click 'Done'

4. Delete the search term. This will remove the black tick marks for matching samples.

Video of steps 1

Video of steps 2-4

Part C

Now that we have our subgroups we will run a Kaplan Meier survival analysis. Note that TCGA survival data is in days, hence the x-axis will be in days.

Ending Screenshot

We can now see that there is no difference in survival between patients with EGFR aberrations and those without.

Steps

1. Click the column menu at the top of column B.

2. Choose 'Kaplan Meier Plot'.

3. Click 'Custom survival time cutoff' at the bottom of the Kaplan Meier plot.

4. Enter 3650, as this is 10 years.

Video of steps

Test your knowledge

Description

This tutorial is made for those who have never used Xena but who have completed Section 1 of the Basic Tutorial. We will cover how to make box plots and bar charts using our Charts and Statistics View and how to add another column of data, in particular phenotype data, to the view.

Prerequisites

This tutorial assumes you have done Basic Tutorial: Section 1. Basic Tutorial: Section 2 is recommended but not required. This tutorial begins where the Basic Tutorial: Section 2 ends. A live link to the end of Basic Tutorial: Section 2 is at the beginning of this tutorial.

Estimated time needed

Part A: 5 min

Part B: 15 min

Learning goals

Part A

• Create a box plot using the Charts and Statistics View

Part B

• Add another column of data to the view

• Add phenotype data to the view

• Create a bar chart using the Charts and Statistics View

Tutorial

In the Basic Tutorial: Section 1 we found that patient's samples that have aberrations in EGFR have higher expression. These aberrations could be mutations or copy number amplifications.

In the Basic Tutorial: Section 2 we created two subgroups: patient's samples that have aberrations in EGFR and those without. We ran a Kaplan Meier survival analysis and found that there was no difference in survival between these two groups.

Now we are going to use the subgroups created in the Basic Tutorial: Section 2 to see if there is a statistical difference in gene expression between the two subgroups. We will also look at whether samples from male or female patients have more aberrations.

Part A

We found that patient's samples that have aberrations in EGFR have higher gene expression. Now we are going to investigate if this difference in gene expression statistically significant.

Ending Screenshot

We can now see that patient's samples with EGFR aberrations have statistically higher gene expression.

Steps

1. Click the 3-dot column menu at the top of the gene expression column (don't worry if you start with another column - you will be selecting the correct columns in the steps ahead).

2. Click 'Compare subgroups', since we want to compare the group of samples who have aberrations in EGFR to the group of samples that do not.

3. Click the dropdown for 'Show data from' and choose 'column C: EGFR - gene expression RNAseq - HTSeq - FPKM-UQ'.

4. Click the dropdown for 'Subgroup samples by' and choose 'column B: (mis OR infra) OR C:>0.5 - Subgroup'.

5. Click 'Done'.

Video of steps

Part B

We will now investigate how EGFR aberrations compare between samples from men and women.

Ending Screenshot

We can now see that EGFR aberrations are more common in samples from females.

Steps

1. Click the 'x' in the upper right corner to exit Chart View.

2. Hover between columns B and C until 'Click to insert a column' becomes visible. Click on it.

3. Choose 'Phenotypic', click in the search bar, and choose 'Advanced'.

4. Type 'gender' into the search bar, select 'gender.demographic' from the dropdown menu, and click 'Done'.

5. Click the column menu at the top of column C and choose 'Chart & Statistics'. Note that this is just another way to enter Chart View.

6. Click 'Compare subgroups', since we want to compare the group of samples who have aberrations in EGFR to the group of samples that do not.

7. 'column C: gender.demographic' should already be selected for 'Show data from'. If not, select it.

8. 'column B: (mis OR infra) OR C:>0.5 - Subgroup' should already be selected for 'Subgroup samples by'. If not, select it.

9. Click 'Done'.

Video of steps 1-4

Video of steps 5-9

Test your knowledge

Description

This tutorial is made for those who have basic knowledge of how to use Xena. We will cover how to use the pick samples feature, how to view multiple genes in a single column, how to enter and view a signature, and how to run a differential expression analysis.

Prerequisites

This tutorial assumes basic knowledge of how to build and read a Visual Spreadsheet. To get this, go through Basic Tutorial: Section 1. It also assumes basic knowledge of filtering. To get this, go through Basic Tutorial: Section 2.

Estimated time needed

Part A: 10 min

Part B: 5 min

Part C: 15 min

Learning goals

Part A

• Create a visual spreadsheet with single column with multiple genes.

• Filter to only Primary Tumor samples using the Pick Samples mode.

• Remove nulls using the option in the filter menu

Part B

• Enter and view a gene expression signature

Part C

• Run a differential expression analysis.

Tutorial

We will investigate the PAM50 molecular subtypes in breast cancer. PAM50 is a 50-gene signature that classifies breast cancer into five molecular intrinsic subtypes: Luminal A, Luminal B, HER2-enriched, Basal-like, and Normal-like.

Part A

We will make a visual spreadsheet where we can explore the relationship between the PAM50 subtype call and the 50 genes that make up the PAM50 subtype call.

Ending Screenshot

Steps

1. Start at https://xenabrowser.net/

2. Type 'TCGA Breast Cancer (BRCA)', select this study from the drop down menu, and click 'To first variable'.

3. Choose 'Phenotypic', select 'sample_type' from the dropdown menu, and click 'To second variable'.

4. Choose 'Phenotypic', click on 'advanced', type 'pam' into the search bar, select 'PAM50Call_RNAseq' from the dropdown menu, and click 'Done'. This will exit the wizard.

5. Click on 'Click to insert a column' after column C. Copy and paste the 50 genes, choose 'Gene Expression', and click 'Done'.

6. Click the handle in the lower right corner of column D, mutation. Move it to the right to make the column bigger.

Video of steps 1-4

Video of steps 5-6

Steps continued

1. Click on the picker icon next to the filter menu to enter pick samples mode.

2. Click on the Primary Tumor samples.

3. Click the filter menu and select 'Keep samples'.

4. Exit pick samples mode by clicking on the picker icon again.

5. Click the filter menu and select 'Remove samples with nulls'.

Video of steps 1-5

Part B

We will now look at the TFAC30 gene signature and see how it relates to the PAM50 subtype calls. This gene expression signature over 30 genes predicts pathologic complete response (pCR) to preoperative weekly paclitaxel and fluorouracil-doxorubicin-cyclophosphamide (T/FAC) chemotherapy.

Ending Screenshot

Steps

1. Click on 'Click to insert a column' after column D. Copy and paste the signature below, choose 'Gene Expression', and click 'Done'. Note you need to include the '=' as this tells Xena that you want the signature rather than to see all the genes individually.

We can now see that patient's samples that are labeled as 'Her2' and 'Basal' are predicted to be more likely to achieve pCR on TFAC chemotherapy.

Video of step 1

Part C

We will run a differential expression analysis comparing Basal samples to Luminal A and Luminal B samples.

Steps

1. Click the column menu for the PAM50 subtype call (column C) and chose 'Differential Expression'. This will open a new tab where we will run the analysis.

2. Choose the first subgroup to be 'Basal' and the second subgroup to be 'LumA' and 'LumB'. Hold the shift key while clicking to select multiple groups.

3. Click 'Submit'.

Video of steps 1-3

Description

This tutorial is made for those who have basic knowledge of how to use Xena. We will cover how to view whole chromosome and how to use the advanced dataset menu to access datasets such as exon expression.

Prerequisites

This tutorial assumes basic knowledge of how to build and read a Visual Spreadsheet. To get this, go through Basic Tutorial: Section 1.

Estimated time needed

10 min‌

Learning goals

Create a visual spreadsheet that with a chromosome-wide column and data from the advanced dataset menu.

Tutorial

We will look at the ERG-TMPRSS2 gene fusion in patients from the TCGA Prostate Cancer study.

ERG is an oncogene that expressed at low levels in normal prostate tissue. Some patient's prostate cancer samples have higher expression of ERG. These samples tend to have an intra-chromosomal deletion that fuses ERG to TMPRSS2. TMPRSS2 is expressed at high levels in normal prostate tissue. This allows ERG to use the TMPRSS2 promoter to increase ERG expression.

​Ending Screenshot​

We can now see that there are many patient's samples with relatively high expression of ERG (column B). This relatively high expression is not uniform across the exons of ERG, but instead is in the exons closer to the 3' end of the gene (column C). Looking at column D, we can see that these samples also have an intra-chromosomal deletion of part of chromosome 21. If we hover over the genes at either end of the deletion, we can see that the end points fall within ERG and TMPRSS2.

Steps

1. Start at https://xenabrowser.net/

2. Type 'TCGA Prostate Cancer (PRAD)', select this study from the drop down menu, and click 'To first variable'.

3. Type 'ERG', select the checkbox for Gene Expression and click 'To second variable'.

4. Type 'ERG', click 'Show Advanced', select the checkbox for 'IlluminaHiSeq' under 'exon expression RNAseq', and click 'Done'.

5. Click the text 'Click to insert a column' after column C. Type 'chr21', select the checkbox for Copy Number and click 'Done'.

6. Click on the filter menu and select 'Remove samples with nulls'

7. Click on the handle in the lower right corner of column E, copy number for chromosome 21. Move it to the right to make the column bigger.

8. Click and drag within column E, copy number for chromosome 21 to zoom into the intra-chromosomal deletion.

Video of steps 1-4

‌Video of steps 5-8

Test your knowledge

Description

This tutorial is made for those who have never used Xena. We will cover how to create a Visual Spreadsheet with gene expression, mutation, and copy number variation data.

Prerequisites

This tutorial assumes basic knowledge of

• gene expression, copy number variation, and mutational genomic sequencing data

• how a change in copy number variation or mutations can lead to a change in gene expression

• The Cancer Genome Atlas (TCGA)

These resources can help you gain basic knowledge of these concepts:

Estimated time needed

Part A: 5 min

Part B: 10 min

Learning goals

Part A

• Create a Visual Spreadsheet

• Compare data across columns

Part B

• Move columns

• Resize columns

• Zoom in and out

Tutorial

We are going to look at EGFR aberrations in patients with lung adenocarcinomas using TCGA data. We will be looking at mutations and copy number aberrations and how they change gene expression.

Part A

Our goal is to build a Visual Spreadsheet and understand the relationship between the columns of data.

Link to Ending Screenshot

Steps

1. Start at our home page http://xena.ucsc.edu/ and click on 'Launch Xena'. You are now in our Visual Spreadsheet Wizard.

2. Type 'GDC TCGA Lung Adenocarcinoma (LUAD)', select this study from the drop down menu, and click 'To first variable'.

3. Type 'EGFR', select the checkboxes for Gene Expression, Copy Number, and Somatic Mutation, and click 'To second variable'.

Video of steps:

Biological interpretation

We can see that samples from TCGA patients that have high expression of EGFR (red, column B) tend to either have amplifications of EGFR (red, column C) or mutations in EGFR (blue tick marks, column D).

Part B

We will now move the columns to change the sort order and resize columns. We will zoom in to the whole Visual Spreadsheet and also within a column.

Steps

1. Move columns. Click column C, copy number variation, and drag it to the left so that it becomes the first column after the samples column (i.e. column B). Note that the samples are now sorted by the values in this column.

2. Resize columns. Click the handle in the lower right corner of column D, mutation. Move it to the right to make the column bigger.

3. Zoom in on a column. Click and drag within column D. Release to zoom.

4. Zoom out on a column. Click the red zoom out text at the top of column D.

5. Zoom in on samples. Click and drag vertically in any column in the Visual Spreadsheet to zoom in on these samples.

6. Zoom out on samples. To zoom out click either 'Zoom out' or 'Clear zoom' at the top of the Visual Spreadsheet.

Video of step 1

Video of step 2

Video of steps 3-6

Test your knowledge

Description

This tutorial is made for those who have basic knowledge of how to use Xena. We will cover how to view tumor and normal samples from healthy and diseased individuals together, and how to compare gene expression for one or more genes between tumor and normal samples.

We will be using both GTEx samples as our normal samples as well as TCGA matched normal samples. More information on GTEx normal samples can be found here:

Prerequisites

This tutorial assumes basic knowledge of how to build and read a . To get this, go through .

Estimated time needed

Part A: 10 min

Part B: 5 min

Learning goals

Part A

• Build a visual spreadsheet with the columns primary site, sample type, study, and gene expression for the TCGA TARGET GTEx study.

• Filter to just colon samples.

Part B

• Create a box plot using the Charts and Statistics View

Tutorial

We will compare MYC gene expression between patient's samples in TCGA colon adenocarcinoma tumor samples and individuals normal colon tissue in GTEx.

Part A

Our goal is to build a visual spreadsheet with the columns 'primary site', 'sample site', 'study', and gene expression for MYC for the TCGA TARGET GTEx study. We will then filter to samples in the colon.

We can now see that normal samples tend to have lower MYC gene expression.

Steps

2. Type 'TCGA TARGET GTEx', select this study from the drop down menu, and click 'To first variable'.

3. Type 'MYC', select the checkbox for Gene Expression and click 'To second variable'.

4. Choose 'Phenotypic' and select the checkboxes for 'sample type', 'study' and 'Primary site', and click 'Done'.

5. Type 'colon' in the samples search bar and choose 'Keep samples'.

Video of steps 1-4

Video of step 5

Part B

Our goal is to see if the difference in gene expression, where normal samples tend to have lower MYC gene expression, is statistically significant.

We can now see that patient's tumor samples, both recurrent, primary, and metastatic, have higher expression compared to normal tissue, both patient's matched normal tissue from TCGA and unmatched individual's normal tissue from GTEx.

Steps

1. Click the column menu for column B (MYC gene expression) and choose 'Charts & Stats'

2. Click 'Compare subgroups', click the dropdown for 'Show data from' and choose 'column B: MYC - gene expression RNAseq - RSEM norm_count' if it is not already selected

3. Click the dropdown for 'Subgroup samples by' and choose 'column C: Sample Type'.

4. Leave the chart type as 'box plot', and click 'Done'.

Video of steps 1-4

Test your knowledge

Description

This tutorial is made for those who have basic knowledge of how to use Xena. We will cover how to load your own data into a Xena hub on your computer. We will then view the data in the Xena Browser

We will be viewing RNAseq and clinical data from the Chinese Glioma Genome Atlas (CGGA).

Prerequisites

To format the datasets you will need access to a spreadsheet application, such as Microsoft Excel.

To load the data into a Local Xena Hub you will need a computer where you have installation privileges.

Estimated time needed

Part A: 10 min

Part B: 15 min

Part C: 10 min

Learning goals

Part A

• Download data from CGGA

• Use Microsoft Excel or another spreadsheet application to make small formatting adjustments. These adjustments are only to enable Kaplan Meier analyses. Data can be visualized as is.

Part B

• Download and install a Local Xena Hub

• Load data into the Xena Hub on your computer

Part C

• Make a visual spreadsheet from the data in the Xena Hub on your computer

• Create a box plot

• Run a Kaplan Meier Analysis

Tutorial

Part A

We will start with downloading the files from the CGGA. These files already conform to our data file requirements. This is because they are matrices that have sample IDs along one axis and probe, gene, or clinical data names along the other. Additionally, the files are tab-delimited.

For more information see:

While we can load the files exactly as is, we will perform a small format adjustment so that we can create a Kaplan Meier plot. Our Kaplan Meier analyses need two columns of clinical data to create a plot: the event/censor column and the time to that event/censor. These columns need to be specially named so that our Kaplan Meier analysis recognizes them. For Overall Survival, the column names need to be 'OS' and 'OS.time'.

For more information on other supported columns for our Kaplan Meier analysis see:

Steps to format the file

2. Click to download the 'Clinical Data' and 'Expression Data from STAR+RSEM'. Unzip the files. The resulting files should be named 'CGGA.mRNAseq_693.RSEM-genes.20200506.txt' and 'CGGA.mRNAseq_693_clinical.20200506.txt'.

3. Open CGGA.mRNAseq_693_clinical.20200506.txt

   in a spreadsheet application like Microsoft Excel. If the spreadsheet application asks, these files are tab-delimited.

4. Rename the column header 'OS' to be 'OS.time'.

5. Rename the column header 'Censor (alive=0; dead=1)' to be 'OS'.

6. Save and close the file.

Part B

Steps

If this is your first time viewing your own data

2. Click 'Open UCSC Xena' to set your computer up to automatically open the Xena Hub when you come to this page in the future.

3. Click on 'download & run a Local Xena Hub' to download the correct installer for your computer.

4. Double-click the installer to install the Xena Hub on your computer. Follow onscreen instructions, which vary by operating system.

If you already have viewed your own data

2. Wait for 30 seconds. If you allowed your browser to open the Xena Hub every time you come to this screen, then it will open the Xena Hub and this dialog box will close. If you did not, you will need to go to your Applications Folder and open UCSC Xena yourself

Whether you have viewed your own data before or not, you should arrive at a screen like this:

Steps to load the data files

1. Click the 'Load Data' button.

2. Click 'Select Data File', choose 'CGGA.mRNAseq_693_clinical.20200506.txt', and click 'Next'.

3. Choose 'Phenotypic Data' and click 'Next'.

4. Choose 'The first column is sample IDs' and click 'Next'

5. Choose 'These are the first data on these samples.', change the study name to 'CGGA', and click 'Import'.

6. Choose 'Load more data'

7. Click 'Select Data File', choose

   'CGGA.mRNAseq_693.RSEM-genes.20200506.txt', and click 'Next'.

8. Choose 'Genomic Data' and click 'Next'.

9. Confirm selection of 'The first row is sample IDs' and click 'Next'

10. Choose 'I have loaded other data on these samples and want to connect to it.', select 'CGGA' from the drop down, and click 'Import'.

Video of steps 1-6

Video of steps 7-10

Part C

We will look at the chromosome 1p-19q co-deletion in Chinese glioma patients and compare this to IDH1 expression.

Ending Screenshot for Visual Spreadsheet (end of step 5)

Ending Screenshot for Box plot (end of step 11)

Ending Screenshot for Kaplan Meier Analysis (end of step 13)

Steps

1. Click on 'Visualization' in the top menu bar.

2. Type 'CGGA', choose 'CGGA' as the study and click 'To first variable'.

3. Enter the gene 'IDH1', choose 'CGGA.mRNAseq_693.RSEM-genes.20200506.txt', and click 'To second variable'

4. Choose 'Phenotypic', click '1p19q_codeletion_status', and click 'Done'

5. The dataset authors annotated samples without a 1p/19q co-deletion status with 'NA'. To remove these samples, type 'NA' in the samples search bar and choose 'Remove Samples' from the filter actions menu drop down.

6. Compare IDH1 expression between samples with a 1p/19q co-deletion and those that do not. To do this, click on the column menu for column B (IDH1 expression) and choose 'Charts & Stats'.

7. Choose 'Compare Subgroups'.

8. Click the dropdown for 'Show data from' and choose 'column B: IDH1 - CGGA.mRNAseq_693.RSEM-genes.20200506.txt'.

9. Click the dropdown for 'Subgroup samples by' and choose 'column C: 1p19q_codeletion_status - CGGA.mRNAseq_693_clinical.20200506.txt'.

10. Click 'Done'.

11. Close the chart using the 'x' in the upper left corner.

12. Run a Kaplan Meier analysis comparing patients with high IDH1 expression to those with low IDH1 expression. To do this, click on the column menu for column B (IDH1 expression) and choose 'KM plot'

Video of steps 2-4

Video of steps 5-10

Video of steps 11-12

Video Tutorials

Overview of Xena (2 min)

Beginner Tutorial (17 min)

Advanced Tutorial (55 min)

Recording of tutorial given for NCI in May 2021. Includes closed captioning.

Workshop Cheatsheet/Handout

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Workshop cheatsheet/handout

Xena mutation views supports examination of both coding and non-coding mutations from whole genome analysis. We support viewing mutations from both gene- or coordinate- centric perspective. In the gene-centric view, you can dynamically toggle to show or hide introns from the view. This figure shows the frequent intron mutations in 321 samples from the ICGC lymphoma cohorts. These 'pile-ups' would be not be visible if viewing mutations only in the exome. These intron mutations overlap with known enhancers regions (Mathelier 2015).‌