Basic Tutorial: Section 3
Learn how to use Chart View and add new columns of data to a view
Last updated
Was this helpful?
Learn how to use Chart View and add new columns of data to a view
Last updated
Was this helpful?
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.
This tutorial assumes you have done . 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.
Part A: 5 min
Part B: 15 min
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
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.
We can now see that patient's samples with EGFR aberrations have statistically higher gene expression.
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).
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.
Click the dropdown for 'Show data from' and choose 'column C: EGFR - gene expression RNAseq - HTSeq - FPKM-UQ'.
Click the dropdown for 'Subgroup samples by' and choose 'column B: (mis OR infra) OR C:>0.5 - Subgroup'.
Click 'Done'.
More information
We will now investigate how EGFR aberrations compare between samples from men and women.
We can now see that EGFR aberrations are more common in samples from females.
Steps
Click the 'x' in the upper right corner to exit Chart View.
Hover between columns B and C until 'Click to insert a column' becomes visible. Click on it.
Choose 'Phenotypic', click in the search bar, and choose 'Advanced'.
Type 'gender' into the search bar, select 'gender.demographic' from the dropdown menu, and click 'Done'.
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.
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.
'column C: gender.demographic' should already be selected for 'Show data from'. If not, select it.
'column B: (mis OR infra) OR C:>0.5 - Subgroup' should already be selected for 'Subgroup samples by'. If not, select it.
Click 'Done'.
More information
Starting at the end of Part A, create a violin plot that compares copy number variation between patient's samples that have EGFR aberrations and those that do not.
Starting at the end of Part B, add the phenotype data 'age_at_earliest_diagnosis_in_years.diagnoses.xena_derived' to the plot.
In the 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 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 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.
To ensure your columns are sorted the same as those in this tutorial, .
