Work In Progress.
Summarize the clinical (or lab) components of your dataset. Get an overview of the associations and correlations in your dataset.
What is clinco?
clinco is designed to give you a comprehensive view of your dataset so that you can design your analyses. It let's you address questions like:
- Does my data have batch effects?
- Is age correlated with expression (or methylation or ...)?
- Does atopy vary by asthma status (correlations within clinical variables)
- Which clinical variables are associated with the first X principal components?
Everything is dispatched from the single command-line executable:
=== clinical data format ===
Clinco expects that your clinical-information is in tab-delimited format with a header indicating the clinical variable in each column one row for each sample. The first column must be some unique ID if the clinical data is to be join to some numerical data such as expression or methylation.
An example with 3 samples would be:
#individual_id age sex date_processed location individual_01 22 M 14-01-2012 Denver individual_02 29 F 22-11-2011 Boston individual_03 20 F 12-03-2012 Denver
=== numerical data format ===
The numerical data is expected to have n_samples * (n_features + 1). Where each feature is a probe or a measurement. The first column is expected to be an ID that maps that sample back to the clinical data. An example matching the above clinical data is:
#individual_id probe1 probe2 probe3 probe4 individual_01 0.11 0.91 0.93 0.14 individual_01 0.01 0.71 0.72 0.01 individual_01 0.14 0.99 0.99 0.16
Using data provided in this repo, the command:
python clinco/pca.py \ -X data/xs.txt \ -c data/clinical.txt \ -k gender \ -f data/ex.pca.png
Does pca on the data in
xs.txt with clinical data described in
It will save a plot of the projected data to
print out the correlations of any of the clinical data columns to the
first 10 principal components.
The plot will vary the colors by gender.
The text output looks like this:
component clinical_var n R anova_groups p_value 1 gender 99 na 2-groups 1.7e-55 3 asthmatic 99 na 2-groups 0.0838
Showing that we have a very strong separation of gender on the first clinical component. This is what we expect because the example data is from the Y chromosome.
From this figure, we can see that the genders separate nicely, corroborating
what we see from the ANOVA p-value above. However, we can also see that there
is a female clustering with the males.
We can turn on labelling by adding
-l Barcode to the command above to see
the Barcode of the outlier and check the data for that individual.