https://github.com/brentp/clinical-components
Summarize the clinical (or lab) components and correlations of your dataset.
https://github.com/brentp/clinical-components
Last synced: 4 months ago
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Summarize the clinical (or lab) components and correlations of your dataset.
- Host: GitHub
- URL: https://github.com/brentp/clinical-components
- Owner: brentp
- Created: 2012-03-19T19:15:36.000Z (about 13 years ago)
- Default Branch: master
- Last Pushed: 2018-06-28T18:03:15.000Z (almost 7 years ago)
- Last Synced: 2025-02-02T04:41:12.802Z (4 months ago)
- Language: Python
- Homepage:
- Size: 390 KB
- Stars: 8
- Watchers: 3
- Forks: 1
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
clinco
======
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:
1. Does my data have batch effects?
2. Is age correlated with expression (or methylation or ...)?
3. Does atopy vary by asthma status (correlations within clinical variables)
4. Which clinical variables are associated with the first X
principal components?
Usage
-----
Everything is dispatched from the single command-line executable:
```Shell
clinco
```
Data Format
-----------
=== 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
```
Example
=======
Using data provided in this repo, the command:
```Shell
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
`clinical.txt`.
It will save a plot of the projected data to `data/ex.pca.png` and
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.
The figure created looks like:
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.