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https://github.com/alexpreynolds/region-selection
Methods for filtering for high-scoring genomic intervals
https://github.com/alexpreynolds/region-selection
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Methods for filtering for high-scoring genomic intervals
- Host: GitHub
- URL: https://github.com/alexpreynolds/region-selection
- Owner: alexpreynolds
- License: mit
- Created: 2022-05-12T22:09:33.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-05-17T06:26:27.000Z (over 2 years ago)
- Last Synced: 2024-04-25T18:41:26.680Z (7 months ago)
- Language: Python
- Size: 34.7 MB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# region-selection
Methods for filtering for high-scoring genomic intervals
## Usage
### Importing the module and creating a Selection instance
```
>>> from region_selection import Selection
>>> s = Selection()
```
### Specify properties
```
>>> s.method = "pq"
>>> s.input_fn = "/Users/areynolds/Developer/Github/region_selection/tests/windows.fixed.25k.bed"
>>> s.bin_size = 200
>>> s.exclusion_span = 24800
```
The `method` can be one of `pq`, `wis`, or `maxmean`, for selecting from one of priority-queue, weighted interval scheduling, or max-mean window sweep methods, respectively.
The `input_fn` property points to a file on the file system. This is optional, unless using the `read()` method.
The `bin_size` and `exclusion_span` properties are integers. These represent the size of elements, and the distance required between them (exclusing the bin, itself).
The default values are 200 and 24800, respectively. This means bins are 200 nt wide, and we require at least 25000 nt of distance between any filtered bins.
### Input data
You can read in data from a four-column, tab-delimited text file:
```
>>> in_df = s.read(s, s.method, s.input_fn)
[region_selection] Reading input file into dataframe...
[region_selection] Read dataframe
```
Otherwise, you must provide a Pandas dataframe containing four columns, each labeled: `Chromosome`, `Start`, `End`, and `Score`, respecively.
In the above snippet, the input dataframe is called `in_df`.
### Running the selection method
Use `run()` to run the specified method on the input dataframe `in_df` (or whatever its name is):
```
>>> out_df = s.run(s, s.method, in_df)
[region_selection] Bin size (nt): 200
[region_selection] Exclusion span (nt): 24800
[region_selection] Exclusion bins: 124
[region_selection] Method: Priority-Queue (PQ)
[region_selection] Constructing heap
[region_selection] Constructing qualifying bin list from heap
[region_selection] Returning sorted bin list
[region_selection] Method (runtime in sec): 140.50703937999998
```
The result is stored as a Pandas dataframe. Here it is called `out_df` and you can call all the usual Pandas properties on this:
```
>>> print(out_df.head())
Chromosome Start End Score
47 chr1 9400 34400 0.41
172 chr1 34400 59400 0.41
304 chr1 60800 85800 0.41
429 chr1 85800 110800 0.41
554 chr1 110800 135800 0.41
```
Or use the `write()` to write to standard output:
```
>>> s.write(out_df)
...
```
Or write with `to_csv()` etc.