Filter genomic regions by score and proximity
pip install .or install with Poetry
poetry add filter-regionsThis module may be used as a library integrated with other Python projects, or as a standalone command-line tool. Here we describe its use as a Python library. Please run filter-regions --help for usage options on the command line.
There are two options for input. Input can be a file or Numpy vector (1d array) containing tab-delimited floating-point values on one line (input type of vector). Alternatively, input may be a BedGraph-formatted (BED3+1) file or Numpy matrix (2d array), where the fourth column contains floats (bedgraph).
Depending on the chosen input type (vector or bedgraph), by default, the output will be either a three- or four-column Pandas dataframe, respectively.
You can specify a ceiling on the number of elements reported via the max_elements parameter. If the number of elements found is greater than this property, the highest-scoring max_elements number of elements are reported. If there are fewer elements found, that smaller number is reported.
In the case of the vector input type, the output will contain half-open start and end indices ([start, end)) and the aggregated score over the half-open interval.
The difference of these indices will equal the window_bins parameter (e.g., 125 in current usage cases).
Here are the column names for the output dataframe:
StartStopScore
In the case of bedgraph input, the output will contain three columns for the chromosome, and start and stop positions (also using BED-like, half-open indexing). The fourth column will contain the aggregated score over the interval.
The difference of the start and stop positions will equal the window_bins parameter, times the size of an individual bin (in units of nt).
Here are the column names for the output dataframe:
ChromosomeStartStopScore
Additional columns may be enabled via the preserve_cols parameter. Please read below for more detail.
There are three filter methods available: pq (priority-queue), wis (weighted-interval scheduling), and maxmean (max-mean sweep).
The max-mean sweep method (MM) is a modification of the priority-queue method, using both the maximum and mean scores over each window to prioritize window selection.
The primary difference between MM and PQ methods is that MM will use an aggregated score over the window to prioritize its selection, while PQ will only use the score at the centermost position within the window.
Once an interval has been selected to be filtered using whatever filter method, its assigned score value can be its original score — whatever the score is at the index in the vector, or bin in the bedgraph file — or an aggregated value that is calculated from applying a function on scores over the filtered window.
The following functions are available via the aggregation_method parameter:
minmaxmeansummedianvariancepercentile
The default is max, i.e., given a filtered window, the score returned is the maximum value of the window. The other functions work accordingly, per their name.
In the case of the percentile aggregation method, the percentile parameter may be specified as a value between 0 and 1. Its default is 0.95; however, another value may be specified. Using 0.5, for example, would be functionally equivalent to applying the median function.
For either input type, if the preserve_cols flag is set to True, additional columns are reported in output:
OriginalIdxRollingMinRollingMaxRollingMeanRollingSumRollingMedianRollingVarianceRollingPercentileMethodIdx
The Rolling* statistics are the results of applying the aggregation function to signal within each window.
The OriginalIdx and MethodIdx values are indices indicating the starting bin index and the index used for window selection. This can be useful for evaluating which bin is used for selection.
Here is a minimal example of how to use the installed package as a library in a standalone script, which reads in and filters a tab-delimited string of signal values from a regular file:
#!/usr/bin/env python
import filter_regions as fr
m = 'maxmean'
i = 'tests/test_example/scores.txt'
t = 'vector'
w = 125
f = fr.Filter(method=m,
input=i,
input_type=t,
window_bins=w)
f.read()
f.filter()
o = f.output_df
print(o.head())
The Filter class property output_df specifies a Pandas dataframe on which all the usual methods may be called (e.g., head() etc.). One can instead use the write() class method to send results to standard output or to a standard file, for example:
f.write() # send columns to standard output stream
Or:
f.write(output='/path/to/output') # write columns to a tab-delimited file
Other options may be used, e.g., to pass in a BedGraph file, use the mean aggregation function, and report all window statistics:
#!/usr/bin/env python
import filter_regions as fr
m = 'maxmean'
i = 'tests/test_example/scores.bed'
t = 'bedgraph'
w = 125
a = 'mean'
p = True
f = fr.Filter(method=m,
input=i,
input_type=t,
window_bins=w,
aggregation_method=a,
preserve_cols=p)
f.read()
f.filter()
o = f.output_df
# ...
Here is an example where a 1d array or vector of floats may be passed in:
#!/usr/bin/env python
import sys
import numpy as np
import filter_regions as fr
m = 'maxmean'
i = np.array([1.0, 2.0, 5.0, 10.0, 5.0, 5.0, 4.0, 8.0, 2.0, 1.0, 10.0, 1.0, 1.0, 1.0, 5.0])
t = 'vector'
w = 3
a = 'max'
p = True
q = False
f = fr.Filter(method=m,
input=i,
input_type=t,
aggregation_method=a,
window_bins=w,
preserve_cols=p,
quiet=q)
f.read()
f.filter()
f.write(output=None)
Finally, here is an example of passing in a BedGraph-like 2d array or matrix:
#!/usr/bin/env python
import sys
import numpy as np
import filter_regions as fr
m = 'maxmean'
i = np.array([
['chr1', 0, 100, 1.0],
['chr1', 100, 200, 2.0],
['chr1', 200, 300, 5.0],
['chr1', 300, 400, 10.0],
['chr1', 400, 500, 5.0],
['chr1', 500, 600, 5.0],
['chr1', 600, 700, 4.0],
['chr1', 700, 800, 8.0],
['chr1', 800, 900, 2.0],
['chr1', 900, 1000, 1.0],
['chr1', 1000, 1100, 10.0],
['chr1', 1100, 1200, 1.0],
['chr1', 1200, 1300, 1.0],
['chr1', 1300, 1400, 1.0],
['chr1', 1400, 1500, 5.0]
])
t = 'bedgraph'
w = 3
a = 'max'
p = True
q = False
f = fr.Filter(method=m,
input=i,
input_type=t,
aggregation_method=a,
window_bins=w,
preserve_cols=p,
quiet=q)
f.read()
f.filter()
f.write(output=None)