In Granges there's a function mcol to access the columns describing various quantities under observation apart from the 'Canonical' position columns ( like "chrom", "start", "end" +optional "strand" etc. ). I'd like to try to access these entities in pyranges as well. For concreteness, take the following example: say I have the following pyranges object:
>>> test_pr
+--------------+-----------+-----------+-------------+-------------+------------+
| Chromosome | Start | End | sample_1 | sample_2 | sample_3 |
| (category) | (int32) | (int32) | (float64) | (float64) | (float64) |
|--------------+-----------+-----------+-------------+-------------+------------|
| chr1 | 10468 | 10470 | nan | 0.1234 | nan |
| chr1 | 10470 | 10472 | 0.714 | 0.8 | 0.12 |
| chr1 | 10483 | 10485 | nan | 0.6 | 0.13 |
| chr1 | 10488 | 10490 | 0.941 | 0.8 | 0.15 |
+--------------+-----------+-----------+-------------+-------------+-------------+
And my task is to take averages at each position across samples. Hence, I'd like to access just the last 3 columns and obtain the following:
>>> output_pr
Chromosome Start End Average
chr1 10468 10470 0.1234
chr1 10470 10472 0.544667
chr1 10483 10485 0.365
chr1 10488 10490 0.630333
so I want something like test_pr.mean(skipna=True), but if I try to access the last three columns with, say, iloc for example, I get this:
>>> test_pr.iloc[:,[3,4,5]]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../pyranges.py", line 270, in __getattr__
return _getattr(self, name)
File ".../pyranges/methods/attr.py", line 66, in _getattr
raise AttributeError("PyRanges object has no attribute", name)
AttributeError: ('PyRanges object has no attribute', 'iloc')
So I understand that pyranges are not standard pandas dataframes, and I can't use iloc, but I'm not sure how else to manipulate the metadata columns and collect statistics, etc. Solving the above example case would, I think, make it clear how to work with this type of data structure in general.
Pyranges objects are dictionaries, where the keys are chromosomes (or tuples of chromosome & strand if a stranded object) and the values are pandas dataframes storing the intervals on that chromosome/chromosome & strand pair.
import pyranges as pr
import pandas as pd
test_pr = pr.from_dict({"Chromosome": ["chr1", "chr1", "chr1", "chr1"],
"Start": [10468, 10470, 10483, 10485],
"End": [10470, 10472, 10485, 10490],
"sample_1": [None, 0.714, None, 0.941],
"sample_2": [0.1234, 0.8, 0.6, 0.8],
"sample_3": [None, 0.12, 0.13, 0.15]})
test_pr.dfs
{'chr1': Chromosome Start End sample_1 sample_2 sample_3
0 chr1 10468 10470 NaN 0.1234 NaN
1 chr1 10470 10472 0.714 0.8000 0.12
2 chr1 10483 10485 NaN 0.6000 0.13
3 chr1 10485 10490 0.941 0.8000 0.15}
Anytime you want to access the dataframes and perform an operation on the metadata, you can use one of pyranges's functions like assign (insert a new column) or apply that take as input a function that is applied to each dataframe in the pyranges object. In these functions you can use your standard pandas-fu to do the operations on the underlying dataframes.
A few ways to answer your question by adapting your .iloc approach (I'm not particularly savvy with pandas so there may be more straightforward solutions).
First, make a new column using pr.assign and an anonymous function applied to dataframe, then a call to pr.drop to remove the sample_* columns:
(test_pr.assign("mean",
lambda df: df.iloc[:, [3,4,5]].mean(axis = 1))
.drop(["sample_1","sample_2","sample_3"]))
Or you could make the new column and drop the metadata columns inside the anonymous function itself, and use pr.apply to return the modified pyranges:
(test_pr.apply(lambda df:
(df.assign(mean = lambda df: df.iloc[:, [3,4,5]].mean(axis=1))
.drop(["sample_1","sample_2","sample_3"], axis = 1))
)
)
Both approaches return your desired output. Again there may be leaner solutions out there, but both get the job done and get you under the hood to the dataframes.
Hope that helps :)
Pyranges objects are dictionaries, where the keys are chromosomes (or tuples of chromosome & strand if a stranded object) and the values are pandas dataframes ...
That was an excellently succinct and helpful first sentence that immediately cleared a lot of things up; Thank you!
Btw, the developper graciously sent me the following solution by email (many thanks to Endre for this):
import pandas as pd
import pyranges as pr
import numpy as np
gr = pr.random()
nums = pd.DataFrame(np.random.rand(len(gr), 3))
gr2 = pr.PyRanges(pd.concat([gr.df, nums], axis=1))
def average(df):
df = df.set_index(["Chromosome", "Start", "End", "Strand"])
mean = df.mean(axis=1)
mean.index = df.index
mean.name = "Average"
return mean.reset_index()
gr2.apply(average)
No worries. Thanks for the share on the solution (and to Endre), that's a really clean approach for taking advantage of pandas's built-in methods, will come in handy for me too!
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