IDR: To identify which part of the signal is reproducible between $n$ samples, we can model the data as a mixture of two $n$ dimensional distributions. The copula framework allows us to decompose each of these two multidimensional distribution as the product of their marginal distribution on each dimension and a copula to model the dependency structure between the marginals. By imposing, one of these copula to be the independence copula, we can then compute the probability of each observation to be reproducible across samples.
Installation
pip install midr
or for the latest development version:
pip install uv
uv pip install git+https://gitbio.ens-lyon.fr/LBMC/physbio/idrpy
Usage
Command line
midr --help
* --csv_input TEXT csv file with data, observation as rows and dimensions as columns [required]
* --csv_output TEXT csv file with data, and two additional columns for IDR and FDR [required]
--ecdf [adjustedDistributionalTransform|distributionalTransform|linear] (default: adjustedDistributionalTransform) choise of eCDF method, to handle ties, linear use the data order, distributional transform randomize ties between upper and lower non-tie values, adjusted distributional transform randomize while keeping ties closer together than their are to the upper and lower values
--copula [empiricalBeta|archmixture|gaussian] (default: empiricalBeta) copula model to use
--pseudo_data use pseudo data (prior to consider higher values more reproducible)
--gpu run on GPU if available
--no_header do not use header in csable header parsing in csv input file
--help Show this message and exit.
Example
midr --csv_input data/input.csv --csv_output results/output.csv
with data/input.csv containing:
V1,V2
0.9250749250749251,0.9040959040959041
0.13586413586413587,0.11288711288711288
0.9820179820179821,0.975024975024975
0.7772227772227772,0.7842157842157842
0.6463536463536463,0.6053946053946054
0.8851148851148851,0.8861138861138861
0.17382617382617382,0.18081918081918083
0.6563436563436563,0.6123876123876124
0.03196803196803197,0.030969030969030968
and results/output.csv containing:
V1,V2,idr,fdr
0.9250749250749251,0.9040959040959041,2.8943635779323093e-11,3.413164596618289e-11
0.13586413586413587,0.11288711288711288,1.0884731099315595e-11,2.6764499871550515e-11
0.9820179820179821,0.975024975024975,3.788097008249572e-12,2.6764499871550515e-11
0.7772227772227772,0.7842157842157842,1.2833902510801593e-11,2.6764499871550515e-11
0.6463536463536463,0.6053946053946054,1.8825313694123784e-11,2.6970363458630062e-11
0.8851148851148851,0.8861138861138861,1.0859802750229041e-11,2.6764499871550515e-11
0.17382617382617382,0.18081918081918083,9.586860680232651e-12,2.6764499871550515e-11
0.6563436563436563,0.6123876123876124,2.2037240785360618e-11,2.8731735052621405e-11
0.03196803196803197,0.030969030969030968,4.258939258330211e-12,2.6764499871550515e-11
Python Example
You can also use the Python API:
import torch
import polars as pl
import midr
data = torch.from_numpy(pl.read_csv("data/input.csv").to_numpy())
idr, fdr = midr.compute_idr(data, copula="archmmixture", pseudo_data=True)
ecdf_data = midr.ecdf(data).detach().numpy()
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Release files for midr 2.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| midr-2.0.1.tar.gz | 16.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| midr-2.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size:32.5 kB
Release files / midr-2.0.1.tar.gz
| Download URL | midr-2.0.1.tar.gz |
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| Size | 16.0 kB |
| Tags | Source |
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| Download URL | midr-2.0.1-py3-none-any.whl |
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| Size | 16.5 kB |
| Tags | Python 3 |
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