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AdaFDR

PyPI version

A fast and covariate-adaptive method for multiple hypothesis testing.

Requirement

  • AdaFDR runs on python 3.6 or later

Installation

pip install adafdr

Version History

  • 0.1.8: latest version.
    • The torch dependency was changed to torch==1.4.0.
    • scheduler.step() goes after optimizer.step()
    • add loss.backward(retain_graph=True)
  • 0.1.7: the version released with the paper.

Usage

Import package

adafdr.method contains all methods while adafdr.data_loader contains the data. They can be imported as

import adafdr.method as md
import adafdr.data_loader as dl

Other ways of importing are usually compatible. For example, one can import the package with import adafdr and call method xxx in the method module via adafdr.method.xxx()

Input format

For a set of N hypotheses, the input data includes the p-values p and the d-dimensional covariate x, with the following format:

  • p: (N,) numpy.ndarray.
  • x: (N,d) numpy.ndarray.

When d=1, x is allowed to be either (N,) numpy.ndarray or (N,1) numpy.ndarray.

Covariate visualization

The covariate visualization method adafdr_explore can be used as

adafdr.method.adafdr_explore(p, x, output_folder=None, covariate_type=None)
  • If the output_folder is a filepath (str) instead of None, the covariate visualization figures will be saved in output_folder. Otherwise, they will show up in the console.

  • covariate_type: a length-d python list with values 0/1. It specifies the type of each covariate: 0 means numerical/ordinal while 1 means categorical. For example, covariate_type=[0,1] means there are 2 covariates, the first is numerical/ordinal and the second is categorical. If not specified, a covariate with more than 75 distinct values is regarded as numerical/ordinal and otherwise categorical.

  • See also doc for more details.

Multiple testing

The multiple hypothesis testing method adafdr_test can be used as

  1. fast version (default, recommended for practical usage):
res = adafdr.method.adafdr_test(p, x, alpha=0.1, covariate_type=None)
  1. regular version:
res = adafdr.method.adafdr_test(p, x, alpha=0.1, fast_mode=False, covariate_type=None)
  1. regular version with multi-core:
res = adafdr.method.adafdr_test(p, x, alpha=0.1, fast_mode=False, single_core=False, covariate_type=None)
  • res is a dictionary containing the results, including:
    • res['decision']: a (N,) boolean vector, decision for each hypothesis with value 1 meaning rejection.
    • res['threshold']: a (N,) float vector, threshold for each hypothesis.
  • If the output_folder is a filepath (str) instead of None, the logfiles and some intermediate results will be saved in output_folder. Otherwise, they will show up in the console.

  • covariate_type: a length-d python list with values 0/1. It specifies the type of each covariate: 0 means numerical/ordinal while 1 means categorical. For example, covariate_type=[0,1] means there are 2 covariates, the first is numerical/ordinal and the second is categorical. If not specified, a covariate with more than 75 distinct values is regarded as numerical/ordinal and otherwise categorical.

  • See also doc for more details.

Example on airway RNA-seq data

The following is an example on the airway RNA-seq data used in the paper.

Import package and load data

Here we load the airway data used in the paper. See vignettes for other data accompanied with the package.

import adafdr.method as md
import adafdr.data_loader as dl
p,x = dl.data_airway()

Covariate visualization using adafdr_explore

md.adafdr_explore(p, x, output_folder=None)

p_scatter ratio

Here, the left is a scatter plot of each hypothesis with p-values (y-axis) plotted against the covariate (x-axis). The right panel shows the estimated null hypothesis distribution (blue) and the estimated alternative hypothesis distribution (orange) with respect to the covariate. Here we can conclude that a hypothesis is more likely to be significant if the covariate (gene expression) value is higher.

Multiple hypothesis testing using adafdr_test

res = md.adafdr_test(p, x, fast_mode=True, output_folder=None)

Here, the learned threshold res['threshold'] looks as follows.

p_scatter

Each orange dot corresponds to the threhsold to one hypothesis. The discrepancy at the right is due to the difference between the thresholds learned by the two folds.

Quick Test

Here is a quick test. First check if the package can be successfully imported:

import adafdr.method as md
import adafdr.data_loader as dl

Next, run a small example which should take a few seconds:

import numpy as np
p,x,h,_,_ = dl.load_1d_bump_slope()
res = md.adafdr_test(p, x, alpha=0.1)
t = res['threshold']
D = np.sum(p<=t)
FD = np.sum((p<=t)&(~h))
print('# AdaFDR successfully finished!')
print('# D=%d, FD=%d, FDP=%0.3f'%(D, FD, FD/D))

It runs AdaFDR-fast on a 1d simulated data. If the package is successfully imported, the result should look like:

# AdaFDR successfully finished! 
# D=837, FD=79, FDP=0.094

R API of python package

R API of this package can be found here.

Citation information

Zhang, Martin J., Fei Xia, and James Zou. "Fast and covariate-adaptive method amplifies detection power in large-scale multiple hypothesis testing." Nature communications 10.1 (2019): 1-11.

Xia, Fei, et al. "Neuralfdr: Learning discovery thresholds from hypothesis features." Advances in Neural Information Processing Systems. 2017.

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