orange-bioinformatics / docs / reference / obiProb.htm

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59``` ``` obiProb: Probability distributions and corrections

obiProb Probability distributions and corrections

obiProb provides the functionality to calculate probability distributions and corrections for multiple hypothesis testing.

Binomial

A class for computing binomial distribution probabilities. Binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p

Methods

__call__(k, N, m, n)
if m out of N experiments are positive return the probability that k out of n experiments are positive using the binomial distribution. (i.e. if p = m/N then return bin(n,k)*(p**k + (1-p)**(n-k)) where bin is the binomial coefficient)
p_value(k, N, m, n)
the probability that k or more tests are positive using the binomial distribution

Hypergeometric

A class for computing hypergeometric distribution probabilities. Hypergeometric distribution is a discrete probability distribution that describes the number of successes in a sequence of n draws from a finite population without replacement

Methods

__call__(k, N, m, n)
if m out of N experiments are positive return the probability that k out of n experiments are positive using the hypergeometric distribution. (i.e. return bin(m, k)*bin(N-m, n-k)/bin(N,n) where bin is the binomial coefficient)
p_value(k, N, m, n)
the probability that k or more tests are positive using the hypergeometric distribution

FDR

A function for preforming False Discovery Rate correction on a ordered list of p-values

Arguments

p_values
an ordered list of p-values
dependent (default False)
use correction for dependent hypotheses
m (default len(p_values))
number of hypotheses tested

Bonferroni

A function for performing Bonferroni correction on a list of p-values

Arguments

p_values
a list of p-values
m (default len(p_values))
number of hypotheses tested
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