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Lukas Käll percolator

  • Science for Life Laboratory, KTH
  • Stockholm, Sweden
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def bootstrap(invec):
idx = npr.randint(0, len(invec), len(invec))
return [invec[i] for i in idx]
def estimatePi0(p, numBoot=100, numLambda=100, maxLambda=0.95):
p.sort()
n=len(p)
lambdas=np.linspace(maxLambda/numLambda,maxLambda,numLambda)
Wls=np.array([n-np.argmax(p>=l) for l in lambdas])
pi0s=np.array([Wls[i] / (n * (1 - lambdas[i])) for i in range(numLambda)])
@percolator
percolator / qvalues.py
Last active November 25, 2018 09:20
Snippet generating q values from p values
def bootstrap(invec):
idx = npr.randint(0, len(invec), len(invec))
return [invec[i] for i in idx]
def estimatePi0(p, numBoot=100, numLambda=100, maxLambda=0.95):
p.sort()
n=len(p)
lambdas=np.linspace(maxLambda/numLambda,maxLambda,numLambda)
Wls=np.array([n-np.argmax(p>=l) for l in lambdas])
pi0s=np.array([Wls[i] / (n * (1 - lambdas[i])) for i in range(numLambda)])
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PAM250 = {
'A': {'A': 2, 'C': -2, 'D': 0, 'E': 0, 'F': -3, 'G': 1, 'H': -1, 'I': -1, 'K': -1, 'L': -2, 'M': -1, 'N': 0, 'P': 1, 'Q': 0, 'R': -2, 'S': 1, 'T': 1, 'V': 0, 'W': -6, 'Y': -3},
'C': {'A': -2, 'C': 12, 'D': -5, 'E':-5, 'F': -4, 'G': -3, 'H': -3, 'I': -2, 'K': -5, 'L': -6, 'M': -5, 'N': -4, 'P': -3, 'Q': -5, 'R': -4, 'S': 0, 'T': -2, 'V': -2, 'W': -8, 'Y': 0},
'D': {'A': 0, 'C': -5, 'D': 4, 'E': 3, 'F': -6, 'G': 1, 'H': 1, 'I': -2, 'K': 0, 'L': -4, 'M': -3, 'N': 2, 'P': -1, 'Q': 2, 'R': -1, 'S': 0, 'T': 0, 'V': -2, 'W': -7, 'Y': -4},
'E': {'A': 0, 'C': -5, 'D': 3, 'E': 4, 'F': -5, 'G': 0, 'H': 1, 'I': -2, 'K': 0, 'L': -3, 'M': -2, 'N': 1, 'P': -1, 'Q': 2, 'R': -1, 'S': 0, 'T': 0, 'V': -2, 'W': -7, 'Y': -4},
'F': {'A': -3, 'C': -4, 'D': -6, 'E':-5, 'F': 9, 'G': -5, 'H': -2, 'I': 1, 'K': -5, 'L': 2, 'M': 0, 'N': -3, 'P': -5, 'Q': -5, 'R': -4, 'S': -3, 'T': -3, 'V': -1, 'W': 0, 'Y': 7},
'G': {'A': 1, 'C': -3, 'D': 1, 'E': 0, 'F': -5, 'G': 5, 'H': -2, 'I': -3, 'K': -
@percolator
percolator / generate_expression_data.py
Last active October 20, 2017 13:00
A sniplet generating data simulating a high throughput expression analysis experiment
import numpy as np
import numpy.random as npr
import pandas as pd
def generate_expression_data(n_analytes=100, n_samples=2, n_replicates=3, p_regulated=0.2, mean_offset=3.0, var=0.2, diff_var=2.0):
# Here we follow a convension, The first sample is the reference i.e. have all label 1
labels = npr.binomial(1, p_regulated, (n_analytes,n_samples-1))
template = np.hstack((np.zeros((n_analytes,1)),labels))
# We expand the template labels into several replicates
@percolator
percolator / index.html
Last active September 8, 2017 12:11
homa-ir.block
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Bokeh Plot</title>
<link rel="stylesheet" href="https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css" type="text/css" />
<script type="text/javascript" src="https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.js"></script>
#!/usr/bin/env python
# coding: utf-8
"""
Test of file to see if it confirms to the iPRG2016 study instructios
Usage: checkiPRGResultFile.py -i input-file [-f fasta-file]
If no fasta file given, iPRG2016.fasta will be used
"" "A1" "A2" "A3" "B1" "B2" "B3" "C1" "C2" "C3" "D1" "D2" "D3"
"HPRR4110123" 0.9499281264973647 1.0 0.9592384671711008 1.0 0.9522402757262433 1.0 1.0 1.0 1.0 0.8138319260152794 0.67724609375 1.0
"HPRR260088" 0.0 1.0 1.0 1.0 1.0 1.0 0.0021141649048625794 1.0 1.0 1.0 1.0 1.0
"HPRR3730024" 0.0 0.0 0.0 0.0 0.0 0.0 0.0021141649048625794 0.0 0.0 1.0 1.0 1.0
"HPRR3730022" 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.9590454692034828 1.0 1.0 1.0
"HPRR4160392" 0.9499281264973647 1.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.5327816677275621 1.0 0.4713896457765668
"HPRR4290990" 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
"HPRR4290991" 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
"HPRR4290996" 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
"HPRR4290997" 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
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