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endrebak / SEP1.md
Last active November 8, 2016 07:27

SEP 1 -- Improved and automated logging

Abstract

This Snakemake Enhancement Proposal (SEP) suggests several enhancements of Snakemake's logging and report generating capabilities.

Introduction

Bioinformaticians often have an enormous library of homemade scripts

import pandas as pd
mir_miR_correspondence = "/local/home/annata/mirna.mature.offset0.txt"
mirna_example_file = "/local/home/annata/SHORTREADS/OFFCONTROL/offcontrol-start/Demux.SRhi10002.Adipocyte%20-%20omental%2c%20donor3.SRhi10002_hg19.11475-119C8.GTGAAA.fastq.gz.filter.shortreads"
### READ FILES
mirna_df = pd.read_table(mirna_example_file, sep="\s+", header=None,
names="id1 id2 nb1 mirna_seq score nb2 short_read_seq type end offset".split(), index_col=0)
grouping <- c("A","A","B","B")
design<-model.matrix(~factor(grouping))
#############################################################
### csaw, with its combined window methodology.
############################################################
xparam <- readParam(dedup=FALSE)
switchTab(n) -> {{ RUNTIME('goToTab', {index: n - 1}); }}
map 1 :call switchTab(1)<CR>
map 2 :call switchTab(2)<CR>
map 3 :call switchTab(3)<CR>
map 4 :call switchTab(4)<CR>
map 5 :call switchTab(5)<CR>
map 6 :call switchTab(6)<CR>
map 7 :call switchTab(7)<CR>
map 8 :call switchTab(8)<CR>
map 9 :call switchTab(9)<CR>
@endrebak
endrebak / simes.py
Last active April 24, 2018 07:42
Simes' method Python
import pandas as pd
# A combined P-value was computed for each peak cluster using Simes’ method
# (19). For a cluster containing n windows, the combined P-value is defined as
# p{s}=min{np{r}/r;r=1,2…,n} where the p{r} are the individual window P-values sorted
# in increasing order. This provides weak control of the family-wise error rate
# across the set of null hypotheses for all windows in the cluster. In other
# words, p{s} represents evidence against the global null hypothesis, i.e. that
# no windows in the cluster are DB.
{
"global": {
"check_for_updates_on_startup": true,
"show_in_menu_bar": true,
"show_profile_name_in_menu_bar": false
},
"profiles": [
{
# Works on very large datasets.
import pandas as pd
try:
import mkl
mkl.set_num_threads(1)
except:
pass
# wget http://big.databio.org/example_data/AIList/AIListTestData.tgz
nrows = 1.5e6
from ncls import NCLS
from ailist import AIList
import numpy as np
import pandas as pd
# ctypedef struct ailist_t:
# int64_t nr, mr # Number of regions
# interval_t *interval_list # Regions data
# uint32_t first, last # Record range of intervals
# int nc, lenC[10], idxC[10]
# uint32_t *maxE
# ...
# uint32_t binary_search(interval_t* As, uint32_t idxS, uint32_t idxE, uint32_t qe) nogil
# Author: denis.engemann@gmail.com
# License: simplified BSD (3 clause)
# Note: code is based on scipy.stats.pearsonr
def ss(a, axis):
return np.sum(a * a, axis=axis)
def compute_corr(x, y):
x = np.asarray(x)
y = np.asarray(y)