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Trying to write a genomic library in Rust

endrebak.ada endrebak

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Trying to write a genomic library in Rust
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@endrebak
endrebak / SEP1.md
Last active November 8, 2016 07:27
View SEP1.md
View pandas_example.py
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)
View csaw_example.R
grouping <- c("A","A","B","B")
design<-model.matrix(~factor(grouping))
#############################################################
### csaw, with its combined window methodology.
############################################################
xparam <- readParam(dedup=FALSE)
View cvim
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
View simes.py
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.
View karabiner.json
{
"global": {
"check_for_updates_on_startup": true,
"show_in_menu_bar": true,
"show_profile_name_in_menu_bar": false
},
"profiles": [
{
View heatmap.py
# Works on very large datasets.
import pandas as pd
try:
import mkl
mkl.set_num_threads(1)
except:
pass
View compare_ai_and_ncls.py
# 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
View ailist.pyx
# 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
View pearson_vectors.py
# 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)