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# Analyse data using a sliding window | |
slideFunct <- function(data, window, step){ | |
total <- length(data) | |
spots <- seq(from=1, to=(total-window), by=step) | |
result <- vector(length = length(spots)) | |
for(i in 1:length(spots)){ | |
result[i] <- median(data[spots[i]:(spots[i]+window)]) | |
} | |
return(result) | |
} |
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git clone <repo-address> | |
git tag -l | |
git checkout <tag-name> | |
git branch -D master | |
git checkout -b master |
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# Sets chmod 755 for folder and all subfolders | |
find /opt/lampp/htdocs -type d -exec chmod 755 {} \; | |
# Sets chmod 655 for all files in this folder & subfolder | |
find /opt/lampp/htdocs -type f -exec chmod 644 {} \; |
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# Sets chmod 755 for folder and all subfolders | |
find /opt/lampp/htdocs -type d -exec chmod 755 {} \; | |
# Sets chmod 655 for all files in this folder & subfolder | |
find /opt/lampp/htdocs -type f -exec chmod 644 {} \; |
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import subprocess | |
import glob | |
# Functions | |
def read_multifasta(file_path): | |
is_entry = False | |
fasta_dict = {} | |
sequence = [] |
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def check_output(file, interval_time, logfile_path): | |
import os | |
import time | |
status = os.path.isfile(file) | |
while status is not True: | |
f_log = open(logfile_path, 'a') | |
f_log.write(time.asctime() + '\t' + 'Still running\n') |
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################################################################# | |
######################## TMHMM Parser ########################### | |
################################################################# | |
# Created by: Kevin Leiss | |
# Last Updated: 14.12.2016 | |
# | |
# License: Feel free to use the script, but please refer to me if | |
# you used it for publication. | |
# |
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#!/bin/bash | |
# The following script parses the 10x chromoium sparse matrix. | |
# It replaces the First column with the ENSEMBL gene ID and the second, | |
# if needed, with the cell barcode (just uncomment the second awk script). | |
# It needs the three 10x chromium outputs as follows: | |
# 1. genes.tsv | |
# 2. matrix.mtx | |
# 3. barcodes.tsv |
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library(rlc) | |
library(matrixStats) | |
library(biomaRt) | |
library(Matrix) | |
library(umap) | |
#sobj <- readRDS("make_analysis_out/SN010_E115/SN010_E115_normalized.rds") | |
makeENSMEBLlink <- function(geneID){ | |
sprintf( | |
"<a href='http://www.ensembl.org/Mus_musculus/Gene/Summary?db=core;g=%s' target='_blank'>%s</a>", |
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# Taken from Simon | |
# compute variances across column or rows for column-sparse matrices | |
library(Matrix) | |
colVars_spm <- function( spm ) { | |
stopifnot( is( spm, "dgCMatrix" ) ) | |
ans <- sapply( seq.int(spm@Dim[2]), function(j) { | |
mean <- sum( spm@x[ (spm@p[j]+1):spm@p[j+1] ] ) / spm@Dim[1] | |
sum( ( spm@x[ (spm@p[j]+1):spm@p[j+1] ] - mean )^2 ) + | |
mean^2 * ( spm@Dim[1] - ( spm@p[j+1] - spm@p[j] ) ) } ) / ( spm@Dim[1] - 1 ) | |
names(ans) <- spm@Dimnames[[2]] |
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