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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]]