tleonardi

import pandas as pd
import numpy as np
from scipy.signal import find_peaks
from collections import OrderedDict

test = "GMM_logit_pvalue"
df = pd.read_csv("out_nanocompore_results.tsv", sep="\t")
df["Peak"] = 0 
df = df[["pos", "chr", "genomicPos", "ref_id", "strand", "ref_kmer", "Peak", test]]
transcripts = set(df["ref_id"])

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from scipy.stats import chi2
import numpy as np

def combine_pvalues_hou(pvalues, weights, cor_mat):
    """ Hou's method for the approximation for the distribution of the weighted
        combination of non-independent or independent probabilities.
        If any pvalue is nan, returns nan.
        https://doi.org/10.1016/j.spl.2004.11.028
        pvalues: list of pvalues to be combined
        weights: the weights of the pvalues

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plotDispEsts2 <- function(dds){
        require(dplyr)
        require(reshape2)
        require(ggplot2)
        as.data.frame(mcols(dds)) %>% 
        select(baseMean, dispGeneEst, dispFit, dispersion) %>% 
                melt(id.vars="baseMean") %>% 
                filter(baseMean>0) %>% 
                ggplot(aes(x=baseMean, y=value, colour=variable)) + 
                        geom_point(size=0.1) +

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# Converts a tab-separated file containing
# DAVID's functional annotation results to
# Markdown format
# It reports: KEGG and GO terms along with
# the gene symbol and name

FILE=
cut -f 1,2,5,6,7,9 $FILE | awk '
BEGIN{OFS=FS="\t"}NR>1{
print "#"$1

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#!/bin/awk -f
function logfactorial(n,    f, i){
  f=0
  for(i=1;i<=n;i++){
    f+=log(i)
   }
  return f
}

# m: Mean

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# Tunnel Display
# This function sets up an ssh tunnel that forwards a local display port
# back to the login node's display port. The tunnel is controlled by the
# socket in $HOME/tmp/ssh-${LSB_SUB_HOST}-${HOSTNAME}-${DISPLAY_NUMBER}.
# The ssh tunnel is in the background and keeps running even after the
# interactive shell is closed, thus preventing completion of the LSF job.
# To avoid this, we setup a trap on SIGINT SIGTERM EXIT that uses the ssh
# control socket to signal the tunnel to exit.
tund(){
if [[ -n $LSB_JOBID ]]; then

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#Flags and scores in Tophat output

Tophat version 2.1.1

There are two functions 'bool rewrite_sam_record()', with different sets of parameters.

The first one is used for singletons the second for paired alignements:

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Keybase proof

I hereby claim:

• I am tleonardi on github.
• I am tleonardi (https://keybase.io/tleonardi) on keybase.
• I have a public key whose fingerprint is E6BA BA6A 0F8A 5B99 24B9 25E0 5FB4 E671 D50A 5A4E

To claim this, I am signing this object:

tleonardi

```{r set-options}
  my_hook <- function(x, options) {
    if (options$fig.show == 'animate') return(hook_plot_html(x, options))
    "%n%" <- knitr:::"%n%"
    base = opts_knit$get('base.url') %n% ''
    cap = knitr:::.img.cap(options)

    if (is.null(w <- options$out.width) & is.null(h <- options$out.height) &
      is.null(s <- options$out.extra) & options$fig.align == 'default') {
      return(sprintf('![%s](%s%s){#fig:%s} ', cap, base, knitr:::.upload.url(x), options$label))

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tanimoto <- function(x, similarity=F) {
	res<-sapply(x, function(x1){
		sapply(x, function(x2) {i=length(which(x1 & x2)) / length(which(x1 | x2)); ifelse(is.na(i), 0, i)})
	})
	if(similarity==T) return(res)
	else return(1-res)
}

x <- data.frame(Samp1=c(0,0,0,1,1,1,0,0,1), Samp2=c(1,1,1,1,1,1,0,0,1), Samp3=c(1,0,0,1,0,0,0,0,0), Samp4=c(1,1,1,1,1,1,1,1,1))
tanimoto(x)