tleonardi

# 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

tleonardi

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) +

tleonardi

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

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"])