hurrialice

# test param:
token="cnv"
iclr_appsession="688128634"
wgs_appsession="686817159"

echo "USAGE:"
echo "get_bam_url_iclr <bssh_profile_name> <bssh_iclr_appsession_id>, e.g. get_bam_url_iclr cnv 688128634"
echo "get_bam_url_wgs <bssh_profile_name> <bssh_wgs_appsession_id>, e.g. get_bam_url_wgs cnv 686817159"

get_bam_url_iclr() {

hurrialice

import pandas as pd
import subprocess
import numpy as np
import sys


def interval_divider(bai_path,  bam_path=None, N=123):
    # get reference length and formal contig name
    x = subprocess.check_output("samtools idxstats " + bai_path[:-4], shell=True)
    ref_df = pd.DataFrame([a.split('\t') for a in x.decode().rstrip().split("\n")]).rename(columns={0: "chr", 1: "length"})

hurrialice

import igv_remote as ir
import keyboard
import numpy as np
import pandas as pd

cohort = "BRCA"
print("cohort: {}".format(cohort))
# generate localhost http format
def format(uuid):
    return("http://localhost:5000/" + uuid + ".bam")

hurrialice

#' better display and logging backend
#' 
#' @param expr expression to evalutate
#' @param tag_width The width of display box of the message tag 
#' @param path where the message goes, default to `stdout()`
#' @param save_conditions if `TRUE`, all conditions will be saved to a list
#' @param quiet if `TRUE`, all messages (error excluded) will be suppressed
#' @examples 
#' test_function <- function(){

hurrialice

library(dplyr)
# generate a raw dataframe for all chosen N, p, p1, p2
df_root = expand.grid(N = c(20, 50, 100),
                      p = seq(from = 0.1, to = 0.9, by = 0.1),
                      p1 = seq(from = 0.1, to = 0.9, by = 0.1),
                      p2 = seq(from = 0.1, to = 0.9, by = 0.1))

# number of replicates for 
nsim <- 200

hurrialice

library(dplyr)
# generate a raw dataframe for all chosen N, p, p1, p2
df_root = expand.grid(N = c(20, 50, 100),
                      p = seq(from = 0.1, to = 0.9, by = 0.1),
                      p1 = seq(from = 0.1, to = 0.9, by = 0.1),
                      p2 = seq(from = 0.1, to = 0.9, by = 0.1))

# number of replicates for 
nsim <- 200

hurrialice

library(readr)
library(dplyr)


reader_raw <- read_tsv("RMBase_hg19_all_mod_RBP_reader.txt", comment = "#", skip = 1, col_names = coln, na = "")

coln <- c("chromosome", "modStart", "modEnd", "modId", "score", "strand", "modName", "modType",
          "supportNum", "supportList", "pubmedIds", "geneName", "geneType", "region", "sequence",
          "RBPname", "clipExpNum", "clipExplist", "RBPtype")

hurrialice

# loop around different ranks
rank_universe <- 2:8
coph <- vector("list", length(rank_universe))
cls <- vector("list", length(rank_universe))

# the short version
sm_short <- filter_site_and_genes(sm, reader_sites$modId)

# set the parallel prarams
.assign_metagene <- function(m){

hurrialice

library(methods)
library(readr)
library(Matrix)
library(foreach)
library(iterators)
library(doParallel)
library(dplyr)

# this a universal script for doparallel 
# never forget to stop the clusters in the end fo main scripts.

hurrialice

## ---------- params set -------------
# define core nums to use when use foreach
no_cores <- 10

# relative importance(higher = more on prior info)
alpha = 0.5

## ----------- load dataset --------------
# load dataset and assign variables
W <- readRDS("string_ppi.rds")