dpryan79

#!/usr/bin/env python
import pyBigWig
import argparse
import os.path
import sys

parser = argparse.ArgumentParser(description="Subset a single chromosome from a bigWig file.")
parser.add_argument("--headerToo", action="store_true", help="Subset the header too, which can expedite some downstream programs but possibly cause problems for others.")
parser.add_argument("input", help="Input bigWig file.")
parser.add_argument("output", help="Output bigWig file.")

clairemcwhite

library(tidygraph)
library(tidyverse)
library(ggraph)
library(dendextend)
library(igraph)

palette_OkabeIto <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#999999")

cut_df <- function(dendrogram, height, c){

GuangchuangYu

## devtools::install_github("stephenturner/msigdf")
library(msigdf)
library(dplyr)
library(clusterProfiler)

c2 <- msigdf.human %>% 
    filter(collection == "c2") %>% select(geneset, entrez) %>% as.data.frame

data(geneList)
de <- names(geneList)[1:100]

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats