djsutherland

4 # number of states
START
COLD
HOT
END

3 # size of vocab
1
2
3

dsparks

# Alternative to a doubly-nested loop
# Imagine I want to perform an operation on a data frame
# once for each combination of two variables, such as Country and Year
# I can do this with a nested loop, or I can do this with (among other
# things) lapply()

# Generate random data:
allCountries <- LETTERS[1:10]
allYears <- 1990:2012

jwaageSnippets

library("rtracklayer")
session <- browserSession("UCSC")
genome(session)<-"mm9"
query <- ucscTableQuery(session, "refGene")
tableName(query) <- "refGene"
getTable(query) -> refseq
refseq[,c(2,3,4,5,6,7,8,13)] -> refseq
refseq$"width" <- refseq$"txEnd"-refseq$"txStart"

as.character(refseq[,1]) -> refseq[,1]

al2na

library(data.table)
library(genomation)
library(ggplot2)


#' Annotate given ranges with genomic features
#' 
#' The function annotates a target GRangesList or GRanges object as overlapping
#' or not with 
#' the elements of named GRangesList. This is useful to annotate your regions

mtmorgan

geneRanges <- 
    function(db, column="ENTREZID")
{
    g <- genes(db, columns=column)
    col <- mcols(g)[[column]]
    genes <- granges(g)[rep(seq_along(g), elementNROWS(col))]
    mcols(genes)[[column]] <- as.character(unlist(col))
    genes
}

giuseppebonaccorso

import matplotlib.pyplot as plt
import numpy as np

from scipy.integrate import odeint

# Set random seed (for reproducibility)
np.random.seed(1000)

# Start and end time (in milliseconds)
tmin = 0.0

slavailn

# test Jaccard

# Create a list gene sets
char1 <- c("gene1", "gene2", "gene5", "gene9", "gene10")
char2 <- c("gene2", "gene3", "gene5", "gene7", "gene10")
char3 <- c("gene7", "gene9", "gene10", "gene11", "gene12", "gene1")

lst <- list(char1, char2, char3)

# Function to calculate Jaccard distance

Balharbi

library(ChIPpeakAnno)
library(biomaRt)
library(genomation)
library(dplyr)


# Read in metilene results 
res <- read.table("results_de_novo_all_10.txt", sep = "\t", header = T)
head(res)

Balharbi

# Alternative to a doubly-nested loop
# Imagine I want to perform an operation on a data frame
# once for each combination of two variables, such as Country and Year
# I can do this with a nested loop, or I can do this with (among other
# things) lapply()

# Generate random data:
allCountries <- LETTERS[1:10]
allYears <- 1990:2012