Bader Alharbi Balharbi

💭

@mengyang2000 your coding style very intresting, mind if I ask abt repo DSS

Aspire to be a lead researcher. learning modular thinking Interest: Research; mainly Genomics/Methylation/epigenetics/ Ph.D. Candidate at GMU

Balharbi / Avoiding a loop.R

Created June 28, 2020 01:44 — forked from dsparks/Avoiding a loop.R

lapply() as an alternative to a multiply-nested loop

	# 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

Balharbi / annotate_metilene_regions.R

Created March 7, 2020 17:10 — forked from slavailn/annotate_metilene_regions.R

annotate RRBS metilene regions

	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)

slavailn / jaccard_genes_cluster.R

Last active August 26, 2020 14:50

Cluster sets of genes based on Jaccard distance (mostly stolen from Biostars post)

	# 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

giuseppebonaccorso / hodgkin-huxley-main.py

Created August 19, 2017 15:06

Hodgkin-Huxley spiking neuron model in Python

	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

mtmorgan / grangesToSymbol

Created July 29, 2016 20:50

Extract ranges of genes, and map ranges to corresponding genes

	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
	}

al2na / annotateWithFeatures.R

Last active June 10, 2020 09:31

new annotation functions for genomation,

	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

jwaageSnippets / gist:5133941

Created March 11, 2013 12:34

Get refseq genes from UCSC, and extract longest transcripts for each gene symbol

	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]

dsparks / Avoiding a loop.R

Created September 12, 2012 13:19

lapply() as an alternative to a multiply-nested loop

	# 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

djsutherland / example.hmm

Created November 1, 2011 05:10

Code for a Hidden Markov Model, along with some sample data / parameters for testing.