Yann Abraham yannabraham

## beanplots.R
## reproduce the figures from http://www.jstatsoft.org/v28/c01/paper using ggplot2

library(ggplot2)

## parameters
set.seed(2710)

## Figure 1
d <- rnorm(50)

## doseResponsePython.ipynb

      
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                yannabraham
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                How to do Dose/Response curve fitting in Python for Drug Discovery
              
          
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## createFlowFrame.R
library(Biobase)
library(flowCore)
library(flowViz)

# simply replace the dummy dta below with your CSV data
dta <- matrix(rnorm(10000),ncol=10)
dimnames(dta)[[2]] <- LETTERS[1:10]

head(dta)

## cytometry_fan.R
## build a fan plot from scratch using dplyr & ggplot2 for visualization of Flow data
## after an original idea by Guy J. Abel
## http://gjabel.wordpress.com/category/r/fanplot/

## libraries
library(flowCore)
library(reshape2)
library(dplyr)
library(ggplot2)
library(RColorBrewer)

## complex_summary_w_tapply.R
data(warpbreaks)

my.summary <- function(vals) {
	return(c(avg=mean(vals),sd=sd(vals)))
}

sum.warpbreaks <- with(warpbreaks,tapply(breaks,list(tension,wool),my.summary))

sum.warpbreaks <- array(unlist(sum.warpbreaks),
	dim=c(length(sum.warpbreaks[1,1][[1]]),dim(sum.warpbreaks)),

## cdlist_parser.R
library(stringr)

screwed <- readLines(con='http://www.uniprot.org/docs/cdlist.txt')
screwed <- screwed[76:521]

parser <- c(0,8,21,29,37,55,1000000L) # use fixed length parsing

screwed <- lapply(screwed,function(scr) {
        sapply(seq(length(parser)-1),function(i) str_trim(substr(scr,parser[i]+1,parser[i+1])))
    }

## andrews.R
## https://en.wikipedia.org/wiki/Andrews_plot
library(dplyr)
library(ggplot2)

do.andrews <- function(x,breaks=30) {
  require(reshape2)

  t <- seq(-pi, pi, pi/breaks)
  m<-nrow(x)
  n<-ncol(x)

## inverted_fan_plots.R
library(ggplot2)
library(dplyr)
library(tidyr)
library(cytofan)
library(bodenmiller)

## let's prepare some data:
cur.mat <- cbind(rbind(refPhenoMat,untreatedPhenoMat),
                 rbind(refFuncMat,untreatedFuncMat))
cur.annot <- bind_rows(refAnnots %>%

## FontLineGenomicsTalk.R
#
# Author: ABRAHYA1
###############################################################################

rm(list=ls())
graphics.off()

## libraries
library(ggplot2)
library(Radviz)

## 20211105_NextGenOmicsTalk.Rmd
---
title: "NextGen OMICS 2021 - The Polyfunctionality Challenge"
author: "Yann Abraham"
date: "2021-11-05"
params:
  fold.lim:
    label: the limit applied to absolute fold change
    value: 1
    input: numeric
  sig.lim:
	## reproduce the figures from http://www.jstatsoft.org/v28/c01/paper using ggplot2

	library(ggplot2)

	## parameters
	set.seed(2710)

	## Figure 1
	d <- rnorm(50)
	library(Biobase)
	library(flowCore)
	library(flowViz)

	# simply replace the dummy dta below with your CSV data
	dta <- matrix(rnorm(10000),ncol=10)
	dimnames(dta)[[2]] <- LETTERS[1:10]

	head(dta)
	## build a fan plot from scratch using dplyr & ggplot2 for visualization of Flow data
	## after an original idea by Guy J. Abel
	## http://gjabel.wordpress.com/category/r/fanplot/

	## libraries
	library(flowCore)
	library(reshape2)
	library(dplyr)
	library(ggplot2)
	library(RColorBrewer)
	data(warpbreaks)

	my.summary <- function(vals) {
	return(c(avg=mean(vals),sd=sd(vals)))
	}

	sum.warpbreaks <- with(warpbreaks,tapply(breaks,list(tension,wool),my.summary))

	sum.warpbreaks <- array(unlist(sum.warpbreaks),
	dim=c(length(sum.warpbreaks[1,1][[1]]),dim(sum.warpbreaks)),
	library(stringr)

	screwed <- readLines(con='http://www.uniprot.org/docs/cdlist.txt')
	screwed <- screwed[76:521]

	parser <- c(0,8,21,29,37,55,1000000L) # use fixed length parsing

	screwed <- lapply(screwed,function(scr) {
	sapply(seq(length(parser)-1),function(i) str_trim(substr(scr,parser[i]+1,parser[i+1])))
	}
	## https://en.wikipedia.org/wiki/Andrews_plot
	library(dplyr)
	library(ggplot2)

	do.andrews <- function(x,breaks=30) {
	require(reshape2)

	t <- seq(-pi, pi, pi/breaks)
	m<-nrow(x)
	n<-ncol(x)
	library(ggplot2)
	library(dplyr)
	library(tidyr)
	library(cytofan)
	library(bodenmiller)

	## let's prepare some data:
	cur.mat <- cbind(rbind(refPhenoMat,untreatedPhenoMat),
	rbind(refFuncMat,untreatedFuncMat))
	cur.annot <- bind_rows(refAnnots %>%
	#
	# Author: ABRAHYA1
	###############################################################################

	rm(list=ls())
	graphics.off()

	## libraries
	library(ggplot2)
	library(Radviz)
	---
	title: "NextGen OMICS 2021 - The Polyfunctionality Challenge"
	author: "Yann Abraham"
	date: "2021-11-05"
	params:
	fold.lim:
	label: the limit applied to absolute fold change
	value: 1
	input: numeric
	sig.lim: