cdiener

ES <- function(p, w, pws, both=FALSE) {
    n <- length(pws)
    nr <- sum(abs(w[pws == p]))
    nh <- sum(pws == p)

    scores <- vector(length=n)
    scores[pws == p] <- abs(w[pws == p])/nr
    scores[pws != p] <- -1/(n - nh)
    r <- range(cumsum(scores))
    i <- which.max(abs(r))

cdiener

#/usr/bin/env python

from cobra.test import create_test_model
from cobra.flux_analysis import single_gene_deletion

cobra_model = create_test_model("textbook")
dels = {"b0008": 0.87, "b0114": 0.71, "b0116": 0.56, "b2276": 0.11, "b1779": 0.00}

rates, statuses = single_gene_deletion(cobra_model, gene_list=dels.keys(),
    method="moma", solver="mosek")

cdiener

Rcpp::sourceCpp("matrix_reduce.cpp")

#' Reduces an ExpressionSet by an n-to-n map of features to groups. All entries
#' in \code{features} must exist in \code{eset}. \code{features} and 
#' \code{groups} must have the same length.
#'
#' @param eset An ExpressionSet object.
#' @param features A character vector of features to be grouped.
#' @param groups A factor or character vector mapping the entries in 
#'  \code{features} to groups.

cdiener

# Caching operator executes command if there is no saved version of the data
# just delete the saved file and code will be run again

'%c%' = function(ex, file) if(file.exists(file), env=parent.frame(4)) load(file) else evalq(ex))

# Example

# first execution
{ x <- rnorm(1e6); save(x, file="cache.Rd") } %c% "cache.Rd" # executes the sampling

cdiener

library(scales)
library(Hmisc)


emsa_plot = ggplot(emsa, aes(x=Name, y=intensity, shape=charge_ratio, col=charge_ratio, width=0.8)) + 
      geom_point(size=3, position=position_dodge(width=0.8)) +  
      stat_summary(fun.data=mean_sdl, mult=0, color="black", geom="errorbar", size=1, position=position_dodge(width=0.8)) +
      scale_color_grey(start=0, end=0.5,name="charge ratio") +
      scale_shape(name="charge ratio") +  
      scale_y_continuous(limits=c(-0.05,1), breaks=seq(0,1,by=0.2), label=percent) +