Swarchal

source_https <- function(url, ...) {
  # load package
  require(RCurl)
 
  # parse and evaluate each .R script
  sapply(c(url, ...), function(u) {
    eval(
        parse(
            text = getURL(u, followlocation = TRUE,
            cainfo = system.file("CurlSSL",

Swarchal

path <- paste(getwd(), "\\", sep = "")
files <- list.files(path=path, pattern="*.csv")
for(file in files)
{
  perpos <- which(strsplit(file, "")[[1]]==".")
  assign(
    gsub(" ","",substr(file, 1, perpos-1)), 
    read.csv(paste(path,file,sep="")))
}

Swarchal

import matplotlib.pyplot as plt
from skimage.io import imread
from skimage.morphology import disk
from skimage.filters import rank
import numpy as np

im_all = imread("/home/scott/Dropbox/wally/ww2.jpg")
all_red = im_all[:, :, 0]
im_wally = imread("/home/scott/Dropbox/wally/wally2.png")
wally_red = im_wally[:, :, 0]

Swarchal

import matplotlib.pyplot as plt
from skimage.io import imread
from skimage.feature import match_template
import matplotlib.pyplot as plt
import numpy as np

image = imread("image.jpg")
wally = imread("wally.png")

# select just the red channel

Swarchal

library(raster)

# load images
image_DNA   <- raster('~/BBBC007_v1_images/A9/A9 p5d.tif')
image_actin <- raster('~/BBBC007_v1_images/A9/A9 p5f.tif')

Swarchal

# display images
plot(image_DNA, col = gray.colors(max(values(image_DNA))))
plot(image_actin, col = gray.colors(max(values(image_actin))))

Swarchal

# merge channels
image_stack <- brick(image_DNA, image_actin)

# display merged image
plotRGB(image_stack, stretch = 'lin')

Swarchal

# change colours of channels
plotRGB(image_stack, stretch = 'lin' g = 2, b = 1)

Swarchal

library(ggplot2)
library(reshape2)

df_expression <- read.csv("expression.csv")
df_molten <- melt(df_expression)
ggplot(data = df_molten,
       aes(x = variable, y = MouseID, fill = value)) + 
    geom_raster() +
    xlab("Protein") + 
    scale_fill_distiller(palette = "RdYlBu", trans = "log10") +

Swarchal

dat <- df_expression[,2:78]  # numerical columns
rownames(dat) <- df_expression[,1]
row.order <- hclust(dist(dat))$order # clustering
col.order <- hclust(dist(t(dat)))$order
dat_new <- dat[row.order, col.order] # re-order matrix accoring to clustering

df_molten_dat <- melt(as.matrix(dat_new)) # reshape into dataframe
names(df_molten_dat)[c(1:2)] <- c("MouseID", "Protein")

ggplot(data = df_molten_dat,