from IPython.display import Image
Image('https://raw.githubusercontent.com/jmchilton/ipython_notebooks/master/images/launch_ipython_cropped.png')

# Load IPython magic for R integration.
%load_ext rpy2.ipython
%R library(cummeRbund)

get(72, True) # Download Galaxy history id number 72 as file in current directory with name '72'

%R cuff <- readCufflinks(dbFile='72')  # Load history downloaded item (cuffdiff for cummeRbund)

%R print(fpkmSCVPlot(genes(cuff))) # Demonstrate plotting with R

# Find all differentially expressed genes at a given alpha
%R sig <- getSig(cuff, alpha=0.01, level='genes')
%R sigGenes <- getGenes(cuff,sig) 
%R print(length(sig))

# Print a heatmap here and then save as PNG in Galaxy's history.
%R print(csHeatmap(sigGenes, cluster='both'))
%R png(filename = 'siggene_heatmap.png', width = 900, height = 1000, units = 'px');  \
   print(csHeatmap(sigGenes, cluster='both')); \
   dev.off()
put('siggene_heatmap.png')

# Describe gene density.
%R dens <- csDensity(genes(cuff))
%R print(dens)

# Now pull that dens data structure out of R and make available as a numpy structure in Python
%Rpull dens

# Iterate through it in Python and create a file 'gene_fpkm.tsv' and upload it to Galaxy history.
with open('gene_fpkm.tsv', "w") as f:
  for val in zip(dens[0]['gene_id'], dens[0]['fpkm']):
        f.write("\t".join(map(str, val)) + "\n")
put("gene_fpkm.tsv")

from IPython.display import Image
Image('https://raw.githubusercontent.com/jmchilton/ipython_notebooks/master/images/save_notebook_cropped.png')