A visualization of the RNA frequency in principal component space, the "clip" function is specific to fluidigm's BioMark platform. This code outputs a .m file containing matrices to be plotted in mat-lab with mesh(mat).

GeneFields.py

###########################################################    ____                   ______ _      _     _     
##   Author : Kevin Nelson                             ####  / ____|                 |  ____(_)    | |   | |    
#					                ### | |  __  ___ _ __   ___  | |__   _  ___| | __| |___ 
##Projecting gene Fields into principal Component space#### | | |_ |/ _ \ '_ \ / _ \ |  __| | |/ _ \ |/ _` / __|
########################################################### | |__| |  __/ | | |  __/ | |    | |  __/ | (_| \__ \
###########################################################  \_____|\___|_| |_|\___| |_|    |_|\___|_|\__,_|___/    

import os
import numpy as np
import sys
import mdp
import csv
import thread
import time

n = 50
has = 0		
needs = 0
lle=True

def is_number(s):
	try:
		float(s)
		return True
	except ValueError:
		return False

def boolienize(data):
	BData={}
	n=0
	for k in range(0,len(data),1):					#boolienize Data
		row=[]
		for p in data[k]:
			if(is_number(p)):
				if(float(p)>0):
					row.append(1)
				else:
					row.append(0)
			else:
				row.append(p)
		BData[k]=row
	return BData

def clip(D):
	BM96={}
	n=0
	b=12
	while(is_number(D[b][2])):
		b+=1
	for k in range(11,b):
		row=[]
		for p in range (1,98,1):
			if(is_number(D[k][p])):
				if(float(D[k][p])>0 and float(D[k][p])!=999):
					row.append(float(D[k][p]))
				else:
					row.append(0)
			else:
				row.append(D[k][p])
		BM96[k-11]=row
	return BM96

def readFileC(n):
	try:
		fileIN=sys.argv[n]						#This is only a kosher tactic if you don't name your files numerically
	except(TypeError):
		fileIN=n
	Data={}
	with open(fileIN) as csvfile:
		tableMain=csv.reader(csvfile,delimiter=',')
		k=0
		for row in tableMain:
			Data[k]=row
			k+=1							#Read the Data into dict Data
	return(Data)

def readFiles():
	Data={}
	files = [f for f in os.listdir('.') if os.path.isfile(f)]
	print '1.1 - ReadingFiles:'
	for f in files:
		if f[0]=='_':
			Data[f]=boolienize(clip(readFileC(f)))			#Mark files for input with "_" as the first charactor in their name
	return Data
def FindMin(a):
	Min=None
	for k in a:
		Min = k if(k < Min or Min is None)else Min
	return Min

def FindMax(a):
	Max=None
	for k in a:
		Max = k if(k > Max or Max is None)else Max
	return Max

def EucAB(a,b):
	dist=0.
	if(len(a)!=len(b)):raise ValueError('Vectors are not of similar dimensionality.')
	for k in range(0,len(a)):
		dist+=(a[k]-b[k])**2
	#dist=dist**0.5
	return(dist)

def PCA(data):
	NBD=np.zeros((len(data)-1,len(data[0])-1))
	for k in range(1,len(data),1):
		row=[]
		for k1 in range(1,len(data[0]),1):
			if(is_number(data[k][k1])):
				row.append(float(data[k][k1]))
		NBD[k-1]=row
	pca=mdp.pca(NBD,svd=True)
	return pca

def octPrt(sdmB,name):
	op=name+'=['
	for k in sdmB:
		if(op[len(op)-1]==','):op=op[0:len(op)-1]+';'
		for k2 in sdmB[k]:
			op += str(sdmB[k][k2])
			op += ','
	if(op[len(op)-1]==','):op=op[0:len(op)-1]+';'
	op+=']\n'
	return(op)

def octPrts(array4d):
	s=""
	for k in array4d:
		for k2 in array4d[k]:
			temp = str(k)[0:len(str(k))-4]
			s+=octPrt(array4d[k][k2],temp+"_"+k2)
	return(s)
	
def geneFields(Data):
	global has
	global needs
	PCAs={}
	Util={}
	GeneFields={}
	for f in Data:
		PCAs[f]=PCA(Data[f])
		Util[f]={}
		Util[f]['Pc1Min']=FindMin(PCAs[f][0:len(PCAs[f])][0])
		Util[f]['Pc1Max']=FindMax(PCAs[f][0:len(PCAs[f])][0])
		Util[f]['Pc2Min']=FindMin(PCAs[f][0:len(PCAs[f])][1])
		Util[f]['Pc2Max']=FindMax(PCAs[f][0:len(PCAs[f])][1])
		Util[f]['Pc1TestPoints']=np.zeros(n+1)
		Util[f]['Pc2TestPoints']=np.zeros(n+1)
		temp=(Util[f]['Pc1Max']-Util[f]['Pc1Min']+2)/n								#Makes test points with a buffer on each side of the data.
		for k in range(0,n+1):
			Util[f]['Pc1TestPoints'][k]=Util[f]['Pc1Min']-1.+k*temp
		temp=(Util[f]['Pc2Max']-Util[f]['Pc2Min']+2)/n
		for k in range(0,n+1):
			Util[f]['Pc2TestPoints'][k]=Util[f]['Pc1Min']-1.+k*temp
		
		GeneFields[f]={}
		for k0 in range (1,len(Data[f][0])):									#for each gene
			g = Data[f][0][k0]
			print (f + " : " + g)
			GeneFields[f][g]={}
			##Def
			if(lle):		
				thread.start_new_thread(makeField,(Data,f,g,Util,PCAs,k0,GeneFields))
				needs=needs+1
			else:	
				_k=0
				for k in Util[f]['Pc1TestPoints']:
					GeneFields[f][g][_k]={}
					_k2=0
					for k2 in Util[f]['Pc2TestPoints']:							#for each pair of test points
						top=0.
						bottom=0.
						for k3 in range(1,len(Data[f])):						#for each sample
							temp = EucAB([k,k2],[PCAs[f][k3-1][0] , PCAs[f][k3-1][1]])
							top+=Data[f][k3][k0]/temp
							bottom+=1/temp
						GeneFields[f][g][_k][_k2]=top/bottom
						_k2=_k2+1
					_k=_k+1
	was=-1
	while(has<needs):
		time.sleep(1)
		#print str(has)+"/"+str(needs)
		if(was!=has):
			print str(has)+"/"+str(needs)
			was=has
	return(GeneFields)

def makeField(Data,f,g,Util,PCAs,k0,GeneFields):
	_k=0
	for k in Util[f]['Pc1TestPoints']:
		GeneFields[f][g][_k]={}
		_k2=0	
		for k2 in Util[f]['Pc2TestPoints']:							#for each pair of test points
			top=0.
			bottom=0.
			for k3 in range(1,len(Data[f])):						#for each sample
				temp = EucAB([k,k2],[PCAs[f][k3-1][0] , PCAs[f][k3-1][1]])
				top+=Data[f][k3][k0]/temp
				bottom+=1/temp
			GeneFields[f][g][_k][_k2]=top/bottom
			_k2=_k2+1
		_k=_k+1
	global has
	has=has+1
	pass

def doEverything():
	output=open("geneFields.m","w")
	output.write(octPrts(geneFields(readFiles())))
	output.close()

print "importsComplete"
doEverything()
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