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generate_illumina_snp_stats.py
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generate_illumina_snp_stats.py
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from fpdf import FPDF
import re
import pandas
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import statistics as stats
import collections
import numpy as np
import seaborn as sns
def illumina_snp_overview(inputFile, pdf, clusterSep, aatmean, aatdev, bbtmean, bbtdev, aarmean, abrmean, bbrmean, callrate, outDir):
print "Running Illumina SNP QC"
snp_qc_table = pandas.read_table(inputFile)
snp_qc_table[['Chr']] = snp_qc_table[['Chr']].astype(str)
chr_strings = [str(chrm) for chrm in range(1, 23)]
chr_strings_nonauto = [str(chrm) for chrm in range(0, 23)]
autosomes_only = snp_qc_table.loc[snp_qc_table['Chr'].isin(chr_strings)] # make a dataframe for chromosomes 1-22
non_autosomes = snp_qc_table.loc[~snp_qc_table['Chr'].isin(chr_strings_nonauto)] # make a dataframe for MT, X, Y, X*Y
missing_chr = snp_qc_table.loc[snp_qc_table['Chr'] == '0'] # make a dataframe for chr 0 only (missing chr values)
# calculates total number of snps analzyed and breaks down by category
total_snps = len(list(snp_qc_table['Name']))
total_autosomes = len(list(autosomes_only['Name']))
total_non_autosomes = len(list(non_autosomes['Name']))
total_missing_chr = len(list(missing_chr['Name']))
print("total snps: {}".format(total_snps))
print("total autosomes: {}".format(total_autosomes))
print("total non autosomes: {}".format(total_non_autosomes))
print("total missing chromosomes: {}".format(total_missing_chr))
columns_for_analysis = []
print " Extracting headers needed for analysis"
# extract all header column names need to SNP analysis
for header in list(snp_qc_table):
if re.search('(.*(Cluster Sep))', header):
clus_sep = re.search('(.*(Cluster Sep))', header)
columns_for_analysis.append(clus_sep.group(0))
elif re.search('(.*(AA T Mean))', header):
AATmean = re.search('(.*(AA T Mean))', header)
columns_for_analysis.append(AATmean.group(0))
elif re.search('(.*(AA T Dev))', header):
AATdev = re.search('(.*(AA T Dev))', header)
columns_for_analysis.append(AATdev.group(0))
elif re.search('(.*(BB T Mean))', header):
BBTmean = re.search('(.*(BB T Mean))', header)
columns_for_analysis.append(BBTmean.group(0))
elif re.search('(.*(BB T Dev))', header):
BBTdev = re.search('(.*(BB T Dev))', header)
columns_for_analysis.append(BBTdev.group(0))
elif re.search('(.*(AA R Mean))', header):
AARmean = re.search('(.*(AA R Mean))', header)
columns_for_analysis.append(AARmean.group(0))
elif re.search('(.*(AB R Mean))', header):
ABRmean = re.search('(.*(AB R Mean))', header)
columns_for_analysis.append(ABRmean.group(0))
elif re.search('(.*(BB R Mean))', header):
BBRmean = re.search('(.*(BB R Mean))', header)
columns_for_analysis.append(BBRmean.group(0))
# snp failture stats across ALL chromosomes
print " Calculating five-number summary statstics"
all_stats = {}
for header_stats in columns_for_analysis:
print header_stats
all_stats[header_stats] = [snp_qc_table[header_stats].median(skipna=True), snp_qc_table[header_stats].mean(skipna=True), snp_qc_table[header_stats].std(skipna=True),
snp_qc_table[header_stats].min(skipna=True), snp_qc_table[header_stats].max(skipna=True)]
print " Extracting failed SNPs"
dicts_to_merge = []
############ cluster separation calculations ###############
# make dataframe of failed SNPs
snps_fail_clus_sep_dataframe = snp_qc_table[snp_qc_table[clus_sep.group(0)] <= float(clusterSep)]
# append string to front of value of failed threshold to prepare for dictionary format
snps_fail_clus_sep_dataframe[clus_sep.group(0)] = 'failed_cluster_separation: '+snps_fail_clus_sep_dataframe[clus_sep.group(0)].astype(str)
# make dictionary of snp (key) and reasons with value of why snp failed (value)
snps_fail_clust_dict = snps_fail_clus_sep_dataframe.set_index('Name')[clus_sep.group(0)].to_dict()
# make dictionary value an iterable to easy merging at end
snps_fail_clust_dict_appendable = {key:[value] for key, value in snps_fail_clust_dict.iteritems()}
# list of snps that fail cluster separation
snps_fail_clus_sep = [key for key in snps_fail_clust_dict]
total_snps_passing_clust = total_snps - len(snps_fail_clus_sep)
# append dictionary to list of dictionaries that need merging
dicts_to_merge.append(snps_fail_clust_dict_appendable)
############### AA_T mean score calculations ##################
snps_fail_AATmean_dataframe = snp_qc_table[snp_qc_table[AATmean.group(0)] > float(aatmean)]
snps_fail_AATmean_dataframe[AATmean.group(0)] = 'failed AAT mean threshold: '+ snps_fail_AATmean_dataframe[AATmean.group(0)].astype(str)
snps_fail_AATmean_dict = snps_fail_AATmean_dataframe.set_index('Name')[AATmean.group(0)].to_dict()
snps_fail_AATmean_dict_appendable = {key:[value] for key, value in snps_fail_AATmean_dict.iteritems()}
snps_fail_AATmean = [key for key in snps_fail_AATmean_dict]
total_snps_passing_AATmean = total_snps - len(snps_fail_AATmean)
dicts_to_merge.append(snps_fail_AATmean_dict_appendable)
################ AA_T dev score calculations ###############
snps_fail_AATdev_dataframe = snp_qc_table[snp_qc_table[AATdev.group(0)] > float(aatdev)]
snps_fail_AATdev_dataframe[AATdev.group(0)] = 'failed AAT dev threshold: ' + snps_fail_AATdev_dataframe[AATdev.group(0)].astype(str)
snps_fail_AATdev_dict = snps_fail_AATdev_dataframe.set_index('Name')[AATdev.group(0)].to_dict()
snps_fail_AATdev_dict_appendable = {key:[value] for key, value in snps_fail_AATdev_dict.iteritems()}
snps_fail_AATdev = [key for key in snps_fail_AATdev_dict]
total_snps_passing_AATdev = total_snps - len(snps_fail_AATdev)
dicts_to_merge.append(snps_fail_AATdev_dict_appendable)
################ BB T mean score calculations ##################
snps_fail_BBTmean_dataframe = snp_qc_table[snp_qc_table[BBTmean.group(0)] < float(bbtmean)]
snps_fail_BBTmean_dataframe[BBTmean.group(0)] = 'failed BBT mean threshold: ' + snps_fail_BBTmean_dataframe[BBTmean.group(0)].astype(str)
snps_fail_BBTmean_dict = snps_fail_BBTmean_dataframe.set_index('Name')[BBTmean.group(0)].to_dict()
snps_fail_BBTmean_dict_appendable = {key:[value] for key, value in snps_fail_BBTmean_dict.iteritems()}
snps_fail_BBTmean = [key for key in snps_fail_BBTmean_dict]
total_snps_passing_BBTmean = total_snps - len(snps_fail_BBTmean)
dicts_to_merge.append(snps_fail_BBTmean_dict_appendable)
################# BB T dev score calculations ######################
snps_fail_BBTdev_dataframe = snp_qc_table[snp_qc_table[BBTdev.group(0)] > float(bbtdev)]
snps_fail_BBTdev_dataframe[BBTdev.group(0)] = 'failed BBT dev treshold: ' + snps_fail_BBTdev_dataframe[BBTdev.group(0)].astype(str)
snps_fail_BBTdev_dict = snps_fail_BBTdev_dataframe.set_index('Name')[BBTdev.group(0)].to_dict()
snps_fail_BBTdev_dict_appendable = {key:[value] for key, value in snps_fail_BBTdev_dict.iteritems()}
snps_fail_BBTdev = [key for key in snps_fail_BBTdev_dict]
total_snps_passing_BBTdev = total_snps - len(snps_fail_BBTdev)
dicts_to_merge.append(snps_fail_BBTdev_dict_appendable)
################# AA R mean score calculations ######################
snps_fail_AARmean_dataframe = snp_qc_table[snp_qc_table[AARmean.group(0)] <= float(aarmean)]
snps_fail_AARmean_dataframe[AARmean.group(0)] = 'failed AAR mean threshold: ' + snps_fail_AARmean_dataframe[AARmean.group(0)].astype(str)
snps_fail_AARmean_dict = snps_fail_AARmean_dataframe.set_index('Name')[AARmean.group(0)].to_dict()
snps_fail_AARmean_dict_appendable = {key:[value] for key, value in snps_fail_AARmean_dict.iteritems()}
snps_fail_AARmean = [key for key in snps_fail_AARmean_dict]
total_snps_passing_AARmean = total_snps - len(snps_fail_AARmean)
dicts_to_merge.append(snps_fail_AARmean_dict_appendable)
################ AB R mean score calculations #######################
snps_fail_ABRmean_dataframe = snp_qc_table[snp_qc_table[ABRmean.group(0)] <= float(abrmean)]
snps_fail_ABRmean_dataframe[ABRmean.group(0)] = 'failed ABR mean threshold: ' + snps_fail_ABRmean_dataframe[ABRmean.group(0)].astype(str)
snps_fail_ABRmean_dict = snps_fail_ABRmean_dataframe.set_index('Name')[ABRmean.group(0)].to_dict()
snps_fail_ABRmean_dict_appendable = {key:[value] for key, value in snps_fail_ABRmean_dict.iteritems()}
snps_fail_ABRmean = [key for key in snps_fail_ABRmean_dict]
total_snps_passing_ABRmean = total_snps - len(snps_fail_ABRmean)
dicts_to_merge.append(snps_fail_ABRmean_dict_appendable)
################# BB R mean score calculations #######################3
snps_fail_BBRmean_dataframe = snp_qc_table[snp_qc_table[BBRmean.group(0)] <= float(bbrmean)]
snps_fail_BBRmean_dataframe[BBRmean.group(0)] = 'failed BBR mean threshold: ' + snps_fail_BBRmean_dataframe[BBRmean.group(0)].astype(str)
snps_fail_BBRmean_dict = snps_fail_BBRmean_dataframe.set_index('Name')[BBRmean.group(0)].to_dict()
snps_fail_BBRmean_dict_appendable = {key:[value] for key, value in snps_fail_BBRmean_dict.iteritems()}
snps_fail_BBRmean = [key for key in snps_fail_BBRmean_dict]
total_snps_passing_BBRmean = total_snps - len(snps_fail_BBRmean)
dicts_to_merge.append(snps_fail_BBRmean_dict_appendable)
# keep track of snps failing and why failing
reasons_snps_fail = {}
# merge all dictionaries without overwriting values
for dictionary in dicts_to_merge:
for key, value in dictionary.iteritems():
try:
reasons_snps_fail.setdefault(key, []).extend(value)
except TypeError: # in the event the dictionary is not iterable (list)
reasons_snps_fail[key].append(value)
# concatenate all failing SNPs
snps_to_remove = list(set(snps_fail_clus_sep + snps_fail_AATmean + snps_fail_AATdev + snps_fail_BBTmean + snps_fail_BBTdev + snps_fail_AARmean + snps_fail_ABRmean + snps_fail_BBRmean))
# calculate passing SNP stats for top table on page
autosomes_remain_table = autosomes_only.loc[~autosomes_only['Name'].isin(snps_to_remove)]
autosomes_remain = len(autosomes_remain_table.index)
non_auto_remain_table = non_autosomes.loc[~non_autosomes['Name'].isin(snps_to_remove)]
non_auto_remain = len(non_auto_remain_table.index)
missing_remain_table = missing_chr.loc[~missing_chr['Name'].isin(snps_to_remove)]
missing_remain = len(missing_remain_table.index)
print " Writing SNP QC to PDF"
# create running title for SNP quality
pdf.add_page()
pdf.set_font('Arial', 'B', 24)
pdf.cell(0, 30, "Illumina SNP Quality Assessment", 0, 1, 'L')
pdf.line(20, 32, 190, 32)
pdf.set_fill_color(200)
pdf.set_font('Arial', 'B', 14)
# writes totals into PDF format
pdf.multi_cell(0, 8, "Total SNPs analyzed: "+str(total_snps), 1, 'L', True)
pdf.set_font('Arial', '', 14)
pdf.set_x(30)
pdf.multi_cell(0, 8, "Total autosomal SNPs analyzed: "+str(total_autosomes), 1, 1, 'L')
pdf.set_x(30)
pdf.multi_cell(0, 8, "Total non-autosomal SNPs analyzed: "+str(total_non_autosomes), 1, 1, 'L')
pdf.set_x(30)
pdf.multi_cell(0, 8, "Total missing chromosome ID SNPs analyzed: "+str(total_missing_chr), 1, 1, 'L')
pdf.set_font('Arial', 'B', 14)
pdf.multi_cell(0, 8, "Total SNPs passing QC: "+str(total_snps - len(snps_to_remove)) + ' ' +
'('+str("%.2f" % round((float(total_snps - len(snps_to_remove))/float(total_snps)) * 100, 2))+'%)' , 1, 'L', True)
pdf.set_font('Arial', '', 14)
try:
pdf.set_x(30)
pdf.multi_cell(0, 8, "Autosomal SNPs remaining: "+str(autosomes_remain) + ' (' +str("%.2f" % round((float(autosomes_remain)/float(total_autosomes))*100, 2))+'%)', 1, 1, 'L')
except ZeroDivisionError:
pdf.set_x(30)
pdf.multi_cell(0, 8, "Autosomal SNPs remaining: "+str(autosomes_remain) + ' (0%)', 1, 1, 'L')
try:
pdf.set_x(30)
pdf.multi_cell(0, 8, "Non-autosomal SNPs remaining: "+str(non_auto_remain) + ' (' +str("%.2f" % round((float(non_auto_remain)/float(total_non_autosomes))*100, 2))+'%)', 1, 1, 'L')
except ZeroDivisionError:
pdf.set_x(30)
pdf.multi_cell(0, 8, "Non-autosomal SNPs remaining: "+str(non_auto_remain) + ' (0%)', 1, 1, 'L')
try:
pdf.set_x(30)
pdf.multi_cell(0, 8, "Missing chromosome ID SNPs remaining: "+str(missing_remain) + ' (' +str("%.2f" % round((float(missing_remain)/float(total_missing_chr))*100, 2))+'%)', 1, 1, 'L')
except ZeroDivisionError:
pdf.set_x(30)
pdf.multi_cell(0, 8, "Missing chromosome ID SNPs remaining: "+str(missing_remain) + ' (0%)', 1, 1, 'L')
pdf.multi_cell(0, 8, '\n\n\n', 0, 1, 'L')
# write cluster sep stats
pdf.set_font('Arial', 'UB', 14)
pdf.multi_cell(0, 10, "Cluster Separation Statistics", 0, 1, 'L')
pdf.set_font('Arial', '', 12)
pdf.multi_cell(0, 8, "Total SNPs passing cluster separation threshold: "+str(total_snps_passing_clust) + ' '
+ '('+str("%.2f" % round((float(total_snps_passing_clust)/float(total_snps))*100, 2))+'%)', 0, 1, 'L')
pdf.multi_cell(0, 8, "Summary Stats on Original Data:")
pdf.set_x(40)
pdf.multi_cell(0, 5, "Median cluster separation: "+ str(all_stats[clus_sep.group(0)][0]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Mean cluster separation: "+ str(all_stats[clus_sep.group(0)][1]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Standard deviation of cluster separation: "+ str(all_stats[clus_sep.group(0)][2]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Minimum cluster separation: "+ str(all_stats[clus_sep.group(0)][3]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Maximum cluster sep: "+ str(all_stats[clus_sep.group(0)][4]), 0, 1, 'L')
# write AA_T mean score stats
pdf.set_font('Arial', 'UB', 14)
pdf.cell(0, 15, "AA T mean score statistics", 0, 1, 'L')
pdf.set_font('Arial', '', 12)
pdf.cell(0, 8, "Total SNPs passing AA T mean threshold: "+str(total_snps_passing_AATmean) + ' '
+ '('+str("%.2f" % round((float(total_snps_passing_AATmean)/float(total_snps))*100, 2))+'%)', 0, 1, 'L')
pdf.multi_cell(0, 8, "Summary Stats on Original Data:")
pdf.set_x(40)
pdf.multi_cell(0, 5, "Median normalized AA theta mean: "+ str(all_stats[AATmean.group(0)][0]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Mean normalized AA theta mean: "+ str(all_stats[AATmean.group(0)][1]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Standard deviation of normalized AA theta mean: "+ str(all_stats[AATmean.group(0)][2]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Minimum normalized AA theta mean: "+ str(all_stats[AATmean.group(0)][3]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Maximum normalized AA theta mean: "+ str(all_stats[AATmean.group(0)][4]), 0, 1, 'L')
# write AA_T dev score stats
pdf.set_font('Arial', 'UB', 14)
pdf.cell(0, 15, "AA T dev score statistics", 0, 1, 'L')
pdf.set_font('Arial', '', 12)
pdf.cell(0, 8, "Total SNPs passing AA T dev threshold: "+str(total_snps_passing_AATdev) + ' '
+ '('+str("%.2f" % round((float(total_snps_passing_AATdev)/float(total_snps))*100, 2))+'%)', 0, 1, 'L')
pdf.multi_cell(0, 8, "Summary Stats on Original Data:")
pdf.set_x(40)
pdf.multi_cell(0, 5, "Median normalized AA theta deviation: "+ str(all_stats[AATdev.group(0)][0]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Mean normalized AA theta deviation: "+ str(all_stats[AATdev.group(0)][1]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Standard deviation of normalized AA theta deviation: "+ str(all_stats[AATdev.group(0)][2]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Minimum normalized AA theta deviation: "+ str(all_stats[AATdev.group(0)][3]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Maximum normalized AA theta deviation: "+ str(all_stats[AATdev.group(0)][4]), 0, 1, 'L')
# write BB_T mean score stats
pdf.set_font('Arial', 'UB', 14)
pdf.cell(0, 15, "BB T mean score statistics", 0, 1, 'L')
pdf.set_font('Arial', '', 12)
pdf.cell(0, 8, "Total SNPs passing BB T mean threshold: "+str(total_snps_passing_BBTmean) + ' '
+ '('+str("%.2f" % round((float(total_snps_passing_BBTmean)/float(total_snps))*100, 2))+'%)', 0, 1, 'L')
pdf.multi_cell(0, 8, "Summary Stats on Original Data:")
pdf.set_x(40)
pdf.multi_cell(0, 5, "Median normalized BB theta mean: "+ str(all_stats[BBTmean.group(0)][0]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Mean normalized BB theta mean: "+ str(all_stats[BBTmean.group(0)][1]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Standard deviation of normalized BB theta mean: "+ str(all_stats[BBTmean.group(0)][2]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Minimum normalized BB theta mean: "+ str(all_stats[BBTmean.group(0)][3]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Maximum normalized BB theta mean: "+ str(all_stats[BBTmean.group(0)][4]), 0, 1, 'L')
# write BB_T dev score stats
pdf.set_font('Arial', 'UB', 14)
pdf.cell(0, 15, "BB T dev score statistics", 0, 1, 'L')
pdf.set_font('Arial', '', 12)
pdf.cell(0, 8, "Total SNPs passing BB T dev threshold: "+str(total_snps_passing_BBTdev) + ' '
+ '('+str("%.2f" % round((float(total_snps_passing_BBTdev)/float(total_snps))*100, 2))+'%)', 0, 1, 'L')
pdf.multi_cell(0, 8, "Summary Stats on Original Data:")
pdf.set_x(40)
pdf.multi_cell(0, 5, "Median normalized BB theta deviation: "+ str(all_stats[BBTdev.group(0)][0]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Mean normalized BB theta deviation: "+ str(all_stats[BBTdev.group(0)][1]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Standard deviation of normalized BB theta deviation: "+ str(all_stats[BBTdev.group(0)][2]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Minimum normalized BB theta deviation: "+ str(all_stats[BBTdev.group(0)][3]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Maximum normalized BB theta deviation: "+ str(all_stats[BBTdev.group(0)][4]), 0, 1, 'L')
# write AA R mean score stats
pdf.set_font('Arial', 'UB', 14)
pdf.cell(0, 15, "AA R mean score statistics", 0, 1, 'L')
pdf.set_font('Arial', '', 12)
pdf.cell(0, 8, "Total SNPs passing AA R mean threshold: "+str(total_snps_passing_AARmean) + ' '
+ '('+str("%.2f" % round((float(total_snps_passing_AARmean)/float(total_snps))*100, 2))+'%)', 0, 1, 'L')
pdf.multi_cell(0, 8, "Summary Stats on Original Data:")
pdf.set_x(40)
pdf.multi_cell(0, 5, "Median normalized AA intesity mean: "+ str(all_stats[AARmean.group(0)][0]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Mean normalized AA intesity mean: "+ str(all_stats[AARmean.group(0)][1]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Standard deviation of normalized AA intesity mean: "+ str(all_stats[AARmean.group(0)][2]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Minimum normalized AA intesity mean: "+ str(all_stats[AARmean.group(0)][3]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Maximum normalized AA intesity mean: "+ str(all_stats[AARmean.group(0)][4]), 0, 1, 'L')
# write AB R mean score stats
pdf.set_font('Arial', 'UB', 14)
pdf.cell(0, 15, "AB R mean score statistics", 0, 1, 'L')
pdf.set_font('Arial', '', 12)
pdf.cell(0, 8, "Total SNPs passing AB R mean threshold: "+str(total_snps_passing_ABRmean) + ' '
+ '('+str("%.2f" % round((float(total_snps_passing_ABRmean)/float(total_snps))*100, 2))+'%)', 0, 1, 'L')
pdf.multi_cell(0, 8, "Summary Stats on Original Data:")
pdf.set_x(40)
pdf.multi_cell(0, 5, "Median normalized AB intesity mean: "+ str(all_stats[ABRmean.group(0)][0]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Mean normalized AB intesity mean: "+ str(all_stats[ABRmean.group(0)][1]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Standard deviation of normalized AB intesity mean: "+ str(all_stats[ABRmean.group(0)][2]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Minimum normalized AB intesity mean: "+ str(all_stats[ABRmean.group(0)][3]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Maximum normalized AB intesity mean: "+ str(all_stats[ABRmean.group(0)][4]), 0, 1, 'L')
# write BB R mean score stats
pdf.set_font('Arial', 'UB', 14)
pdf.cell(0, 15, "BB R mean score statistics", 0, 1, 'L')
pdf.set_font('Arial', '', 12)
pdf.cell(0, 8, "Total SNPs passing BB R mean threshold: "+str(total_snps_passing_BBRmean) + ' '
+ '('+str("%.2f" % round((float(total_snps_passing_BBRmean)/float(total_snps))*100, 2))+'%)', 0, 1, 'L')
pdf.multi_cell(0, 8, "Summary Stats on Original Data:")
pdf.set_x(40)
pdf.multi_cell(0, 5, "Median normalized BB intesity mean: "+ str(all_stats[BBRmean.group(0)][0]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Mean normalized BB intesity mean: "+ str(all_stats[BBRmean.group(0)][1]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Standard deviation of normalized BB intesity mean: "+ str(all_stats[BBRmean.group(0)][2]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Minimum normalized BB intesity mean: "+ str(all_stats[BBRmean.group(0)][3]), 0, 1, 'L')
pdf.set_x(40)
pdf.multi_cell(0, 5, "Maximum normalized BB intesity mean: "+ str(all_stats[BBRmean.group(0)][4]), 0, 1, 'L')
print " ...Finished writing Illumina SNP QC statistics..."
return snps_to_remove, reasons_snps_fail