### OASIS - A Novel GWAS Analysis Method 
### Copyright (C) GPL-3.0 (2016) Dr. Mohammad Saeed

## Citation: Mohammad Saeed (2017). Novel linkage disequilibrium clustering algorithm identifies new lupus genes on meta-analysis of GWAS datasets. Immunogenetics.

## This program is free software: you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 3 of the License, or
## (at your option) any later version.

## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
## GNU General Public License for more details.

## The GNU General Public License can be accessed at https://opensource.org/licenses/GPL-3.0.

# Installing libraries

# Import libraries

# Download these libraries: numpy, matplotlib, six, dateutil, pyparsing, ptz
# http://stackoverflow.com/users/5179477/mohammad-saeed
# Then click on the link: Can't install Matplotlib for Python


from __future__ import print_function
from datetime import date
import math
import numpy as np

# comment out next two lines if you wish to view the graphs instead of saving them
import matplotlib
matplotlib.use('Agg')

import matplotlib.pyplot as plt

## User Info

print ('OASIS - A Novel GWAS Analysis Method')
print ('Copyright (C) GPL-3.0 (2016) Dr. Mohammad Saeed')
print ('Contact: rheumdocpk@gmail.com or saeed.khan@arkanalabs.com')
print ('')
print ('Please cite OASIS paper in published works as:')
print ('Mohammad Saeed (2017). Novel linkage disequilibrium clustering algorithm identifies new lupus genes on meta-analysis of GWAS datasets. Immunogenetics.')
print ('')
print ('The GNU General Public License can be accessed at https://opensource.org/licenses/GPL-3.0.')
print ('')
print ('')
print ('For OASIS you need these libraries: numpy, matplotlib, six, dateutil, pyparsing, ptz')
print ('If you have any difficulty please refer to http://stackoverflow.com/users/5179477/mohammad-saeed')
print ("For detailed instructions click on the link: Can't install Matplotlib for Python")
print ('')
print ('')
print ('OASIS input file (.csv) has to be in the following format:')
print ('The .csv Excel sheet has to sorted according to chromosome and position.')
print ('Data has to be in the following 4 columns (without header)')
print ('Chromosome, SNP, Position (bp), P-value')
print ('')
print ('')
print ('OASIS Module 3 input file (.csv) has to be in the following format:')
print ('This input file is prepared by merging QRD.txt files for two GWAS from OASIS Analysis (Modules 1 & 2)')
print ('This is the reason for performing OASIS Analysis in separate folders by placing the OASIS program file in them') 
print ('The file has to have a single line header with the variables below:')
print ('Serial, Dataset, Initial_chr, Initial_loc, Initial_SNP, End_loc, End_SNP, Max_SNP, Max_-log(p), OASIS, Oasis_percent, Quadrant')
print ('')
print ('')
print ('Please be advised that data entry is generally case sensitive (capital vs simple letters)')
print ('')
print ('')

## Global variables

# A Dictionary (var) that holds as keys the basepair locations of SNPs in a single chromosome
# A List (chrom) that holds the numbers of chromosomes whose data is present in the input file

chrom = []
var = {}

# glop and goasis are lists for storing the entire gwas data of these two variables for determining the graph axes later
glop = []
goasis = []

# These are chromosome specific lists for storing the relevant variables and are nullified at each cycle (reading of one chromosome)
snp_pos = []
lop = []
oasis_scr = []
chrom_rng = range(1, 23)

# These variables are for labeling chromosome graphs. They do not have any calculation function though bar* are generated by mean+sd calculations on glop/goasis
c = 1
bar_lop = 0
bar_oasis = 0



## OASIS Module 1

# Define a function 'ch_chk' which checks i.e reads the number of chromosomes in the PLINK / dbGAP GWAS file
# It stores these numbers in the list called chrom

def ch_chk():
    global chrom
    fgwas = open (fname)
    
    for line_chk in fgwas:
        line_chk = line_chk.rstrip()
        
        # Splits the read line from the input file into 'variables' 
        var_chk = line_chk.split(',')

        # Appends the chromosome number to the list 'chrom' as the input file is read
        if var_chk[0] not in chrom:
            chrom.append(var_chk[0])
            
    fgwas.close()
    return chrom


## Define a 'read_file' function which:
# Reads the input file line by line, identifies the variables and
# Store variables in a dictionary called var with location as key
# Thus all relevant data for each chromosome is stored in the dictionary 'var' - this process is iterated over the list chrom
# Therefore the dictionary var is made for one chromosome at a time (and reset after 'oasis_process')
# The function 'oasis_process' is called to process data in the dictionary 'var' one chromosome at a time

def read_file():

    # Imports the chromsome list 'chrom' and the dictionary 'var'
    
    global var, chrom

    # Iterates over the list of chromosomes i.e stores all relevant data for one chromosome at a time in the dictionary 'var'
    
    for chr_n in chrom:
        chint = int(chr_n)
        
        fgwas = open (fname)      
        for line in fgwas:
            line = line.rstrip()
            variant = line.split(',')
            chno = int(variant[0])

            # At its turn each chromosome's relevant data (chromosome number, snp, chi square, p-value, odd ratio) is stored agaist the key (pos)
            
            if chno == chint:
                pos = int(variant[2])
                var[pos] = (variant[0], variant[1], variant[3])
                # chromosome, snp, position, p-value
            
        
        fgwas.close()
        print ("        Chromosome: ", chint)
        
        # Calls the function oasis_process to perform the required calculations
        
        oasis_process()

        # Resets the chromosome dictionary 'var'
        
        var = {}
    return

        
    
## Process the variables in the chromosome dictionary as oasis blocks
# This is the main OASIS calculator

def oasis_process():

    # Imports the chromosome dictionary and sets local variables
    
    global var
    oasis = 0
    epos = 0
    total_snp = 0
    last_pos = 0
    block = {}

    # Main Loop for reading the chromosome dictionary sorted by the basepair location key (pos)
    
    for pos, (chm, snp, pval) in sorted(var.items()):
        loc = int(pos)
        p = float(pval)
        
        # Loop to calculate oasis within the window
        
        if epos > 0 and loc <= epos:
            if p <= 0.05:
                total_snp += 1
                oasis += 1
                block[p] = (snp) 

            # Continue to add number of SNPs for final calculation of Oasis_percent
            
            elif p > 0.05:
                total_snp += 1

        # Loop activated when window has ended
        # It prints the end of block stats to the file
        # It also resets all local variables
        
        elif epos > 0 and loc > epos:
            mx_p = min(block)
            mx_snp = block[mx_p]
            mx_lg_p = math.log(mx_p, 10)*-1
            oasis_pc = (oasis *100 / total_snp)
            print (pos, snp, mx_snp, mx_lg_p, oasis, oasis_pc, file=fout)
            last_pos = loc
            total_snp = 0
            oasis = 0
            epos = 0
            block = {}

        # Initiates OASIS block and determines window size
        # This happens only on a new read from 'var' ensured by the expression 'last_pos != loc'
        
        if epos == 0 and p <= 0.05 and last_pos != loc:
            bpos = loc
            epos = loc + win*10**3
            print (chm, pos, snp, end=' ', file=fout)
            total_snp += 1
            oasis += 1
            block[p] = (snp)
                               
    # Activated for the last oasis block in the chromosome dictionary when the block is less than the window size
    
    if oasis > 0:
        mx_p = min(block)
        mx_snp = block[mx_p]
        oasis_pc = (oasis *100 / total_snp)
        print (pos, snp, mx_snp, mx_lg_p, oasis, oasis_pc, file=fout)
       


## OASIS Module 2

## Generate data from the GWAS dataset for the graph function    

def xy():

    global snp_pos, oasis_scr, lop, c, bar_lop, bar_oasis, glop, goasis, f_result

    # Iterates over the list of chromosomes i.e stores all relevant data for one chromosome at a time in respective lists

    print ("Creating graphs for:")
    print ("")
    
    for ch_no in chrom_rng:
        c = ch_no
                
        foasis = open (f_result)
        head = 7
        while head>0:
            foasis.readline()
            head = head -1
                
        for line in foasis:
            line = line.rstrip()
            v = line.split()
            chno = int(v[0])

        # At its turn each chromosome's relevant data (chromosome position, p-value, oasis) is stored against their respective lists

            l = int(v[1])
            p = float(v[6])
            b = int(v[7])
            
            glop.append(p)
            goasis.append(b)
            
            if chno == ch_no:
## In case you want to limit the oasis_scr or lop data you can arbitrary set cut offs here
##                if p >= 1 or b >= 1:
                snp_pos.append(l)
                lop.append(p)
                oasis_scr.append(b)
                
        
        foasis.close()

        # Calculates the 3-sigma rule cut off for -log[p] and oasis_scr

        mlop = np.mean(glop)
        sdlop = np.std(glop)
        moasis = np.mean(goasis)
        sdoasis = np.std(goasis)
        r3s_lop = mlop + (3*sdlop)
        r3s_oasis = moasis + (3*sdoasis)
        bar_lop = float("{0:.1f}".format(r3s_lop))
        bar_oasis = float("{0:.1f}".format(r3s_oasis))
        
        # Calls the graph function to generate the graphs for each chromosome

        print ("        Chromosome: ", c)
        graph()
        plt.close()
        
        # Resets the chromosome lists

        snp_pos = []
        lop = []
        oasis_scr = []
        
    return snp_pos, lop, oasis_scr, c, bar_lop, bar_oasis, glop, goasis



## Create graphs for OASIS and -log[p] data  

def graph():
    global snp_pos, oasis_scr, lop, bar_lop, bar_oasis
    x = max(snp_pos)+10**7
    y = max(glop)+1
    plt.figure()
    plt.subplot(211)
    plt.plot(snp_pos, lop, 'bo')
    plt.axis([0, x, 0, y])
    plt.plot((0, x), (bar_lop, bar_lop), 'm--')
    plt.title('Chromosome: %s' % c)
    plt.grid(True)
    plt.ylabel('- log [P]')
    plt.subplot(212)
    yy = max(goasis)+1
    plt.plot(snp_pos, oasis_scr, 'ro')
    plt.axis([0, x, 0, yy])
    plt.plot((0, x), (bar_oasis, bar_oasis), 'm--')
    plt.grid(True)
    plt.xlabel('Position (bp)')
    plt.ylabel('OASIS')
    plt.tight_layout()
    # comment out next line if you wish to view the graphs instead of saving them
    plt.savefig('chr%s' % c)
    plt.figure()
    plt.title('Chromosome: %s' % c)
    plt.scatter(oasis_scr, lop, c = u'r')
    plt.axis([0, yy, 0, y])
    plt.plot((bar_oasis, bar_oasis), (0, y), 'm--')
    plt.plot((0, yy), (bar_lop, bar_lop), 'm--')
    plt.grid(True)
    plt.xlabel('OASIS')
    plt.ylabel('- log [P]')
    # comment out next line if you wish to view the graphs instead of saving them
    plt.savefig('chr%s_scatter' % c)

    # New code for full GWAS scatter plot
    plt.figure()
    plt.title(r'GWAS (3$\sigma$) scatter plot')
    plt.scatter(goasis, glop, c=u'b', marker='s')
    plt.axis([0, yy, 0, y])
    plt.plot((bar_oasis, bar_oasis), (0, y), 'r--')
    plt.plot((0, yy), (bar_lop, bar_lop), 'r--')
    plt.grid(True)
    plt.xlabel('OASIS')
    plt.ylabel('- log [P]')
    # comment out next line if you wish to view the graphs instead of saving them
    plt.savefig('GWAS_scatter')
    
    plt.show()
    plt.close()


## Open OASIS results file and categorize Quadrants A, B, C

def quads():
    
    foasis = open (f_result)
    head = 7
    while head>0:
        foasis.readline()
        head = head -1
        
    fqrd= open ("QRD.txt", 'w')
    print ("OASIS outfile file:", f_result, file=fqrd)
    print ('Stats:   3-sigma(-log[p]):', bar_lop, '   3-sigma(oasis):', bar_oasis, file=fqrd)
    print ('Axis_loc', 'Initial_chr', 'Initial_loc', 'Initial_SNP', 'End_loc', 'End_SNP', 'Max_SNP', 'Max_-log(p)', 'OASIS', 'Oasis_percent', 'Quadrant', file=fqrd)


    a_axis = []
    b_axis = []
    c_axis = []
    a_qrd = []
    b_qrd = []
    c_qrd = []

    for line in foasis:
        line = line.rstrip()
        var = line.split()

        cr = float(var[0])
        iloc = float(var[1])
        xloc = float(cr + (iloc / 10**9))
            
        lp = float(var[6])
        ob = int(var[7])

           
        if lp >= bar_lop and ob >= bar_oasis:
            print (xloc, line, "A", file=fqrd)
            a_qrd.append(lp)
            a_axis.append(xloc)
            
        elif lp >= bar_lop:
            print (xloc, line, "B", file=fqrd)
            b_qrd.append(lp)
            b_axis.append(xloc)
            
        elif ob >= bar_oasis:
            print (xloc, line, "C", file=fqrd)
            c_qrd.append(lp)
            c_axis.append(xloc)

    foasis.close()
    fqrd.close()

    amax = max(a_qrd)+1
    bmax = max(b_qrd)+1
    cmax = max(c_qrd)+1
    ymax = max(amax, bmax, cmax)

    plt.figure()

    plt.subplot(311)
    plt.title(r'Quadrant A: Oasis and p-value >> 3$\sigma$')
    plt.plot(a_axis, a_qrd, 'ro')
    plt.axis([0, 23, 0, ymax])
    plt.grid(b=True, which='major', color='k', linestyle='-')
    plt.grid(b=True, which='minor', color='k', linestyle='-', alpha=0.2)
    plt.minorticks_on()
    plt.ylabel('-log[p]')

    plt.subplot(312)
    plt.title(r'Quadrant B: p-value >> 3$\sigma$')
    plt.plot(b_axis, b_qrd, 'go')
    plt.axis([0, 23, 0, ymax])
    plt.grid(b=True, which='major', color='k', linestyle='-')
    plt.grid(b=True, which='minor', color='k', linestyle='-', alpha=0.2)
    plt.minorticks_on()
    plt.ylabel('-log[p]')

    plt.subplot(313)
    plt.title(r'Quadrant C: Oasis >> 3$\sigma$')
    plt.plot(c_axis, c_qrd, 'bo')
    plt.axis([0, 23, 0, ymax])
    plt.grid(b=True, which='major', color='k', linestyle='-')
    plt.grid(b=True, which='minor', color='k', linestyle='-', alpha=0.2)
    plt.minorticks_on()
    plt.xlabel('Chromosome position')
    plt.ylabel('-log[p]')
    plt.tight_layout()
    plt.savefig('GWAS_Quadrants')
    plt.show()



## OASIS Module 3
# Module 3 is the program Maplink
# Maplink allows users to group together OASIS results from two GWAS datasets into a single html table and highlighting (*) regions from different datasets within 200kb  
# Maplink has to be run directly after loading this OASIS program by calling this function in the Python shell thus: maplink()
# The input file for Maplink is a .csv table combining QRD results from two GWAS datasets (this file has to be prepared manually by merging two QRD.txt files in Excel)
# Two columns have to be added to this .csv file viz 'Serial' and 'Dataset'
# The 'Dataset' symbols should be inserted after pasting the two QRD files into a single sheet (one dataset QRD results stacked over the other)
# The file should be sorted according to chromosome and position and then numbered serially in the 'Serial column'


def maplink():
    
    # Inserting Mapview hyperlinks to OASIS results (Composite QRD Excel (.csv) sheet)

    ## Open file and read content line by line

    print ("")
    
    fn_qrd = raw_input('Please enter name of file (.csv) with composite OASIS (QRD) GWAS results: ')
    qrd_name = str(fn_qrd+'.csv')

    fmap = open (qrd_name, 'r')
    fmap.readline()

    ## Store values in variables (dictionary)
    oamap = {}
    title = ['Serial', 'Dataset', 'Initial_chr', 'Initial_loc', 'Initial_SNP', 'End_loc', 'End_SNP', 'Max_SNP', 'Max_-log(P)', 'OASIS', 'Oasis%', 'Quadrant', 'Select', 'Mapview_link']

    db1 = 0
    db2 = 0
    last_pos1 = 0
    last_pos2 = 0
    select = ""
    slt = 0
    how_far=0
    chrm = 1

    for hit in fmap:
        hit = hit.rstrip()
        var = hit.split(',')
        serial = float(var[0])
        oamap[serial] = (var[1], var[2], var[3], var[4], var[5], var[6],var[7], var[8], var[9], var[10], var[11])

    fmap.close()

    ## Links

    fn_html = raw_input('Please enter name of HTML output file (with Mapview links): ')
    html_name = str(fn_html+'.html')

    fhtml = open (html_name, 'w')

    datcode = raw_input('Please enter the code for the dataset to be highlighted in the HTML table: ')

    total_hit = (len(oamap)) # prints the total number of oasis hits / records

    series = oamap.keys()
    series.sort()

    part1 ="http://www.ncbi.nlm.nih.gov/projects/mapview/maps.cgi?TAXID=9606&CHR="
    part2="&MAPS=genes-r%2Csnp-r&QUERY="
    part3="%2BOR%2B"
    part4="&BEG="
    part5="M&END="
    part6="M&oview=default"

    # Preparing HTML file headers

    print ('<table><align="center">', file=fhtml)
    print ('<TABLE BORDER="3" CELLSPACING="1" CELLPADDING="5">', file=fhtml)
    print ('<tr bgcolor="#006400"><td align="center" colspan="14"><B><H1><p style="color:#FFFFFF";>OASIS</B></H1><H4><I><p style="color:#FFFFFF";>A Novel GWAS Meta-Analysis Algorithm by Dr. Mohammad Saeed</I></H4></td></tr>', file=fhtml)
    print ('<align="center">', file=fhtml)
    print ('<tr bgcolor="#9ACD32"><td>'+title[0]+'</td><td>'+title[1]+'</td><td>'+title[2]+'</td><td>'+title[3]+'</td><td>'+title[4]+'</td><td>'+title[5]+'</td><td>'+title[6]+'</td><td>'+title[7]+'</td><td>'+title[8]+'</td><td>'+title[9]+'</td><td>'+title[10]+'</td><td>'+title[11]+'</td><td>'+title[12]+'</td><td>'+title[13]+'</td></tr>', file=fhtml)


    # Printing the data to the HTML file as a Table

    serial = 1

    for ser in series:
        region = oamap[ser]

        beg=(float(region[2])/10**6)-1
        begr = round(beg, 1)
        begn = str(begr)
        enz=(float(region[4])/10**6)+1
        enzr=round(enz, 1)
        endz = str(enzr)

        maplnk = part1+region[1]+part2+region[3]+part3+region[5]+part3+region[6]+part4+begn+part5+endz+part6

        snp_pos = int(region[4])

        if serial == 1:
            db1 = region[0]
            last_pos1 = snp_pos
            how_far = 5*10**6
            
        elif serial == 2:
            db2 = region[0]
            last_pos2 = snp_pos
            how_far = last_pos2 - last_pos1
            
        else:
            db2 = region[0]
            last_pos2 = snp_pos
            how_far = last_pos2 - last_pos1

        if chrm == region[1]:
            if db1 != db2 and how_far < 2*10**6:
                select = "**"
                slt += 1
        else:
            chrm = region[1]
            how_far = 5*10**6
            
        if region[0]==datcode:
            print ('<tr bgcolor="#FFF8DC"><td>'+str(serial)+'</td><td align="center">'+region[0]+'</td><td align="center">'+region[1]+'</td><td>'+region[2]+'</td><td>'+region[3]+'</td><td>'+region[4]+'</td><td>'+region[5]+'</td><td>'+region[6]+'</td><td>'+region[7]+'</td><td align="center">'+region[8]+'</td><td align="center">'+region[9]+'</td><td align="center">'+region[10]+'</td><td>'+select+'</td><td align="center">'+'<a href='+maplnk+'>map</a>'+'</td></tr>', file=fhtml)

        else:
            print ('<tr><td>'+str(serial)+'</td><td align="center">'+region[0]+'</td><td align="center">'+region[1]+'</td><td>'+region[2]+'</td><td>'+region[3]+'</td><td>'+region[4]+'</td><td>'+region[5]+'</td><td>'+region[6]+'</td><td>'+region[7]+'</td><td align="center">'+region[8]+'</td><td align="center">'+region[9]+'</td><td align="center">'+region[10]+'</td><td>'+select+'</td><td align="center">'+'<a href='+maplnk+'>map</a>'+'</td></tr>', file=fhtml)

        serial += 1
        select = ""

        if serial > 2:
            db1 = db2
            last_pos1 = last_pos2
        
    total_select = str(slt)
    total_hits = str(total_hit)

    print ('</table>', file=fhtml)
    print ('<tr><td align="left">Regions replicated: '+total_select+'</td></tr>', file=fhtml)
    print ('<tr><td align="left">Total Hits: '+total_hits+'</td></tr>', file=fhtml)

    fhtml.close()
    raise SystemExit()
    


## Command and control function
# It does not contain any significant code but calls the above functions in an ordered fashion

# Check if User wishes to use Module 3 only

module3 = raw_input('Do you wish to use Module 3 (Maplink) [M3] or proceed to OASIS Analysis [M1] (M1 OR M3): ')
if module3 == "M3" or module3 == "m3":
    maplink()
print ("")
        
# Open the PLINK / dbGAP GWAS association results file using User input

fn = raw_input('Please enter name of file (.csv) with PLINK / dbGAP GWAS association results: ')
fname = str(fn+'.csv')

print ("")


# Open the OASIS results output file using User input

fo = raw_input('Please enter name of OASIS results output file:')
f_result = str(fo+'.txt')
fout = open (f_result, 'w')

print ("")


# User to determine the OASIS window size

win = int(raw_input('Please enter window size in kb (default 200kb):'))

print ("")
        
def Main():

    print ('Intializing OASIS')
    print ("")
    print ('OASIS - Novel Method of GWAS Analysis. Dr. Mohammad Saeed. Copyright (C) GNU GPL-3.0 (2016)', file=fout)
    print ("", file=fout)
    print ('OASIS Analysis dated:', date.today(), file=fout)
    print ("", file=fout)
    print ('Window Size:', win, 'kb',  file=fout)
    print ("", file=fout)
    print ('Initial_chr', 'Initial_loc', 'Initial_SNP', 'End_loc', 'End_SNP', 'Max_SNP', 'Max_-log(p)', 'OASIS', 'Oasis_percent', file=fout)
    ch_chk()
    read_file()
    fout.close()
    print ("")
    print ("")
    xy()
    quads()
    


## Starts the program

Main()