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Bloodtypes of PGP Participants





In [85]:



    


%matplotlib inline
import matplotlib.pyplot as plt
import sqlite3
import pandas as pd
import numpy as np
from scipy import stats
import sqlite3



    











In [86]:



    


# Set default plot stype for matplotlib
plt.style.use('ggplot')

# Set default figure size for matplotlib
plt.rcParams['figure.figsize'] = (16, 8)



    











In [87]:



    


# connect to untap database, found here: 
#https://workbench.su92l.arvadosapi.com/collections/22d61dd43786c65cd175b04ad6954af0+3119/html/index.html#

conn = sqlite3.connect('Database/untap.db')
c = conn.cursor()
c.execute('SELECT * FROM demographics')
rows = c.fetchall()
data = pd.DataFrame(rows,columns=zip(*c.description)[0])
conn.close()
data



    


















    
Out[87]:










  
    

      
      id
      human_id
      date_of_birth
      gender
      weight
      height
      blood_type
      race
    

  
  
    

      0
      1
      hu43860C
      1954-08-28 (61 years old)
      Male
      246lbs (112kg)
      6ft 5in (195cm)
      O+
      White
    

    

      1
      2
      huC30901
      1962-05-23 (53 years old)
      Male
      175lbs (79kg)
      6ft 2in (187cm)
      O-
      White
    

    

      2
      3
      hu9385BA
      1955-07-05 (60 years old)
      Male
      155lbs (70kg)
      
      A+
      
    

    

      3
      4
      hu04FD18
      1954-09-18 (61 years old)
      Male
      
      
      O+
      White
    

    

      4
      5
      huAE6220
      1949-04-24 (66 years old)
      Male
      
      
      B+
      White
    

    

      5
      6
      hu604D39
      1958-01-19 (58 years old)
      Male
      245lbs (111kg)
      5ft 8in (172cm)
      AB+
      Black or African American
    

    

      6
      7
      hu6E4515
      1921-07-09 (94 years old)
      Male
      180lbs (82kg)
      6ft (182cm)
      O+
      White
    

    

      7
      8
      huA90CE6
      1958-09-25 (57 years old)
      Male
      
      
      
      White
    

    

      8
      9
      hu34D5B9
      1973-01-20 (43 years old)
      
      170lbs (77kg)
      5ft 11in (180cm)
      
      
    

    

      9
      10
      hu2FEC01
      1985-10-06 (30 years old)
      Male
      143lbs (65kg)
      5ft 9in (175cm)
      A+
      White
    

    

      10
      11
      huAA16BD
      1927-11-17 (88 years old)
      
      
      
      
      
    

    

      11
      12
      hu2DBF2D
      1982-12-07 (33 years old)
      Male
      190lbs (86kg)
      5ft 10in (177cm)
      O-
      White
    

    

      12
      13
      hu342A08
      1943-09-18 (72 years old)
      Female
      160lbs (73kg)
      5ft 9in (175cm)
      
      White
    

    

      13
      14
      hu4339C0
      
      
      
      
      
      
    

    

      14
      15
      hu72A81D
      1968-11-01 (47 years old)
      Female
      145lbs (66kg)
      5ft 10in (177cm)
      A+
      White
    

    

      15
      16
      hu0E64A1
      1985-02-20 (30 years old)
      Male
      120lbs (54kg)
      
      
      White
    

    

      16
      17
      hu3CAB43
      1967-11-30 (48 years old)
      Male
      115lbs (52kg)
      5ft 6in (167cm)
      
      White
    

    

      17
      18
      hu2D6140
      1942-01-05 (74 years old)
      Female
      142lbs (64kg)
      5ft 6in (167cm)
      O+
      White
    

    

      18
      19
      huA0E089
      1942-11-22 (73 years old)
      Female
      139lbs (63kg)
      5ft 4in (162cm)
      A+
      White
    

    

      19
      20
      hu8229AE
      1969-09-11 (46 years old)
      Female
      153lbs (69kg)
      5ft 2in (157cm)
      O+
      White
    

    

      20
      21
      huCA017E
      1949-03-19 (66 years old)
      Male
      142lbs (64kg)
      5ft 6in (167cm)
      A+
      Asian
    

    

      21
      22
      hu92C40A
      1944-01-13 (72 years old)
      Female
      132lbs (60kg)
      5ft 7in (170cm)
      
      American Indian or Alaska Native
    

    

      22
      23
      hu38168C
      1949-07-08 (66 years old)
      Female
      105lbs (48kg)
      5ft 3in (160cm)
      O+
      Asian
    

    

      23
      24
      huB1FD55
      1958-09-19 (57 years old)
      Male
      
      
      
      White
    

    

      24
      25
      huD81F3D
      1969-04-24 (46 years old)
      Male
      210lbs (95kg)
      6ft 3in (190cm)
      A+
      White
    

    

      25
      26
      huD37D14
      1970-02-20 (45 years old)
      Female
      147lbs (67kg)
      5ft 6in (167cm)
      O+
      White
    

    

      26
      27
      huBAAC98
      1980-07-30 (35 years old)
      Male
      240lbs (109kg)
      6ft 2in (187cm)
      O+
      White
    

    

      27
      28
      huB4940E
      
      
      
      
      
      
    

    

      28
      29
      huEC6EEC
      1964-07-10 (51 years old)
      Female
      180lbs (82kg)
      5ft 4in (162cm)
      O+
      Hispanic or Latino
    

    

      29
      30
      huF5E0B6
      1971-12-12 (44 years old)
      Male
      185lbs (84kg)
      6ft (182cm)
      A+
      White
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      1303
      1304
      hu4A6650
      1943-10-26 (72 years old)
      Male
      227lbs (103kg)
      5ft 11in (180cm)
      A+
      White
    

    

      1304
      1305
      hu594129
      1962-02-20 (53 years old)
      Male
      163lbs (74kg)
      5ft 11in (180cm)
      B+
      White
    

    

      1305
      1306
      huB256A2
      1982-04-14 (33 years old)
      
      115lbs (52kg)
      
      
      
    

    

      1306
      1307
      hu7D6AB1
      1948-07-30 (67 years old)
      Female
      93lbs (42kg)
      5ft (152cm)
      O+
      Asian
    

    

      1307
      1308
      huFEC65A
      1969-11-25 (46 years old)
      Male
      
      
      
      White
    

    

      1308
      1309
      huF9A043
      1957-01-08 (59 years old)
      
      162lbs (73kg)
      
      
      
    

    

      1309
      1310
      huF9C3EE
      1985-11-20 (30 years old)
      Male
      170lbs (77kg)
      5ft 10in (177cm)
      A+
      Asian
    

    

      1310
      1311
      huD0127A
      1964-05-22 (51 years old)
      
      212lbs (96kg)
      
      
      
    

    

      1311
      1312
      hu5C4EBA
      1966-10-27 (49 years old)
      
      270lbs (122kg)
      
      
      
    

    

      1312
      1313
      huD66092
      1948-01-02 (68 years old)
      
      116lbs (53kg)
      5ft 4in (162cm)
      
      
    

    

      1313
      1314
      hu039674
      1981-04-18 (34 years old)
      Male
      165lbs (75kg)
      5ft 6in (167cm)
      
      White
    

    

      1314
      1315
      hu941A10
      1946-03-01 (69 years old)
      Female
      140lbs (64kg)
      5ft 3in (160cm)
      
      White
    

    

      1315
      1316
      hu1D278B
      1976-11-26 (39 years old)
      Male
      190lbs (86kg)
      5ft 11in (180cm)
      O+
      Hispanic/Latino
    

    

      1316
      1317
      hu045168
      1980-06-04 (35 years old)
      
      143lbs (65kg)
      5ft 9in (175cm)
      
      
    

    

      1317
      1318
      hu2561FE
      1955-01-08 (61 years old)
      
      
      5ft 9in (175cm)
      
      
    

    

      1318
      1319
      huD8A5B6
      1956-01-28 (60 years old)
      
      184lbs (83kg)
      
      
      
    

    

      1319
      1320
      hu2CA680
      1964-12-24 (51 years old)
      
      140lbs (64kg)
      
      
      
    

    

      1320
      1321
      hu631D32
      1937-10-19 (78 years old)
      Male
      156lbs (71kg)
      5ft 7in (170cm)
      AB+
      White
    

    

      1321
      1322
      hu934217
      1962-10-01 (53 years old)
      Male
      191lbs (87kg)
      5ft 6in (167cm)
      O+
      White
    

    

      1322
      1323
      hu524B5B
      1961-05-26 (54 years old)
      Female
      147lbs (67kg)
      5ft 3in (160cm)
      O+
      White
    

    

      1323
      1324
      hu78E6A9
      1963-05-20 (52 years old)
      Female
      142lbs (64kg)
      5ft 5in (165cm)
      O+
      White
    

    

      1324
      1325
      huDF8970
      1975-07-16 (40 years old)
      
      
      
      
      
    

    

      1325
      1326
      hu313A20
      1982-05-29 (33 years old)
      Male
      165lbs (75kg)
      6ft (182cm)
      O+
      White
    

    

      1326
      1327
      huFBD57F
      1962-03-27 (53 years old)
      Male
      201lbs (91kg)
      5ft 9in (175cm)
      A+
      White
    

    

      1327
      1328
      huC5203C
      1978-12-28 (37 years old)
      Female
      98lbs (44kg)
      5ft (152cm)
      
      White
    

    

      1328
      1329
      huABB4CD
      1975-01-08 (41 years old)
      
      174lbs (79kg)
      
      
      
    

    

      1329
      1330
      huDD1522
      1986-02-06 (29 years old)
      
      153lbs (69kg)
      5ft 4in (162cm)
      
      
    

    

      1330
      1331
      hu25C433
      1951-08-17 (64 years old)
      
      
      
      
      
    

    

      1331
      1332
      hu419539
      1974-07-22 (41 years old)
      
      161lbs (73kg)
      6ft (182cm)
      
      
    

    

      1332
      1333
      hu7FC773
      
      
      108lbs (49kg)
      
      
      
    

  



1333 rows × 8 columns



















In [88]:



    


# Group data by blood type
bloodtype = data.groupby('blood_type')['human_id'].count()
bloodData = pd.DataFrame(columns=['PGP'])
bloodData.PGP = bloodtype[1:]



    











In [89]:



    


# Compare distribution of blood type in PGP with that of the general US population
## Note: Blood type distribution taken from American Red Cross website
## http://givebloodgivelife.org/education/bloodtypes.php

bTotal = bloodtype[1:].sum()
bloodData['USA'] = [0.34 ,.06, .03, 0.01, .09, .02, .38, .07];
bloodData['USA'] = bTotal*bloodData['USA']



    











In [90]:



    


bloodData2 = bloodData/bTotal
bloodData2



    


















    
Out[90]:










  
    

      
      PGP
      USA
    

    

      blood_type
      
      
    

  
  
    

      A+
      0.313618
      0.34
    

    

      A-
      0.077029
      0.06
    

    

      AB+
      0.038514
      0.03
    

    

      AB-
      0.011004
      0.01
    

    

      B+
      0.107290
      0.09
    

    

      B-
      0.013755
      0.02
    

    

      O+
      0.356259
      0.38
    

    

      O-
      0.082531
      0.07
    

  




















In [91]:



    


# Plot comparison as a bar plot
ax = bloodData2.plot(kind = 'bar')
labels = ax.get_xticklabels()
ax.set_xticklabels(bloodData.index,rotation=45);
ax.set_xlabel('blood type');
plt.gcf().subplots_adjust(bottom=0.2)

plt.savefig('Images/bloodtypeAll.svg',format='svg')



    


















    
























In [92]:



    


# Calculate p-value
chi2, pvalue = stats.chisquare(bloodData['PGP'].values, f_exp= bloodData['USA'].values, axis=None)
pvalue



    


















    
Out[92]:








0.063511368112054573

















In [93]:



    


# Now look at comparing results for just White participants
idx = data['race'].str.contains('White',na=False)
dataW = data[idx]



    











In [102]:



    


bloodtypeW = dataW.groupby('blood_type')['human_id'].count()
bloodDataW = pd.DataFrame(columns=['PGPWhite'])
bloodDataW.PGPWhite = bloodtypeW[1:]



    











In [103]:



    


# Compare distribution of blood type in PGP with that of the general US population
## Note: Blood type distribution taken from American Red Cross website
## http://www.redcrossblood.org/learn-about-blood/blood-types

bTotalW = bloodtypeW[1:].sum()
bloodDataW['White'] = [0.33 ,.07, .03, 0.01, .09, .02,.37, .08];
bloodDataW['White'] = bTotalW*bloodDataW['White']



    











In [114]:



    


bloodData2W = bloodDataW/bTotalW
bloodData2W['PGPAll']= bloodData2['PGP']



    











In [115]:



    


# Plot comparison as a bar plot
ax = bloodData2W[['PGPAll','PGPWhite','White']].plot(kind = 'bar')
labels = ax.get_xticklabels()
ax.set_xticklabels(bloodData.index,rotation=45);
ax.set_xlabel('blood type');
plt.gcf().subplots_adjust(bottom=0.2)

plt.savefig('Images/bloodtypeWhite.svg',format='svg')



    


















    
























In [98]:



    


# Calculate p-value
chi2, pvalue = stats.chisquare(bloodDataW['PGPWhite'].values, f_exp= bloodDataW['White'].values, axis=None)
pvalue



    


















    
Out[98]:








0.53666552766244691

Content source: swzCuroverse/PGPGraphics

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