Gene-age distributions by algorithm

This notebook was used to generate data for Figure 1



In [1]:

    
import pandas as pd
from matplotlib import pyplot as plt
%matplotlib inline



In [2]:

    
binAges = pd.read_csv("../binAges_HUMAN.csv",index_col=0)
binAges = binAges[['InParanoid', 'InParanoidCore', 'PANTHER8_LDO','OMA_Groups', 'OMA_Pairs', 
    'RSD', 'EggNOG', 'Orthoinspector', 'Hieranoid_2', 'EnsemblCompara_v2', 
    'Metaphors', 'PhylomeDB','PANTHER8_all']]
binAges.head()









    Out[2]:






  
    
      
      InParanoid
      InParanoidCore
      PANTHER8_LDO
      OMA_Groups
      OMA_Pairs
      RSD
      EggNOG
      Orthoinspector
      Hieranoid_2
      EnsemblCompara_v2
      Metaphors
      PhylomeDB
      PANTHER8_all
    
  
  
    
      Q8TEA1
      Cellular_organisms
      Euk_Archaea
      Euk_Archaea
      Eukaryota
      Eukaryota
      Cellular_organisms
      Euk_Archaea
      Cellular_organisms
      Eukaryota
      Euk_Archaea
      Cellular_organisms
      Cellular_organisms
      Cellular_organisms
    
    
      A6NIH7
      Eumetazoa
      Eukaryota
      Eukaryota
      Eukaryota
      Eumetazoa
      Eukaryota
      Eukaryota
      Eukaryota
      Eukaryota
      Eukaryota
      Eukaryota
      Eukaryota
      Eukaryota
    
    
      Q96HJ5
      Mammalia
      Mammalia
      Mammalia
      Mammalia
      Mammalia
      Mammalia
      None
      Mammalia
      Eumetazoa
      Vertebrata
      Eumetazoa
      Eumetazoa
      Opisthokonta
    
    
      O94913
      Eukaryota
      Eukaryota
      Eukaryota
      Vertebrata
      Eumetazoa
      Eukaryota
      Opisthokonta
      Eukaryota
      Eukaryota
      Eukaryota
      Eukaryota
      Vertebrata
      Eukaryota
    
    
      P37837
      Cellular_organisms
      Euk+Bac
      Eukaryota
      Euk+Bac
      Euk+Bac
      Cellular_organisms
      Cellular_organisms
      Cellular_organisms
      Euk+Bac
      Cellular_organisms
      Cellular_organisms
      Cellular_organisms
      Cellular_organisms

Get value counts for each database, normalize, and plot



In [3]:

    
ageCounts = binAges.apply(pd.value_counts)
ageCounts.drop(["None"],inplace=True)



In [4]:

    
ageFreqs = ageCounts.apply(lambda x: x/float(x.sum()))



In [5]:

    
cols = ["Cellular_organisms","Euk+Bac",
  "Euk_Archaea",
  "Eukaryota",
  "Opisthokonta",
  "Eumetazoa",
  "Vertebrata",
  "Mammalia"]

ageFreqsFlip = ageFreqs.T[cols]
ageFreqsFlip









    Out[5]:






  
    
      
      Cellular_organisms
      Euk+Bac
      Euk_Archaea
      Eukaryota
      Opisthokonta
      Eumetazoa
      Vertebrata
      Mammalia
    
  
  
    
      InParanoid
      0.019371
      0.032303
      0.007983
      0.135065
      0.039221
      0.136342
      0.388058
      0.241658
    
    
      InParanoidCore
      0.013931
      0.038579
      0.007769
      0.157477
      0.061834
      0.233885
      0.254354
      0.232171
    
    
      PANTHER8_LDO
      0.024186
      0.040328
      0.010237
      0.184544
      0.040047
      0.175994
      0.329490
      0.195174
    
    
      OMA_Groups
      0.006954
      0.025822
      0.005676
      0.122718
      0.041980
      0.213274
      0.335992
      0.247584
    
    
      OMA_Pairs
      0.013173
      0.042494
      0.008233
      0.149846
      0.062414
      0.200414
      0.312918
      0.210507
    
    
      RSD
      0.037012
      0.070050
      0.015331
      0.233430
      0.071650
      0.223054
      0.172672
      0.176802
    
    
      EggNOG
      0.018813
      0.050924
      0.010250
      0.296659
      0.073046
      0.313526
      0.221083
      0.015699
    
    
      Orthoinspector
      0.037235
      0.056948
      0.010901
      0.245721
      0.063366
      0.275418
      0.143949
      0.166463
    
    
      Hieranoid_2
      0.023249
      0.056222
      0.008611
      0.322190
      0.090817
      0.264904
      0.125867
      0.108140
    
    
      EnsemblCompara_v2
      0.043274
      0.060271
      0.019656
      0.291293
      0.063608
      0.282117
      0.125391
      0.114390
    
    
      Metaphors
      0.048588
      0.074412
      0.013940
      0.334553
      0.078574
      0.284812
      0.093065
      0.072055
    
    
      PhylomeDB
      0.045949
      0.103793
      0.022158
      0.321080
      0.074182
      0.257416
      0.111247
      0.064175
    
    
      PANTHER8_all
      0.082978
      0.144612
      0.013757
      0.347527
      0.087728
      0.211049
      0.081068
      0.031281



In [6]:

    
ax = ageFreqsFlip.plot(kind='bar',colormap='Greys', ylim=(0,.7),legend=False)
ax.grid(False)
xLeft,xRight = ax.get_xlim()

plt.tight_layout()
plt.legend(loc=0,ncol=3,prop={'size':9})

#plt.savefig("ageDistributions.svg")



In [ ]:

	InParanoid	InParanoidCore	PANTHER8_LDO	OMA_Groups	OMA_Pairs	RSD	EggNOG	Orthoinspector	Hieranoid_2	EnsemblCompara_v2	Metaphors	PhylomeDB	PANTHER8_all
Q8TEA1	Cellular_organisms	Euk_Archaea	Euk_Archaea	Eukaryota	Eukaryota	Cellular_organisms	Euk_Archaea	Cellular_organisms	Eukaryota	Euk_Archaea	Cellular_organisms	Cellular_organisms	Cellular_organisms
A6NIH7	Eumetazoa	Eukaryota	Eukaryota	Eukaryota	Eumetazoa	Eukaryota	Eukaryota	Eukaryota	Eukaryota	Eukaryota	Eukaryota	Eukaryota	Eukaryota
Q96HJ5	Mammalia	Mammalia	Mammalia	Mammalia	Mammalia	Mammalia	None	Mammalia	Eumetazoa	Vertebrata	Eumetazoa	Eumetazoa	Opisthokonta
O94913	Eukaryota	Eukaryota	Eukaryota	Vertebrata	Eumetazoa	Eukaryota	Opisthokonta	Eukaryota	Eukaryota	Eukaryota	Eukaryota	Vertebrata	Eukaryota
P37837	Cellular_organisms	Euk+Bac	Eukaryota	Euk+Bac	Euk+Bac	Cellular_organisms	Cellular_organisms	Cellular_organisms	Euk+Bac	Cellular_organisms	Cellular_organisms	Cellular_organisms	Cellular_organisms

	Cellular_organisms	Euk+Bac	Euk_Archaea	Eukaryota	Opisthokonta	Eumetazoa	Vertebrata	Mammalia
InParanoid	0.019371	0.032303	0.007983	0.135065	0.039221	0.136342	0.388058	0.241658
InParanoidCore	0.013931	0.038579	0.007769	0.157477	0.061834	0.233885	0.254354	0.232171
PANTHER8_LDO	0.024186	0.040328	0.010237	0.184544	0.040047	0.175994	0.329490	0.195174
OMA_Groups	0.006954	0.025822	0.005676	0.122718	0.041980	0.213274	0.335992	0.247584
OMA_Pairs	0.013173	0.042494	0.008233	0.149846	0.062414	0.200414	0.312918	0.210507
RSD	0.037012	0.070050	0.015331	0.233430	0.071650	0.223054	0.172672	0.176802
EggNOG	0.018813	0.050924	0.010250	0.296659	0.073046	0.313526	0.221083	0.015699
Orthoinspector	0.037235	0.056948	0.010901	0.245721	0.063366	0.275418	0.143949	0.166463
Hieranoid_2	0.023249	0.056222	0.008611	0.322190	0.090817	0.264904	0.125867	0.108140
EnsemblCompara_v2	0.043274	0.060271	0.019656	0.291293	0.063608	0.282117	0.125391	0.114390
Metaphors	0.048588	0.074412	0.013940	0.334553	0.078574	0.284812	0.093065	0.072055
PhylomeDB	0.045949	0.103793	0.022158	0.321080	0.074182	0.257416	0.111247	0.064175
PANTHER8_all	0.082978	0.144612	0.013757	0.347527	0.087728	0.211049	0.081068	0.031281