在pandas中，如何在3个具有匹配行和列的独立数据帧之间建立相关矩阵？

Question

每个数据帧具有基因名称的行索引和细胞系的列索引，表达水平填充每个细胞。这3个数据帧具有相同的对应基因名称和细胞系，我希望找到相应行的三元组之间的相关性(即细胞系如何影响3个数据帧之间每个特定基因的表达)。我如何找到新数据帧中的相关系数，然后使用热图进行可视化？

谢谢!

DATAFRAME1 = pd.DataFrame({"GENENAME":[GENE1,GENE2,GENE3,GENE4,GENE5],"CELLLINE1":[34,12,78,84,26], "CELLLINE2":[54,87,35,25,82], "CELLLINE3":[56,78,0,14,13], "CELLLINE4":[0,23,72,56,14], "CELLLINE5":[78,12,31,0,34]})
DATAFRAME2 = pd.DataFrame({"GENENAME":[GENE1,GENE2,GENE3,GENE4,GENE5],"CELLLINE1":[45,24,65,65,65], "CELLLINE2":[45,87,65,52,12], "CELLLINE3":[98,52,32,32,12], "CELLLINE4":[0,23,1,365,53], "CELLLINE5":[24,12,65,3,65]})
DATAFRAME3 = pd.DataFrame({"GENENAME":[GENE1,GENE2,GENE3,GENE4,GENE5],"CELLLINE1":[14,96,25,2,25], "CELLLINE2":[47,7,5,58,34], "CELLLINE3":[85,45,65,53,53], "CELLLINE4":[3,35,12,56,236], "CELLLINE5":[68,10,45,46,85]})

Answer 1

如果我理解正确，您可以执行以下操作：

DATAFRAME1 = pd.DataFrame({"GENENAME":['GENE1','GENE2','GENE3','GENE4','GENE5'],"CELLLINE1":[34,12,78,84,26], "CELLLINE2":[54,87,35,25,82], "CELLLINE3":[56,78,0,14,13], "CELLLINE4":[0,23,72,56,14], "CELLLINE5":[78,12,31,0,34]})
DATAFRAME2 = pd.DataFrame({"GENENAME":['GENE1','GENE2','GENE3','GENE4','GENE5'],"CELLLINE1":[45,24,65,65,65], "CELLLINE2":[45,87,65,52,12], "CELLLINE3":[98,52,32,32,12], "CELLLINE4":[0,23,1,365,53], "CELLLINE5":[24,12,65,3,65]})
DATAFRAME3 = pd.DataFrame({"GENENAME":['GENE1','GENE2','GENE3','GENE4','GENE5'],"CELLLINE1":[14,96,25,2,25], "CELLLINE2":[47,7,5,58,34], "CELLLINE3":[85,45,65,53,53], "CELLLINE4":[3,35,12,56,236], "CELLLINE5":[68,10,45,46,85]})

df1=DATAFRAME1.set_index("GENENAME").T
df2=DATAFRAME2.set_index("GENENAME").T
df3=DATAFRAME3.set_index("GENENAME").T

现在，对于每个基因，您可以执行以下操作：

pd.concat([df1[['GENE1']],df2[['GENE1']],df3[['GENE1']]],axis=1).corr()

GENENAME     GENE1     GENE1     GENE1
GENENAME                              
GENE1     1.000000  0.449474  0.843977
GENE1     0.449474  1.000000  0.695770
GENE1     0.843977  0.695770  1.000000

对于所有基因，您可以执行以下操作：

for i in DATAFRAME1['GENENAME']:
    print(i)
    print(pd.concat([df1[[i]],df2[[i]],df3[[i]]],axis=1).corr())
    print("="*50)

GENE1
GENENAME     GENE1     GENE1     GENE1
GENENAME                              
GENE1     1.000000  0.449474  0.843977
GENE1     0.449474  1.000000  0.695770
GENE1     0.843977  0.695770  1.000000
==================================================
GENE2
GENENAME     GENE2     GENE2     GENE2
GENENAME                              
GENE2     1.000000  0.932963 -0.373474
GENE2     0.932963  1.000000 -0.335923
GENE2    -0.373474 -0.335923  1.000000
==================================================
GENE3
GENENAME     GENE3     GENE3     GENE3
GENENAME                              
GENE3     1.000000 -0.113161 -0.690468
GENE3    -0.113161  1.000000  0.012654
GENE3    -0.690468  0.012654  1.000000
==================================================
GENE4
GENENAME     GENE4     GENE4     GENE4
GENENAME                              
GENE4     1.000000  0.454716 -0.694046
GENE4     0.454716  1.000000  0.230386
GENE4    -0.694046  0.230386  1.000000
==================================================
GENE5
GENENAME     GENE5     GENE5     GENE5
GENENAME                              
GENE5     1.000000 -0.392969 -0.439636
GENE5    -0.392969  1.000000  0.293649
GENE5    -0.439636  0.293649  1.000000
==================================================