如何解决在熊猫中，如何在3个独立的数据帧之间建立相关矩阵，并匹配行和列？
每个数据框都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个数据框具有相同的对应基因名称和细胞系，我想仅找到对应行的三胞胎之间的相关性（即细胞系如何影响3个数据框之间每个特定基因的表达）。我如何才能将相关系数找到一个新的数据框，然后使用热图将其可视化？
谢谢！
DATAFRAME1 = pd.DataFrame({"GENENAME":[GENE1,GENE2,GENE3,GENE4,GENE5],"CELLLINE1":[34,12,78,84,26],"CELLLINE2":[54,87,35,25,82],"CELLLINE3":[56,14,13],"CELLLINE4":[0,23,72,56,14],"CELLLINE5":[78,31,34]})
DATAFRAME2 = pd.DataFrame({"GENENAME":[GENE1,"CELLLINE1":[45,24,65,65],"CELLLINE2":[45,52,12],"CELLLINE3":[98,32,1,365,53],"CELLLINE5":[24,3,65]})
DATAFRAME3 = pd.DataFrame({"GENENAME":[GENE1,"CELLLINE1":[14,96,2,25],"CELLLINE2":[47,7,5,58,34],"CELLLINE3":[85,45,53,"CELLLINE4":[3,236],"CELLLINE5":[68,10,46,85]})
解决方法
如果我理解正确，则可以执行以下操作：
DATAFRAME1 = pd.DataFrame({"GENENAME":['GENE1','GENE2','GENE3','GENE4','GENE5'],"CELLLINE1":[34,12,78,84,26],"CELLLINE2":[54,87,35,25,82],"CELLLINE3":[56,14,13],"CELLLINE4":[0,23,72,56,14],"CELLLINE5":[78,31,34]})
DATAFRAME2 = pd.DataFrame({"GENENAME":['GENE1',"CELLLINE1":[45,24,65,65],"CELLLINE2":[45,52,12],"CELLLINE3":[98,32,1,365,53],"CELLLINE5":[24,3,65]})
DATAFRAME3 = pd.DataFrame({"GENENAME":['GENE1',"CELLLINE1":[14,96,2,25],"CELLLINE2":[47,7,5,58,34],"CELLLINE3":[85,45,53,"CELLLINE4":[3,236],"CELLLINE5":[68,10,46,85]})
df1=DATAFRAME1.set_index("GENENAME").T
df2=DATAFRAME2.set_index("GENENAME").T
df3=DATAFRAME3.set_index("GENENAME").T
现在，对于每个Gene，您都可以执行以下操作：
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
==================================================
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