python - 数据帧中每行的相关系数和 p 值

Question

我有一个矩阵，如下所示，

foo = pd.DataFrame(
        [['ASP1',12.45,12.65,1.54,1.56],
        ['ASP2',4.5,1.4,0.03,1.987],
        ['ASP3',0.12,0.34,0.45,0.9],
        ['ASP4',0.65,0.789,0.01,0.876]],
        columns = ('Sam','C1','C2','B1','B2'))
foo
    Sam C1  C2  B1  B2
0   ASP1    12.45   12.650  1.54    1.560
1   ASP2    4.50    1.400   0.03    1.987
2   ASP3    0.12    0.340   0.45    0.900
3   ASP4    0.65    0.789   0.01    0.876

我想对 Sam 中 C1..C2 和 B1..B2 列之间的每一行进行相关性测试。最后，我的目标是生成如下矩阵，

foo_result = pd.DataFrame(
        [['C',0.76,0.06],
        ['B',0.34,0.10]],
        columns = ('Gen','Correlation_coefficent','P-value'))
foo_result

    Gene    Correlation_coefficent  P-value
0   C   0.76    0.060
1   B   0.34    0.100

任何建议或解决方案都会很棒。谢谢

Answer 1

这应该可以做到:

from scipy.stats import pearsonr

c_values = [column for column in foo.columns.tolist() if column.startswith('C')]
b_values = [column for column in foo.columns.tolist() if column.startswith('B')]

foo['Correlation_coefficent'], foo['P-value'] = zip(*foo.T.apply(lambda x: pearsonr(x[c_values], x[b_values])))
foo_result = foo[['Sam', 'Correlation_coefficent','P-value']]

输出:

    Sam  Correlation_coefficent  P-value
0  ASP1                     1.0      0.0
1  ASP2                    -1.0      0.0
2  ASP3                     1.0      0.0
3  ASP4                     1.0      0.0

获得这些结果的原因是变量的数量。希望您的原件至少有 3 个值。