pca.py 文件源码

def normalize_and_transpose(matrix):
    matrix.tocsc()

    m = normalize_by_umi(matrix)

    # Use log counts
    m.data = np.log2(1 + m.data)

    # Transpose
    m = m.T

    # compute centering (mean) and scaling (stdev)
    (c,v) = summarize_columns(m)
    s = np.sqrt(v)

    return (m, c, s)