比较两个文件中的文本并在字段中追加文本

Question

我有两个文件： 文件A看起来像

ProbeID rsID    chr bp  strand  alleleA alleleB
SNP_A-1780270   rs987435    7   78599583    -   C   G
SNP_A-1780271   rs345783    15  33395779    -   C   G
SNP_A-1780272   rs955894    1   189807684   -   G   T
SNP_A-1780274   rs6088791   20  33907909    -   A   G
SNP_A-1780277   rs11180435  12  75664046    +   C   T
SNP_A-1780278   rs17571465  1   218890658   -   A   T
SNP_A-1780283   rs17011450  4   127630276   -   C   T
SNP_A-1780285   rs6919430   6   90919465    +   A   C
SNP_A-1780286   rs41528453  --- --- --- A   G
SNP_A-1780287   rs2342723   16  5748791 +   C   T

文件B看起来像

^{pr2}$

我想要的输出看起来像：

ProbeID call    genotype
SNP_A-1780270   2   G G
SNP_A-1780271   0   C C
SNP_A-1780272   2   T T 
SNP_A-1780274   1   A G
SNP_A-1780277   0   C C
SNP_A-1780278   2   T T
SNP_A-1780283   2   T T 
SNP_A-1780285   2   C C
SNP_A-1780286   0   A A
SNP_A-1780287   0   C C

本质上，这与两个列表中的ProbeID匹配，在文件B中，检查call列中对应的“call”值。当call=0时，在相邻列中打印两次等位基因的值。当call=1时，打印等位基因A和等位基因B的值。当call=2时，打印等位基因b的值两次。在

Answer 1

使用pandas：

import pandas as pd
import re

A = pd.read_csv('FileA', delimiter = r'\s+')
B = pd.read_csv('FileB', delimiter = r'\s+')
A = A.set_index(['ProbeID'])
B = B.set_index(['ProbeID'])
C = pd.concat([A,B], axis = 1)

idx = C['call'] == 0
C['alleleB'][idx]  = C['alleleA'][idx]
idx = C['call'] == 2
C['alleleA'][idx]  = C['alleleB'][idx]
print(C[['call', 'alleleA', 'alleleB']])

收益率

^{pr2}$

---

如果您有许多Bfiles，您可以使用如下方法：

import pandas as pd
import re

A = pd.read_csv('FileA', delimiter = r'\s+')
A = A.set_index(['ProbeID'])

BFiles = ['FileB1', 'FileB2', 'FileB3']
for i, bfile in enumerate(BFiles):
    B = pd.read_csv('FileB', delimiter = r'\s+')
    B = B.set_index(['ProbeID'])
    C = pd.concat([A,B], axis = 1)

    idx = C['call'] == 0
    C['alleleB'][idx]  = C['alleleA'][idx]
    idx = C['call'] == 2
    C['alleleA'][idx]  = C['alleleB'][idx]
    cfile = 'FileC{i}'.format(i = i)
    with open(cfile, 'w') as f:
        f.write(C[['call', 'alleleA', 'alleleB']])

将cfile更改为适当的值。在

Answer 2

使用嵌套字典可以很容易地完成此任务：

data = {}
with open(fileA) as fA:
    header = next(fA).split()
    attributes = header[1:]
    for line in fA:
        lst = line.split()
        data[lst[0]] = dict(zip(attributes,l[1:])

with open(fileB) as fB:
    header = next(fB).split()
    for line in fB:
        ID,call = line.split()
        data[ID]['call'] = int(call)

现在您只需迭代数据并只打印所需的内容。在

或者，如果这些行完全对应（或者如果使用python3，则只使用普通的zip），则可以使用itertools.izip一次处理一行：

^{pr2}$

Answer 3

这是一个R解决方案。在

my.data <- merge(df1, df2, by = "ProbeID")

# select rows based on call
zero <- my.data$call == 0
one <- my.data$call == 1
two <- my.data$call == 2

# subset rows based on previous condition and calculate genotype
my.data[zero, "genotype"] <- paste(my.data$alleleA[zero], my.data$alleleA[zero], sep = " ")
my.data[one, "genotype"] <- paste(my.data$alleleA[one], my.data$alleleB[one], sep = " ")
my.data[two, "genotype"] <- paste(my.data$alleleB[two], my.data$alleleB[two], sep = " ")

my.data[, c("ProbeID", "call", "genotype")]


        ProbeID call genotype
1  SNP_A-1780270    2      G G
2  SNP_A-1780271    0      C C
3  SNP_A-1780272    2      T T
4  SNP_A-1780274    1      A G
5  SNP_A-1780277    0      C C
6  SNP_A-1780278    2      T T
7  SNP_A-1780283    2      T T
8  SNP_A-1780285    2      C C
9  SNP_A-1780286    0      A A
10 SNP_A-1780287    0      C C