如何使用python读取和修改（.gct）文件？

Question

Which libraries would help me read a gct file in python and edit it like removing the rows with NaN values. And how will the following code change if I apply it to a .gct file?

data = pd.read_csv('PAAD1.csv')
new_data = data.dropna(axis = 0, how ='any')
print("Old data frame length:", len(data), "\nNew data frame length:",  
       len(new_data), "\nNumber of rows with at least 1 NA value: ", 
       (len(data)-len(new_data)))
new_data.to_csv('EditedPAAD.csv')

Answer 1

You should use the cmapPy package for this. Compared to read_csv it gives you more freedom and domain specific utilities. Eg if your *.gct looks like this

#1.2            
22215   2       
Name    Description Tumor_One   Normal_One
1007_s_at   na  -0.214548   -0.18069
1053_at "RFC2 : replication factor C (activator 1) 2, 40kDa |@RFC2|"    0.868853    -1.330921
117_at  na  1.124814    0.933021
121_at  PAX8 : paired box gene 8 |@PAX8|    -0.825381   0.102078
1255_g_at   GUCA1A : guanylate cyclase activator 1A (retina) |@GUCA1A|  -0.734896   -0.184104
1294_at UBE1L : ubiquitin-activating enzyme E1-like |@UBE1L|    -0.366741   -1.209838
1316_at "THRA : thyroid hormone receptor, alpha (erythroblastic leukemia viral (v-erb-a) oncogene homolog, avian) |@THRA|"  -0.126108   1.486972
1320_at "PTPN21 : protein tyrosine phosphatase, non-receptor type 21 |@PTPN21|" 3.083681    -0.086705
...

You can extract only rows with a desired probeset id (row id), eg ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at UBE1L']

So to read a file, remove the nan in the description and save it again, do:

from cmapPy.pandasGEXpress.parse_gct import parse
from cmapPy.pandasGEXpress.write_gct import write

data = parse('example.gct', rid=['1007_s_at', '1053_at',
                                 '117_at', '121_at',
                                 '1255_g_at', '1294_at  UBE1L'])
# remove nan values from row_metadata (description column)
data.row_metadata_df.dropna(inplace=True)
# remove the entries of .data_df where nan values are in row_metadata
data.data_df = data.data_df.loc[data.row_metadata_df.index]

# Can only write GCT version 1.3
write(data, 'new_example.gct')

The new_example.gct looks then like this:

#1.3
3   2   1   0
id  Description Tumor_One   Normal_One

1053_at RFC2 : replication factor C (activator 1) 2, 40kDa |@RFC2|  0.8689  -1.3309

121_at  PAX8 : paired box gene 8 |@PAX8|    -0.8254 0.1021

1255_g_at   GUCA1A : guanylate cyclase activator 1A (retina) |@GUCA1A|  -0.7349 -0.1841

Answer 2

Quick search in google will give you the following: https://pypi.org/project/cmapPy/

Regarding to the code, if you don't care about the metadata in the 2 first rows, it seems to work for your purpose, but you should first indicate that the delimiter is TAB and skip the 2 first rows - pandas.read_csv(PATH_TO_GCT_FILE, sep='\\t',skiprows=2)