哪些库可以帮助我读取python中的gct文件并对其进行编辑,就像删除带有NaN值的行一样。如果我将下面的代码应用到.gct文件中,它会发生怎样的变化?
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')
发布于 2020-02-13 18:15:44
为此,您应该使用cmapPy
包。与read_csv
相比,它为您提供了更多的自由度和特定领域的实用程序。例如,如果您的*.gct
看起来像这样
#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
...
您只能提取具有所需的探测集id (行id)的行,例如['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at UBE1L']
因此,要读取文件,请删除description
中的nan
并再次保存它,请执行以下操作:
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')
然后new_example.gct
看起来像这样:
#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
发布于 2020-02-13 16:09:50
在谷歌中快速搜索会给你提供以下信息:https://pypi.org/project/cmapPy/
关于代码,如果您不关心前两行中的元数据,它似乎可以满足您的需要,但是您应该首先指出分隔符为TAB
,并跳过前两行- pandas.read_csv(PATH_TO_GCT_FILE, sep='\t',skiprows=2)
https://stackoverflow.com/questions/60202996
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