删除表达式数据集中的值 python

Question

我有这个微阵列数据集。我想绕过我在这个管道的早期版本中遇到的一个问题，(https://geoparse.readthedocs.io/en/latest/Analyse_hsa-miR-124a-3p_transfection_time-course.html) 我已经创建了一个实验文件并将其作为数据框读取。我想消除表达式 table 中不再作为字符串值存在于我读入的数据框的列加入中的每一列。

# Import tools
import GEOparse
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# download datasets
gse1 = GEOparse.get_GEO(geo="GSE99039", destdir="C:/Users/Highf_000/PycharmProjects/TFTest")
gse2 = GEOparse.get_GEO(geo="GSE6613", destdir="C:/Users/Highf_000/PycharmProjects/TFTest")
gse3 = GEOparse.get_GEO(geo="GSE72267", destdir="C:/Users/Highf_000/PycharmProjects/TFTest")

# import all GSM data for each GSE file
with open("GSE99039_GPL570.csv") as f:
    GSE99039_GPL570 = f.read().splitlines()
with open("GSE6613_GPL96.csv") as f:
    GSE6613_GPL96 = f.read().splitlines()
with open("GSE72267_GPL571.csv") as f:
    GSE72267_GPL571 = f.read().splitlines()

# gse1
gse1.gsm = gse1.phenotype_data
print(gse1.gsm.head())

# gse1
gse1.details = pd.read_csv('GSE99039_MicroarrayDetails.csv', delimiter = ',')
print(gse1.details.head())
gse1.detailsv1 = gse1.details[(gse1.details.values == "CONTROL") | (gse1.details.values == "IPD") | (gse1.details.values == "GPD") ]
print(gse1.detailsv1.head())

# gse1
pivoted_control_samples = gse1.pivot_samples('VALUE')[GSE99039_GPL570]
print(pivoted_control_samples)


# gse1
# Pulls the probes out
pivoted_control_samples_average = pivoted_control_samples.median(axis=1)
# Print number of probes before filtering
print("Number of probes before filtering: ", len(pivoted_control_samples_average))
# Extract all probes > 0.25
expression_threshold = pivoted_control_samples_average.quantile(0.25)
expressed_probes = pivoted_control_samples_average[pivoted_control_samples_average >= expression_threshold].index.tolist()
# Print probes above cut off
print("Number of probes above threshold: ", len(expressed_probes))
# confirm filtering worked
samples = gse1.pivot_samples("VALUE").loc[expressed_probes]
print(samples.head())

# print phenotype data
print(gse1.phenotype_data[["title", "source_name_ch1", "Disease_Label", "Sex" ]])

这是我创建的数据框的样子，在脚本中命名为 gse1.detailsv1：

   Accession       Title  Source name  ... Subject_id Disease label     Sex
0  GSM2630758  E7R_039a01  Whole blood  ...      L3012       CONTROL  Female
1  GSM2630759  E7R_039a02  Whole blood  ...      L2838           IPD    Male
2  GSM2630760  E7R_039a03  Whole blood  ...      L2540           IPD  Female
3  GSM2630761  E7R_039a04  Whole blood  ...      L3015       CONTROL  Female
4  GSM2630762  E7R_039a05  Whole blood  ...      L2884           IPD  Female

[5 rows x 7 columns]

这就是我的表达式 table 的样子，在脚本中命名为 samples：

name       GSM2630758  GSM2630759  ...  GSM2631314  GSM2631315
ID_REF                             ...                        
1007_s_at       5.397       4.952  ...       5.567       5.529
1053_at         5.199       5.198  ...       5.706       5.078
117_at          8.327       8.589  ...       8.511       8.458
121_at          7.042       6.935  ...       7.526       7.673
1294_at         7.753       8.210  ...       7.537       7.418

[5 rows x 558 columns]

假设，如果 GSM2630758 在第一个数据帧的 Accession 列中不存在，我想删除 GSM2630758。我需要遍历它并消除所有不再存在的值。

Answer 1

samples.drop(set(samples.columns[1:]) - set(gse1.detailsv.Accession.unique()), axis=1)

Answer 2

如果 gse1.detailsv1 数据集足够小，您可以创建所有 Accession 的列表并选择这些列：

cols = set(gse1.detailsv1["Accesion"].unique()) & set(samples.columns)
samples = samples[cols]

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