标签：... 01 df seconds 使用 PyDESeq2 condition

PyDESeq2

Python版的DESeq2已上线，以后就可以使用Python来做差异分析了。目前文章还在bioRxiv。我来简单尝尝鲜。

安装

使用mamba或者conda来新建一个虚拟环境，然后使用pip安装。

mamba create -n pydeseq2 python
mamba activate pydeseq2
pip install pydeseq2

用法

作为Python版的DESeq2， 用法和R里差不多。

数据读取

count_file = "test_counts.csv"
condition_file = "test_clinical.csv"

counts_df = pd.read_csv(count_file, index_col=0).T
condition_df = pd.read_csv(condition_file, index_col=0)

数据集是一个100个样本，每个样本10个基因的小测试集。而其中50个样本属于条件A,另50个样本属于条件B。

>>> counts_df.shape
(100, 10)
>>> counts_df.head()
         gene1  gene2  gene3  gene4  gene5  gene6  gene7  gene8  gene9  gene10
sample1     12     22      2    187     15      2     13     57     56       6
sample2     10      6     20     99     55      0     35     96     43       1
sample3      0     28      3     96     38      2      9     54     27      14
sample4      7     28     10    170     16     10     17     38     18      16
sample5      2     31      5    126     23      2     19     53     31      18

>>> condition_df
          condition
sample1           A
sample2           A
sample3           A
sample4           A
sample5           A
...             ...
sample96          B
sample97          B
sample98          B
sample99          B
sample100         B

[100 rows x 1 columns]

构建DeseqDataSet 对象

和DESeq2类似

# 构建DeseqDataSet 对象
dds = DeseqDataSet(counts_df, condition_df, design_factor="condition")
# 离散度和log fold-change评估.
dds.deseq2()
# Fitting size factors...
# ... done in 0.00 seconds.
# Fitting dispersions...
# ... done in 0.64 seconds.
# Fitting dispersion trend curve...
# ... done in 0.03 seconds.
# Fitting MAP dispersions...
# ... done in 0.63 seconds.
# Fitting LFCs...
# ... done in 0.66 seconds.
# Refitting 0 outliers.

统计分析

差异表达统计检验分析

res = DeseqStats(dds)
# 执行统计分析并返回结果
res_df = res.summary()

结果如下

>>> res_df
         baseMean  log2FoldChange     lfcSE       stat        pvalue          padj
gene1   10.306788        1.007045  0.225231   4.471161  7.779603e-06  2.593201e-05
gene2   24.718815       -0.059670  0.165606  -0.360311  7.186146e-01  7.186146e-01
gene3    4.348135       -0.166592  0.325445  -0.511891  6.087275e-01  6.763639e-01
gene4   98.572300       -2.529204  0.136752 -18.494817  2.273125e-76  2.273125e-75
gene5   38.008562        1.236663  0.151824   8.145377  3.781028e-16  1.890514e-15
gene6    4.734285        0.212656  0.304487   0.698408  4.849222e-01  6.061527e-01
gene7   30.011855       -0.445855  0.150575  -2.961017  3.066249e-03  5.110415e-03
gene8   59.330642        0.372080  0.118911   3.129070  1.753603e-03  3.507207e-03
gene9   46.779546        0.547280  0.124922   4.380966  1.181541e-05  2.953853e-05
gene10  11.963156        0.494775  0.229494   2.155940  3.108836e-02  4.441194e-02

最后

PyDESeq2目前主要的核心功能差异分析已完成，虽然相比DESeq2而言还在起步阶段, 不过很明显可以发现Python的组学分析生态正逐渐完善~

参考

https://github.com/owkin/PyDESeq2

标签：...,01,df,seconds,使用,PyDESeq2,condition
From： https://www.cnblogs.com/huanping/p/17001567.html