用rpy2修改r对象 [英] Modify r object with rpy2
问题描述
我正在尝试使用rpy2在python中使用DESeq2 R/Bioconductor软件包.
I'm trying to use rpy2 to use the DESeq2 R/Bioconductor package in python.
我在写问题时实际上解决了我的问题(使用do_slots可以访问r对象的属性)，但是我认为该示例可能对其他人有用，所以这是我在R中的工作方式以及它的翻译方式python:
I actually solved my problem while writing my question (using do_slots allows access to the r objects attributes), but I think the example might be useful for others, so here is how I do in R and how this translates in python:
我可以从两个数据帧中创建一个"DESeqDataSet"，如下所示:
I can create a "DESeqDataSet" from two data frames as follows:
counts_data <- read.table("long/path/to/file",
header=TRUE, row.names="gene")
head(counts_data)
## WT_RT_1 WT_RT_2 prg1_RT_1 prg1_RT_2
## aap-1 406 311 41 95
## aat-1 5 8 2 0
## aat-2 1 1 0 0
## aat-3 13 12 0 1
## aat-4 6 6 2 3
## aat-5 3 1 1 0
col_data <- DataFrame(lib = c("WT", "WT", "prg1", "prg1"),
treat = c("RT", "RT", "RT", "RT"),
rep = c("1", "2", "1", "2"),
row.names = colnames(counts_data))
head(col_data)
## DataFrame with 4 rows and 3 columns
## lib treat rep
## <character> <character> <character>
## WT_RT_1 WT RT 1
## WT_RT_2 WT RT 2
## prg1_RT_1 prg1 RT 1
## prg1_RT_2 prg1 RT 2
dds <- DESeqDataSetFromMatrix(countData = counts_data,
colData = col_data,
design = ~ lib)
## Warning message:
## In DESeqDataSet(se, design = design, ignoreRank) :
## some variables in design formula are characters, converting to factors
dds
## class: DESeqDataSet
## dim: 18541 4
## metadata(1): version
## assays(1): counts
## rownames(18541): aap-1 aat-1 ... WBGene00255550 WBGene00255553
## rowData names(0):
## colnames(4): WT_RT_1 WT_RT_2 prg1_RT_1 prg1_RT_2
## colData names(3): lib treat rep
为了确保分析将使用正确的控件，我需要relevel一个可以使用双括号"语法访问的因子:
To ensure the analysis will use the correct control, I need to relevel a factor that can be accessed using the "double brackets" syntax:
dds[["lib"]]
## [1] WT WT prg1 prg1
## Levels: prg1 WT
dds[["lib"]] <- relevel(dds[["lib"]], ref="WT")
dds[["lib"]]
## [1] WT WT prg1 prg1
## Levels: WT prg1
然后我可以运行分析:
dds <- DESeq(dds)
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
res <- results(dds)
我查看给定基因的结果:
I look at the results for a given gene:
res["his-10",]
## log2 fold change (MAP): lib prg1 vs WT
## Wald test p-value: lib prg1 vs WT
## DataFrame with 1 row and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## his-10 586.5464 3.136174 0.2956132 10.60904 2.705026e-26 8.78785e-25
在Python中
现在，我想在Python中使用rpy2进行同样的操作.
In Python
Now, I would like to do the same in python with rpy2.
我似乎已经从pandas数据框中成功创建了对象:
I seem to successfully create the object from pandas dataframes:
import pandas as pd
from rpy2.robjects import r, pandas2ri, Formula
as_df = r("as.data.frame")
from rpy2.robjects.packages import importr
deseq2 = importr("DESeq2")
counts_data = pd.read_table("long/path/to/file", index_col=0)
col_data = pd.DataFrame({
"cond_names" : counts_data.columns,
"lib" : ["WT", "WT", "prg1", "prg1"],
"rep" : ["1", "1", "2", "2"],
"treat" : ["RT", "RT", "RT", "RT"]})
col_data.set_index("cond_names", inplace=True)
pandas2ri.activate() # makes some conversions automatic
dds = deseq2.DESeqDataSetFromMatrix(
countData=counts_data,
colData=col_data,
design=Formula("~lib"))
在IPython(我实际上在其中运行了先前的命令)中，我可以使用do_slots查找对象内部，以尝试确定需要重新调平的因素:
In IPython (where I actually ran the previous commands), I can look inside the object using do_slots to try to identify the factor which needs relevelling:
In [229]: tuple(dds.do_slot("colData").slotnames())
Out[229]: ('rownames', 'nrows', 'listData', 'elementType', 'elementMetadata', 'metadata')
In [230]: dds.do_slot("colData").do_slot("listData")
Out[230]:
R object with classes: ('list',) mapped to:
<ListVector - Python:0x7f2ae2590a08 / R:0x108fcdd0>
[FactorVector, FactorVector, FactorVector]
lib: <class 'rpy2.robjects.vectors.FactorVector'>
R object with classes: ('factor',) mapped to:
<FactorVector - Python:0x7f2ae20f1c08 / R:0x136a3920>
[ 2, 2, 1, 1]
rep: <class 'rpy2.robjects.vectors.FactorVector'>
R object with classes: ('factor',) mapped to:
<FactorVector - Python:0x7f2a9600c948 / R:0x136a30f0>
[ 1, 1, 2, 2]
treat: <class 'rpy2.robjects.vectors.FactorVector'>
R object with classes: ('factor',) mapped to:
<FactorVector - Python:0x7f2a9600ccc8 / R:0x136a3588>
[ 1, 1, 1, 1]
我认为relevel的因素是第一个因素，因为"lib"是传递给deseq2.DESeqDataSetFromMatrix函数的col_data数据帧中的第一列(我意识到，"lib"实际上是写在r对象的说明).
I suppose the factor to relevel is the first one because "lib" was the first column in the col_data dataframe passed to the deseq2.DESeqDataSetFromMatrix function ( I realize that "lib" is actually written in the description of the r object).
通过do_slots访问的属性上的relevel似乎有效果:
The relevel on attributes accessed via do_slots seems to have effects:
In [231]: dds.do_slot("colData").do_slot("listData")[0] = r.relevel(dds.do_slot("colData").do_slot("listData")[0], ref="WT")
In [232]: dds.do_slot("colData").do_slot("listData")
Out[232]:
R object with classes: ('list',) mapped to:
<ListVector - Python:0x7f2a95078508 / R:0x108fcdd0>
[FactorVector, FactorVector, FactorVector]
lib: <class 'rpy2.robjects.vectors.FactorVector'>
R object with classes: ('factor',) mapped to:
<FactorVector - Python:0x7f2a9600bb88 / R:0x12a7ff60>
[ 1, 1, 2, 2]
rep: <class 'rpy2.robjects.vectors.FactorVector'>
R object with classes: ('factor',) mapped to:
<FactorVector - Python:0x7f2ae2568888 / R:0x136a30f0>
[ 1, 1, 2, 2]
treat: <class 'rpy2.robjects.vectors.FactorVector'>
R object with classes: ('factor',) mapped to:
<FactorVector - Python:0x7f2ae2568848 / R:0x136a3588>
[ 1, 1, 1, 1]
然后我运行分析部分:
In [233]: dds = deseq2.DESeq(dds)
/home/bli/.local/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:186: RRuntimeWarning: estimating size factors
warnings.warn(x, RRuntimeWarning)
/home/bli/.local/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:186: RRuntimeWarning: estimating dispersions
warnings.warn(x, RRuntimeWarning)
/home/bli/.local/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:186: RRuntimeWarning: gene-wise dispersion estimates
warnings.warn(x, RRuntimeWarning)
/home/bli/.local/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:186: RRuntimeWarning: mean-dispersion relationship
warnings.warn(x, RRuntimeWarning)
/home/bli/.local/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:186: RRuntimeWarning: final dispersion estimates
warnings.warn(x, RRuntimeWarning)
/home/bli/.local/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:186: RRuntimeWarning: fitting model and testing
warnings.warn(x, RRuntimeWarning)
In [234]: res = pandas2ri.ri2py(as_df(deseq2.results(dds)))
In [235]: res.index.names = ["gene"]
dds = deseq2.DESeq(dds)
res = pandas2ri.ri2py(as_df(deseq2.results(dds)))
res.index.names = ["gene"]
现在，检查测试基因的结果:
Now, check the results for a test gene:
In [236]: res.loc["his-10"]
Out[236]:
baseMean 5.865464e+02
log2FoldChange 3.136174e+00
lfcSE 2.956132e-01
stat 1.060904e+01
pvalue 2.705026e-26
padj 8.787850e-25
Name: his-10, dtype: float64
python返回的结果与R中的结果相同.
The results returned by python are the same as from R.
推荐答案
I found code examples in the rpy2 documentation that helped me solve the problem: http://rpy2.readthedocs.io/en/version_2.8.x/rinterface.html#pass-by-value-paradigm.
一个人可以通过do_slots方法访问r个对象的属性，该方法将属性名称作为参数.有关完整的解决方案，请参见问题.
One can access attributes of r objects via the do_slots method, which takes as argument the attribute name. See in the question for the full solution.
还有一个do_slot_assign方法，例如，可以用来更改设计公式:
There also is a do_slot_assign method that can be used for instance to change the design formula:
>>> dds.do_slot("design").r_repr()
'~lib'
>>> dds.do_slot_assign("design", Formula("~ treat"))
>>> dds.do_slot("design").r_repr()
'~treat'
这篇关于用rpy2修改r对象的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
