NormalizeData.default 运行 DoubletFinder 在 R 中集成的 seurat 对象上出错
Error in NormalizeData.default running DoubletFinder on an integrated seurat object in R
我正在尝试 运行 DoubletFinder 处理由各种数据集集成产生的 seurat 对象。
Seurat 对象有 2 个化验:RNA 和整合。
集成的seurat对象已完全处理:
归一化和 FindVariableFeature 预积分
集成对象上的ScaleData、RunPCA、FindNeighbors、FindClusters、RunUMAP。
DoubletFinder 的 paramSweep_v3() 函数给出以下输出:
sweep.res.list <- paramSweep_v3(integrated.seu, PCs = 1:38, sct = FALSE)
Loading required package: fields
Loading required package: spam
Loading required package: dotCall64
Loading required package: grid
Spam version 2.5-1 (2019-12-12) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.
Attaching package: ‘spam’
The following object is masked from ‘package:R.utils’:
cleanup
The following objects are masked from ‘package:base’:
backsolve, forwardsolve
Loading required package: maps
See https://github.com/NCAR/Fields for
an extensive vignette, other supplements and source code
[1] "Creating artificial doublets for pN = 5%"
[1] "Creating Seurat object..."
[1] "Normalizing Seurat object..."
Error in NormalizeData.default(object = GetAssayData(object = object, :
trying to get slot "params" from an object of a basic class ("NULL") with no slots
为什么这表明我的 Seurat 对象中没有插槽?
DoubletFinder 自述文件明确指出您不应该 运行 它在聚合数据集上。它会产生虚假的人工双峰:
[https://github.com/chris-mcginnis-ucsf/DoubletFinder][1]
Do not apply DoubletFinder to aggregated scRNA-seq data representing
multiple distinct samples (e.g., multiple 10X lanes). For example, if
you run DoubletFinder on aggregated data representing WT and mutant
cell lines sequenced across different 10X lanes, artificial doublets
will be generated from WT and mutant cells, which cannot exist in your
data. These artificial doublets will skew results. Notably, it is okay
to run DoubletFinder on data generated by splitting a single sample
across multiple 10X lanes.
我通过读取单个样本,将它们单独聚类,运行 DoubletFinder,删除双峰,然后 运行 集成工作流程来做到这一点。
我正在尝试 运行 DoubletFinder 处理由各种数据集集成产生的 seurat 对象。
Seurat 对象有 2 个化验:RNA 和整合。
集成的seurat对象已完全处理:
归一化和 FindVariableFeature 预积分
集成对象上的ScaleData、RunPCA、FindNeighbors、FindClusters、RunUMAP。
DoubletFinder 的 paramSweep_v3() 函数给出以下输出:
sweep.res.list <- paramSweep_v3(integrated.seu, PCs = 1:38, sct = FALSE)
Loading required package: fields
Loading required package: spam
Loading required package: dotCall64
Loading required package: grid
Spam version 2.5-1 (2019-12-12) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.
Attaching package: ‘spam’
The following object is masked from ‘package:R.utils’:
cleanup
The following objects are masked from ‘package:base’:
backsolve, forwardsolve
Loading required package: maps
See https://github.com/NCAR/Fields for
an extensive vignette, other supplements and source code
[1] "Creating artificial doublets for pN = 5%"
[1] "Creating Seurat object..."
[1] "Normalizing Seurat object..."
Error in NormalizeData.default(object = GetAssayData(object = object, :
trying to get slot "params" from an object of a basic class ("NULL") with no slots
为什么这表明我的 Seurat 对象中没有插槽?
DoubletFinder 自述文件明确指出您不应该 运行 它在聚合数据集上。它会产生虚假的人工双峰:
[https://github.com/chris-mcginnis-ucsf/DoubletFinder][1]
Do not apply DoubletFinder to aggregated scRNA-seq data representing multiple distinct samples (e.g., multiple 10X lanes). For example, if you run DoubletFinder on aggregated data representing WT and mutant cell lines sequenced across different 10X lanes, artificial doublets will be generated from WT and mutant cells, which cannot exist in your data. These artificial doublets will skew results. Notably, it is okay to run DoubletFinder on data generated by splitting a single sample across multiple 10X lanes.
我通过读取单个样本,将它们单独聚类,运行 DoubletFinder,删除双峰,然后 运行 集成工作流程来做到这一点。