单细胞测序攻略：二聚体过滤——DoubletDecon包攻略
DoubletDecon介绍提醒：1.一直到2020年7月一直在更新，直接对接seurat比较好用2.需要单个样本全部seurat流程走一遍，导出所需文件后进行去除二聚体过程3.去除二聚体以后用subset取去除二聚体后文件的细胞名称，即可得到取出后的交集4.整个包的使用流程参考链接里的protocol文章，本篇只是对代码进行入门介绍
注意：不要用Improved_Seurat_Pre_Process，会报错代码来源：参考文献：
第一步 安装包第二步 加载包第三步 读取数据第四步 质量控制第五步 标准流程 Pipeline第六步 Seurat数据导出——Output第七步 数据预处理（读入DoubletDecon）Seurat_Pre_Process()注意：Improved_Seurat_Pre_Process会报错，别用。先把需要的数据导出来
第八步 二聚体分析第九步 提取过滤二聚体后的细胞名称第十步 细胞名称连接符号变了，要换回seurat的版本第十一步 取交集，所谓去除二聚体后的seurat对象包信息作者简介
DoubletDecon介绍
提醒：
1.一直到2020年7月一直在更新，直接对接seurat比较好用
2.需要单个样本全部seurat流程走一遍，导出所需文件后进行去除二聚体过程
3.去除二聚体以后用subset取去除二聚体后文件的细胞名称，即可得到取出后的交集
4.整个包的使用流程参考链接里的protocol文章，本篇只是对代码进行入门介绍
注意：不要用Improved_Seurat_Pre_Process，会报错
代码来源：
https://github.com/JonathanShor/DoubletDetection https://github.com/EDePasquale/DoubletDecon/blob/master/seurat-3.0.R
参考文献：
https://www.cell.com/cell-reports/fulltext/S2211-1247(19)31286-0 https://www.biorxiv.org/content/10.1101/2020.04.23.058156v1.full
第一步 安装包
if(!require(devtools
)){
install
.packages("devtools") # If not already installed
}
devtools
::install_github('EDePasquale/DoubletDecon')
第二步 加载包
library(plyr
)
library(dplyr
)
library(Matrix
)
library(ggplot2
)
library(cowplot
)
library(Seurat
)
library(harmony
)
library(DoubletDecon
)
library(clusterProfiler
)
第三步 读取数据
sce
.10x
<- Read10X(data
.dir
= 'D:/HSW/HD/scRNA-seq/')
testdata_1
<- CreateSeuratObject(counts
= sce
.10x
,
project
= "testdata_1",
min
.cells
=3, min
.features
=500)
testdata_1
第四步 质量控制
testdata_1
[["percent.mt"]] <- PercentageFeatureSet(testdata_1
, pattern
= "^MT-")
testdata_1
[["percent.HB"]]<-PercentageFeatureSet(testdata_1
,features
="HBB")
VlnPlot(testdata_1
, features
= c("nFeature_RNA", "nCount_RNA", "percent.mt","percent.HB"), ncol
= 2)
plot1
<- FeatureScatter(testdata_1
, feature1
= "nCount_RNA", feature2
= "percent.mt")
plot2
<- FeatureScatter(testdata_1
, feature1
= "nCount_RNA", feature2
= "nFeature_RNA")
CombinePlots(plots
= list(plot1
, plot2
))
testdata_1
<- subset(testdata_1
,
subset
= nFeature_RNA
> 200 & nFeature_RNA
< 4000 & percent
.mt
< 10 & percent
.HB < 7)
dim(testdata_1
)
testdata_1
第五步 标准流程 Pipeline
testdata_1
<- NormalizeData(testdata_1
)
testdata_1
<- FindVariableFeatures(testdata_1
, selection
.method
= "vst", nfeatures
= 3000, verbose
= FALSE)
testdata_1
<- ScaleData(testdata_1
, verbose
= FALSE)
testdata_1
<- RunPCA(testdata_1
, features
= VariableFeatures(object
=testdata_1
))
testdata_1
<- JackStraw(testdata_1
, num
.replicate
= 100,dims
= 50)
testdata_1
<- ScoreJackStraw(testdata_1
, dims
= 1:20)
JackStrawPlot(testdata_1
, dims
=1:20)
ElbowPlot(testdata_1
,ndims
=50)
testdata_1
<- FindNeighbors(testdata_1
, dims
= 1:10)
testdata_1
<- FindClusters(testdata_1
, resolution
= 0.6)
testdata_1
<- RunTSNE(testdata_1
, dims
= 1:10)
testdata_1
<- RunUMAP(testdata_1
, dims
= 1:10)
testdata_1
.markers
<- FindAllMarkers(testdata_1
, only
.pos
= TRUE, min
.pct
= 0.25, logfc
.threshold
= 0.25)
testdata_1
.markers
%>% group_by(cluster
) %>% top_n(n
= 2, wt
= avg_logFC
)
top50
<- testdata_1
.markers
%>% group_by(cluster
) %>% top_n(n
= 50, wt
= avg_logFC
)
第六步 Seurat数据导出——Output
write
.table(top50
,file
="Top50Genes.txt",sep
="\t",col
.names
= NA)
write
.table(x
= Idents(object
= testdata_1
),"Cluster.txt",sep
="\t",col
.names
= NA)
data
<- testdata_1@assays$
RNA@data
write
.table(data
,file
="counts.txt",sep
="\t",col
.names
= NA)
第七步 数据预处理（读入DoubletDecon）Seurat_Pre_Process()
注意：Improved_Seurat_Pre_Process会报错，别用。先把需要的数据导出来
location
="D:/HSW/HD/scRNA-seq/"
expressionFile
=paste0(location
, "counts.txt")
genesFile
=paste0(location
, "Top50Genes.txt")
clustersFile
=paste0(location
, "Cluster.txt")
newFiles
=Seurat_Pre_Process(expressionFile
, genesFile
, clustersFile
)
filename
="test_example"
第八步 二聚体分析
results
=Main_Doublet_Decon(rawDataFile
=newFiles$newExpressionFile
,
groupsFile
=newFiles$newGroupsFile
,
filename
=filename
,
location
=location
,
fullDataFile
=NULL,
removeCC
=FALSE,
species
="hsa",
rhop
=1.1,
write
=TRUE,
PMF=TRUE,
useFull
=FALSE,
heatmap
=FALSE,
centroids
=TRUE,
num_doubs
=100,
only50
=FALSE,
min_uniq
=4,
nCores
=-1)
第九步 提取过滤二聚体后的细胞名称
LIST<-row
.names(results$Final_nondoublets_groups
)
head(LIST)
第十步 细胞名称连接符号变了，要换回seurat的版本
LIST=gsub('[.]','-',LIST)
第十一步 取交集，所谓去除二聚体后的seurat对象
testdata_1_RemoveDoublet
<-subset(x
= testdata_1
, cells
=LIST)
包信息
sessionInfo()
R version
4.0.2 (2020-06-22)
Platform
: x86_64
-w64
-mingw32
/x64 (64-bit
)
Running under
: Windows
10 x64 (build
19041)
Matrix products
: default
locale
:
[1] LC_COLLATE=Chinese (Simplified
)_China
.936 LC_CTYPE=Chinese (Simplified
)_China
.936
[3] LC_MONETARY=Chinese (Simplified
)_China
.936 LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified
)_China
.936
attached base packages
:
[1] stats4 parallel grid stats graphics grDevices utils datasets methods
[10] base
other attached packages
:
[1] Matrix_1
.2-18 shiny_1
.5.0 stringr_1
.4.0 doParallel_1
.0.15
[5] iterators_1
.0.12 foreach_1
.5.0 R.utils_2
.10.1 R.oo_1
.24.0
[9] R.methodsS3_1
.8.1 tidyr_1
.1.1 mygene_1
.24.0 GenomicFeatures_1
.40.1
[13] AnnotationDbi_1
.50.3 GenomicRanges_1
.40.0 GenomeInfoDb_1
.24.2 IRanges_2
.22.2
[17] S4Vectors_0
.26.1 MCL_1.0 plyr_1
.8.6 gplots_3
.0.4
[21] DeconRNASeq_1
.30.0 pcaMethods_1
.80.0 Biobase_2
.48.0 BiocGenerics_0
.34.0
[25] limSolve_1
.5.6 dplyr_1
.0.1 clusterProfiler_3
.16.1 ROCR_1.0-11
[29] KernSmooth_2
.23-17 fields_11
.5 spam_2
.5-1 dotCall64_1
.0-0
[33] DoubletFinder_2
.0.3 harmony_1
.0 Rcpp_1
.0.5 Seurat_3
.2.0
[37] sctransform_0
.2.1 cowplot_1
.0.0 ggplot2_3
.3.2 DoubletDecon_1
.1.6
loaded via a
namespace (and not attached
):
[1] rappdirs_0
.3.1 rtracklayer_1
.48.0 knitr_1
.29
[4] bit64_4
.0.5 DelayedArray_0
.14.1 irlba_2
.3.3
[7] data
.table_1
.13.0 rpart_4
.1-15 RCurl_1
.98-1.2
[10] generics_0
.0.2 callr_3
.4.3 usethis_1
.6.1
[13] RSQLite_2
.2.0 RANN_2.6.1 europepmc_0
.4
[16] future_1
.18.0 chron_2
.3-56 bit_4
.0.4
[19] enrichplot_1
.8.1 spatstat
.data_1
.4-3 xml2_1
.3.2
[22] httpuv_1
.5.4 SummarizedExperiment_1
.18.2 assertthat_0
.2.1
[25] viridis_0
.5.1 xfun_0
.16 hms_0
.5.3
[28] promises_1
.1.1 fansi_0
.4.1 progress_1
.2.2
[31] caTools_1
.18.0 dbplyr_1
.4.4 igraph_1
.2.5
[34] DBI_1.1.0 htmlwidgets_1
.5.1 purrr_0
.3.4
[37] ellipsis_0
.3.1 RSpectra_0
.16-0 backports_1
.1.8
[40] biomaRt_2
.44.1 deldir_0
.1-28 vctrs_0
.3.2
[43] remotes_2
.2.0 abind_1
.4-5 withr_2
.2.0
[46] ggforce_0
.3.2 triebeard_0
.3.0 checkmate_2
.0.0
[49] GenomicAlignments_1
.24.0 prettyunits_1
.1.1 goftest_1
.2-2
[52] cluster_2
.1.0 DOSE_3.14.0 ape_5
.4-1
[55] lazyeval_0
.2.2 crayon_1
.3.4 pkgconfig_2
.0.3
[58] labeling_0
.3 tweenr_1
.0.1 nlme_3
.1-148
[61] pkgload_1
.1.0 nnet_7
.3-14 devtools_2
.3.1
[64] rlang_0
.4.7 globals_0
.12.5 lifecycle_0
.2.0
[67] miniUI_0
.1.1.1 downloader_0
.4 BiocFileCache_1
.12.1
[70] rsvd_1
.0.3 rprojroot_1
.3-2 polyclip_1
.10-0
[73] matrixStats_0
.56.0 lmtest_0
.9-37 urltools_1
.7.3
[76] zoo_1
.8-8 base64enc_0
.1-3 ggridges_0
.5.2
[79] processx_3
.4.3 png_0
.1-7 viridisLite_0
.3.0
[82] bitops_1
.0-6 Biostrings_2
.56.0 blob_1
.2.1
[85] qvalue_2
.20.0 jpeg_0
.1-8.1 gridGraphics_0
.5-0
[88] scales_1
.1.1 lpSolve_5
.6.15 memoise_1
.1.0
[91] magrittr_1
.5 ica_1
.0-2 gdata_2
.18.0
[94] zlibbioc_1
.34.0 compiler_4
.0.2 scatterpie_0
.1.5
[97] RColorBrewer_1
.1-2 fitdistrplus_1
.1-1 Rsamtools_2
.4.0
[100] cli_2
.0.2 XVector_0
.28.0 listenv_0
.8.0
[103] patchwork_1
.0.1 pbapply_1
.4-2 ps_1
.3.4
[106] htmlTable_2
.1.0 Formula_1
.2-3 MASS_7.3-51.6
[109] mgcv_1
.8-31 tidyselect_1
.1.0 stringi_1
.4.6
[112] GOSemSim_2
.14.2 askpass_1
.1 latticeExtra_0
.6-29
[115] ggrepel_0
.8.2 fastmatch_1
.1-0 tools_4
.0.2
[118] future
.apply_1
.6.0 rstudioapi_0
.11 foreign_0
.8-80
[121] gridExtra_2
.3 farver_2
.0.3 Rtsne_0
.15
[124] ggraph_2
.0.3 digest_0
.6.25 rvcheck_0
.1.8
[127] BiocManager_1
.30.10 proto_1
.0.0 quadprog_1
.5-8
[130] later_1
.1.0.1 RcppAnnoy_0
.0.16 httr_1
.4.2
[133] colorspace_1
.4-1 XML_3.99-0.5 fs_1
.5.0
[136] tensor_1
.5 reticulate_1
.16 splines_4
.0.2
[139] uwot_0
.1.8 expm_0
.999-5 spatstat
.utils_1
.17-0
[142] graphlayouts_0
.7.0 ggplotify_0
.0.5 plotly_4
.9.2.1
[145] sessioninfo_1
.1.1 xtable_1
.8-4 jsonlite_1
.7.0
[148] spatstat_1
.64-1 tidygraph_1
.2.0 testthat_2
.3.2
[151] R6_2.4.1 Hmisc_4
.4-1 gsubfn_0
.7
[154] pillar_1
.4.6 htmltools_0
.5.0 mime_0
.9
[157] glue_1
.4.1 fastmap_1
.0.1 BiocParallel_1
.22.0
[160] codetools_0
.2-16 maps_3
.3.0 fgsea_1
.14.0
[163] pkgbuild_1
.1.0 utf8_1
.1.4 lattice_0
.20-41
[166] tibble_3
.0.3 sqldf_0
.4-11 curl_4
.3
[169] leiden_0
.3.3 gtools_3
.8.2 GO.db_3
.11.4
[172] openssl_1
.4.2 survival_3
.2-3 limma_3
.44.3
[175] desc_1
.2.0 munsell_0
.5.0 DO.db_2
.9
[178] GenomeInfoDbData_1
.2.3 reshape2_1
.4.4 gtable_0
.3.0
作者简介
何世伟 复旦大学医学博士生 厦门大学公共卫生硕士 研究方向：儿科学、生物信息学、表观遗传流行病学、循证医学 联系方式：swheok@foxmail.com
