表达矩阵处理—表达QC(reads)

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书籍翻译历史目录

⊙引言—关于课程

⊙scRNA-seq简介

⊙  scRNA-seq原始数据的质控

⊙  scRNA-seq数据处理—文件格式小结

⊙  scRNA-seq数据处理—demultiplexing

⊙  scRNA-seq数据的处理—STAR

⊙  scRNA-seq数据处理—Kallisto

⊙  scRNA-seq表达矩阵的构建

⊙  数据处理必备—R安装

⊙  数据处理基础—数据类型了解一下

⊙  数据处理基础—什么是整齐数据和Rich Data

⊙  数据处理基础—ggplot2了解一下

⊙  实例数据练习—Tabula Murlis

⊙表达矩阵处理—表达质量的控制

正

文

7. 清理表达矩阵

7.2

表达QC（reads）

library(SingleCellExperiment)
library(scater)
options(stringsAsFactors = FALSE)
reads <- read.table("tung/reads.txt", sep = "\t")
anno <- read.table("tung/annotation.txt", sep = "\t", header = TRUE)
head(reads[ , 1:3])
## NA19098.r1.A01 NA19098.r1.A02 NA19098.r1.A03
## ENSG00000237683 0 0 0
## ENSG00000187634 0 0 0
## ENSG00000188976 57 140 1
## ENSG00000187961 0 0 0
## ENSG00000187583 0 0 0
## ENSG00000187642 0 0 0
head(anno)
## individual replicate well batch sample_id
## 1 NA19098 r1 A01 NA19098.r1 NA19098.r1.A01
## 2 NA19098 r1 A02 NA19098.r1 NA19098.r1.A02
## 3 NA19098 r1 A03 NA19098.r1 NA19098.r1.A03
## 4 NA19098 r1 A04 NA19098.r1 NA19098.r1.A04
## 5 NA19098 r1 A05 NA19098.r1 NA19098.r1.A05
## 6 NA19098 r1 A06 NA19098.r1 NA19098.r1.A06
reads <- SingleCellExperiment(
assays = list(counts = as.matrix(reads)),
colData = anno
)
keep_feature <- rowSums(counts(reads) > 0) > 0
reads <- reads[keep_feature, ]
isSpike(reads, "ERCC") <- grepl("^ERCC-", rownames(reads))
isSpike(reads, "MT") <- rownames(reads) %in%
c("ENSG00000198899", "ENSG00000198727", "ENSG00000198888",
"ENSG00000198886", "ENSG00000212907", "ENSG00000198786",
"ENSG00000198695", "ENSG00000198712", "ENSG00000198804",
"ENSG00000198763", "ENSG00000228253", "ENSG00000198938",
"ENSG00000198840")
reads <- calculateQCMetrics(
reads,
feature_controls = list(
ERCC = isSpike(reads, "ERCC"),
MT = isSpike(reads, "MT")
)
)
hist(
reads$total_counts,
breaks = 100
)
abline(v = 1.3e6, col = "red")

filter_by_total_counts <- (reads$total_counts > 1.3e6)
table(filter_by_total_counts)
## filter_by_total_counts
## FALSE TRUE
## 180 684
hist(
reads$total_features,
breaks = 100
)
abline(v = 7000, col = "red")

filter_by_expr_features <- (reads$total_features > 7000)
table(filter_by_expr_features)
## filter_by_expr_features
## FALSE TRUE
## 116 748
plotPhenoData(
  reads,
  aes_string(
      x = "total_features",
      y = "pct_counts_MT",
      colour = "batch"
  )
)

plotPhenoData(
  reads,
  aes_string(
      x = "total_features",
      y = "pct_counts_ERCC",
      colour = "batch"
  )
)

filter_by_ERCC <-
reads$batch != "NA19098.r2" & reads$pct_counts_ERCC < 25
table(filter_by_ERCC)
## filter_by_ERCC
## FALSE TRUE
## 103 761
filter_by_MT <- reads$pct_counts_MT < 30
table(filter_by_MT)## filter_by_MT
## FALSE TRUE
## 18 846
reads$use <- (
# sufficient features (genes)
filter_by_expr_features &
# sufficient molecules counted
filter_by_total_counts &
# sufficient endogenous RNA
filter_by_ERCC &
# remove cells with unusual number of reads in MT genes
filter_by_MT
)
table(reads$use)
##
## FALSE TRUE
## 258 606
reads <- plotPCA(
  reads,
  size_by = "total_features",
  shape_by = "use",
  pca_data_input = "pdata",
  detect_outliers = TRUE,
  return_SCE = TRUE
)

table(reads$outlier)
##
## FALSE TRUE
## 756 108
library(limma)
##
## Attaching package: 'limma'
## The following object is masked from 'package:scater':
##
## plotMDS
## The following object is masked from 'package:BiocGenerics':
##
## plotMA
auto <- colnames(reads)[reads$outlier]
man <- colnames(reads)[!reads$use]
venn.diag <- vennCounts(
cbind(colnames(reads) %in% auto,
colnames(reads) %in% man)
)
vennDiagram(
venn.diag,
names = c("Automatic", "Manual"),
circle.col = c("blue", "green")
)

plotQC(reads, type = "highest-expression")

filter_genes <- apply(
counts(reads[, colData(reads)$use]),
1,
function(x) length(x[x > 1]) >= 2
)
rowData(reads)$use <- filter_genes
table(filter_genes)
## filter_genes
## FALSE TRUE
## 2664 16062
dim(reads[rowData(reads)$use, colData(reads)$use])
## [1] 16062 606
assay(reads, "logcounts_raw") <- log2(counts(reads) + 1)
reducedDim(reads) <- NULL
saveRDS(reads, file = "tung/reads.rds")

通过比较图7.6和图7.13，很明显基于read的过滤比基于UMI的分析去除了更多的细胞。如果您返回并比较结果，您应该能够得出结论，ERCC和MT过滤器对于基于read的分析更严格。

sessionInfo()
## R version 3.4.3 (2017-11-30)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 9 (stretch)
##
## Matrix products: default
## BLAS: /usr/lib/openblas-base/libblas.so.3
## LAPACK: /usr/lib/libopenblasp-r0.2.19.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 methods stats graphics grDevices utils
## [8] datasets base
##
## other attached packages:
## [1] limma_3.34.9 scater_1.6.3
## [3] ggplot2_2.2.1 SingleCellExperiment_1.0.0
## [5] SummarizedExperiment_1.8.1 DelayedArray_0.4.1
## [7] matrixStats_0.53.1 Biobase_2.38.0
## [9] GenomicRanges_1.30.3 GenomeInfoDb_1.14.0
## [11] IRanges_2.12.0 S4Vectors_0.16.0
## [13] BiocGenerics_0.24.0 knitr_1.20
##
## loaded via a namespace (and not attached):
## [1] backports_1.1.2 plyr_1.8.4 lazyeval_0.2.1
## [4] sp_1.2-7 shinydashboard_0.6.1 splines_3.4.3
## [7] digest_0.6.15 htmltools_0.3.6 viridis_0.5.0
## [10] magrittr_1.5 memoise_1.1.0 cluster_2.0.6
## [13] prettyunits_1.0.2 colorspace_1.3-2 blob_1.1.0
## [16] rrcov_1.4-3 xfun_0.1 dplyr_0.7.4
## [19] RCurl_1.95-4.10 tximport_1.6.0 lme4_1.1-15
## [22] bindr_0.1 zoo_1.8-1 glue_1.2.0
## [25] gtable_0.2.0 zlibbioc_1.24.0 XVector_0.18.0
## [28] MatrixModels_0.4-1 car_2.1-6 kernlab_0.9-25
## [31] prabclus_2.2-6 DEoptimR_1.0-8 SparseM_1.77
## [34] VIM_4.7.0 scales_0.5.0 sgeostat_1.0-27
## [37] mvtnorm_1.0-7 DBI_0.7 GGally_1.3.2
## [40] edgeR_3.20.9 Rcpp_0.12.15 sROC_0.1-2
## [43] viridisLite_0.3.0 xtable_1.8-2 progress_1.1.2
## [46] laeken_0.4.6 bit_1.1-12 mclust_5.4
## [49] vcd_1.4-4 httr_1.3.1 RColorBrewer_1.1-2
## [52] fpc_2.1-11 modeltools_0.2-21 pkgconfig_2.0.1
## [55] reshape_0.8.7 XML_3.98-1.10 flexmix_2.3-14
## [58] nnet_7.3-12 locfit_1.5-9.1 labeling_0.3
## [61] rlang_0.2.0 reshape2_1.4.3 AnnotationDbi_1.40.0
## [64] munsell_0.4.3 tools_3.4.3 RSQLite_2.0
## [67] pls_2.6-0 evaluate_0.10.1 stringr_1.3.0
## [70] cvTools_0.3.2 yaml_2.1.17 bit64_0.9-7
## [73] robustbase_0.92-8 bindrcpp_0.2 nlme_3.1-129
## [76] mime_0.5 quantreg_5.35 biomaRt_2.34.2
## [79] compiler_3.4.3 pbkrtest_0.4-7 beeswarm_0.2.3
## [82] e1071_1.6-8 tibble_1.4.2 robCompositions_2.0.6
## [85] pcaPP_1.9-73 stringi_1.1.6 highr_0.6
## [88] lattice_0.20-34 trimcluster_0.1-2 Matrix_1.2-7.1
## [91] nloptr_1.0.4 pillar_1.2.1 lmtest_0.9-35
## [94] data.table_1.10.4-3 cowplot_0.9.2 bitops_1.0-6
## [97] httpuv_1.3.6.1 R6_2.2.2 bookdown_0.7
## [100] gridExtra_2.3 vipor_0.4.5 boot_1.3-18
## [103] MASS_7.3-45 assertthat_0.2.0 rhdf5_2.22.0
## [106] rprojroot_1.3-2 rjson_0.2.15 GenomeInfoDbData_1.0.0
## [109] diptest_0.75-7 mgcv_1.8-23 grid_3.4.3
## [112] class_7.3-14 minqa_1.2.4 rmarkdown_1.8
## [115] mvoutlier_2.0.9 shiny_1.0.5 ggbeeswarm_0.6.0