evaluation of simulated 10X PBMC dataset
The following model formulae were used in the dispersion calculations for the different data sets. Note that if a count matrix or data frame was provided in place of a DESeqDataSet for some data set, the corresponding design formula is set to ~1
, thus assuming that all samples are to be treated as replicates.
original : ~ celltype
zingeR : ~ grp
These bar plots show the number of samples (columns) and variables (rows) in each data set.
Disperson/BCV plots show the association between the average abundance and the dispersion or “biological coefficient of variation” (sqrt(dispersion)), as calculated by edgeR
(Robinson, McCarthy, and Smyth 2010) and DESeq2
(Love, Huber, and Anders 2014). In the edgeR
plot, the estimate of the prior degrees of freedom is indicated.
The black dots represent the tagwise dispersion estimates, the red line the common dispersion and the blue curve represents the trended dispersion estimates. For further information about the dispersion estimation in edgeR
, see Chen, Lun, and Smyth (2014).
No statistics were calculated, since the ‘calculateStatistics’ argument to ‘countsimQCReport()’ was set to FALSE. To perform pairwise quantitative comparisons between data sets, set this argument to TRUE. Note, however, that this increases the runtime significantly.
The black dots are the gene-wise dispersion estimates, the red curve the fitted mean-dispersion relationship and the blue circles represent the final dispersion estimates.For further information about the dispersion estimation in DESeq2
, see Love, Huber, and Anders (2014).
No statistics were calculated, since the ‘calculateStatistics’ argument to ‘countsimQCReport()’ was set to FALSE. To perform pairwise quantitative comparisons between data sets, set this argument to TRUE. Note, however, that this increases the runtime significantly.
These scatter plots show the relation between the empirical mean and variance of the variables. The difference between these mean-variance plots and the mean-dispersion plots above is that the plots in this section do not take the information about the experimental design and sample grouping into account, but simply display the mean and variance of log2(CPM) estimates across all samples, calculated using the cpm
function from edgeR
(Robinson, McCarthy, and Smyth 2010), with a prior count of 2.
No statistics were calculated, since the ‘calculateStatistics’ argument to ‘countsimQCReport()’ was set to FALSE. To perform pairwise quantitative comparisons between data sets, set this argument to TRUE. Note, however, that this increases the runtime significantly.
These plots illustrate the distribution of the total read count per sample, i.e., the column sums of the respective data matrices.
No statistics were calculated, since the ‘calculateStatistics’ argument to ‘countsimQCReport()’ was set to FALSE. To perform pairwise quantitative comparisons between data sets, set this argument to TRUE. Note, however, that this increases the runtime significantly.
The plots below show the distribution of the TMM normalization factors (Robinson and Oshlack 2010), intended to adjust for differences in RNA composition, as calculated by edgeR
(Robinson, McCarthy, and Smyth 2010).
No statistics were calculated, since the ‘calculateStatistics’ argument to ‘countsimQCReport()’ was set to FALSE. To perform pairwise quantitative comparisons between data sets, set this argument to TRUE. Note, however, that this increases the runtime significantly.
These plots show the distribution of the “effective library sizes”, defined as the total count per sample multiplied by the corresponding TMM normalization factor.
No statistics were calculated, since the ‘calculateStatistics’ argument to ‘countsimQCReport()’ was set to FALSE. To perform pairwise quantitative comparisons between data sets, set this argument to TRUE. Note, however, that this increases the runtime significantly.
The plots in this section show the distribution of average abundance values for the variables. The abundances are log CPM values calculated by edgeR
.
No statistics were calculated, since the ‘calculateStatistics’ argument to ‘countsimQCReport()’ was set to FALSE. To perform pairwise quantitative comparisons between data sets, set this argument to TRUE. Note, however, that this increases the runtime significantly.
These plots show the distribution of the fraction of zeros observed per sample (column) in the count matrices.
No statistics were calculated, since the ‘calculateStatistics’ argument to ‘countsimQCReport()’ was set to FALSE. To perform pairwise quantitative comparisons between data sets, set this argument to TRUE. Note, however, that this increases the runtime significantly.
These plots illustrate the distribution of the fraction of zeros observed per variable (row) in the count matrices.
No statistics were calculated, since the ‘calculateStatistics’ argument to ‘countsimQCReport()’ was set to FALSE. To perform pairwise quantitative comparisons between data sets, set this argument to TRUE. Note, however, that this increases the runtime significantly.
The plots below show the distribution of Spearman correlation coefficients for pairs of samples, calculated from the log(CPM) values obtained via the cpm
function from edgeR
, with a prior.count of 2. If there are more than r maxNForCorr
samples in a data set, the pairwise correlations between r maxNForCorr
randomly selected samples are shown.
No statistics were calculated, since the ‘calculateStatistics’ argument to ‘countsimQCReport()’ was set to FALSE. To perform pairwise quantitative comparisons between data sets, set this argument to TRUE. Note, however, that this increases the runtime significantly.
These plots illustrate the distribution of Spearman correlation coefficients for pairs of variables, calculated from the log(CPM) values obtained via the cpm
function from edgeR
, with a prior.count of 2. Only non-constant variables are considered, and if there are more than 500 such variables in a data set, the pairwise correlations between 500 randomly selected variables are shown.
No statistics were calculated, since the ‘calculateStatistics’ argument to ‘countsimQCReport()’ was set to FALSE. To perform pairwise quantitative comparisons between data sets, set this argument to TRUE. Note, however, that this increases the runtime significantly.
These scatter plots show the association between the total count (column sums) and the fraction of zeros observed per sample.
No statistics were calculated, since the ‘calculateStatistics’ argument to ‘countsimQCReport()’ was set to FALSE. To perform pairwise quantitative comparisons between data sets, set this argument to TRUE. Note, however, that this increases the runtime significantly.
These scatter plots show the association between the average abundance and the fraction of zeros observed per variable. The abundance is defined as the log(CPM) values as calculated by edgeR
.
No statistics were calculated, since the ‘calculateStatistics’ argument to ‘countsimQCReport()’ was set to FALSE. To perform pairwise quantitative comparisons between data sets, set this argument to TRUE. Note, however, that this increases the runtime significantly.
## R version 3.4.1 (2017-06-30)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: OS X El Capitan 10.11.6
##
## Matrix products: default
## BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] bindrcpp_0.2 cellrangerRkit_2.0.0
## [3] Rmisc_1.5 plyr_1.8.4
## [5] lattice_0.20-35 bit64_0.9-7
## [7] bit_1.1-12 Matrix_1.2-11
## [9] countsimQC_0.5.0 MultiAssayExperiment_1.2.1
## [11] mgcv_1.8-18 nlme_3.1-131
## [13] cowplot_0.8.0 ggplot2_2.2.1
## [15] knitr_1.17 RColorBrewer_1.1-2
## [17] MAST_1.2.1 genefilter_1.58.1
## [19] iCOBRA_1.4.0 DESeq2_1.17.13
## [21] scales_0.5.0 edgeR_3.19.3
## [23] limma_3.32.5 doParallel_1.0.10
## [25] iterators_1.0.8 foreach_1.4.3
## [27] BiocParallel_1.10.1 zinbwave_0.99.6
## [29] SummarizedExperiment_1.6.3 DelayedArray_0.2.7
## [31] matrixStats_0.52.2 Biobase_2.36.2
## [33] GenomicRanges_1.28.4 GenomeInfoDb_1.12.2
## [35] IRanges_2.10.2 S4Vectors_0.14.3
## [37] BiocGenerics_0.22.0
##
## loaded via a namespace (and not attached):
## [1] Rtsne_0.13 colorspace_1.3-2
## [3] rprojroot_1.2 htmlTable_1.9
## [5] XVector_0.16.0 base64enc_0.1-3
## [7] gsl_1.9-10.3 DT_0.2
## [9] AnnotationDbi_1.38.2 mvtnorm_1.0-6
## [11] codetools_0.2-15 splines_3.4.1
## [13] zingeR_0.1.0 geneplotter_1.54.0
## [15] Formula_1.2-2 annotate_1.54.0
## [17] cluster_2.0.6 pheatmap_1.0.8
## [19] shinydashboard_0.6.1 stabledist_0.7-1
## [21] copula_0.999-17 shiny_1.0.5
## [23] compiler_3.4.1 backports_1.1.0
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## [27] acepack_1.4.1 htmltools_0.3.6
## [29] tools_3.4.1 gtable_0.2.0
## [31] glue_1.1.1 GenomeInfoDbData_0.99.0
## [33] reshape2_1.4.2 dplyr_0.7.4
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## [41] gtools_3.5.0 XML_3.98-1.9
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## [53] stringi_1.1.5 RSQLite_2.0
## [55] pcaPP_1.9-72 checkmate_1.8.3
## [57] caTools_1.17.1 rlang_0.1.2
## [59] pkgconfig_2.0.1 bitops_1.0-6
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## [63] ROCR_1.0-7 bindr_0.1
## [65] labeling_0.3 htmlwidgets_0.9
## [67] magrittr_1.5 R6_2.2.2
## [69] gplots_3.0.1 Hmisc_4.0-3
## [71] ADGofTest_0.3 DBI_0.7
## [73] foreign_0.8-69 survival_2.41-3
## [75] abind_1.4-5 RCurl_1.95-4.8
## [77] nnet_7.3-12 tibble_1.3.4
## [79] pspline_1.0-18 KernSmooth_2.23-15
## [81] rmarkdown_1.6 locfit_1.5-9.1
## [83] grid_3.4.1 data.table_1.10.4-2
## [85] blob_1.1.0 digest_0.6.12
## [87] xtable_1.8-2 tidyr_0.7.1
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## [91] munsell_0.4.3 glmnet_2.0-10
Chen, Yunshun, Aaron TL Lun, and Gordon K Smyth. 2014. “Differential Expression Analysis of Complex RNA-Seq Experiments Using edgeR.” In Statistical Analysis of Next Generation Sequence Data. Somnath Datta and Daniel S Nettleton (Eds), Springer, New York. https://link.springer.com/chapter/10.1007%2F978-3-319-07212-8_3.
Love, Michael I, Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2.” Genome Biology 15: 550. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8.
Robinson, Mark D, and Alicia Oshlack. 2010. “A Scaling Normalization Method for Differential Expression Analysis of RNA-Seq Data.” Genome Biology 11: R25. https://genomebiology.biomedcentral.com/articles/10.1186/gb-2010-11-3-r25.
Robinson, Mark D, Davis J McCarthy, and Gordon K Smyth. 2010. “edgeR-a Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data.” Bioinformatics 26: 139–40. https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btp616.