Comparison of characteristic features across count data sets
Description
Comparison of simulated transcript-level counts
Design formulae
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.
sim : ~ condition
GEUVADIS : ~ center + population Data set dimensions
These bar plots show the number of samples (columns) and features (rows) in each data set.
Number of samples (columns)
Number of features (rows)
Dispersion/BCV plots
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. If there are more than 50 samples in a data set, the dispersions are calculated using 50 randomly selected samples.
edgeR
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).
Pairwise comparisons - 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.
DESeq2
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).
Pairwise comparisons - DESeq2
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.
Mean-variance plots
These scatter plots show the relation between the empirical mean and variance of the features. 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.
Separate scatter plots
Overlaid scatter plots
Pairwise comparisons
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.
Library sizes
These plots illustrate the distribution of the total read count per sample, i.e., the column sums of the respective data matrices.
Separate histograms
Overlaid histograms
Density plots
Density plots (filled)
Box plots
Violin plots
Empirical cumulative distribution function
Pairwise comparisons
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.
TMM normalization factors
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).
Separate histograms
Overlaid histograms
Density plots
Density plots (filled)
Box plots
Violin plots
Empirical cumulative distribution function
Pairwise comparisons
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.
Effective library sizes
These plots show the distribution of the “effective library sizes”, defined as the total count per sample multiplied by the corresponding TMM normalization factor.
Separate histograms
Overlaid histograms
Density plots
Density plots (filled)
Box plots
Violin plots
Empirical cumulative distribution function
Pairwise comparisons
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.
Expression distributions (average log CPM)
The plots in this section show the distribution of average abundance values for the features. The abundances are log CPM values calculated by edgeR.
Separate histograms
Overlaid histograms
Density plots
Density plots (filled)
Box plots
Violin plots
Empirical cumulative distribution function
Pairwise comparisons
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.
Fraction zeros per sample
These plots show the distribution of the fraction of zeros observed per sample (column) in the count matrices.
Separate histograms
Overlaid histograms
Density plots
Density plots (filled)
Box plots
Violin plots
Empirical cumulative distribution function
Pairwise comparisons
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.
Fraction zeros per feature
These plots illustrate the distribution of the fraction of zeros observed per feature (row) in the count matrices.
Separate histograms
Overlaid histograms
Density plots
Density plots (filled)
Box plots
Violin plots
Empirical cumulative distribution function
Pairwise comparisons
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.
Sample-sample correlations
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 50 samples in a data set, the pairwise correlations between 50 randomly selected samples are shown.
Separate histograms
Overlaid histograms
Density plots
Density plots (filled)
Box plots
Violin plots
Empirical cumulative distribution function
Pairwise comparisons
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.
Feature-feature correlations
These plots illustrate the distribution of Spearman correlation coefficients for pairs of features, calculated from the log(CPM) values obtained via the cpm function from edgeR, with a prior.count of 2. Only non-constant features are considered, and if there are more than 50 such features in a data set, the pairwise correlations between 50 randomly selected features are shown.
Separate histograms
Overlaid histograms
Density plots
Density plots (filled)
Box plots
Violin plots
Empirical cumulative distribution function
Pairwise comparisons
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.
Library size vs fraction zeros
These scatter plots show the association between the total count (column sums) and the fraction of zeros observed per sample.
Separate scatter plots
Overlaid scatter plots
Pairwise comparisons
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.
Mean expression vs fraction zeros
These scatter plots show the association between the average abundance and the fraction of zeros observed per feature. The abundance is defined as the log(CPM) values as calculated by edgeR.
Separate scatter plots
Overlaid scatter plots
Pairwise comparisons
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.
Session info
## R version 3.5.0 (2018-04-23)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: OS X El Capitan 10.11.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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 datasets utils
## [8] methods base
##
## other attached packages:
## [1] bindrcpp_0.2.2 countsimQC_0.5.2
## [3] DESeq2_1.20.0 SummarizedExperiment_1.10.1
## [5] DelayedArray_0.6.0 BiocParallel_1.14.1
## [7] matrixStats_0.53.1 Biobase_2.40.0
## [9] GenomicRanges_1.32.2 GenomeInfoDb_1.16.0
## [11] IRanges_2.14.9 S4Vectors_0.18.1
## [13] BiocGenerics_0.26.0 rmarkdown_1.9
## [15] BiocInstaller_1.30.0 devtools_1.13.5
##
## loaded via a namespace (and not attached):
## [1] httr_1.3.1 tidyr_0.8.1 edgeR_3.22.2
## [4] bit64_0.9-7 splines_3.5.0 assertthat_0.2.0
## [7] Formula_1.2-3 latticeExtra_0.6-28 blob_1.1.1
## [10] GenomeInfoDbData_1.1.0 yaml_2.1.19 pillar_1.2.2
## [13] RSQLite_2.1.1 backports_1.1.2 lattice_0.20-35
## [16] limma_3.36.1 glue_1.2.0 digest_0.6.15
## [19] RColorBrewer_1.1-2 XVector_0.20.0 checkmate_1.8.5
## [22] colorspace_1.3-2 htmltools_0.3.6 Matrix_1.2-14
## [25] plyr_1.8.4 pkgconfig_2.0.1 XML_3.98-1.11
## [28] genefilter_1.62.0 zlibbioc_1.26.0 purrr_0.2.5
## [31] xtable_1.8-2 scales_0.5.0 git2r_0.21.0
## [34] htmlTable_1.11.2 tibble_1.4.2 annotate_1.58.0
## [37] ggplot2_2.2.1 DT_0.4 withr_2.1.2
## [40] nnet_7.3-12 lazyeval_0.2.1 survival_2.42-3
## [43] magrittr_1.5 memoise_1.1.0 evaluate_0.10.1
## [46] foreign_0.8-70 tools_3.5.0 data.table_1.11.2
## [49] stringr_1.3.1 munsell_0.4.3 locfit_1.5-9.1
## [52] cluster_2.0.7-1 AnnotationDbi_1.42.1 compiler_3.5.0
## [55] caTools_1.17.1 rlang_0.2.1 randtests_1.0
## [58] grid_3.5.0 RCurl_1.95-4.10 rstudioapi_0.7
## [61] htmlwidgets_1.2 labeling_0.3 bitops_1.0-6
## [64] base64enc_0.1-3 gtable_0.2.0 curl_3.2
## [67] DBI_1.0.0 R6_2.2.2 gridExtra_2.3
## [70] knitr_1.20 dplyr_0.7.5 bit_1.1-12
## [73] bindr_0.1.1 Hmisc_4.1-1 rprojroot_1.3-2
## [76] stringi_1.2.2 Rcpp_0.12.17 geneplotter_1.58.0
## [79] rpart_4.1-13 acepack_1.4.1References
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.