Quality Control
4 May 2023
library(sesame)
sesameDataCache()Calculate Quality Metrics
The main function to calculate the quality metrics is
sesameQC_calcStats. This function takes a SigDF, calculates
the QC statistics, and returns a single S4 sesameQC object,
which can be printed directly to the console. To calculate QC metrics on
a given list of samples or all IDATs in a folder, one can use
sesameQC_calcStats within the standard
openSesame pipeline. When used with
openSesame, a list of sesameQCs will be
returned. Note that one should turn off preprocessing using
prep="":
## calculate metrics on all IDATs in a specific folder
qcs = openSesame(idat_dir, prep="", func=sesameQC_calcStats)SeSAMe divides sample quality metrics into multiple groups. These groups are listed below and can be referred to by short keys. For example, “intensity” generates signal intensity-related quality metrics.
| Short.Key | Description |
|---|---|
| detection | Signal Detection |
| numProbes | Number of Probes |
| intensity | Signal Intensity |
| channel | Color Channel |
| dyeBias | Dye Bias |
| betas | Beta Value |
By default, sesameQC_calcStats calculates all QC groups.
To save time, one can compute a specific QC group by specifying one or
multiple short keys in the funs= argument:
sdfs <- sesameDataGet("EPIC.5.SigDF.normal")[1:2] # get two examples
## only compute signal detection stats
qcs = openSesame(sdfs, prep="", func=sesameQC_calcStats, funs="detection")
qcs[[1]]##
## =====================
## | Detection
## =====================
## N. Probes w/ Missing Raw Intensity : 0 (num_dtna)
## % Probes w/ Missing Raw Intensity : 0.0 % (frac_dtna)
## N. Probes w/ Detection Success : 838020 (num_dt)
## % Detection Success : 96.7 % (frac_dt)
## N. Detection Succ. (after masking) : 838020 (num_dt_mk)
## % Detection Succ. (after masking) : 96.7 % (frac_dt_mk)
## N. Probes w/ Detection Success (cg) : 835491 (num_dt_cg)
## % Detection Success (cg) : 96.7 % (frac_dt_cg)
## N. Probes w/ Detection Success (ch) : 2471 (num_dt_ch)
## % Detection Success (ch) : 84.3 % (frac_dt_ch)
## N. Probes w/ Detection Success (rs) : 58 (num_dt_rs)
## % Detection Success (rs) : 98.3 % (frac_dt_rs)We consider signal detection the most important QC metric.
One can retrieve the actual stat numbers from sesameQC
using the sesameQC_getStats (the following generates the fraction of
probes with detection success):
sesameQC_getStats(qcs[[1]], "frac_dt")## [1] 0.9666915After computing the QCs, one can optionally combine the
sesameQC objects into a data frame for easy comparison.
## combine a list of sesameQC into a data frame
head(do.call(rbind, lapply(qcs, as.data.frame)))Note that when the input is an SigDF object, calling
sesameQC_calcStats within openSesame and as a
standalone function are equivalent.
sdf <- sesameDataGet('EPIC.1.SigDF')
qc = openSesame(sdf, prep="", func=sesameQC_calcStats, funs=c("detection"))
## equivalent direct call
qc = sesameQC_calcStats(sdf, c("detection"))
qc##
## =====================
## | Detection
## =====================
## N. Probes w/ Missing Raw Intensity : 0 (num_dtna)
## % Probes w/ Missing Raw Intensity : 0.0 % (frac_dtna)
## N. Probes w/ Detection Success : 834922 (num_dt)
## % Detection Success : 96.3 % (frac_dt)
## N. Detection Succ. (after masking) : 834922 (num_dt_mk)
## % Detection Succ. (after masking) : 96.3 % (frac_dt_mk)
## N. Probes w/ Detection Success (cg) : 832046 (num_dt_cg)
## % Detection Success (cg) : 96.4 % (frac_dt_cg)
## N. Probes w/ Detection Success (ch) : 2616 (num_dt_ch)
## % Detection Success (ch) : 89.2 % (frac_dt_ch)
## N. Probes w/ Detection Success (rs) : 58 (num_dt_rs)
## % Detection Success (rs) : 98.3 % (frac_dt_rs)Rank Quality Metrics
SeSAMe features comparison of your sample with public data sets. The
sesameQC_rankStats() function ranks the input
sesameQC object with sesameQC calculated from
public datasets. It shows the rank percentage of the input sample as
well as the number of datasets compared.
sdf <- sesameDataGet('EPIC.1.SigDF')
qc <- sesameQC_calcStats(sdf, "intensity")
qc##
## =====================
## | Signal Intensity
## =====================
## Mean sig. intensity : 3171.21 (mean_intensity)
## Mean sig. intensity (M+U) : 6342.41 (mean_intensity_MU)
## Mean sig. intensity (Inf.II) : 2991.85 (mean_ii)
## Mean sig. intens.(I.Grn IB) : 3004.33 (mean_inb_grn)
## Mean sig. intens.(I.Red IB) : 4670.97 (mean_inb_red)
## Mean sig. intens.(I.Grn OOB) : 318.55 (mean_oob_grn)
## Mean sig. intens.(I.Red OOB) : 606.99 (mean_oob_red)
## N. NA in M (all probes) : 0 (na_intensity_M)
## N. NA in U (all probes) : 0 (na_intensity_U)
## N. NA in raw intensity (IG) : 0 (na_intensity_ig)
## N. NA in raw intensity (IR) : 0 (na_intensity_ir)
## N. NA in raw intensity (II) : 0 (na_intensity_ii)sesameQC_rankStats(qc, platform="EPIC")##
## =====================
## | Signal Intensity
## =====================
## Mean sig. intensity : 3171.21 (mean_intensity) - Rank 15.7% (N=636)
## Mean sig. intensity (M+U) : 6342.41 (mean_intensity_MU)
## Mean sig. intensity (Inf.II) : 2991.85 (mean_ii) - Rank 15.6% (N=636)
## Mean sig. intens.(I.Grn IB) : 3004.33 (mean_inb_grn) - Rank 7.5% (N=636)
## Mean sig. intens.(I.Red IB) : 4670.97 (mean_inb_red) - Rank 21.2% (N=636)
## Mean sig. intens.(I.Grn OOB) : 318.55 (mean_oob_grn) - Rank 4.2% (N=636)
## Mean sig. intens.(I.Red OOB) : 606.99 (mean_oob_red) - Rank 3.6% (N=636)
## N. NA in M (all probes) : 0 (na_intensity_M)
## N. NA in U (all probes) : 0 (na_intensity_U)
## N. NA in raw intensity (IG) : 0 (na_intensity_ig)
## N. NA in raw intensity (IR) : 0 (na_intensity_ir)
## N. NA in raw intensity (II) : 0 (na_intensity_ii)Quality Control Plots
SeSAMe provides functions to create QC plots. Some functions takes sesameQC as input while others directly plot the SigDF objects. Here are some examples:
sesameQC_plotBar()takes a list of sesameQC objects and creates bar plot for each metric calculated.sesameQC_plotRedGrnQQ()graphs the dye bias between the two color channels.sesameQC_plotIntensVsBetas()plots the relationship between β values and signal intensity and can be used to diagnose artificial readout and influence of signal background.sesameQC_plotHeatSNPs()plots SNP probes and can be used to detect sample swaps.
More about quality control plots can be found in Supplemental Vignette.
Session Info
sessionInfo()## R version 4.3.0 (2023-04-21)
## Platform: aarch64-apple-darwin22.3.0 (64-bit)
## Running under: macOS Ventura 13.2.1
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## Matrix products: default
## BLAS: /Users/zhouw3/.Renv/versions/4.3.0/lib/R/lib/libRblas.dylib
## LAPACK: /Users/zhouw3/.Renv/versions/4.3.0/lib/R/lib/libRlapack.dylib; LAPACK version 3.11.0
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## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
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## time zone: America/New_York
## tzcode source: internal
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] knitr_1.42 sesame_1.18.4 sesameData_1.18.0
## [4] ExperimentHub_2.8.0 AnnotationHub_3.8.0 BiocFileCache_2.8.0
## [7] dbplyr_2.3.2 BiocGenerics_0.46.0 rmarkdown_2.21
## [10] R6_2.5.1
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## [3] blob_1.2.4 filelock_1.0.2
## [5] Biostrings_2.68.0 bitops_1.0-7
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## [29] RColorBrewer_1.1-3 BiocParallel_1.34.0
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## [39] xtable_1.8-4 ggplot2_3.4.2
## [41] scales_1.2.1 SummarizedExperiment_1.30.1
## [43] cli_3.6.1 crayon_1.5.2
## [45] generics_0.1.3 reshape2_1.4.4
## [47] httr_1.4.5 tzdb_0.3.0
## [49] DBI_1.1.3 cachem_1.0.8
## [51] stringr_1.5.0 zlibbioc_1.46.0
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## [93] pkgconfig_2.0.3