Quality Control
29 October 2024
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
sesameQCtoDF(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):
## [1] 0.9666915After computing the QCs, one can optionally combine the
sesameQC objects into a data frame for easy comparison.
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.
##
## =====================
## | 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)##
## =====================
## | 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
## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [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
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ggplot2_3.5.1 tibble_3.2.1
## [3] SummarizedExperiment_1.37.0 Biobase_2.67.0
## [5] GenomicRanges_1.59.0 GenomeInfoDb_1.43.0
## [7] IRanges_2.41.0 S4Vectors_0.45.0
## [9] MatrixGenerics_1.19.0 matrixStats_1.4.1
## [11] knitr_1.48 sesame_1.25.0
## [13] sesameData_1.23.0 ExperimentHub_2.15.0
## [15] AnnotationHub_3.15.0 BiocFileCache_2.15.0
## [17] dbplyr_2.5.0 BiocGenerics_0.53.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 farver_2.1.2 dplyr_1.1.4
## [4] blob_1.2.4 filelock_1.0.3 Biostrings_2.75.0
## [7] fastmap_1.2.0 digest_0.6.37 lifecycle_1.0.4
## [10] KEGGREST_1.47.0 RSQLite_2.3.7 magrittr_2.0.3
## [13] compiler_4.5.0 rlang_1.1.4 sass_0.4.9
## [16] tools_4.5.0 utf8_1.2.4 yaml_2.3.10
## [19] labeling_0.4.3 S4Arrays_1.7.0 bit_4.5.0
## [22] curl_5.2.3 DelayedArray_0.33.0 plyr_1.8.9
## [25] RColorBrewer_1.1-3 abind_1.4-8 BiocParallel_1.41.0
## [28] withr_3.0.2 purrr_1.0.2 grid_4.5.0
## [31] preprocessCore_1.69.0 fansi_1.0.6 wheatmap_0.2.0
## [34] colorspace_2.1-1 scales_1.3.0 cli_3.6.3
## [37] rmarkdown_2.28 crayon_1.5.3 generics_0.1.3
## [40] reshape2_1.4.4 httr_1.4.7 tzdb_0.4.0
## [43] DBI_1.2.3 cachem_1.1.0 stringr_1.5.1
## [46] zlibbioc_1.53.0 parallel_4.5.0 AnnotationDbi_1.69.0
## [49] BiocManager_1.30.25 XVector_0.47.0 vctrs_0.6.5
## [52] Matrix_1.7-1 jsonlite_1.8.9 hms_1.1.3
## [55] ggrepel_0.9.6 bit64_4.5.2 fontawesome_0.5.2
## [58] jquerylib_0.1.4 glue_1.8.0 codetools_0.2-20
## [61] stringi_1.8.4 gtable_0.3.6 BiocVersion_3.21.1
## [64] UCSC.utils_1.3.0 munsell_0.5.1 pillar_1.9.0
## [67] rappdirs_0.3.3 htmltools_0.5.8.1 GenomeInfoDbData_1.2.13
## [70] R6_2.5.1 evaluate_1.0.1 lattice_0.22-6
## [73] highr_0.11 readr_2.1.5 png_0.1-8
## [76] BiocStyle_2.35.0 memoise_2.0.1 bslib_0.8.0
## [79] Rcpp_1.0.13 SparseArray_1.7.0 xfun_0.48
## [82] pkgconfig_2.0.3