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] 0.9666915

After 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 4.4.0 alpha (2024-03-27 r86216)
## Platform: aarch64-apple-darwin20
## Running under: macOS Ventura 13.6.5
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] wheatmap_0.2.0              tidyr_1.3.1                
##  [3] dplyr_1.1.4                 ggplot2_3.5.0              
##  [5] tibble_3.2.1                SummarizedExperiment_1.34.0
##  [7] Biobase_2.64.0              GenomicRanges_1.56.0       
##  [9] GenomeInfoDb_1.40.0         IRanges_2.38.0             
## [11] S4Vectors_0.42.0            MatrixGenerics_1.16.0      
## [13] matrixStats_1.2.0           knitr_1.45                 
## [15] sesame_1.22.0               sesameData_1.21.10         
## [17] ExperimentHub_2.12.0        AnnotationHub_3.12.0       
## [19] BiocFileCache_2.12.0        dbplyr_2.5.0               
## [21] BiocGenerics_0.50.0        
## 
## loaded via a namespace (and not attached):
##  [1] DBI_1.2.2               rlang_1.1.3             magrittr_2.0.3         
##  [4] compiler_4.4.0          RSQLite_2.3.5           mgcv_1.9-1             
##  [7] png_0.1-8               vctrs_0.6.5             reshape2_1.4.4         
## [10] stringr_1.5.1           pkgconfig_2.0.3         crayon_1.5.2           
## [13] fastmap_1.1.1           XVector_0.44.0          fontawesome_0.5.2      
## [16] labeling_0.4.3          utf8_1.2.4              rmarkdown_2.26         
## [19] tzdb_0.4.0              UCSC.utils_1.0.0        preprocessCore_1.66.0  
## [22] purrr_1.0.2             bit_4.0.5               xfun_0.43              
## [25] zlibbioc_1.50.0         cachem_1.0.8            jsonlite_1.8.8         
## [28] blob_1.2.4              highr_0.10              DelayedArray_0.30.0    
## [31] BiocParallel_1.38.0     parallel_4.4.0          R6_2.5.1               
## [34] bslib_0.6.2             stringi_1.8.3           RColorBrewer_1.1-3     
## [37] jquerylib_0.1.4         Rcpp_1.0.12             readr_2.1.5            
## [40] splines_4.4.0           Matrix_1.7-0            tidyselect_1.2.1       
## [43] abind_1.4-5             yaml_2.3.8              codetools_0.2-19       
## [46] curl_5.2.1              lattice_0.22-6          plyr_1.8.9             
## [49] withr_3.0.0             KEGGREST_1.44.0         evaluate_0.23          
## [52] Biostrings_2.72.0       pillar_1.9.0            BiocManager_1.30.22    
## [55] filelock_1.0.3          generics_0.1.3          BiocVersion_3.19.1     
## [58] hms_1.1.3               munsell_0.5.0           scales_1.3.0           
## [61] BiocStyle_2.32.0        glue_1.7.0              tools_4.4.0            
## [64] grid_4.4.0              AnnotationDbi_1.66.0    colorspace_2.1-0       
## [67] nlme_3.1-164            GenomeInfoDbData_1.2.12 cli_3.6.2              
## [70] rappdirs_0.3.3          fansi_1.0.6             S4Arrays_1.4.0         
## [73] gtable_0.3.4            sass_0.4.9              digest_0.6.35          
## [76] SparseArray_1.4.0       ggrepel_0.9.5           farver_2.1.1           
## [79] memoise_2.0.1           htmltools_0.5.8         lifecycle_1.0.4        
## [82] httr_1.4.7              bit64_4.0.5             MASS_7.3-60.2