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 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## 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] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] ggplot2_3.4.4               tibble_3.2.1               
##  [3] SummarizedExperiment_1.32.0 Biobase_2.62.0             
##  [5] MatrixGenerics_1.14.0       matrixStats_1.0.0          
##  [7] knitr_1.44                  S4Vectors_0.40.0           
##  [9] IRanges_2.36.0              GenomicRanges_1.54.0       
## [11] sesame_1.20.0               sesameData_1.19.0          
## [13] ExperimentHub_2.10.0        AnnotationHub_3.10.0       
## [15] BiocFileCache_2.10.0        dbplyr_2.3.4               
## [17] BiocGenerics_0.48.0        
## 
## loaded via a namespace (and not attached):
##  [1] DBI_1.1.3                     bitops_1.0-7                 
##  [3] rlang_1.1.1                   magrittr_2.0.3               
##  [5] compiler_4.3.1                RSQLite_2.3.1                
##  [7] png_0.1-8                     vctrs_0.6.4                  
##  [9] reshape2_1.4.4                stringr_1.5.0                
## [11] pkgconfig_2.0.3               crayon_1.5.2                 
## [13] fastmap_1.1.1                 XVector_0.42.0               
## [15] ellipsis_0.3.2                fontawesome_0.5.2            
## [17] labeling_0.4.3                utf8_1.2.4                   
## [19] promises_1.2.1                rmarkdown_2.25               
## [21] tzdb_0.4.0                    preprocessCore_1.64.0        
## [23] purrr_1.0.2                   bit_4.0.5                    
## [25] xfun_0.40                     zlibbioc_1.48.0              
## [27] cachem_1.0.8                  GenomeInfoDb_1.38.0          
## [29] jsonlite_1.8.7                blob_1.2.4                   
## [31] later_1.3.1                   DelayedArray_0.28.0          
## [33] BiocParallel_1.36.0           interactiveDisplayBase_1.40.0
## [35] parallel_4.3.1                R6_2.5.1                     
## [37] bslib_0.5.1                   stringi_1.7.12               
## [39] RColorBrewer_1.1-3            jquerylib_0.1.4              
## [41] Rcpp_1.0.11                   wheatmap_0.2.0               
## [43] readr_2.1.4                   httpuv_1.6.12                
## [45] Matrix_1.6-1.1                tidyselect_1.2.0             
## [47] abind_1.4-5                   yaml_2.3.7                   
## [49] codetools_0.2-19              curl_5.1.0                   
## [51] lattice_0.22-5                plyr_1.8.9                   
## [53] shiny_1.7.5.1                 withr_2.5.1                  
## [55] KEGGREST_1.42.0               evaluate_0.22                
## [57] Biostrings_2.70.0             pillar_1.9.0                 
## [59] BiocManager_1.30.22           filelock_1.0.2               
## [61] stats4_4.3.1                  generics_0.1.3               
## [63] RCurl_1.98-1.12               BiocVersion_3.18.0           
## [65] hms_1.1.3                     munsell_0.5.0                
## [67] scales_1.2.1                  BiocStyle_2.30.0             
## [69] xtable_1.8-4                  glue_1.6.2                   
## [71] tools_4.3.1                   grid_4.3.1                   
## [73] AnnotationDbi_1.64.0          colorspace_2.1-0             
## [75] GenomeInfoDbData_1.2.11       cli_3.6.1                    
## [77] rappdirs_0.3.3                fansi_1.0.5                  
## [79] S4Arrays_1.2.0                dplyr_1.1.3                  
## [81] gtable_0.3.4                  sass_0.4.7                   
## [83] digest_0.6.33                 SparseArray_1.2.0            
## [85] ggrepel_0.9.4                 farver_2.1.1                 
## [87] memoise_2.0.1                 htmltools_0.5.6.1            
## [89] lifecycle_1.0.3               httr_1.4.7                   
## [91] mime_0.12                     bit64_4.0.5