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="":

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:

## 
## =====================
## | 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] 0.9666915

After 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.

## 
## =====================
## | 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 version 4.2.2 (2022-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
## 
## locale:
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##  [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       
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] ggplot2_3.4.0               tibble_3.1.8               
##  [3] SummarizedExperiment_1.28.0 Biobase_2.58.0             
##  [5] GenomicRanges_1.50.1        GenomeInfoDb_1.34.3        
##  [7] IRanges_2.32.0              S4Vectors_0.36.0           
##  [9] MatrixGenerics_1.10.0       matrixStats_0.62.0         
## [11] knitr_1.40                  sesame_1.16.1              
## [13] sesameData_1.16.0           ExperimentHub_2.6.0        
## [15] AnnotationHub_3.6.0         BiocFileCache_2.6.0        
## [17] dbplyr_2.2.1                BiocGenerics_0.44.0        
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##  [7] bslib_0.4.1                   utf8_1.2.2                   
##  [9] R6_2.5.1                      DBI_1.1.3                    
## [11] colorspace_2.0-3              withr_2.5.0                  
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## [15] bit_4.0.4                     curl_4.3.3                   
## [17] compiler_4.2.2                cli_3.4.1                    
## [19] DelayedArray_0.24.0           labeling_0.4.2               
## [21] sass_0.4.2                    scales_1.2.1                 
## [23] readr_2.1.3                   rappdirs_0.3.3               
## [25] stringr_1.4.1                 digest_0.6.30                
## [27] rmarkdown_2.18                XVector_0.38.0               
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## [31] highr_0.9                     fastmap_1.1.0                
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## [55] grid_4.2.2                    blob_1.2.3                   
## [57] ggrepel_0.9.2                 parallel_4.2.2               
## [59] promises_1.2.0.1              crayon_1.5.2                 
## [61] lattice_0.20-45               Biostrings_2.66.0            
## [63] hms_1.1.2                     KEGGREST_1.38.0              
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## [67] codetools_0.2-18              glue_1.6.2                   
## [69] BiocVersion_3.16.0            evaluate_0.18                
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