# Quality Control

    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 `sesameQC`s 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))
    ## or a list of prefixes, with parallel processing
    sesameQCtoDF(openSesame(sprintf("%s/%s", idat_dir, idat_prefixes), prep="",
        func=sesameQC_calcStats, BPPARAM=BiocParallel::MulticoreParam(24)))

The results display `frac_dt_cg`, `RGratio`, `RGdistort` by default. For other QC metrics, 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)))

    ##           num_dtna frac_dtna num_dt   frac_dt num_dt_mk frac_dt_mk num_dt_cg
    ## PrEC.Rep1        0         0 838020 0.9666915    838020  0.9666915    835491
    ## PrEC.Rep2        0         0 838961 0.9677770    838961  0.9677770    836403
    ##           frac_dt_cg num_dt_ch frac_dt_ch num_dt_rs frac_dt_rs
    ## PrEC.Rep1  0.9671109      2471  0.8427694        58  0.9830508
    ## PrEC.Rep2  0.9681666      2501  0.8530014        57  0.9661017

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 \(\beta\) 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 4.6.1 (2026-06-24)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 26.04 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so;  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
    ##  [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: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] knitr_1.51                  wheatmap_0.2.4
    ##  [3] ggplot2_4.0.3               tidyr_1.3.2
    ##  [5] dplyr_1.2.1                 SummarizedExperiment_1.43.0
    ##  [7] Biobase_2.73.1              GenomicRanges_1.65.1
    ##  [9] Seqinfo_1.3.0               IRanges_2.47.2
    ## [11] S4Vectors_0.51.5            MatrixGenerics_1.25.0
    ## [13] matrixStats_1.5.0           sesame_1.31.3
    ## [15] sesameData_1.31.0           ExperimentHub_3.3.1
    ## [17] AnnotationHub_4.3.2         BiocFileCache_3.3.0
    ## [19] dbplyr_2.6.0                BiocGenerics_0.59.10
    ## [21] generics_0.1.4
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] tidyselect_1.2.1      farver_2.1.2          blob_1.3.0
    ##  [4] filelock_1.0.3        Biostrings_2.81.5     S7_0.2.2
    ##  [7] fastmap_1.2.0         digest_0.6.39         lifecycle_1.0.5
    ## [10] KEGGREST_1.53.5       RSQLite_3.53.3        magrittr_2.0.5
    ## [13] compiler_4.6.1        rlang_1.3.0           sass_0.4.10
    ## [16] tools_4.6.1           utf8_1.2.6            yaml_2.3.12
    ## [19] labeling_0.4.3        S4Arrays_1.13.0       bit_4.6.0
    ## [22] curl_7.1.0            DelayedArray_0.39.3   plyr_1.8.9
    ## [25] RColorBrewer_1.1-3    abind_1.4-8           BiocParallel_1.47.0
    ## [28] withr_3.0.3           purrr_1.2.2           sys_3.4.3
    ## [31] grid_4.6.1            preprocessCore_1.75.0 colorspace_2.1-3
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