Quality Control
5 May 2023
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):
## [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 version 4.3.0 RC (2023-04-13 r84269)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
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## BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
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