In xiaolicbs/samplyzer:

This is a R Markdown Notebook for utilzing functions of the genotypeQC R library. When you execute code within the notebook, the results appear beneath the code.

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Install required libraries

This library required R version >= 3.3.2.

#install.packages('devtools')
library(devtools)
install_github('xiaolicbs/samplyzer')
library(samplyzer)

Load input files

setwd('/Users/xiaoli/samplyzer/vignettes/data')
bamQcMetr = read.csv('bamQcMetr.tsv', sep = '\t')
vcfQcMetr = read.csv('vcfQcMetr.tsv', sep = '\t')
annotations = read.csv('sampleAnnotations.tsv', sep = '\t')
samplepc = read.csv('samplePCs.tsv', sep = '\t')
refpc = read.csv('refPCs.tsv', sep ='\t')
stratify = c('ANCESTRY', 'SeqTech')

Construct an Sample Dataset object

sds = sampleDataset(bamQcMetr = bamQcMetr, vcfQcMetr = vcfQcMetr, 
                    annotations = annotations, primaryID = 'SampleID')

Attributes of sample data set

A Sample Data Set object contains several attributes, including a data frame, which contains all the data for samples and several other attributes that contains metadata, such as qcMetrics, bamQcMetr, vcfQcMetr and PrimaryID. To check attributes of your current SDS:

attributes(sds)

Modify attributes of sample data set by

Function setAttr can be used to modify attributes of SampleDataset. For example, to add genotype PCs to the SampleDataset:

sds = setAttr(sds, attributes = 'PC', data = samplepc, primaryID = 'SampleID')

Access attributes of a SampleDataset

Function 'getAttr' was designed to access attributes of a SampleDataset, for example, you can access sample annotations via:

getAttr(sds, 'PC', showID = T)

To know how many attributes are there, use:

attributes(sds)
print(sds)

Infer ancestry from

sds = inferAncestry(sds, trainSet = refpc[,c('PC1', 'PC2', 'PC3')], knownAncestry = refpc$group )
getAttr(sds, 'inferredAncestry', showID = T)

Perform a stratified QC metrics analysis

sds = calZscore(sds, strat = c('inferredAncestry', 'SeqTech'), qcMetrics = sds$vcfQcMetr)

Filter samples with hard cutoffs and z-scores

cutoffs = data.frame(
  qcMetrics = c('Percent_lt_30bp_frags', 'Contamination_Estimation', 'Percent_Chimeric_Reads'),
  value = c(0.01, 0.02, 0.05),
  greater = c(T, T, T),
  stringsAsFactors = F
)
sds = flagSamples(sds, cutoffs = cutoffs, zscore = 4)
getAttr(sds, 'flaggedReason', showID = T) # show samples flagged

Save SampleDataset

A S3 class function save is used to save SampleDataset to different formats.

prefix = 'examples'
save(sds, RDS = paste(prefix, 'RDS', sep = '.'),
     tsv = paste(prefix, 'tsv', sep = '.'),
     xls = paste(prefix, 'xls', sep = '.'))

Visualize a QC metric by annotations

plt = sampleQcPlot(sds, annotation = 'inferredAncestry', qcMetrics = 'nHets', geom = 'scatter',
             main = 'nHet by inferredAncestry', outliers = 'Sample-001', show = T)

nhet = sampleQcPlot(sds, annotation = 'inferredAncestry', qcMetrics = 'nHets', geom = 'scatter',
             main = 'nHet by inferredAncestry', outliers = 'Sample-040', show = T)

plots = PCplots(sds)

Generate multipanel plots for

grobList = sampleQcPlot(sds, qcMetrics = sds$bamQcMetr, annotation = 'LCSETMax', geom = 'scatter', ncols = 3, outliers = 'Sample-001')
ggplot2::ggsave('QcPlot.pdf', grobList, width = 12, height = 6)