Nothing
In GWASinspector: Comprehensive and Easy to Use Quality Control of GWAS Results
htmltools::img(src = knitr::image_uri(system.file("extdata", "DNA.png", package = "GWASinspector")),
alt = 'logo',
style = 'position:absolute; top:0; right:20%; padding:10px;')
QC Start Time: r format(.QC$thisStudy$starttime, "%b %d %Y - %X")
QC End time: r format( .QC$thisStudy$endtime, "%b %d %Y - %X")
Script version: r .QC$script.version
Alterative header file: r basename(.QC$config$supplementaryFiles$header_translations)
Allele frequency reference dataset: r basename(.QC$config$supplementaryFiles$allele_ref_std)
`r if(!is.na(.QC$config$supplementaryFiles$allele_ref_alt)){
paste(sprintf("Allele frequency alternative reference dataset:\t%s" ,
basename(.QC$config$supplementaryFiles$allele_ref_alt)))
}
`
`r if(!is.na(.QC$config$supplementaryFiles$beta_ref_std)){
paste(sprintf("Effect-size reference dataset:\t%s" ,
basename(.QC$config$supplementaryFiles$beta_ref_std)))
}
`
Filter values for selecting High-Quality (HQ) variants
`r count.table <- t(data.table(
"Allele frequency" = format(.QC$config$filters$HQfilter_FRQ,
scientific = FALSE),
"HWE p-value" = format(.QC$config$filters$HQfilter_HWE,
scientific = FALSE),
"Call-rate" = format(.QC$config$filters$HQfilter_cal,
scientific = FALSE),
"Imputation quality" = format(.QC$config$filters$HQfilter_imp,
scientific = FALSE)))
colnames(count.table) <- 'Value'
kable(count.table,escape='FALSE', align = "c",format = "html")
`
Input File Description
Input File: r basename(.QC$thisStudy$file.path)
`r count.table <- t(data.table(
"input variant count" = format(.QC$thisStudy$input.data.rowcount, big.mark="," , scientific = FALSE),
"Missing crucial variable" = calculatePercent(.QC$thisStudy$missing.crucial.rowcount,
.QC$thisStudy$input.data.rowcount,
pretty = T),
"Duplicated variants" = calculatePercent(.QC$thisStudy$duplicate.count,
.QC$thisStudy$input.data.rowcount,
pretty = T),
"variant count after step 1"= calculatePercent(.QC$thisStudy$rowcount.step1,
.QC$thisStudy$input.data.rowcount,
decimal.place=3,
pretty = T),
"Monomorphic variants" = calculatePercent(.QC$thisStudy$monomorphic.count,
.QC$thisStudy$input.data.rowcount,
pretty = T)))
colnames(count.table) <- 'Count'
rownames(count.table)[1] <- "Input variant count"
rownames(count.table)[4] <- sprintf("Variant count after step 1")
if (.QC$thisStudy$x.chr.count.removed > 0)
count.table <- rbind(count.table, t(data.table("Removed X variants" = calculatePercent(.QC$thisStudy$x.chr.count.removed,
.QC$thisStudy$input.data.rowcount,
pretty = T))))
if (.QC$thisStudy$y.chr.count.removed > 0)
count.table <- rbind(count.table, t(data.table("Removed Y variants" = calculatePercent(.QC$thisStudy$y.chr.count.removed,
.QC$thisStudy$input.data.rowcount,
pretty = T))))
if (.QC$thisStudy$xy.chr.count.removed > 0)
count.table <- rbind(count.table, t(data.table("Removed XY variants" = calculatePercent(.QC$thisStudy$xy.chr.count.removed,
.QC$thisStudy$input.data.rowcount,
pretty = T))))
if (.QC$thisStudy$m.chr.count.removed > 0)
count.table <- rbind(count.table, t(data.table("Removed M variants" = calculatePercent(.QC$thisStudy$m.chr.count.removed,
.QC$thisStudy$input.data.rowcount,
pretty = T))))
count.table2 <- t(data.table(
"variant count after step 2" = calculatePercent(.QC$thisStudy$rowcount.step2,
.QC$thisStudy$input.data.rowcount,
decimal.place=3,
pretty = T),
"variant count after step 3"= calculatePercent(.QC$thisStudy$rowcount.step3,
.QC$thisStudy$input.data.rowcount,
decimal.place=4,
pretty = T)))
rownames(count.table2)[1] <- sprintf("Variant count after step 2 ")
rownames(count.table2)[2] <- sprintf("Variant count after step 3 ")
count.table <- rbind(count.table,count.table2)
kable(count.table,escape='FALSE', align = "l",format = "html")
`
Column names
`r column.tbl <- rbind(.QC$thisStudy$original.File.Columns.sorted,
.QC$thisStudy$renamed.File.Columns.sorted)
rownames(column.tbl) <- c('Original', 'Renamed')
kable(column.tbl, align = "c" , format = "html")
`
Header values are translated according to 'header_translations' file.
- missing columns =
r paste(.QC$thisStudy$missing.Columns,collapse = ' | ')
Variant counts
`r count.table <- t(data.table(
"High Quality variants" = calculatePercent(.QC$thisStudy$HQ.count,
.QC$thisStudy$rowcount.step3,
pretty = T),
"Low Quality variants" = calculatePercent(.QC$thisStudy$LQ.count,
.QC$thisStudy$rowcount.step3,
pretty = T),
"Palindromic variants" = calculatePercent(.QC$thisStudy$palindromic.rows,
.QC$thisStudy$rowcount.step3,
pretty = T),
"Non-Palindromic variants" = calculatePercent(.QC$thisStudy$non.palindromic.rows,
.QC$thisStudy$rowcount.step3,
pretty = T),
"Palindromic variants with high allele frequency difference (> 0.15)" = calculatePercent(.QC$thisStudy$palindormicHighDiffEAF,
.QC$thisStudy$palindromic.rows,
pretty = T),
"Non-palindromic variants with high allele frequency difference (> 0.15)" = calculatePercent(.QC$thisStudy$nonpalindormicHighDiffEAF ,
.QC$thisStudy$non.palindromic.rows,
pretty = T,
"Palindromic variants with opposite allele frequency \"compared to the reference\" (> 0.65 for the input file and < 0.35 for the reference, or vice versa)" = calculatePercent(.QC$thisStudy$palindormicExtremeDiffEAF ,
.QC$thisStudy$palindromic.rows,
pretty = T)))
colnames(count.table) <- 'Count'
kable(count.table,escape='FALSE', align = "l",format = "html")
`
The results from matching variants with reference datasets
References used for variant matching
`r match.table1 <- .QC$thisStudy$tables$match.ref.table
colnames(match.table1)[colnames(match.table1) == 'Std_ref'] <- 'Standard Reference'
match.table <- data.table(apply(match.table1,2, function(x)
return(calculatePercent(x,
.QC$thisStudy$rowcount.step2,
pretty = T,
decimal.place = 3)
)
))
match.table <- cbind(colnames(match.table1),match.table)
colnames(match.table) <- c('Reference' ,'Count')
kable(match.table,escape='FALSE', align = "l",format = "html")
`
Variant types
r if(nrow(.QC$thisStudy$tables$multi_allele_count_preProcess) > 1)
kable(.QC$thisStudy$tables$multi_allele_count_preProcess,escape='FALSE', align = "l",format = "html")
Result of matching with standard reference dataset
Allele frequency reference dataset: r basename(.QC$config$supplementaryFiles$allele_ref_std)
`r count.table1 <- t(data.table(
"Verified variants" = calculatePercent(.QC$thisStudy$found.rows.std,
.QC$thisStudy$rowcount.step2,
decimal.place=3,
pretty=TRUE),
"Not-found variants" = calculatePercent(.QC$thisStudy$not.found.rows.std,
.QC$thisStudy$rowcount.step2,
decimal.place=3,
pretty=TRUE),
"Flipped variants" = calculatePercent(.QC$thisStudy$flipped.rows.std,
.QC$thisStudy$found.rows.std,
pretty=TRUE),
"Switched variants" = calculatePercent(.QC$thisStudy$switched.rows.std,
.QC$thisStudy$found.rows.std,
pretty=TRUE),
"Allele frequency correlation" = '',
"r (all variants)" = .QC$thisStudy$AFcor.std_ref,
"r (palindromic)" = .QC$thisStudy$AFcor.palindromic.std_ref,
"r (non-palindromic)" = .QC$thisStudy$AFcor.non.palindromic.std_ref,
"r (INDEL)" = .QC$thisStudy$AFcor.std_ref.indel ))
colnames(count.table1) <- 'Count'
kable(count.table1,escape='FALSE', align = "c",format = "html")
`
r
if(file.exists(.QC$thisStudy$stdMafPlotPath) & .QC$graphic.device != 'tiff')
{
paste(' <h4 class="header2">Standard reference allele-frequency comparison plot</h4>')
htmltools::img(src = knitr::image_uri(.QC$thisStudy$stdMafPlotPath),
alt = 'MAF',
style = 'width:100%')
}
r if(!is.na(.QC$config$supplementaryFiles$allele_ref_alt))
paste('<h4 class="header2">Result of matching with alternative reference dataset</h4>')
`r if(!is.na(.QC$config$supplementaryFiles$allele_ref_alt)){
count.table2 <- t(data.table(
"Verified variants" = calculatePercent(.QC$thisStudy$found.rows.alt ,
.QC$thisStudy$not.found.rows.std,
decimal.place=3,
pretty=TRUE),
"Not-found variants" = calculatePercent(.QC$thisStudy$not.found.rows.alt ,
.QC$thisStudy$not.found.rows.std,
decimal.place=3,
pretty=TRUE),
"Flipped variants" = calculatePercent(.QC$thisStudy$flipped.rows.alt ,
.QC$thisStudy$found.rows.alt,
pretty=TRUE),
"Switched variants" = calculatePercent(.QC$thisStudy$switched.rows.alt ,
.QC$thisStudy$found.rows.alt,
pretty=TRUE),
"Allele frequency correlation" = '',
"r (all variants)" = .QC$thisStudy$AFcor.alt_ref,
"r (palindromic)" = .QC$thisStudy$AFcor.palindromic.alt_ref,
"r (non-palindromic)" = .QC$thisStudy$AFcor.non.palindromic.alt_ref))
colnames(count.table2) <- 'Count'
kable(count.table2,escape='FALSE', align = "c",format = "html")
}
`
r
if(file.exists(.QC$thisStudy$altMafPlotPath) & .QC$graphic.device != 'tiff')
{
paste(' <h4 class="header2">Standard reference allele-frequency comparison plot</h4>')
htmltools::img(src = knitr::image_uri(.QC$thisStudy$altMafPlotPath),
alt = 'MAF',
style = 'width:100%')
}
QC summary statistics
P-value correlation (observed vs expected)
Note: Only variants with a valid P-value are used for P-value correlation calculation.
`r count.table1 <- t(data.table(
"Included variants" = calculatePercent(.QC$thisStudy$rownum.PVcor,
.QC$thisStudy$rowcount.step3,
pretty = T),
"r (all variants)" = .QC$thisStudy$PVcor))
colnames(count.table1) <- 'Value'
kable(count.table1,escape='FALSE', align = "c",format = "html")
`
`r
if(file.exists(.QC$thisStudy$pvalCorPlotPath) & .QC$graphic.device != 'tiff')
{
paste('
P-value correlation plot
')
htmltools::img(src = knitr::image_uri(.QC$thisStudy$pvalCorPlotPath),
alt = 'MAF',
style = 'width:50%')
}
`
Distribution statistics
`r count.table2 <- t(data.table(
"Skewness" = .QC$thisStudy$skewness,
"Skewness (HQ)" = .QC$thisStudy$skewness.HQ,
"Kurtosis" = .QC$thisStudy$kurtosis,
"Kurtosis (HQ)" = .QC$thisStudy$kurtosis.HQ,
"Visscher's stat" = .QC$thisStudy$Visschers.stat ,
"Visscher's stat (HQ)" = .QC$thisStudy$Visschers.stat.HQ,
"Lambda - total" = .QC$thisStudy$lambda ,
"Lambda - genotyped" = .QC$thisStudy$lambda.gen,
"Lambda - imputed" = .QC$thisStudy$lambda.imp,
"Sample Size (max)" = .QC$thisStudy$MAX_N_TOTAL,
"Fixed HWE P-value" = .QC$thisStudy$fixed.hwep,
"Fixed Imputation Quality" = .QC$thisStudy$fixed.impq,
"Fixed Call Rate" = .QC$thisStudy$fixed.callrate,
"Fixed Sample Size" = .QC$thisStudy$fixed.n_total))
colnames(count.table2) <- 'Value'
kable(count.table2,escape='FALSE', align = "c",format = "html")
`
r
kable(t(.QC$thisStudy$tables$variable.summary), format = "html")
Variable statistics
`r b <- t(data.frame('CHR' = c(abs(study$column.NA.list$CHR - study$column.INVALID.list$CHR) ,
study$column.INVALID.list$CHR,
' '),
'POSITION' = c(abs(study$column.NA.list$POSITION - study$column.INVALID.list$POSITION) ,
study$column.INVALID.list$POSITION,
' '),
'EFFECT_ALL' = c(abs(study$column.NA.list$EFFECT_ALL - study$column.INVALID.list$EFFECT_ALL) ,
study$column.INVALID.list$EFFECT_ALL,
' '),
'OTHER_ALL' = c(abs(study$column.NA.list$OTHER_ALL - study$column.INVALID.list$OTHER_ALL) ,
study$column.INVALID.list$OTHER_ALL,
' '),
'EFFECT' = c(abs(study$column.NA.list$EFFECT - study$column.INVALID.list$EFFECT) ,
study$column.INVALID.list$EFFECT,
' '),
'STDERR' = c(abs(study$column.NA.list$STDERR - study$column.INVALID.list$STDERR - study$column.INVALID.list$zero.STDERR) ,
study$column.INVALID.list$STDERR,
study$column.INVALID.list$zero.STDERR),
'EFF_ALL_FREQ' = c(abs(study$column.NA.list$EFF_ALL_FREQ - study$column.INVALID.list$EFF_ALL_FREQ - study$column.INVALID.list$minusone.EFF_ALL_FREQ),
study$column.INVALID.list$EFF_ALL_FREQ,
study$column.INVALID.list$minusone.EFF_ALL_FREQ),
'HWE_PVAL' = c(abs(study$column.NA.list$HWE_PVAL - study$column.INVALID.list$HWE_PVAL - study$column.INVALID.list$minusone.HWE_PVAL) ,
study$column.INVALID.list$HWE_PVAL,
study$column.INVALID.list$minusone.HWE_PVAL),
'PVALUE' = c(abs(study$column.NA.list$PVALUE - study$column.INVALID.list$PVALUE - study$column.INVALID.list$minusone.PVALUE) ,
study$column.INVALID.list$PVALUE,
study$column.INVALID.list$minusone.PVALUE),
'IMPUTED' = c(abs(study$column.NA.list$IMPUTED - study$column.INVALID.list$IMPUTED),
study$column.INVALID.list$IMPUTED,
' '),
'IMP_QUALITY' = c(abs(study$column.NA.list$IMP_QUALITY - study$column.INVALID.list$IMP_QUALITY) ,
study$column.INVALID.list$IMP_QUALITY,
' '),
'MARKER' = c(abs(study$column.NA.list$MARKER - study$column.INVALID.list$MARKER) ,
study$column.INVALID.list$MARKER,
' '),
'N_TOTAL' = c(abs(study$column.NA.list$N_TOTAL - study$column.INVALID.list$N_TOTAL) ,
study$column.INVALID.list$N_TOTAL,
' '),
'STRAND' = c(abs(study$column.NA.list$STRAND - study$column.INVALID.list$STRAND) ,
study$column.INVALID.list$STRAND,
' '),
'CALLRATE' = c(abs(study$column.NA.list$CALLRATE - study$column.INVALID.list$CALLRATE - study$column.INVALID.list$minusone.CALLRATE),
study$column.INVALID.list$CALLRATE ,
study$column.INVALID.list$minusone.CALLRATE)
))
colnames(b) <- c('NA values','Invalid values','Uncertain values')
kable(b, align = "c", escape= FALSE ,format = "html")
`
Chromosome
-
Variant count for each chromosome: `r
if(!is.na(study$tables$CHR.tbl))
{
tbl = t(.QC$thisStudy$tables$CHR.tbl)
rownames(tbl) <- c('Chromosome','Count')
kable(tbl, align = "c",format = "html")
} else{ paste('CHR column not found!')}
`
Effect Allele
- Allele distribution: `r
tbl = merge(.QC$thisStudy$tables$EFFECT_ALL.tbl,
.QC$thisStudy$tables$EFFECT_ALL.post.matching.tbl,
by="EFFECT_ALL",
all = T)
tbl = t(tbl)
rownames(tbl) <- c('Allele','Count (input file)','Count (post-matching)')
kable(tbl, align = "c",format = "html")
`
Other Allele
- Allele distribution: `r
tbl = merge(.QC$thisStudy$tables$OTHER_ALL.tbl,
.QC$thisStudy$tables$OTHER_ALL.post.matching.tbl,
by="OTHER_ALL",
all = T)
tbl = t(tbl)
rownames(tbl) <- c('Allele','Count (input file)','Count (post-matching)')
kable(tbl, align = "c",format = "html")
`
Strand
- Negative strand variants =
r .QC$thisStudy$neg.strand.count
Imputation status
`r tbl = .QC$thisStudy$tables$imputed.tbl
colnames(tbl) <- c('','Count')
kable(tbl, align = "c",format = "html")
`
POSITION
- Invalid values (< 0)
EFFECT
- Invalid values ( = -1)
STDERR
-
uncertain values ( = 0)
-
Invalid values ( < 0)
PVALUE
-
uncertain values ( = -1)
-
Invalid values ( > 1 or <= 0)
Allele frequency
-
Allele frequency = 0: r .QC$thisStudy$column.INVALID.list$zero.EFF_ALL_FREQ
-
Allele frequency = 1: r .QC$thisStudy$column.INVALID.list$one.EFF_ALL_FREQ
-
uncertain values ( = -1)
-
Invalid values ( < 0 or > 1)
HWE_PVAL
-
uncertain values ( = -1)
-
Invalid values ( > 1 or <= 0)
IMP_QUALITY
valid range for Imputation Quality is between r .QC$config$filters$minimal_impQ_value and r .QC$config$filters$maximal_impQ_value
CALLRATE
-
uncertain values (CALLRATE = -1)
-
Invalid values (> 1 or < 0)
N_TOTAL
- Invalid values (<= 0)
r if(!is.na(.QC$config$supplementaryFiles$beta_ref_std)){
paste('<h3 class="header1">Effect-size correlation</h3>')
}
`r if(!is.na(.QC$config$supplementaryFiles$beta_ref_std)){
paste('* r =', .QC$thisStudy$effect.rho_4)
}
`
`r
if(file.exists(.QC$thisStudy$effPlotPath) & .QC$graphic.device != 'tiff')
{
htmltools::img(src = knitr::image_uri(.QC$thisStudy$effPlotPath),
alt = 'MAF',
style = 'width:50%')
}
`
r if(file.exists(.QC$thisStudy$histPlotPath) & .QC$graphic.device != 'tiff')
paste('<h3 class="header1">Plots</h3>')
r
if(file.exists(.QC$thisStudy$histPlotPath) & .QC$graphic.device != 'tiff')
{
htmltools::img(src = knitr::image_uri(.QC$thisStudy$histPlotPath),
alt = 'MAF',
style = 'width:100%')
}
`r
if(file.exists(.QC$thisStudy$QQPlotPath) & .QC$graphic.device != 'tiff'){
htmltools::img(src = knitr::image_uri(.QC$thisStudy$QQPlotPath),
alt = 'MAF',
style = 'width:100%')
}
`
`r
if(file.exists(.QC$thisStudy$manPlotPath) & .QC$graphic.device != 'tiff')
{
htmltools::img(src = knitr::image_uri(.QC$thisStudy$manPlotPath),
alt = 'MAF',
style = 'width:100%')
}
`
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GWASinspector documentation built on Sept. 28, 2023, 1:06 a.m.
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htmltools::img(src = knitr::image_uri(system.file("extdata", "DNA.png", package = "GWASinspector")), alt = 'logo', style = 'position:absolute; top:0; right:20%; padding:10px;')
QC Start Time: r format(.QC$thisStudy$starttime, "%b %d %Y - %X")
QC End time: r format( .QC$thisStudy$endtime, "%b %d %Y - %X")
Script version: r .QC$script.version
Alterative header file: r basename(.QC$config$supplementaryFiles$header_translations)
Allele frequency reference dataset: r basename(.QC$config$supplementaryFiles$allele_ref_std)
`r if(!is.na(.QC$config$supplementaryFiles$allele_ref_alt)){ paste(sprintf("Allele frequency alternative reference dataset:\t%s" , basename(.QC$config$supplementaryFiles$allele_ref_alt))) }
`
`r if(!is.na(.QC$config$supplementaryFiles$beta_ref_std)){ paste(sprintf("Effect-size reference dataset:\t%s" , basename(.QC$config$supplementaryFiles$beta_ref_std))) }
`
Filter values for selecting High-Quality (HQ) variants
`r count.table <- t(data.table( "Allele frequency" = format(.QC$config$filters$HQfilter_FRQ, scientific = FALSE), "HWE p-value" = format(.QC$config$filters$HQfilter_HWE, scientific = FALSE), "Call-rate" = format(.QC$config$filters$HQfilter_cal, scientific = FALSE), "Imputation quality" = format(.QC$config$filters$HQfilter_imp, scientific = FALSE)))
colnames(count.table) <- 'Value' kable(count.table,escape='FALSE', align = "c",format = "html") `
Input File Description
Input File: r basename(.QC$thisStudy$file.path)
`r count.table <- t(data.table( "input variant count" = format(.QC$thisStudy$input.data.rowcount, big.mark="," , scientific = FALSE), "Missing crucial variable" = calculatePercent(.QC$thisStudy$missing.crucial.rowcount, .QC$thisStudy$input.data.rowcount, pretty = T), "Duplicated variants" = calculatePercent(.QC$thisStudy$duplicate.count, .QC$thisStudy$input.data.rowcount, pretty = T), "variant count after step 1"= calculatePercent(.QC$thisStudy$rowcount.step1, .QC$thisStudy$input.data.rowcount, decimal.place=3, pretty = T), "Monomorphic variants" = calculatePercent(.QC$thisStudy$monomorphic.count, .QC$thisStudy$input.data.rowcount, pretty = T)))
colnames(count.table) <- 'Count' rownames(count.table)[1] <- "Input variant count" rownames(count.table)[4] <- sprintf("Variant count after step 1")
if (.QC$thisStudy$x.chr.count.removed > 0) count.table <- rbind(count.table, t(data.table("Removed X variants" = calculatePercent(.QC$thisStudy$x.chr.count.removed, .QC$thisStudy$input.data.rowcount, pretty = T))))
if (.QC$thisStudy$y.chr.count.removed > 0)
count.table <- rbind(count.table, t(data.table("Removed Y variants" = calculatePercent(.QC$thisStudy$y.chr.count.removed,
.QC$thisStudy$input.data.rowcount,
pretty = T))))
if (.QC$thisStudy$xy.chr.count.removed > 0)
count.table <- rbind(count.table, t(data.table("Removed XY variants" = calculatePercent(.QC$thisStudy$xy.chr.count.removed,
.QC$thisStudy$input.data.rowcount,
pretty = T))))
if (.QC$thisStudy$m.chr.count.removed > 0)
count.table <- rbind(count.table, t(data.table("Removed M variants" = calculatePercent(.QC$thisStudy$m.chr.count.removed,
.QC$thisStudy$input.data.rowcount,
pretty = T))))
count.table2 <- t(data.table(
"variant count after step 2" = calculatePercent(.QC$thisStudy$rowcount.step2,
.QC$thisStudy$input.data.rowcount,
decimal.place=3,
pretty = T),
"variant count after step 3"= calculatePercent(.QC$thisStudy$rowcount.step3,
.QC$thisStudy$input.data.rowcount,
decimal.place=4,
pretty = T)))
rownames(count.table2)[1] <- sprintf("Variant count after step 2 ") rownames(count.table2)[2] <- sprintf("Variant count after step 3 ")
count.table <- rbind(count.table,count.table2)
kable(count.table,escape='FALSE', align = "l",format = "html")
`
Column names
`r column.tbl <- rbind(.QC$thisStudy$original.File.Columns.sorted, .QC$thisStudy$renamed.File.Columns.sorted) rownames(column.tbl) <- c('Original', 'Renamed')
kable(column.tbl, align = "c" , format = "html") `
Header values are translated according to 'header_translations' file.
- missing columns =
r paste(.QC$thisStudy$missing.Columns,collapse = ' | ')
Variant counts
`r count.table <- t(data.table(
"High Quality variants" = calculatePercent(.QC$thisStudy$HQ.count,
.QC$thisStudy$rowcount.step3,
pretty = T),
"Low Quality variants" = calculatePercent(.QC$thisStudy$LQ.count,
.QC$thisStudy$rowcount.step3,
pretty = T),
"Palindromic variants" = calculatePercent(.QC$thisStudy$palindromic.rows,
.QC$thisStudy$rowcount.step3,
pretty = T),
"Non-Palindromic variants" = calculatePercent(.QC$thisStudy$non.palindromic.rows,
.QC$thisStudy$rowcount.step3,
pretty = T),
"Palindromic variants with high allele frequency difference (> 0.15)" = calculatePercent(.QC$thisStudy$palindormicHighDiffEAF,
.QC$thisStudy$palindromic.rows,
pretty = T),
"Non-palindromic variants with high allele frequency difference (> 0.15)" = calculatePercent(.QC$thisStudy$nonpalindormicHighDiffEAF ,
.QC$thisStudy$non.palindromic.rows,
pretty = T,
"Palindromic variants with opposite allele frequency \"compared to the reference\" (> 0.65 for the input file and < 0.35 for the reference, or vice versa)" = calculatePercent(.QC$thisStudy$palindormicExtremeDiffEAF ,
.QC$thisStudy$palindromic.rows,
pretty = T)))
colnames(count.table) <- 'Count'
kable(count.table,escape='FALSE', align = "l",format = "html")
`
The results from matching variants with reference datasets
References used for variant matching
`r match.table1 <- .QC$thisStudy$tables$match.ref.table
colnames(match.table1)[colnames(match.table1) == 'Std_ref'] <- 'Standard Reference' match.table <- data.table(apply(match.table1,2, function(x) return(calculatePercent(x, .QC$thisStudy$rowcount.step2, pretty = T, decimal.place = 3) ) ))
match.table <- cbind(colnames(match.table1),match.table) colnames(match.table) <- c('Reference' ,'Count')
kable(match.table,escape='FALSE', align = "l",format = "html") `
Variant types
r if(nrow(.QC$thisStudy$tables$multi_allele_count_preProcess) > 1)
kable(.QC$thisStudy$tables$multi_allele_count_preProcess,escape='FALSE', align = "l",format = "html")
Result of matching with standard reference dataset
Allele frequency reference dataset:r basename(.QC$config$supplementaryFiles$allele_ref_std)
`r count.table1 <- t(data.table( "Verified variants" = calculatePercent(.QC$thisStudy$found.rows.std, .QC$thisStudy$rowcount.step2, decimal.place=3, pretty=TRUE), "Not-found variants" = calculatePercent(.QC$thisStudy$not.found.rows.std, .QC$thisStudy$rowcount.step2, decimal.place=3, pretty=TRUE), "Flipped variants" = calculatePercent(.QC$thisStudy$flipped.rows.std, .QC$thisStudy$found.rows.std, pretty=TRUE), "Switched variants" = calculatePercent(.QC$thisStudy$switched.rows.std, .QC$thisStudy$found.rows.std, pretty=TRUE), "Allele frequency correlation" = '', "r (all variants)" = .QC$thisStudy$AFcor.std_ref, "r (palindromic)" = .QC$thisStudy$AFcor.palindromic.std_ref, "r (non-palindromic)" = .QC$thisStudy$AFcor.non.palindromic.std_ref, "r (INDEL)" = .QC$thisStudy$AFcor.std_ref.indel ))
colnames(count.table1) <- 'Count' kable(count.table1,escape='FALSE', align = "c",format = "html") `
r
if(file.exists(.QC$thisStudy$stdMafPlotPath) & .QC$graphic.device != 'tiff')
{
paste(' <h4 class="header2">Standard reference allele-frequency comparison plot</h4>')
htmltools::img(src = knitr::image_uri(.QC$thisStudy$stdMafPlotPath),
alt = 'MAF',
style = 'width:100%')
}
r if(!is.na(.QC$config$supplementaryFiles$allele_ref_alt))
paste('<h4 class="header2">Result of matching with alternative reference dataset</h4>')
`r if(!is.na(.QC$config$supplementaryFiles$allele_ref_alt)){
count.table2 <- t(data.table( "Verified variants" = calculatePercent(.QC$thisStudy$found.rows.alt , .QC$thisStudy$not.found.rows.std, decimal.place=3, pretty=TRUE), "Not-found variants" = calculatePercent(.QC$thisStudy$not.found.rows.alt , .QC$thisStudy$not.found.rows.std, decimal.place=3, pretty=TRUE), "Flipped variants" = calculatePercent(.QC$thisStudy$flipped.rows.alt , .QC$thisStudy$found.rows.alt, pretty=TRUE), "Switched variants" = calculatePercent(.QC$thisStudy$switched.rows.alt , .QC$thisStudy$found.rows.alt, pretty=TRUE), "Allele frequency correlation" = '', "r (all variants)" = .QC$thisStudy$AFcor.alt_ref, "r (palindromic)" = .QC$thisStudy$AFcor.palindromic.alt_ref, "r (non-palindromic)" = .QC$thisStudy$AFcor.non.palindromic.alt_ref))
colnames(count.table2) <- 'Count' kable(count.table2,escape='FALSE', align = "c",format = "html")
} `
r
if(file.exists(.QC$thisStudy$altMafPlotPath) & .QC$graphic.device != 'tiff')
{
paste(' <h4 class="header2">Standard reference allele-frequency comparison plot</h4>')
htmltools::img(src = knitr::image_uri(.QC$thisStudy$altMafPlotPath),
alt = 'MAF',
style = 'width:100%')
}
QC summary statistics
P-value correlation (observed vs expected)
Note: Only variants with a valid P-value are used for P-value correlation calculation.
`r count.table1 <- t(data.table(
"Included variants" = calculatePercent(.QC$thisStudy$rownum.PVcor,
.QC$thisStudy$rowcount.step3,
pretty = T),
"r (all variants)" = .QC$thisStudy$PVcor))
colnames(count.table1) <- 'Value'
kable(count.table1,escape='FALSE', align = "c",format = "html") `
`r if(file.exists(.QC$thisStudy$pvalCorPlotPath) & .QC$graphic.device != 'tiff') { paste('
P-value correlation plot
')htmltools::img(src = knitr::image_uri(.QC$thisStudy$pvalCorPlotPath),
alt = 'MAF',
style = 'width:50%')
} `
Distribution statistics
`r count.table2 <- t(data.table( "Skewness" = .QC$thisStudy$skewness, "Skewness (HQ)" = .QC$thisStudy$skewness.HQ, "Kurtosis" = .QC$thisStudy$kurtosis, "Kurtosis (HQ)" = .QC$thisStudy$kurtosis.HQ, "Visscher's stat" = .QC$thisStudy$Visschers.stat , "Visscher's stat (HQ)" = .QC$thisStudy$Visschers.stat.HQ, "Lambda - total" = .QC$thisStudy$lambda , "Lambda - genotyped" = .QC$thisStudy$lambda.gen, "Lambda - imputed" = .QC$thisStudy$lambda.imp, "Sample Size (max)" = .QC$thisStudy$MAX_N_TOTAL, "Fixed HWE P-value" = .QC$thisStudy$fixed.hwep, "Fixed Imputation Quality" = .QC$thisStudy$fixed.impq, "Fixed Call Rate" = .QC$thisStudy$fixed.callrate, "Fixed Sample Size" = .QC$thisStudy$fixed.n_total))
colnames(count.table2) <- 'Value' kable(count.table2,escape='FALSE', align = "c",format = "html")
`
r
kable(t(.QC$thisStudy$tables$variable.summary), format = "html")
Variable statistics
`r b <- t(data.frame('CHR' = c(abs(study$column.NA.list$CHR - study$column.INVALID.list$CHR) , study$column.INVALID.list$CHR, ' '),
'POSITION' = c(abs(study$column.NA.list$POSITION - study$column.INVALID.list$POSITION) ,
study$column.INVALID.list$POSITION,
' '),
'EFFECT_ALL' = c(abs(study$column.NA.list$EFFECT_ALL - study$column.INVALID.list$EFFECT_ALL) ,
study$column.INVALID.list$EFFECT_ALL,
' '),
'OTHER_ALL' = c(abs(study$column.NA.list$OTHER_ALL - study$column.INVALID.list$OTHER_ALL) ,
study$column.INVALID.list$OTHER_ALL,
' '),
'EFFECT' = c(abs(study$column.NA.list$EFFECT - study$column.INVALID.list$EFFECT) ,
study$column.INVALID.list$EFFECT,
' '),
'STDERR' = c(abs(study$column.NA.list$STDERR - study$column.INVALID.list$STDERR - study$column.INVALID.list$zero.STDERR) ,
study$column.INVALID.list$STDERR,
study$column.INVALID.list$zero.STDERR),
'EFF_ALL_FREQ' = c(abs(study$column.NA.list$EFF_ALL_FREQ - study$column.INVALID.list$EFF_ALL_FREQ - study$column.INVALID.list$minusone.EFF_ALL_FREQ),
study$column.INVALID.list$EFF_ALL_FREQ,
study$column.INVALID.list$minusone.EFF_ALL_FREQ),
'HWE_PVAL' = c(abs(study$column.NA.list$HWE_PVAL - study$column.INVALID.list$HWE_PVAL - study$column.INVALID.list$minusone.HWE_PVAL) ,
study$column.INVALID.list$HWE_PVAL,
study$column.INVALID.list$minusone.HWE_PVAL),
'PVALUE' = c(abs(study$column.NA.list$PVALUE - study$column.INVALID.list$PVALUE - study$column.INVALID.list$minusone.PVALUE) ,
study$column.INVALID.list$PVALUE,
study$column.INVALID.list$minusone.PVALUE),
'IMPUTED' = c(abs(study$column.NA.list$IMPUTED - study$column.INVALID.list$IMPUTED),
study$column.INVALID.list$IMPUTED,
' '),
'IMP_QUALITY' = c(abs(study$column.NA.list$IMP_QUALITY - study$column.INVALID.list$IMP_QUALITY) ,
study$column.INVALID.list$IMP_QUALITY,
' '),
'MARKER' = c(abs(study$column.NA.list$MARKER - study$column.INVALID.list$MARKER) ,
study$column.INVALID.list$MARKER,
' '),
'N_TOTAL' = c(abs(study$column.NA.list$N_TOTAL - study$column.INVALID.list$N_TOTAL) ,
study$column.INVALID.list$N_TOTAL,
' '),
'STRAND' = c(abs(study$column.NA.list$STRAND - study$column.INVALID.list$STRAND) ,
study$column.INVALID.list$STRAND,
' '),
'CALLRATE' = c(abs(study$column.NA.list$CALLRATE - study$column.INVALID.list$CALLRATE - study$column.INVALID.list$minusone.CALLRATE),
study$column.INVALID.list$CALLRATE ,
study$column.INVALID.list$minusone.CALLRATE)
))
colnames(b) <- c('NA values','Invalid values','Uncertain values')
kable(b, align = "c", escape= FALSE ,format = "html")
`
Chromosome
-
Variant count for each chromosome: `r if(!is.na(study$tables$CHR.tbl)) { tbl = t(.QC$thisStudy$tables$CHR.tbl) rownames(tbl) <- c('Chromosome','Count')
kable(tbl, align = "c",format = "html") } else{ paste('CHR column not found!')} `
Effect Allele
- Allele distribution: `r
tbl = merge(.QC$thisStudy$tables$EFFECT_ALL.tbl, .QC$thisStudy$tables$EFFECT_ALL.post.matching.tbl, by="EFFECT_ALL", all = T) tbl = t(tbl)
rownames(tbl) <- c('Allele','Count (input file)','Count (post-matching)')
kable(tbl, align = "c",format = "html") `
Other Allele
- Allele distribution: `r
tbl = merge(.QC$thisStudy$tables$OTHER_ALL.tbl, .QC$thisStudy$tables$OTHER_ALL.post.matching.tbl, by="OTHER_ALL", all = T) tbl = t(tbl)
rownames(tbl) <- c('Allele','Count (input file)','Count (post-matching)')
kable(tbl, align = "c",format = "html") `
Strand
- Negative strand variants =
r .QC$thisStudy$neg.strand.count
Imputation status
`r tbl = .QC$thisStudy$tables$imputed.tbl
colnames(tbl) <- c('','Count')
kable(tbl, align = "c",format = "html")
`
POSITION
- Invalid values (< 0)
EFFECT
- Invalid values ( = -1)
STDERR
-
uncertain values ( = 0)
-
Invalid values ( < 0)
PVALUE
-
uncertain values ( = -1)
-
Invalid values ( > 1 or <= 0)
Allele frequency
-
Allele frequency = 0:
r .QC$thisStudy$column.INVALID.list$zero.EFF_ALL_FREQ -
Allele frequency = 1:
r .QC$thisStudy$column.INVALID.list$one.EFF_ALL_FREQ -
uncertain values ( = -1)
-
Invalid values ( < 0 or > 1)
HWE_PVAL
-
uncertain values ( = -1)
-
Invalid values ( > 1 or <= 0)
IMP_QUALITY
valid range for Imputation Quality is between r .QC$config$filters$minimal_impQ_value and r .QC$config$filters$maximal_impQ_value
CALLRATE
-
uncertain values (CALLRATE = -1)
-
Invalid values (> 1 or < 0)
N_TOTAL
- Invalid values (<= 0)
r if(!is.na(.QC$config$supplementaryFiles$beta_ref_std)){
paste('<h3 class="header1">Effect-size correlation</h3>')
}
`r if(!is.na(.QC$config$supplementaryFiles$beta_ref_std)){ paste('* r =', .QC$thisStudy$effect.rho_4) }
`
`r if(file.exists(.QC$thisStudy$effPlotPath) & .QC$graphic.device != 'tiff') {
htmltools::img(src = knitr::image_uri(.QC$thisStudy$effPlotPath),
alt = 'MAF',
style = 'width:50%')
}
`
r if(file.exists(.QC$thisStudy$histPlotPath) & .QC$graphic.device != 'tiff')
paste('<h3 class="header1">Plots</h3>')
r
if(file.exists(.QC$thisStudy$histPlotPath) & .QC$graphic.device != 'tiff')
{
htmltools::img(src = knitr::image_uri(.QC$thisStudy$histPlotPath),
alt = 'MAF',
style = 'width:100%')
}
`r if(file.exists(.QC$thisStudy$QQPlotPath) & .QC$graphic.device != 'tiff'){ htmltools::img(src = knitr::image_uri(.QC$thisStudy$QQPlotPath), alt = 'MAF', style = 'width:100%') }
`
`r if(file.exists(.QC$thisStudy$manPlotPath) & .QC$graphic.device != 'tiff') { htmltools::img(src = knitr::image_uri(.QC$thisStudy$manPlotPath), alt = 'MAF', style = 'width:100%') }
`
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