htmltools::img(src = knitr::image_uri(system.file("extdata", "DNA.png", package = "GWASinspector")), 
               alt = 'logo', 
               style = 'position:absolute; top:0; right:20%; padding:10px;')


Start Time: r format(.QC$config$new_items$starttime, "%b %d %Y - %X")

End time: r format(.QC$config$new_items$endtime, "%b %d %Y - %X")

Script version: r .QC$script.version

System Information : r .QC$r.version


Input GWAS Files

`r report.table <- data.table(sapply(.QC$qc.study.list, function(x) return(paste0("",basename(x$file.path),""))))

report.table <- cbind(sapply(.QC$qc.study.list, function(x) return(x$number)) , report.table) colnames(report.table) <- c('File number', 'Input file name') row.names(report.table) <- sapply(.QC$qc.study.list, function(x) return(x$number))

kable(report.table, align = "c" ,escape = FALSE, format = "html") %>% kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = FALSE, position = "left") %>% kableExtra::column_spec(1, bold = TRUE,width="10em",extra_css='text-align:center', border_right = T) %>% kableExtra::column_spec(2, extra_css='text-align:left') `


Summary of the input GWAS files

`r report.table <- t(data.table( "Sample size (Max)" = sapply(.QC$qc.study.list, function(x) return(thousand.sep(x$MAX_N_TOTAL))),

    "<span class='mainItem'>Missing columns</span>" = sapply(.QC$qc.study.list, function(x) return(paste(x$missing.Columns, collapse = ' | '))),

   "<span class='mainItem'>Variants in input file</span>" = sapply(.QC$qc.study.list, function(x) return(thousand.sep(x$input.data.rowcount))), 
    "<span class='item'>Missing crucial variable</span>" = sapply(.QC$qc.study.list, function(x) return(calculatePercent(x$missing.crucial.rowcount,
                                              x$input.data.rowcount,
                                              pretty = T))),

   "<span class='mainItem'>Variant count after step 1</span>" = sapply(.QC$qc.study.list, function(x) return(calculatePercent(x$rowcount.step1,
                                                 x$input.data.rowcount,
                                                 decimal.place=3,
                                                 pretty = T))),

   "<span class='item'>Monomorphic variants</span>" = sapply(.QC$qc.study.list, function(x) return(calculatePercent(x$monomorphic.count,x$rowcount.step3,pretty=TRUE))),

   "<span class='mainItem'>Variant count after step 2</span>" = sapply(.QC$qc.study.list, function(x) return(calculatePercent(x$rowcount.step2,
                                                  x$input.data.rowcount,
                                                  decimal.place=3,
                                                  pretty = T))),
   "<span class='mainItem'>Variant count after step 3</span>" = sapply(.QC$qc.study.list, function(x) return(calculatePercent(x$rowcount.step3,
                                                   x$input.data.rowcount,
                                                   decimal.place=4,
                                                   pretty = T))),
   "<span class='mainItem'>SNP variants</span>" = sapply(.QC$qc.study.list, function(x) return(calculatePercent(sum(as.numeric(gsub(x=x$tables$multi_allele_count_preProcess[1:3],
                                                                                            pattern = ",",
                                                                                            replacement = ""))),
                                                   x$rowcount.step3,
                                                   decimal.place=4,
                                                   pretty = T))),
   "<span class='mainItem'>Non-SNP variants</span>" = sapply(.QC$qc.study.list, function(x) return(calculatePercent(sum(as.numeric(gsub(x=x$tables$multi_allele_count_preProcess[4:6],
                                                                                            pattern = ",",
                                                                                            replacement = ""))),
                                                   x$rowcount.step3,
                                                   decimal.place=4,
                                                   pretty = T))),
   "<span class='mainItem'>Palindromic variants</span>" = sapply(.QC$qc.study.list, function(x) return(calculatePercent(x$palindromic.rows,x$rowcount.step3,pretty=TRUE))),
   "<span class='mainItem'>Genotyped variants</span>" = sapply(.QC$qc.study.list, function(x) return(ifelse(x$tables$imputed.tbl[IMPUTED=='genotyped',.N] == 0,
                                             "NA",                                                 calculatePercent(as.numeric(x$tables$imputed.tbl[IMPUTED=="genotyped",N]),x$rowcount.step3,pretty=TRUE)))),
   "<span class='mainItem'>Imputed variants</span>" = sapply(.QC$qc.study.list, function(x) return(ifelse(x$tables$imputed.tbl[IMPUTED=='imputed',.N] == 0,
                                             "NA",                                                 calculatePercent(as.numeric(x$tables$imputed.tbl[IMPUTED=="imputed",N]),x$rowcount.step3,pretty=TRUE)))),
   "<span class='mainItem'>Negative-strand variants</span>" = sapply(.QC$qc.study.list, function(x) return(calculatePercent(x$neg.strand.count,x$rowcount.step3,pretty=TRUE))),
   "<span class='mainItem'>Allele frequency correlation</span>" = '',
   "<span class='item'>Standard reference</span>" = sapply(.QC$qc.study.list, function(x) return(x$AFcor.std_ref)),
   "<span class='item'>Alternative reference</span>" = sapply(.QC$qc.study.list, function(x) return(x$AFcor.alt_ref)),
   "<span class='mainItem'>Palindromic allele frequency correlation</span>" = '',
   "<span class='item'>Standard reference</span>" = sapply(.QC$qc.study.list, function(x) return(x$AFcor.palindromic.std_ref)),
   "<span class='item'>Alternative reference</span>" = sapply(.QC$qc.study.list, function(x) return(x$AFcor.palindromic.alt_ref)),
   "<span class='mainItem'>Lambda - total</span>" = sapply(.QC$qc.study.list, function(x) return(x$lambda)),
   "<span class='mainItem'>Lambda - genotyped</span>" = sapply(.QC$qc.study.list, function(x) return(x$lambda.gen)),
   "<span class='mainItem'>Lambda - imputed</span>" = sapply(.QC$qc.study.list, function(x) return(x$lambda.imp)),
   "<span class='mainItem'>P-value correlation (obs. vs exp.)</span>" = sapply(.QC$qc.study.list, function(x) return(x$PVcor)),
   "<span class='mainItem'>Visscher's statistic (HQ variants)</span>" = sapply(.QC$qc.study.list, function(x) return(x$Visschers.stat.HQ)),
          "<span class='mainItem'>Fixed HWE P-value</span>" = sapply(.QC$qc.study.list, function(x) return(x$fixed.hwep)),
   "<span class='mainItem'>Fixed imputation quality</span>" = sapply(.QC$qc.study.list, function(x) return(x$fixed.impq)),
   "<span class='mainItem'>Fixed sample size</span>" = sapply(.QC$qc.study.list, function(x) return(x$fixed.n_total)),
   "<span class='mainItem'>Fixed call rate</span>" = sapply(.QC$qc.study.list, function(x) return(x$fixed.callrate)),
   "<span class='mainItem'>Effect size</span>" = ' ',
   "<span class='item'>Min.</span>" = sapply(.QC$qc.study.list, function(x) return(x$tables$variable.summary['Min.',  .QC$config$input_parameters$effect_type_string])),
   "<span class='item'>1st Qu.</span>" = sapply(.QC$qc.study.list, function(x) return(x$tables$variable.summary['1st Qu.',  .QC$config$input_parameters$effect_type_string])),
   "<span class='item'>Median</span>" = sapply(.QC$qc.study.list, function(x) return(x$tables$variable.summary['Median', .QC$config$input_parameters$effect_type_string])),
   "<span class='item'>Mean</span>" = sapply(.QC$qc.study.list, function(x) return(x$tables$variable.summary['Mean', .QC$config$input_parameters$effect_type_string])),
   "<span class='item'>3rd Qu.</span>" = sapply(.QC$qc.study.list, function(x) return(x$tables$variable.summary['3rd Qu.', .QC$config$input_parameters$effect_type_string])),
   "<span class='item'>Max.</span>" = sapply(.QC$qc.study.list, function(x) return(x$tables$variable.summary['Max.', .QC$config$input_parameters$effect_type_string])),
   "<span class='item'>Min. (HQ variants)</span>" = sapply(.QC$qc.study.list, function(x) return(ifelse(nrow(x$tables$variable.summary.HQ) > 0,
                                                                                               x$tables$variable.summary.HQ['Min.',  .QC$config$input_parameters$effect_type_string], 
                                                                                               NA))),
   "<span class='item'>Max. (HQ variants)</span>" = sapply(.QC$qc.study.list, function(x) return(ifelse(nrow(x$tables$variable.summary.HQ) > 0,
                                                                                               x$tables$variable.summary.HQ['Max.', .QC$config$input_parameters$effect_type_string], 
                                                                                               NA))),
    "<span class='mainItem'>Standard error (median)</span>" = sapply(.QC$qc.study.list, function(x) return(x$tables$variable.summary['Median','STDERR'])),
    "<span class='mainItem'>Standard error (median) (HQ variants)</span>" = sapply(.QC$qc.study.list, function(x) return(ifelse(nrow(x$tables$variable.summary.HQ) > 0,
                                                                                               x$tables$variable.summary.HQ['Median','STDERR'], 
                                                                                               NA)))
   ))

    colnames(report.table) <- sapply(.QC$qc.study.list, function(x) return(x$number))
    study.col.count <- length(.QC$qc.study.list) + 1

prc <- sprintf("%s%%", study.col.count * 15)

kable(report.table,escape = FALSE, align = "c" , format = "html") %>% kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = TRUE, position = "left") %>% kableExtra::column_spec(1, bold = TRUE, width="20em", border_right = T,extra_css="vertical-align:middle" ) %>% kableExtra::column_spec(2:study.col.count, width="20em" , border_right = T ) %>% kableExtra::scroll_box(width = prc)

`

step1: removing variants with missing crucial values and duplicated lines.
step2: removing monomorphic variants and specified chromosomes.
step3: removing mismatched, ambiguous and multi-allelic variants that could not be verified.

Plots

`r if(file.exists(.QC$config$paths$precisionPlotPath) & .QC$graphic.device != 'tiff') htmltools::img(src = knitr::image_uri(.QC$config$paths$precisionPlotPath), alt = 'MAF',width='80%')

`


`r if(file.exists(.QC$config$paths$skew_kurt) & .QC$graphic.device != 'tiff') htmltools::img(src = knitr::image_uri(.QC$config$paths$skew_kurt), alt = 'MAF',width='80%')

`


Effect-size box plot

r paste0("<a target='_blank' href='",basename(.QC$config$paths$effsizePlotPath),"'><font color='blue'>click for full-size plot</font></a>")

`r if(file.exists(.QC$config$paths$effsizePlotPath) & .QC$graphic.device != 'tiff' ) htmltools::img(src = knitr::image_uri(.QC$config$paths$effsizePlotPath), alt = 'MAF',height='100%')

`



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GWASinspector documentation built on March 23, 2021, 1:10 a.m.