#Knitr settings knitr::opts_chunk$set( warning=FALSE, message=FALSE, echo=TRUE, tidy=FALSE, error=FALSE, fig.show='hold', fig.width=3.5, fig.height = 3.5 ) options(width=150)
if(is.null(experiment)) experiment <- "Sequencing data"
r Sys.Date()
r qc.path
r packageDescription("fastqcr")$Version
r experiment
library(goldclipData) library(fastqcr) library(dplyr) library(ggplot2) library(cowplot)
qc <- qc_aggregate(qc.path, progressbar = FALSE) #qc
qcgs <- qc_stats(qc) qcgs <- mutate(qcgs, tot.seq = prettyNum(tot.seq, big.mark = ",", scientific = FALSE), pct.dup = paste0(pct.dup, '%'), pct.gc = paste0(pct.gc, '%')) names(qcgs) <- c('Sample', 'Duplication', 'GC', 'Total_sequences', 'Length (nt)') qcgs
Column names:
```{block, type = "block"} The table shows, for each sample, some general statistics such as the total number of reads, the length of reads, the percentage of GC content and the percentage of duplicate reads
## Summary ```r summary(qc)
Column names:
```{block, type = "block"} The table shows, for each FastQC module, the number and the name of samples that failed or warned.
## Inspecting Problems ### Failed modules in the most samples ```r qc_fails(qc, "module")
```{block, type = "block"} For each module, the number of problems (failures) and the name of samples, that failed, are shown.
### Warned module in the most samples ```r qc_warns(qc, "module")
qc_problems(qc, "sample")
# 1. base quality # 2. sequence content qc.file <- fastqcFiles(qc.path) temp <- lapply(qc.file, function(f) { p <- fastqcPlot(f) print(p) })
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