R|fastqcr QC数据处理

转自：https://mp.weixin.qq.com/s?__biz=MzIyNDI1MzgzOQ==&mid=2650393841&idx=1&sn=edffe1a043b34a36e9f052d955bbddbc&chksm=f01cae11c76b2707d1ea34bc68facae4d3c332ec7d72f484b74400fd05c926bb62e9bcf48210&scene=21#wechat_redirect

FastQC是一款较常用的高通量数据质控软件，每个样本会得到一个zip和html的结果文件，查看略有不便。

fastqcr R包可以整理并分析多样不的zip格式的结果报告，当然也可以直接做fastQC分析。

一 加载安装R包

install.packages("fastqcr")
library(fastqcr)

二 R跑FastQC程序

fastqc(fq.dir = "~/Documents/FASTQ", # FASTQ files directory
       qc.dir = "~/Documents/FASTQC", # Results direcory
       threads = 4                    # Number of threads
       )

三 Fastcq结果整理绘图

3.1 读入fastqc的zip结果文件

qc.dir <- system.file("fastqc_results", package = "fastqcr")
qc.dir
# [1] "C:/Users/MJ/Documents/R/win-library/3.3/fastqcr/fastqc_results"

查看文件夹中文件

list.files(qc.dir) 
#[1] "A1.R2.clean_fastqc.zip" "A1_R1.clean_fastqc.zip" "A2.R1.clean_fastqc.zip"
#[4] "A2.R2.clean_fastqc.zip"

3.2 整体统计结果 Aggregate & summary

1)Aggregate函数，Sample的基本统计；qc_stats()类似

c <- qc_aggregate(qc.dir)
head(qc,3)
  sample                      module        status tot.seq seq.length pct.gc pct.dup
  <chr>                       <chr>         <chr>  <chr>   <chr>       <dbl>   <dbl>
1 170197A-Ti-1.R1.clean.fq.gz Basic Statis~ PASS   364589~ 150           51.    18.0
2 170197A-Ti-1.R1.clean.fq.gz Per base seq~ PASS   364589~ 150           51.    18.0
3 170197A-Ti-1.R1.clean.fq.gz Per tile seq~ WARN   364589~ 150           51.    18.0

其中：

sample: sample names
module: fastqc modules
status: fastqc module status for each sample
tot.seq: total sequences (i.e.: the number of reads)
seq.length: sequence length
pct.gc: percentage of GC content
pct.dup: percentage of duplicate reads

2)summary函数，Module的基本统计

head(summary(qc),3)

module           nb_samples nb_fail nb_pass nb_warn failed                  warned

  <chr>                 <dbl>   <dbl>   <dbl>   <dbl> <chr>                   <chr> 
1 Adapter Content          4.      0.      4.      0. NA                      NA    
2 Basic Statistics         4.      0.      4.      0. NA                      NA    
3 Kmer Content             4.      4.      0.      0. 170197A-Ti-1.R1.clean.~ NA        

其中：

module: fastqc modules
nb_samples: the number of samples tested
nb_pass, nb_fail, nb_warn: the number of samples that passed, failed and warned, respectively.
failed, warned: the name of samples that failed and warned, respectively.

3)dplyr函数

aggregated完可以使用dplyr得到重点关注的信息：如WARN和FALL样本：

library(dplyr)
qc %>%select(sample, module, status) %>% filter(status %in% c("WARN", "FAIL")) %>%arrange(sample)

   sample                       module                    status
   <chr>                        <chr>                     <chr> 
 1 170197A-Ti-1.R1.clean.fq.gz  Per tile sequence quality WARN  
 2 170197A-Ti-1.R1.clean.fq.gz  Per sequence GC content   WARN  
 3 170197A-Ti-1.R1.clean.fq.gz  Kmer Content              FAIL  

附：dplyr简单介绍 dplyr

3.3 异常信息统计

1) Module异常：qc_warns & qc_problems函数

qc_warns(qc, "module") # See which module warned in the most samples
  module                    nb_problems sample                                      
  <chr>                           <int> <chr>                                       
1 Per sequence GC content             4 170197A-Ti-1.R1.clean.fq.gz, 170197A-Ti-1.R~
2 Per tile sequence quality           2 170197A-Ti-1.R1.clean.fq.gz, 170197A-Ti-10.~

查看特定模块的异常

qc_problems(qc, "module",  name = "Per sequence GC content")
  module                  nb_problems sample                       status
  <chr>                         <int> <chr>                        <chr> 
1 Per sequence GC content           4 170197A-Ti-1.R1.clean.fq.gz  WARN  
2 Per sequence GC content           4 170197A-Ti-1.R2.clean.fq.gz  WARN  
3 Per sequence GC content           4 170197A-Ti-10.R1.clean.fq.gz WARN  
4 Per sequence GC content           4 170197A-Ti-10.R2.clean.fq.gz WARN  

2)Sample 异常 ： qc_fails & qc_problems 函数

qc_problems(qc, "sample", compact = FALSE) #fail 和 warn的 ，compact展示方式 
sample                       nb_problems module                    status
   <chr>                              <int> <chr>                     <chr> 
 1 170197A-Ti-1.R1.clean.fq.gz            3 Kmer Content              FAIL  
 2 170197A-Ti-1.R1.clean.fq.gz            3 Per tile sequence quality WARN  
 3 170197A-Ti-1.R1.clean.fq.gz            3 Per sequence GC content   WARN  
 4 170197A-Ti-1.R2.clean.fq.gz            3 Per tile sequence quality FAIL  
 5 170197A-Ti-1.R2.clean.fq.gz            3 Kmer Content              FAIL  
 6 170197A-Ti-1.R2.clean.fq.gz            3 Per sequence GC content   WARN  

四 绘图及生成报告

4.1 导入单个样本fastq结果

qc.file <- system.file("fastqc_results", "170197A-Ti-1.R1.clean_fastqc.zip", package = "fastqcr")
qc <- qc_read(qc.file) #读入qc的结果，后续绘图使用
names(qc) #查看包含的元素

4.2 绘制样本基本图形

qc_plot(qc, "Per sequence GC content")
qc_plot(qc, "Per base sequence quality")
qc_plot(qc, "Per sequence quality scores")
qc_plot(qc, "Per base sequence content")
qc_plot(qc, "Sequence duplication levels")

4.3 生成单样本网页报告

1)图形有解释信息的报告

qc_report(qc.file, result.file = "one-sample-report-with-interpretation",interpret = TRUE)

在工作目录下生成html的网页报告，图形含有解释信息（如下），表格可交互。

2)图形无解释信息的报告

qc_report(qc.file, result.file = "one-sample-report",interpret = FALSE)

4.4 多样本网页报告

qc.dir <- system.file("fastqc_results", package = "fastqcr")
qc_report(qc.dir, result.file = "~/Desktop/multi-qc-result",experiment = "Exome sequencing")