Metabolomics Data Analysis

# Metabolomics Data Analysis
## Demo
### Miao Yu
### 2018/07/13

---

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  ga('send', 'pageview');

</script>

# Don't Panic

<div class="figure" style="text-align: center">
<img src="https://yufree.github.io/presentation/figure/onecode.gif" alt="When you change one parameter in your code..." width="72%" />
<p class="caption">When you change one parameter in your code...</p>
</div>

---

# Workflow

---

# Project Setup

## Data

- Different groups should assign different sub-folder

## Anno

- For annotation results

## Workflow.Rmd

- Templete script for reproducible research

---

# Data conversion

<div class="figure" style="text-align: center">
<img src="https://yufree.github.io/presentation/figure/MSconvert.png" alt="MSconvert" width="60%" />
<p class="caption">MSconvert</p>
</div>

.half[
Kessner, D., Chambers, M., Burke, R., Agus, D., & Mallick, P. (2008). ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics, 24(21), 2534–2536. doi:10.1093/bioinformatics/btn323
]

---

# Session Info

```r
# record the software version
sessionInfo()
```

```
R version 3.5.0 (2018-04-23)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.18     plyr_1.8.4       compiler_3.5.0   pillar_1.3.0    
 [5] later_0.7.3      highr_0.7        bindr_0.1.1      tools_3.5.0     
 [9] digest_0.6.15    jsonlite_1.5     evaluate_0.11    tibble_1.4.2    
[13] rcrossref_0.8.4  pkgconfig_2.0.1  rlang_0.2.1      bibtex_0.4.2    
[17] shiny_1.1.0      curl_3.2         crul_0.6.0       yaml_2.2.0      
[21] xfun_0.3.5       bindrcpp_0.2.2   xml2_1.2.0       stringr_1.3.1   
[25] dplyr_0.7.6      knitr_1.20       htmlwidgets_1.2  triebeard_0.3.0 
[29] rprojroot_1.3-2  DT_0.4           tidyselect_0.2.4 httpcode_0.2.0  
[33] glue_1.3.0       R6_2.2.2         rmarkdown_1.10   xaringan_0.7    
[37] purrr_0.2.5      magrittr_1.5     urltools_1.7.1   backports_1.1.2 
[41] codetools_0.2-15 promises_1.0.1   htmltools_0.3.6  assertthat_0.2.0
[45] mime_0.5         xtable_1.8-2     httpuv_1.4.5     stringi_1.2.4   
[49] miniUI_0.1.1.1   crayon_1.3.4    
```

---

# Optimization

```r
library(IPO)
library(xcms)
# the path should be some pool QC samples
path <- list.files('D:/metademo/data/oq/',full.names = T,recursive = T)
# find the parameters for peak picking algorithm
peakpickingParameters <- getDefaultXcmsSetStartingParams('centWave')
# for obitrap, ppm should be 5
peakpickingParameters$ppm <- 15
resultPeakpicking <- 
  optimizeXcmsSet(files = path, 
                  params = peakpickingParameters,
                  nSlaves = 1,
                  subdir = NULL)
# find the parameters for retention time correction algorithm, obiwarp
optimizedXcmsSetObject <- resultPeakpicking$best_settings$xset
retcorGroupParameters <- getDefaultRetGroupStartingParams()
resultRetcorGroup <-
  optimizeRetGroup(xset = optimizedXcmsSetObject, 
                   params = retcorGroupParameters, 
                   subdir = NULL)
writeRScript(resultPeakpicking$best_settings$parameters, 
             resultRetcorGroup$best_settings)
```

---

### IPO package

`$$Peak\ Picking\ Score = \frac{reliable\ peaks^2}{all\ peaks - low\ intensity\ peaks}$$`

- RPs are defined as peaks that belong to an isotopologue.

- mass range
  
  - retention time
  
  - intensity

`$$retention\ time\ correction\ and\ grouping\ target\ value  = norm(RCS) + norm(GS)$$`

- Retention time correction could also be optimized

---

# Wrap function

```r
library(xcms)
getrtmz <- function(path,index = NULL) {
  path <- list.files(path, full.names = T, recursive = T)
  if(!is.null(index)){path <- path[index]}
  xset <- xcmsSet(
    files = path,
  method = "centWave",peakwidth = c(9.9264, 91.7),ppm = 15, 
  noise = 0, snthresh = 10, mzdiff = 0.0020888, 
  prefilter = c(3, 100), mzCenterFun = "wMean", integrate = 1,
  fitgauss = FALSE,verbose.columns = FALSE) # peak extraction
  xset <- retcor(
    xset,
    method = "obiwarp", plottype = "none", distFunc = "cor_opt",
    profStep = 1, center = 1, response = 1, gapInit = 0.928,
    gapExtend = 2.7, factorDiag = 2, factorGap = 1,
    localAlignment = 0) # retention time correction
  xset <- group(
    xset,
    method  = "density", bw = 0.879999999999999, 
    mzwid   = 0.01932, minfrac = 1, minsamp = 1, max = 50) # peak grouping
  xset <- fillPeaks(xset) # peak filling
  return(xset)
}
```

---

# Peaks list

```r
# get the xcmsset object
pos <- getrtmz('D:/metademo/data/')
# back up the xcmsset object
save(pos,file = 'pos.Rdata')
load('pos.Rdata')
# get the EIC, boxplot and diffreport, eixmax should be equal to the numbers of peaks groups in the pos objects

report <- annotateDiffreport(pos,filebase = 'peaklistpos',metlin = T, eicmax = 3094, classeic = xset@phenoData$class)
# save the report as a csv file
write.csv(report,file = 'all.csv')
# get the csv file for Metaboanalyst.ca
enviGCMS::getupload(pos,name = 'metabo')
```

---

# Peaks filtering

```r
# get the peak intensity, m/z, retention time and group information as list
mzrt <- enviGCMS::getmzrt(pos)

# get the mean and rsd for each group
mzrtm <- enviGCMS::getdoe(mzrt)
gm <- mzrtm$groupmean
gr <- mzrtm$grouprsd

# find the blank group and pool QC group
blk <- grepl('blk',colnames(gm)))
pqc <- grepl('qc',colnames(gm))

# filter by pool QC and blank's group mean intensity(pool QC should larger than three times of blank), return numbers and index
sum(indexmean <- apply(gm,1,function(x) all(x[pqc]>= 3*x[blk])))

# filt by pool qc rsd%, return numbers and index
rsdcf <- 30
sum(indexrsd <- apply(gm,1,function(x) ifelse(is.na(x[pqc]),T,x[pqc]<rsdcf)))

# overlap with rsd% and mean filter
sum(index <- indexmean&indexrsd)
```

---

# Peaks filtering

```r
# new list, update group and remove pool qc/blk
qcindex <- grepl('blk',mzrt$group$class) | grepl('qc',mzrt$group$class)
mzrtfilter <- list(data = mzrt$data[index,!qcindex],
                   mz = mzrt$mz[index],
                   rt = mzrt$rt[index],
                   group = mzrt$group)
mzrtfilter$group$class <- mzrt$group$class[!qcindex]
```

- The average intensities in pool QC for each peak should larger than three tims of the average intensities in blank sample

- RSD% less than 30% in Pooled QC to remove batch effects

---

# Normalization

```r
# visulize the batch effect
mzrtsim::rlaplot(mzrt$data,lv = mzrt$group$class)
mzrtsim::ridgesplot(mzrt$data,lv = mzrt$group$class)
# get the simulation data and test on NOREVA
sim <- mzrtsim::simmzrt(mzrt$data)
mzrtsim::simdata(sim)
# correct the batch effect by sva
mzrtcor <- mzrtsim::svacor(mzrt$data,lv = mzrt$group$class)
# visulize the batch effect correction
li <- mzrtsim::limmaplot(mzrtcor,lv = mzrt$group$class)
# return the corrected data
mzrt$data <- mzrtcor$dataCorrected
```

---

# Statistical analysis

## Exploratory data analysis

```r
# visulize the data
enviGCMS::plotmr(mzrtfilter)
# PCA
enviGCMS::plotpca(mzrtfilter$data,
                  as.character(mzrtfilter$group$class))
```

- Find the confonding factors or inner correlationship among variables/samples

- Correlationship/Cluster/Similarity/Dimension reducing analysis

---
# Statistical analysis

## Linear mixed model

- Fixed effects: Treatment/Control

- Random effects: individual variances

- `$$y = Fixed\ effects + (random\ slope\ variable|random \ baseline\ variable)$$`

- [More](https://web.stanford.edu/class/psych252/section/Mixed_models_tutorial.html#getting-p-values)
---

# Statistical analysis

## Split the data

```r
library(caret)
## Spliting data
trainIndex <- createDataPartition(variable, p = .8, 
                                  list = FALSE, 
                                  times = 1)
## Get the training and testing datasets
Train <- data[ trainIndex,]
Test  <- data[-trainIndex,]
```

- Variables should be larger than sample numbers
- Train datasets are used to build the model
- Test datasets are used to evaluate the final model
- [More](http://topepo.github.io/caret/)

---
# Statistical analysis

## Tune the model

```r
fitControl <- trainControl(## 10-fold CV
                           method = "repeatedcv",
                           number = 10,
                           ## repeated ten times
                           repeats = 10)
```

- General parameters training control

- Cross validation

- Performance Metrics(RMSE or ROC)

---
# Statistical analysis

## Tune the specific model

```r
# extra papameters for GBM 
gbmGrid <-  expand.grid(interaction.depth = c(1, 5, 9), 
                        n.trees = (1:30)*50, 
                        shrinkage = 0.1,
                        n.minobsinnode = 20)
```

- Different models have different parameters

- Use `expand.grid` to add the tuning for them

---
# Statistical analysis

## Train the model

```r
set.seed(825)
gbmFit <- train(Class ~ ., data = training, 
                 method = "gbm", 
                 trControl = fitControl, 
                 verbose = FALSE, 
                 ## Now specify the exact models 
                 ## to evaluate:
                 tuneGrid = gbmGrid)
# show the fitting process
plot(gbmFit)  
```

- Fit the model based on the tuning parameters

- Show the fitting results

---
# Statistical analysis

## Model selection

```r
# ANOVA analysis
anova(fit1,fit2)
```

- Check which model show improvements of the variances explained

---
# Statistical analysis

## Variable importance

```r
Imp <- varImp(fit)
plot(Imp)
```

- Not all models have the variable impartance evaluation

- `varImp` could be used to evaluate variable importance if possible

---

# Annotation

```r
library(xMSannotator)
num_nodes = 10
data("adduct_weights")
annolipid <- xsetplus::fanno(pos,outloc = 'D:/storage/data/anno', mode = 'pos', db_name = 'hmdb')
```

---

# Pathway Analysis

```r
# get the file
xsetplus::mumdata(pos,lv = mzrt$group$class)
# http://mummichog.org/index.html
mummichog1 -f 'test.txt' -o myResult
```

- [More](http://mummichog.org/index.html)

---

# XCMS online

https://xcmsonline.scripps.edu/

---

# Metaboanalyst

http://www.metaboanalyst.ca/

---

# NOREVA

http://idrb.zju.edu.cn/noreva/

---

# Reproducible research

- Report software version

- Avoid GUI

- Report script

- Share raw data

<a href="data:text/x-markdown;base64,---
title: "Meta-Workflow"
author: "Miao Yu"
date: "August 16, 2016"
output:
  html_document:
    df_print: paged
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,cache = T,message=T,warning=F)
sessionInfo()
```

# Optimization

```{r IPOpos,eval=FALSE}
library(IPO)
library(xcms)
peakpickingParameters <- getDefaultXcmsSetStartingParams('centWave')
path <- list.files('D:/metademo/data/oq/',full.names = T,recursive = T)

peakpickingParameters$ppm <- 15
resultPeakpicking <- 
  optimizeXcmsSet(files = path[c(1,2,3)], 
                  params = peakpickingParameters,
                  nSlaves = 1,
                  subdir = NULL)

optimizedXcmsSetObject <- resultPeakpicking$best_settings$xset
retcorGroupParameters <- getDefaultRetGroupStartingParams()
resultRetcorGroup <-
  optimizeRetGroup(xset = optimizedXcmsSetObject, 
                   params = retcorGroupParameters, 
                   subdir = NULL)
writeRScript(resultPeakpicking$best_settings$parameters, 
             resultRetcorGroup$best_settings)
para <- capture.output(writeRScript(resultPeakpicking$best_settings$parameters, resultRetcorGroup$best_settings), type = "message")
save(para,file = 'para.RData')
sessionInfo()
```

# Wrap function

```{r eval=T}
library(xcms)
library(Rmpi)
library(stringr)
getrtmz <- function(path,index = NULL){
  load('para.RData')
peakwidth <- as.numeric(unlist(str_extract_all(para[grepl('peakwidth',para)],'\\d+\\.*\\d*')))
ppm <- as.numeric(unlist(str_extract_all(para[grepl('ppm',para)],'\\d+')))
noise <- as.numeric(unlist(str_extract_all(para[grepl('noise',para)],'\\d+')))
snthresh <- as.numeric(unlist(str_extract_all(para[grepl('snthresh',para)],'\\d+')))
mzdiff <- as.numeric(unlist(str_extract_all(para[grepl('mzdiff',para)],'\\d+\\.*\\d*')))
prefilter <- as.numeric(unlist(str_extract_all(para[grepl('prefilter',para)],'\\d+\\.*\\d*')))
integrate <- as.numeric(unlist(str_extract_all(para[grepl('integrate',para)],'\\d+')))
profStep <- round(as.numeric(unlist(str_extract_all(para[grepl('profStep',para)],'\\d+\\.*\\d*'))),1)
center <- as.numeric(unlist(str_extract_all(para[grepl('center',para)],'\\d+')))
response <- as.numeric(unlist(str_extract_all(para[grepl('response',para)],'\\d+')))
gapInit <- as.numeric(unlist(str_extract_all(para[grepl('gapInit',para)],'\\d+\\.*\\d*')))
gapExtend <- as.numeric(unlist(str_extract_all(para[grepl('gapExtend',para)],'\\d+\\.*\\d*')))
factorDiag <- as.numeric(unlist(str_extract_all(para[grepl('factorDiag',para)],'\\d+')))
factorGap <- as.numeric(unlist(str_extract_all(para[grepl('factorGap',para)],'\\d+')))
localAlignment <- as.numeric(unlist(str_extract_all(para[grepl('localAlignment',para)],'\\d+')))
bw <- as.numeric(unlist(str_extract_all(para[grepl('bw',para)],'\\d+\\.*\\d*')))
mzwid <- as.numeric(unlist(str_extract_all(para[grepl('mzwid',para)],'\\d+\\.*\\d*')))
minfrac <- as.numeric(unlist(str_extract_all(para[grepl('minfrac',para)],'\\d+\\.*\\d*')))
minsamp <- as.numeric(unlist(str_extract_all(para[grepl('minsamp',para)],'\\d+')))
max <-  as.numeric(unlist(str_extract_all(para[grepl('max',para)],'\\d+')))
  files <- list.files(path,full.names = T,recursive = T)
  if(!is.null(index)){
    files <- files[index]
  }
  xset <- xcmsSet(files,
  method = "centWave",
  peakwidth       = peakwidth,
  ppm             = ppm,
  noise           = noise,
  snthresh        = snthresh,
  mzdiff          = mzdiff,
  prefilter       = prefilter,
  mzCenterFun     = "wMean",
  integrate       = integrate,
  fitgauss        = FALSE,
  verbose.columns = FALSE)
xset <- retcor( 
  xset,
  method         = "obiwarp",
  plottype       = "none",
  distFunc       = "cor_opt",
  profStep       = profStep,
  center         = center,
  response       = response,
  gapInit        = gapInit,
  gapExtend      = gapExtend,
  factorDiag     = factorDiag,
  factorGap      = factorGap,
  localAlignment = localAlignment)
xset <- group( 
  xset,
  method  = "density",
  bw      = bw,
  mzwid   = mzwid,
  minfrac = minfrac,
  minsamp = minsamp,
  max     = max)

xset <- fillPeaks(xset)
return(xset)
}
```

# Peaks list

```{r eval=F}
# get the xcmsset object
neg <- getrtmz('D:/metademo/data/')
# back up the xcmsset object
save(neg,file = 'neg.Rdata')
# get the number
npeaks <- nrow(neg@groups)
# get the EIC, boxplot and diffreport, eixmax should be equal to the numbers of peaks groups in the pos objects 
report <- CAMERA::annotateDiffreport(neg,filebase = 'peaklistneg',metlin = T, eicmax = npeaks, classeic = neg@phenoData$class)
# save the report as a csv file
write.csv(report,file = 'all.csv')
# get the csv file for Metaboanalyst.ca
enviGCMS::getupload(neg,name = 'metabo')
```

# Peaks filtering

```{r}
load('neg.Rdata')
# get the peak intensity, m/z, retention time and group information as list
mzrt <- enviGCMS::getmzrt(neg)
# get the mean and rsd for each group
mzrtm <- enviGCMS::getdoe(mzrt)
gm <- mzrtm$groupmean
gr <- mzrtm$grouprsd
# find the blank group and pool QC group
blk <- grepl('k',colnames(gm))
pqc <- grepl('q',colnames(gm))
# filter by pool QC and blank's group mean intensity(pool QC should larger than three times of blank), return numbers and index
sum(indexmean <- apply(gm,1,function(x) all(x[pqc]>= 3*x[blk])))
# filt by pool qc rsd%, return numbers and index
rsdcf <- 30
sum(indexrsd <- apply(gr,1,function(x) ifelse(is.na(x[pqc]),T,x[pqc]<rsdcf)))
# overlap with rsd% and mean filter
sum(index <- indexmean&indexrsd)

# new list, update group and remove pool qc/blk
qcindex <- grepl('k',mzrt$group$class) | grepl('q',mzrt$group$class)
mzrtfilter <- list(data = mzrt$data[index,!qcindex],
                   mz = mzrt$mz[index],
                   rt = mzrt$rt[index],
                   group = droplevels(mzrt$group$class[!qcindex,drop =T]))
# get the filtered csv
enviGCMS::getupload(mzrtfilter,name = 'peakfilter')

```

# Normalization (Optional)

```{r}
# visulize the batch effect
mzrtsim::rlaplot(mzrt$data,lv = mzrt$group$class)
mzrtsim::ridgesplot(mzrt$data,lv = mzrt$group$class)
# get the simulation data and test on NOREVA
sim <- mzrtsim::simmzrt(mzrt$data)
mzrtsim::simdata(sim)
# correct the batch effect by sva
mzrtcor <- mzrtsim::svacor(mzrt$data,lv = mzrt$group$class)
# visulize the batch effect correction
li <- mzrtsim::limmaplot(mzrtcor,lv = mzrt$group$class)
# return the corrected data
mzrt$data <- mzrtcor$dataCorrected
```

# Statistical analysis

```{r}
# visulize the data
enviGCMS::plotmr(mzrtfilter,inscf = 4,rsdcf = 50)
# PCA
#enviGCMS::plotpca(mzrtfilter$data,
#                  as.character(mzrtfilter$group))
enviGCMS::plotpca(mzrtfilter$data,c('b','b','b','M','M','M','H','H','H','L','L','L'))
```

# Annotation

```{r eval=FALSE}
library(xMSannotator)
num_nodes = 10
data("adduct_weights")
negsub <- getrtmz('D:/metademo/data/oq/')
anno <- xsetplus::fanno(negsub,outloc = 'D:/metademo/anno',mode = 'neg')
# annolipid <- xsetplus::fanno(neg,outloc = 'D:/storage/data/anno',
#                              db_name = 'LipidMaps')
```

# Pathway Analysis

```{r pathway}
# get the file
xsetplus::mumdata(neg,lv = mzrt$group$class)
# http://mummichog.org/index.html
# mummichog1 -f 'test.txt' -o myResult
```

# PMD analysis

```{r pmd}
load('neg.Rdata')
enviGCMS::getmzrt(neg,name = 'pmd')
options(shiny.maxRequestSize = 100*1024^2)
pmd::runPMD()
```

" download="workflow.Rmd">Download workflow.Rmd</a>

---

# Q&A

## yufreecas@gmail.com

## https://yufree.cn/metaworkflow/