version_0_9_2.Rmd

---
title: "Version 0.9.2"
author:
- name: Xiaotao Shen (https://www.shenxt.info/)
date: "Created on 2020-04-01 and updated on `r Sys.Date()`"
output:
  html_document:
    df_print: paged
    toc: no
  pdf_document:
    toc: no
vignette: >
  %\VignetteIndexEntry{version_0_9_2}
  %\VignettePackage{masscleaner}
  % \VignetteEngine{knitr::rmarkdown}
  % \usepackage[utf8]{inputenc}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE, echo=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = TRUE,
  out.width = "100%"
)
```

***

In this version, we just added a new function `output_eic()`. This can be used to generate peaks in some samples after you run `process_data()`. For example, you analyzed data, and found some features are very important, so you want to check the peak shapes of them in QC samples, so you can use `output_eic()` function.

## **Set work directory**

First, you need to set the work directory to the folder which you used to run the `process_data()` function. For example, in our [example for `process_Data`](https://jaspershen.github.io/masscleaner/articles/raw_data_processing.html), we set the work directory in `example/POS`, so here, we also set work directory in this folder.

## **Run `output_eic()`**

Then we can run `output_eic()` function. 

```{r,eval=FALSE,warning=FALSE, R.options="", message=FALSE,cache = TRUE}
library(masscleaner)

output_eic(path = ".", 
           query_sample_name = c("QC_1"), 
           query_peak_name = c("M70T54_POS", "M70T579_POS"),
           polarity = "positive", 
           threads = 4)
```

Then the peak shape of plots will be outputted in `example/POS/Result`.

# **Session information**

```{r,eval=TRUE,warning=FALSE, R.options="", message=FALSE, cache=TRUE}
sessionInfo()
```