Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/OKdll/mpwR

Compare workflows in mass spectrometry based bottom-up proteomics.
https://github.com/OKdll/mpwR

Last synced: 1 day ago
JSON representation

Compare workflows in mass spectrometry based bottom-up proteomics.

Awesome Lists containing this project

README 

        
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "80%"

)

```



# mpwR 



[![R-CMD-check](https://github.com/OKdll/mpwR/workflows/R-CMD-check/badge.svg)](https://github.com/OKdll/mpwR/actions)

[![CRAN status](https://www.r-pkg.org/badges/version/mpwR)](https://CRAN.R-project.org/package=mpwR)



mpwR [ɪmˈpaʊə(r)] offers a systematic approach for comparing proteomic workflows and empowers the researcher to effortlessly access valuable information about identifications, data completeness, quantitative precision, and other performance indicators across an unlimited number of analyses and multiple software tools. It can be used to analyze label-free mass spectrometry-based experiments with data-dependent or data-independent spectral acquisition.



## Applications - RMarkdown or Shiny

The functions of mpwR provide a great foundation to generate customized reports e.g. with RMarkdown or to build shiny apps/dashboards for downstream data analysis. An example for a shiny dashboard is also available - you can access the dashboard [here](https://okdll.shinyapps.io/mpwR/). 



## Installation



Install the development version from [GitHub](https://github.com/OKdll/mpwR) using the [`devtools`](https://github.com/r-lib/devtools) package by using the following commands:



```{r, eval = FALSE}

# install.packages("devtools") #remove "#" if you do not have devtools package installed yet

devtools::install_github("OKdll/mpwR", dependencies = TRUE) # use dependencies TRUE to install all required packages for mpwR

```



## Preparation



### Requirements 

As input the standard outputs of ProteomeDiscoverer, Spectronaut, DIA-NN or MaxQuant are supported by mpwR. Details about further requirements are listed in the vignette [Requirements](https://okdll.github.io/mpwR/articles/Requirements.html).



### Import 

Importing the output files from each software can be easily performed with `prepare_mpwR`. Further details about importing data are highlighted in the vignette [Import](https://okdll.github.io/mpwR/articles/Import.html).



```{r import, eval = FALSE}

files <- prepare_mpwR(path = "Path_to_Folder_with_files")

```



### Load packages

```{r libraries, message=FALSE, warning=FALSE}

library(mpwR)

library(flowTraceR)

library(magrittr) 

library(dplyr)

library(tidyr)

library(stringr)

library(tibble)

library(ggplot2)

```



## Example - Workflow



This is a basic example which shows the downstream analysis for number of identifications and data completeness. Please check the vignette [Workflow](https://okdll.github.io/mpwR/articles/Workflow.html) for a detailed analysis pipeline and more functionalities.



```{r example}

#get example

files <- create_example()

```



# Number of Identifications



## Report

The number of identifications can be determined with `get_ID_Report`. 

```{r ID-Report}

ID_Reports <- get_ID_Report(input_list = files)

```

 


For each analysis an ID Report is generated and stored in a list. Each ID Report entry can be easily accessed:

```{r show-ID-Report}

ID_Reports[["DIA-NN"]]

```

 


## Plot



### Individual

Each ID Report can be plotted with `plot_ID_barplot` from precursor- to proteingroup-level. The generated barplots are stored in a list.

```{r plot-ID-barplot}

ID_Barplots <- plot_ID_barplot(input_list = ID_Reports, level = "ProteinGroup.IDs")

```

 


The individual barplots can be easily accessed:

```{r show-ID-barplot}

ID_Barplots[["DIA-NN"]]

```

 


### Summary

As a visual summary a boxplot can be generated with `plot_ID_boxplot`.

```{r plot-ID-boxplot}

plot_ID_boxplot(input_list = ID_Reports, level = "ProteinGroup.IDs")

```

 


# Data Completeness



## Report

Data Completeness can be determined with `get_DC_Report` for absolute numbers or in percentage. 



```{r DC-Report}

DC_Reports <- get_DC_Report(input_list = files, metric = "absolute")

DC_Reports_perc <- get_DC_Report(input_list = files, metric = "percentage")

``` 

 


For each analysis a DC Report is generated and stored in a list. Each DC Report entry can be easily accessed:

```{r show-DC-Report}

DC_Reports[["DIA-NN"]]

```

 


## Plot



### Individual

#### Absolute

Each DC Report can be plotted with `plot_DC_barplot` from precursor- to proteingroup-level. The generated barplots are stored in a list.

```{r plot-DC-barplot}

DC_Barplots <- plot_DC_barplot(input_list = DC_Reports, level = "ProteinGroup.IDs", label = "absolute")

```

 


The individual barplots can be easily accessed:

```{r show-DC-barplot}

DC_Barplots[["DIA-NN"]]

```

 


#### Percentage

```{r show-DC-barplot-percentage}

plot_DC_barplot(input_list = DC_Reports_perc, level = "ProteinGroup.IDs", label = "percentage")[["DIA-NN"]]

```

 


### Summary

As a visual summary a stacked barplot can be generated with `plot_DC_stacked_barplot`.



#### Absolute

```{r plot-DC-stacked-barplot}

plot_DC_stacked_barplot(input_list = DC_Reports, level = "ProteinGroup.IDs", label = "absolute")

```

 


#### Percentage

```{r plot-DC-stacked-barplot-percentage}

plot_DC_stacked_barplot(input_list = DC_Reports_perc, level = "ProteinGroup.IDs", label = "percentage")

```