---
title: "artMS: Analytical R Tools for Mass Spectrometry"
author: "David Jimenez-Morales"
date: "`r Sys.Date()`"
output: 
  BiocStyle::html_document:
  # toc: true
  # # pdf_document: true
    toc_float: true
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    toc_depth: 3
    number_sections: true
    theme: lumen
  
vignette: >
    %\VignetteIndexEntry{Learn to use artMS}
    %\usepackage[utf8]{inputenc}
    %\VignetteEngine{knitr::rmarkdown}
---


```{r set-options, echo=FALSE, cache=FALSE}
options(width = 2000)
```

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# OVERVIEW

[artMS](http://bioconductor.org/packages/release/bioc/html/artMS.html) is a [Bioconductor](http://bioconductor.org) package 
that provides a set of tools for the analysis and integration of 
large-scale proteomics (mass-spectrometry-based) datasets 
obtained using the popular proteomics software 
[MaxQuant](https://www.maxquant.org/). 

The functions available in artMS, which can be grouped into the 
following categories:

- Multiple quality control (QC) functions.
- Relative quantification using [MSstats](http://msstats.org/).
- Downstream analysis and integration of quantifications (enrichment, 
clustering, PCA, summary plots, etc)
- Generation of input files for other tools, including 
[SAINTq](http://saint-apms.sourceforge.net/Main.html), [SAINTexpress](http://saint-apms.sourceforge.net/Main.html), 
[Photon](https://github.com/jdrudolph/photon), 
and [Phosfate](http://phosfate.com/)

[Click here for details about all the functions available in artMS](https://biodavidjm.github.io/artMS/reference/index.html).

For a graphical overview check 
[the slides presented at the 2019 SUMS-RAS](https://mass-spec.stanford.edu/sites/g/files/sbiybj8411/f/sites/default/files/2019SUMS-RAS_artMS_Jimenez-Morales.pdf) 
(Stanford University Mass Spectrometry - Research Application Symposium)


`r Biocpkg("artMS")` also perfoms basic quality control and relative 
quantification for **metabolomics** datasets obtained using the alignment 
table generated by 
[MarkerView](https://sciex.com/products/software/markerview-software).
However, this functionality is not stable.

## What's new?

Check the repo [NEWS file](https://github.com/biodavidjm/artMS/blob/master/NEWS) to be up to date with the new features, improvements, bug fixes affecting the package.

## How to install


Before you begin, ensure that your system is running an `R version >= 3.6` 
or the installation of `artMS` won't work. 
You can check the R version running on your system 
by executing the function `getRversion()`

If the outcome is `>= 3.6.0`, congratulations! you can move forward. 
If it is not, then you need to 
[install the latest version of R in your system](https://www.r-project.org/).

Two options to install `r Biocpkg("artMS")`:

- **Official BioConductor releases (recommended)**. `r Biocpkg("artMS")` is available in [BioConductor](https://www.bioconductor.org/). Instructions on how to install the package are available `r Biocpkg("artMS", label = "here")`.

(Why Bioconductor? [Here you can find a nice summary of many good reasons](https://bioinformatics.stackexchange.com/questions/639/why-bioconductor)).

- **Development version from Github**. (***Warning***: not stable, but it has the latest). Assuming that you have an `R (>= 3.6)` version running on your system, 
follow these steps:

```
install.packages("devtools")
library(devtools)
install_github("biodavidjm/artMS")
```

Once installed, the package can be loaded and attached to your current 
workspace as follows:

```{r, eval=TRUE}
library(artMS)
```

## Input files

`r Biocpkg("artMS")` performs the different analyses taking as input the 
following files:

- **evidence.txt** file: output of the quantitative proteomics software 
package **MaxQuant**. 
- **keys.txt** (tab-delimited) txt file generated **by the user** describing 
the experimental design.
- **contrast.txt** (tab-delimited) txt file generated **by the user** with the 
comparisons between conditions to be quantified.

Check [below](#inputfiles) to find out more about generating the input files.


## Configuration file

[//]: # (Having a large number of arguments in an R function or pipeline 
can make the task of providing them a cumbersome endeavor.)
 
`artmsQuantification()` requires a large number of arguments, specially those
related to the statistical package `r Biocpkg("MSstats")`.
To facilite the task of providing all those arguments, the function 
`artmsQuantification()` takes a config file (in `yaml` format) for the 
customization of the parameters for quantification (using `MSstats`) 
and other operations, including QC analyses, charts, and annotations. 

A configuration file template can be generated by running 
`artmsWriteConfigYamlFile()`

Check [below](#configfile) to learn the details of the configuration file.


## Basic workflows

### Proteomics

- **Generate the input files**: Check the [input files](#inputfiles) 
section for details

- **Quality Control**: if you are interested in performing only quality 
control analysis, run the following functions:

    - `artmsQualityControlEvidenceBasic()`: QC based on the `evidence.txt` file
    - `artmsQualityControlEvidenceExtended()`: based on the `evidence.txt` file
    - `artmsQualityControlSummaryExtended()`: based on the `summary.txt` file

- **Relative Quantification**: fill up the configuration file and run the 
following function:

  - `artmsQuantification(yaml_config_file = "config.yaml")` 
  ([here the details](#relquan))

- **Analysis of Quantifications**: performs annotations, clustering analysis,
PCA analysis, enrichment analysis by running the function 

  - `artmsAnalysisQuantifications()` ([here the details](#analysisquan))

- **Miscellaneous functions**: Check [below](#misc) to discover more useful functions
provided by the `artMS` package.


### Metabolomics (unstable)

`r Biocpkg("artMS")` also enables the relative quantification of untargeted 
polar metabolites using the alignment table generated by [MarkerView](https://sciex.com/products/software/markerview-software).
This means that the metabolites do not need to have an `ID`, as the 
`m/z` and `retention time` will be used as identifiers. Typical workflow:

- Run QC on the metabolomics dataset: `artmsQualityControlMetabolomics()`

- Relative quantification: `artmsQuantification()` (notice that a few options
must be changed in the config file before running the function)

Please, keep in mind that most of the functions available in artMS 
don't work for metabolomics data due to annotation issues 
(protein/gene ids are the primary ids for most 
of the functions). Check the [metabolomics section](#metabolomics) to find out
more.


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# REQUIRED INPUT FILES

<a name="inputfiles" />

***IMPORTANT***

Before you begin, please, set the working directory. artMS will work from 
that working directory. For example:

```
setwd("/path/to/my/working_directory/project_proteomics/")
```

Most of the artMS functions will create sub-folders to output files assuming that this working directory has been set and will try to find all the required files in this working directory.


## Input files

Three basic (tab-delimited) files are required to perform the full pack of 
operations:

### `evidence.txt`

The output of the quantitative proteomics software package 
[MaxQuant](http://www.biochem.mpg.de/5111795/maxquant). It combines all the 
information about the identified peptides.

### `keys.txt`

Tab delimited file generated by the user. It summarizes the experimental 
design of the evidence file. `artMS` merges the `keys.txt` and `evidence.txt` 
by the "RawFile" column. Each RawFile corresponds to a unique individual 
experimental technical replicate / biological replicate / Condition / Run. 

For any basic label-free proteomics experiment, the keys file **must contain** 
the following columns and rules:

- **RawFile**: The name of the RAW-file for which the mass spectral data was 
derived from.
- **IsotopeLabelType**: `'L'` for label free experiments (`'H'` will be used 
for SILAC experiments, [see below](#silac))
- **Condition**: The conditions names must follow these rules:
    - Use only letters (A - Z, both uppercase and 
    lowercase) and numbers (0 - 9). The only special character allowed 
    is underscore (`_`).
    - **Very important: A condition name cannot begin with a number 
    (R limitation)**.
- **BioReplicate**: biological replicate number. It is based on the condition 
name. Use as prefix the corresponding  `Condition` name, and add as suffix 
`dash (-)` plus the biological replicate number. 
For example, if condition `H1N1_06H` has too biological replicates, 
name them `H1N1_06H-1` and `H1N1_06H-2`
- **Run**: a **unique number** for all the MS runs (from 1 to the total number 
of raw files). It will be especially useful when having technical replicates. 
For example, in the table below, there are 2 technical replicates of the same 
biological replicate (Cal_33-1, technical replicates 1 and 2). 
A special case is SILAC experiments (H and L label are run simultaneously. 
See [below](#silac) to find out more)

**RawFile**|**IsotopeLabelType**|**Condition**|**BioReplicate**|**Run**
-----|-----|-----|-----|-----
qx006145|L|Cal_33|Cal_33-1|1
qx006146|L|Cal_33|Cal_33-1|2
qx006148|L|Cal_33|Cal_33-2|3
qx006151|L|HSC6|HSC6-1|4
qx006152|L|HSC6|HSC6-2|5

For more examples, check the artMS data object `artms_data_ph_keys`

*Tip*: it is recommended to use Microsoft Excel (OpenOffice Cal / or similar) 
to generate the keys file. *Do not forget* to choose the 
*format = Tab Delimited Text (.txt)* when saving the file 
(use *save as* option)

### `contrast.txt`

The comparisons between conditions that the user wants to quantify.

- Example #1: the comparison for the keys described above would be:

```
HSC6-Cal_33
```

- Example #2: let's quantify changes in protein abundance between wild type 
(`WT_A549`) relative to two additional experimental conditions with drugs 
(`WT_DRUG_A` and `WT_DRUG_B`), but also changes in protein abundance between
`DRUG_A` and `DRUG_B`, the contrast file would look like this:

```
WT_DRUG_A-WT_A549
WT_DRUG_B-WT_A549
WT_DRUG_A-WT_DRUG_B
```

**Requirements**: 

- The two conditions to be compared must be separated by a dash symbol (`-`),
and only one dash symbol is allowed, i.e., only one comparison per line.

As a result of the quantification, the condition on the left will take the 
positive log2FC sign -if the protein is more abundant in condition 
on the left (numerator), and the condition on the right the negative log2FC -if a 
protein is more abundant in condition on the right term (denominator).


**Example of wrong comparisons**

Only condition names are allowed. Individual Bioreplicates cannot be compared.
For example, this is wrong:

```
# WRONG:
HSC6-Cal_33-1
```







<a name="configfile" />

## The artMS configuration file

---

***IMPORTANT***

Before you begin, please, set the working directory. artMS will work from 
that working directory. For example:

```
setwd("/path/to/my/working_directory/project_proteomics/")
```

Most of the artMS functions will create sub-folders to output files assuming that this working directory has been set and will try to find all the required files in this working directory.

---

The configuration file (in `yaml` format) contains a variety of options 
available for the QC, quantification, and annotations performed by `artMS`.

To generate a sample configuration file, go to the project folder 
(`setwd(/path/to/your/working/folder/)`) and execute:

```{r, eval = FALSE}
library(artMS)
artmsWriteConfigYamlFile(config_file_name = "my_config.yaml")
```

Open the `my_config.yaml` file with your favorite editor (RStudio for example). 
*Note: Although the configuration file might look complex, 
the default options work very well*. 

The configuration (`yaml`) file contains the following sections:

### Section: `files`

```
files :
  evidence : /path/to/the/project_name/evidence.txt
  keys : /path/to/the/project_name/keys.txt
  contrasts : /path/to/the/project_name/contrast.txt
  summary: /path/to/the/project_name/summary.txt # Optional
  output : /path/to/the/project_name/results_folder/ph-results.txt
```

The file `path/name` of the required files. Notice that all the files are located in the folder `project_name/`, however, for the results file, an extra folder has been added in this example (`results_folder`): artMS will create the whole folder structure and will save the "ph-results" file (and all the extra outputs) in that folder. This means that you could write another completely different folder for the output (e.g. "/path/to/any/other/folder/results_projectX/results.txt) and artMS will create the whole folder structure for you.
The results file name (e.g. `-results.txt`) will be used as prefix for the 
several files (`txt` and `pdf`) that will be generated.

---

### Section: `qc`

```
qc:
  basic: 1 # 1 = yes; 0 = no
  extended: 1 # 1 = yes; 0 = no
  extendedSummary: 0 # 1 = yes; 0 = no
```

Select to perform both 'basic' and 'extended' quality control based on the
`evidence.txt` file or 'extendedSummary' based on the `summary.txt` file.
[Read below](#qualitycontrol) 
to find out more about the details of each type of analysis.


### Section: `data`

```
data:
  enabled : 1 # 1 = yes; 0 = no
  fractions: 
    enabled : 0 # 1 for protein fractionation
  silac: 
    enabled : 0 # 1 for SILAC experiments
  filters: 
    enabled : 1
    contaminants : 1
    protein_groups : remove # remove, keep
    modifications : AB # PH, UB, AC, AB, APMS
  sample_plots : 1 # correlation plots
```

Let's break it down `data`:

- `enabled : 1`: to pre-process the data provided in the *files* section. 
`0`: won't process the data (and a pre-generated MSstats file will be expected)

- `fractions`: Multiple fractionation or separation methods are often 
combined in proteomics to improve signal-to-noise and proteome coverage 
and to reduce interference between peptides in quantitative proteomics.
    - `enabled : 1` for fractionation dataset. See 
    **Special case: Protein Fractionation** [below](#fractions) for details
    - `enabled : 0` no fractions

- `silac`:
    - `enabled : 1`: check if the files belong to a SILAC experiment. 
    See **Special case: SILAC** [below](#silac) for details
    - `enabled : 0`: no silac experiment (default)

- `filters`:
    - `enabled : 1` Enables filtering (this section)
    - `contaminants : 1` Removes contaminants (`CON__` and `REV__` 
    labeled by MaxQuant). To keep contaminats: `0`
    - `protein_groups : remove` choose whether `remove` or `keep` 
    protein groups
    -  `modifications : ` select if a PTM proteomics experiment, i.e.:
       + `AB`: Global protein abundant (default), i.e, no-modification
       + `PH`, `UB`, or `AC`: shortcuts for the most frequent posttranslational modifications
       + `PTM:XXX:yy`: User defined PTM, i.e. this is another way to select any PTM supported by MaxQuant, including the most frequent PTM. Replace `XXX` with 1 or more 1-letter amino acid codes on which to find modifications (all uppercase). Replace yy with modification name used within the evidence file (require lowercase characters). Example for phosphorylation: `PTM:STY:ph` will find modifications on aa S,T,Y with this example format `_AAGGAPS(ph)PPPPVR_`. This means that you could use the shortcut `modifications: PH` or `modifications: PTM:STY:ph` and both would analyze phosphorylation peptides
       + `APMS`: affinity purification mass-spectrometry  
       
- `sample_plots` 
    - `1` Generate correlation plots
    - `0` otherwise

---

### Section: `MSstats`

```
msstats :
  enabled : 1
  msstats_input : # `-mss.txt` file or blank (default)
  profilePlots : none 
  normalization_method : equalizeMedians
  normalization_reference :  # blank (default) if equalizeMedians
  summaryMethod : TMP 
  censoredInt : NA  
  cutoffCensored : minFeature  
  MBimpute : 1 
  feature_subset: all
```

Let's break it down:

- `enabled : ` Choose `1` to run MSstats, `0` otherwise.
- `msstats_input :` leave it blank if MSstats will be run 
(previous `enabled : 1`). But if MSstats was already run and the 
`evidence-mss.txt` file is available, then choose `enabled : 0` and provide here the `evidence-mss.txt` file path/name
- `profilePlots :` Choose one of the following options:
    * `before` plot before normalization
    * `after` plot after normalization
    * `before-after`: recommended, although computational expensive
    * `none` no normalization plots
- `normalization_method :` available options:
    - `equalizeMedians` (recommended)
    - `quantile`
    - `0`: no normalization (not recommended)
    - `globalStandards` if selected, specified the reference protein in 
    `normalization_reference` (next)
- `normalization_reference :` provide a protein id if and only if `normalization_method: globalStandards` is selected 
as the `normalization_method` (above), otherwise leave blank. If multiple protein IDs are used for normalization, then provide them comma separated (for example `normalization_reference: Q86U42, O75822`)
- `summaryMethod :` TMP # "TMP"(default) means Tukey's median polish, which is robust estimation method. "linear" uses linear mixed model. "logOfSum" conducts log2 (sum of intensities) per run.
- `censoredInt :` 
    - `NA` (default) Missing values are censored or at random. 'NA' assumes 
    that all 'NA's in 'Intensity' column are censored. 
    - `0` uses zero intensities as censored intensity. In this case, 
    NA intensities are missing at random. The output from Skyline should use 
    `0`. Null assumes that all `NA` intensities are randomly missing.
- `cutoffCensored :` 
    - `minFeature` Cutoff value for censoring. Only with `censoredInt : NA`
    or `0`. Default is 'minFeature', which uses minimum value for each feature.
    - `minFeatureNRun` uses the smallest between minimum value of corresponding
    feature and minimum value of corresponding run. 
    - `minRun` uses minimum value for each run.
- `MBimpute :` 
    - `TRUE` only for `summaryMethod="TMP"` and `censoredInt='NA'` or `0`. 
    TRUE (default) imputes 'NA' or '0' (depending on censoredInt option) by 
    Accelerated failure model. 
    - `FALSE` uses the values assigned by cutoffCensored.
- `feature_subset :` 
    - `all` : default
    - `highQuality`  : this option seems to be buggy right now

Check [MSstats documentation](http://msstats.org/) to find out more about 
every option.

---

### Section: `output_extras`

```
  enabled : 1 # if 0, won't process anything on this section
  annotate :  
    enabled: 1 
    species : HUMAN
  plots:
    volcano: 1
    heatmap: 1
    LFC : -1 1 # Range of minimal log2fc
    FDR : 0.05 # adjusted p-value, false discovery rate
    heatmap_cluster_cols : 0
    heatmap_display : log2FC # log2FC or pvalue
```

Extra actions to perform based on the MSstats results, including 
*annotations* and *plots* (heatmaps and volcano plots). 
Let's break it down:

- `enabled : ` 1 (default) enables this section, 0 turns it off
- `annotate :` 
    * `enabled`: 1 (default), will generate a `-results-annotated.txt` 
file that includes `Gene` and `Protein.Name` (only for supported species)
    * `species`: The supported species are: HUMAN, MOUSE, ANOPHELES, 
    ARABIDOPSIS, BOVINE, WORM, CANINE, FLY, ZEBRAFISH, ECOLI_STRAIN_K12, 
    ECOLI_STRAIN_SAKAI, CHICKEN, RHESUS, MALARIA, CHIMP, RAT, YEAST, PIG, 
    XENOPUS
- `plots :` options for additional plots 
    * `volcano :` 1
    * `LFC :` log2 fold change cutoff (minimal negative and positive value)
    * `FDR :` false discovery rate cutoff for significance (recommended: 0.05)
    * `heatmap :` correlation plots
    * `heatmap_cluster_cols :` 1 perfoms clustering of columns, 
    0 (default) doesn't
    * `heatmap_display :` choose to display either `log2FC` or `pvalue`


<a name="fractions" />

## Special case: Protein fractionation

To handle protein fractionation experiments, two options must be activated

1. `keys.txt`: The keys file must contain an additional column named 
"`FractionKey`" with the information about fractions. For example:

**Raw.file**|**IsotopeLabelType**|**Condition**|**BioReplicate**|**Run**|**FractionKey**
:-----:|:-----:|:-----:|:-----:|:-----:|:-----:
S9524\_Fx1|L|AB|AB-1|1|1
S9524\_Fx2|L|AB|AB-1|1|2
S9524\_Fx3|L|AB|AB-1|1|3
S9524\_Fx4|L|AB|AB-1|1|4
S9524\_Fx5|L|AB|AB-1|1|5
S9524\_Fx6|L|AB|AB-1|1|6
S9524\_Fx7|L|AB|AB-1|1|7
S9524\_Fx8|L|AB|AB-1|1|8
S9524\_Fx9|L|AB|AB-1|1|9
S9524\_Fx10|L|AB|AB-1|1|10
S9525\_Fx1|L|AB|AB-2|2|1
S9525\_Fx2|L|AB|AB-2|2|2
S9525\_Fx3|L|AB|AB-2|2|3
S9525\_Fx4|L|AB|AB-2|2|4
S9525\_Fx5|L|AB|AB-2|2|5
S9525\_Fx6|L|AB|AB-2|2|6
S9525\_Fx7|L|AB|AB-2|2|7
S9525\_Fx8|L|AB|AB-2|2|8
S9525\_Fx9|L|AB|AB-2|2|9
S9525\_Fx10|L|AB|AB-2|2|10
S9526\_Fx1|L|AB|AB-3|3|1
S9526\_Fx2|L|AB|AB-3|3|2
S9526\_Fx3|L|AB|AB-3|3|3
S9526\_Fx4|L|AB|AB-3|3|4
S9526\_Fx5|L|AB|AB-3|3|5
S9526\_Fx6|L|AB|AB-3|3|6
S9526\_Fx7|L|AB|AB-3|3|7
S9526\_Fx8|L|AB|AB-3|3|8
S9526\_Fx9|L|AB|AB-3|3|9
S9526\_Fx10|L|AB|AB-3|3|10

2. `config.yaml`: Enable *fractions* in the configuration file as follow:

```
fractions: 
  enabled : 1 # 1 for protein fractions, 0 otherwise
```

<a name="silac" />

## Special case: SILAC

One of the most widely used techniques that enable relative protein 
quantification is *stable isotope labeling by amino acids in cell culture* 
(SILAC). The `keys.txt` file can capture the typical SILAC experiment. 
The following example shows a SILAC experiment with two conditions, 
two biological replicates, and two technical replicates:

**RawFile**|**IsotopeLabelType**|**Condition**|**BioReplicate**|**Run**
:-----:|:-----:|:-----:|:-----:|:-----:
QE20140321-01|H|iso|iso-1|1
QE20140321-02|H|iso|iso-1|2
QE20140321-04|L|iso|iso-2|3
QE20140321-05|L|iso|iso-2|4
QE20140321-01|L|iso\_M|iso\_M-1|1
QE20140321-02|L|iso\_M|iso\_M-1|2
QE20140321-04|H|iso\_M|iso\_M-2|3
QE20140321-05|H|iso\_M|iso\_M-2|4

It is also required to activate the *silac* option in the yaml file as follows:

```
silac: 
  enabled : 1 # 1 for SILAC experiments
```


<a name="qualitycontrol" />

<!--
Quality Control
-------------------------------------------------------------------------------
-->

# QUALITY CONTROL

`artMS` provides 3 functions to perform QC analyses.


<a name="qcbasicevidence" />

## Basic QC (`evidence.txt`-based)

The basic quality control analysis takes as input both the `evidence.txt`
and [keys.txt](#inputfiles) files
and generates several QC plots exploring different aspects of
the MS data. Run it as follows:

```{r, eval=FALSE}
artmsQualityControlEvidenceBasic(
  evidence_file = artms_data_ph_evidence,
  keys_file = artms_data_ph_keys,
  prot_exp = "PH")
```

The following `pdf` can be generated:

- **plotCORMAT (qcBasic_evidence.qcplot.CorrelationMatrix.pdf)** (default):  *Correlation matrix* for *technical replicates* (if available),  *biological replicates*, and conditions based on MS Intensity values
- **plotCORMAT (qcBasic_evidence.qcplot.CorrelationMatrixCluster.pdf** (default):  Clustered version of the *Correlation matrices* as for *.CorrelationMatrix.pdf*
- **plotINTMISC (qcBasic_evidence.qcplot.IntensityStats.pdf)**: several pages, 
including bar plots of 
*Total Sum of Intensities in BioReplicates*, 
*Total Sum of Intensities in Conditions*, 
*Total Peptide Counts in BioReplicates*,  
*Total Peptide Counts in conditions* 
grouped by categories (`CON`: contaminants, `PROT` peptides, `REV` reversed 
sequences used by MaxQuant to estimate the FDR); 
*Box plots* 
of MS Intensity values per biological replicates and conditions; 
*bar plots* 
of total intensity (excluding contaminants) by bioreplicates and conditions; 
Bar plots of *total feature counts* by bioreplicates and conditions.
- **plotPTMSTATS (qcBasic_evidence.qcplot.PTMStats.pdf)**: If any PTM is 
selected (`PH`, `UB`, `AC`, `PTM:##) an extra pdf 
file will be generated with stats related to the selected modification, 
including: *bar plot of peptide counts and intensities*, broken by 
`PTM/other` categories; bar plots of *total sum-up of MS intensity values* by 
other/PTM categories.
- **plotREPRO (qcBasic_evidence.qcplot.BasicReproducibility)**: correlation dot plot for 
all the combinations of biological replicates of every condition, based on MS Intensity values of features (peptide+charge)
- **plotINTDIST (qcBasic_evidence.qcplot.IntensityDistributions.pdf)**: 2 pages. *Box-dot plot* and *Jitter plot* of MS (raw) intensity values for each biological replicate.

Check `?artmsQualityControlEvidenceBasic()` to find out more options. Remember: by default, all the plots are printed to a `pdf` file by running:

```{r, eval=TRUE}
artmsQualityControlEvidenceBasic( 
    evidence_file = artms_data_ph_evidence,
    keys_file = artms_data_ph_keys,
    prot_exp = "PH", 
    plotPTMSTATS = TRUE,
    plotINTDIST = FALSE, plotREPRO = FALSE,
    plotCORMAT = FALSE, plotINTMISC = FALSE,
    printPDF = FALSE, verbose = FALSE)
```


<a name="qcextendedevidence" />

## Extended QC (`evidence.txt`-based)

It takes as [input](#inputfiles) the `evidence.txt` and `keys.txt`
files as follows:

```{r, eval=FALSE}
artmsQualityControlEvidenceExtended(
   evidence_file = artms_data_ph_evidence,
   keys_file = artms_data_ph_keys)
```

and generates the following QC plots:

- **plotCS (qcExtended_evidence.qcplot.ChargeState)**: charge state 
distribution of PSMs confidently identified in each BioReplicate.

- **plotIC (qcExtended_evidence.qcplot.PCA.pdf)**: pairwise intensity correlation and Principal Component Analysis (PCA)
   + Page 1 and 3: pairwise peptide and protein intensity correlation and 
scatter plot between any 2 BioReplicates, respectively. 
   + Page 2 and 4: principal component analysis at the intensity level 
   for both peptide and proteins, respectively. 

- **plotIONS (qcExtended_evidence.qcplot.Ions.pdf)**:
A peptide ion is defined in the context of m/z, in other words, a unique 
peptide sequence may give rise to multiple ions with different charge state 
and/or amino acid modification. 
   + Page 1: number of ions confidently identified in each BioReplicate 
   (top panel: non-contaminants, bottom: contaminants)
   + Page 2: mean number of peptide ions per condition with error bar 
   showing the standard error of the mean 
   (categories: non-contaminants / contaminants)
 
- **plotME (qcExtended_evidence.qcplot.MassError.pdf)**: 
Distribution of precursor error for all PSMs confidently 
identified in each BioReplicate.

- **plotMOCD (qcExtended_evidence.qcplot.MZ.pdf)**: 
Distribution of precursor mass-over-charge for all PSMs 
confidently identified in each BioReplicate.

- **plotPIO (qcExtended_evidence.qcplot.PepIonOversampling.pdf)**: 
   + Page 1: proportion of all peptide ions (including peptides 
matched across runs) fragmented once, twice and thrice or more. 
   + Page 2: proportion of peptide ions (with intensity detected) 
fragmented once, twice and thrice or more. 
   + Page 3: proportion of peptide ions (with intensity detected 
   and MS/MS identification) fragmented once, twice, and 3 or more

- **plotPEPDETECT (qcExtended_evidence.qcplot.PeptideDetection.pdf)**: 
frequency of peptide detection across BioReplicates by condition, 
showing the percentage of peptides detected once, twice, thrice,
and so on (based on the number of bioreplicates for each condition).

- **plotPEPICV (qcExtended_evidence.qcplot.PeptideIntensity.pdf)**: 
Peptide intensity coefficient of variance (CV) plot.  
The CV is calculated for each feature (peptide ion) identified in more 
than one replicate. 
   + Page 1 shows the distribution of CV's for each condition, while 
   + Page 2 shows the distribution of CV's within 4 bins of intensity 
   (i.e., 4 quantiles of average intensity).

- **plotPEPTIDES (qcExtended_evidence.qcplot.Peptides.pdf)**: 
peptide statistics plot.
   + Page 1: number of unique peptide sequences (disregards the charge 
state or amino acid modifications) confidently identified in each 
BioReplicate (top panel: non-contaminants, bottom: contaminants)
   + Page 2: mean number of peptides per condition with error bar showing the 
   standard error of the mean.

- **plotPEPTOVERLAP (qcExtended_evidence..qcplot.PeptidesOverlap.pdf)**: 
peptide overlaps across bioreplicates (page 1) and conditions (page 2)
   
- **plotPROTDETECT (qcExtended_evidence.qcplot.ProteinDetection.pdf)**: 
Protein detection frequency plot:
   + Page 1: protein group frequency of detection across BioReplicates for 
   each condition, showing the percentage of proteins detected per bioreplicates
   + Page 2: feature (peptide ion) intensity distribution within each 
   BioReplicate (potential contaminant proteins are plot separately). 
   + Page 3: density of feature intensity for different feature types 
   (i.e., MULTI-MSMS, MULTI-SECPEP).
   
- **plotPROTICV (qcExtended_evidence.qcplot.ProteinIntensityCV.pdf)**: 
protein intensity coefficient of variance (CV) plot. 
The CV is calculated for each protein (after summing the peptide intensities) 
identified in more than one replicate. 
   + Page 1: istribution of CV's for each condition
   + Page 2: distribution of CV's within 4 bins of 
   intensity (i.e., 4 quantiles of average intensity).

- **plotPROTEINS (qcExtended_evidence.qcplot.Proteins.pdf)**: protein statistics,
   + Page 1: number of protein groups confidently identified in each BioReplicate. 
   + Page 2: mean number of protein groups per condition with error bar showing 
   the standard error of the mean (categories: non-contaminants / contaminants)
   
- **plotPROTOVERLAP (qcExtended_evidence..qcplot.ProteinOverlap.pdf)**: 
Protein overlap across bioreplicates (page 1) and conditions (page 2) 

- **plotPSM (qcExtended_evidence.qcplot.PSM.pdf)** : Peptide-spectrum-matches (PSMs).
   + Page 1: number of PSMs confidently identified in each BioReplicate. 
   + Page 2: mean number of PSMs per condition with error bar showing the 
   standard error of the mean (categories: non-contaminants / contaminants)

- **plotIDoverlap (qcExtended_evidence.qcplot.ID-Overlap.pdf)**: heatmap of pairwise 
identification overlap: 
   + Page 1: peptide identification overlap, only peptides detected and 
   identified between any 2 BioReplicates
   + Page 2: protein identification overlap, only proteins with at least 1 
   peptide detected and identified between any 2 BioReplicates

- **plotSP (qcExtended_evidence.qcplot.SamplePrep.pdf)**: sample quality metrics.
   + Page 1: missing cleavage distribution of all peptides confidently 
   identified in each BioReplicate. 
   + Page 2: fraction of peptides with at least one methionine 
   oxidized in each BioReplicate. 

- **plotTYPE (qcExtended_evidence.qcplot.Type.pdf)**: identification type. 
MaxQuant classifies each peptide identification into different categories 
(e.g., MSMS, MULTI-MSMS, MULTI-SECPEP). This plot shows the distribution of 
identification type in each BioReplicate

Examples: printing `plotTYPE`, `plotPEPICV`, and `plotPCA` plots only:

```{r, eval = TRUE}
artmsQualityControlEvidenceExtended(
  evidence_file = artms_data_ph_evidence,
  keys_file = artms_data_ph_keys,
  plotPCA = TRUE,
  plotTYPE = TRUE,
  plotPEPTIDES = TRUE,
  plotPSM = FALSE,
  plotIONS = FALSE,
  plotPEPTOVERLAP = FALSE,
  plotPROTEINS = FALSE,
  plotPROTOVERLAP = FALSE,
  plotPIO = FALSE,
  plotCS = FALSE,
  plotME = FALSE,
  plotMOCD = FALSE,
  plotPEPICV = FALSE,
  plotPEPDETECT = FALSE,
  plotPROTICV = FALSE,
  plotPROTDETECT = FALSE,
  plotIDoverlap = FALSE,
  plotSP = FALSE,
  printPDF = FALSE,
  verbose = FALSE)
```


<a name="qcextendedsummary" />

## Extended QC (`summary.txt` based)

It requires two files:

- [keys.txt](#inputfiles) file
- MaxQuant `summary.txt` file. As described by MaxQuant's `table.pdf`, the 
summary file contains summary information for all the raw files processed 
with a single MaxQuant run, including statistics on the peak detection. 
The QC analysis of this file  gathers a quick overview on the 
quality of every RawFile based on this `summary.txt`. Run it as follows:

```{r, eval = FALSE}
artmsQualityControlSummaryExtended(summary_file = "summary.txt",
                                    keys_file = artms_data_ph_keys)
```

It generates the following `pdf` plots:

- **plotMS1SCANS (.qcplot.MS1scans.pdf)**: generates MS1 scan counts plot: 
Page 1 shows the number of MS1 scans in each BioReplicate. 
If replicates are present, Page 2 shows the mean number of MS1 scans 
per condition with error bar showing the standard error of the mean. 
If `isFractions` is `TRUE`, each fraction is a stack on the individual 
bar graphs. 

- **plotMS2 (.qcplot.MS2scans.pdf)**: generates MS2 scan counts plot: 
Page 1 shows the number of MSs scans in each BioReplicate. 
If replicates are present, Page 2 shows the mean number of MS1 scans per 
condition with error bar showing the standard error of the mean. 
If `isFractions` is `TRUE`, each fraction is a stack on the individual bar graphs. 

- **plotMSMS (.qcplot.MSMS.pdf)**: generates MS2 identification rate (%) plot: 
Page 1 shows the fraction of MS2 scans confidently identified in each 
BioReplicate. If replicates are present, Page 2 shows the mean rate of MS2 
scans confidently identified per condition with error bar showing the 
standard error of the mean. 
If `isFractions` `TRUE`, each fraction is a stack on the individual bar graphs.

- **plotISOTOPE (.qcplot.Isotope.pdf)**: generates Isotope Pattern counts plot: 
Page 1 shows the number of Isotope Patterns with charge greater than 1 in 
each BioReplicate. If replicates are present, Page 2 shows the mean number 
of Isotope Patterns with charge greater than 1 per condition with error bar 
showing the standard error of the mean. 
If `isFractions` `TRUE`, each fraction is a stack on the individual bar graphs.


<!--
RELATIVE QUANTIFICATION
-------------------------------------------------------------------------------
-->

<a name="relquan" />

# RELATIVE QUANTIFICATION

The relative quantification is a fundamental step in the analysis of MS data. 
`artMS` facilitates and simplifies the analysis using 
[MSstats](http://msstats.org/), a fantastic statistical package for the relative
quantification of Mass-Spectrometry based proteomics.

All the options and parameters required to run a relative quantification 
analysis using `MSstats` (in addition to other options) are summarized in 
`artMS` through a configuration file in `.yaml` format. Check the
[input-files](#inputfiles) section to find out more about each of the options.

Different types of proteomics experiments can be quantified including changes 
in global protein abundance (AB), affinity purification mass spectrometry 
(APMS), and different type of posttranslational modifications, 
including phosphorylation (PH), ubiquitination (UB), and acetylation (AC). 

`artMS` also enables the relative quantification of untargeted polar metabolites 
using the alignment table generated by 
[MarkerView](https://sciex.com/products/software/markerview-software).
This means that `artMS` does not require an ID for the metabolites,
as the m/z and retention time will be combined and used as identifiers.


<a name="globalab" />

## Quantification of Changes in Global Protein Abundance

The quantification of changes in protein abundance between different conditions
requires to fill up the following sections of the
[config file](#configfile):

```
files:
  evidence : /path/to/the/evidence.txt
  keys : /path/to/the/keys.txt
  contrasts : /path/to/the/contrast.txt
  output : /path/to/the/output/results_ptm_global/results.txt
  .
  .
  .
data:
  .
  .
  .
  filters:
    modifications : AB 
```

The remaining options can be left unmodified (and run the default parameters). 
Then run the following `artMS` function:

```
artmsQuantification(
  yaml_config_file = '/path/to/config/file/artms_ab_config.yaml')
```


<a name="globalptm" />

## Quantification of Changes in Global Phosphorylation, Ubiquitination, Acetylation (or any PTM)

***Warning: This quantification is only possible for experiments that have
used methods to enrich for the modified peptides (e.g. phosphorylation) prior to 
the mass spectrometry analysis.***

The **global PTM** quantification analysis 
calculates changes of the PTM at the *protein level*. 
This means that all the **modified** peptides for every protein are used to quantify changes in protein phosphorylation, ubiquitination, or acetylation between different conditions. 
The **site-specific** analysis ([explained next](#siteptm)) 
would quantify changes at the *site level*, i.e., each modified peptide for every PTM site
is / are quantified independently between the different conditions (one or more different peptides could be detected for the same PTM)

Only two sections need to be filled up using the **default** 
`r Biocpkg("artMS", vignette="input-files.html", label="configuration")` 
file:

```
files:
  evidence : /path/to/the/evidence.txt
  keys : /path/to/the/keys.txt
  contrasts : /path/to/the/contrast.txt
  output : /path/to/the/output/results_ptm_global/results.txt
  .
  .
  .
data:
  .
  .
  .
  filters:
    modifications : PH
```

The remaining options can be left unmodified. 

Once the configuration `yaml` file is ready, run the following command:

```
artmsQuantification(
  yaml_config_file = '/path/to/config/file/artms_phglobal_config.yaml')
```


<a name="siteptm" />

## PTM-Site/Peptide-specific Quantification of Changes (PH, UB, AC)

***Warning: This quantification is only possible for experiments that have
used methods to enrich phosphopeptides or ubiquitinated peptides prior to 
the mass spectrometry analysis.***

Abbreviations:

- `PH` = Protein phosphorylation
- `UB` = Protein Ubiquitination
- `AC` = Protein Acetylation
- `PTM:XXX:yy` : User defined PTM (any PTM supported by MaxQuant). 
Replace XXX with 1 or more 1-letter amino acid codes on which to find modifications (all uppercase).  Replace yy with modification name used within the evidence file (require lowercase characters). Example: `PTM:STY:ph` will find modifications on aa S,T,Y with this example format `_AAGGAPS(ph)PPPPVR_`

The `site-specific` analysis quantifies changes at the modified peptide level.
This means that changes in every modified (PH, UB, AC, or PTM) peptide of a given protein
will be quantified individually. The caveat is that the proportion of missing 
values should increase in general relative to a typical non-PTM **global** analysis (protein global abundance quantification). 
Both **sites** and **global** ptm analysis are highly correlated due to the usually only 
one or two peptides drive the overall changes in PTMs for every protein.

To run a site/peptide specific analysis follow these steps:

1. **Important pre-processing step on the 
evidence file to enable the ptm-site/peptide-specific analysis**. 
This step takes any of the Proteins id columns selected by the user
(either `Leading razor protein`, `Leading protein`, or `Proteins`) 
and re-annotates it to incorporate the ptm-site/peptide-specific information.
By default, this function converts the column `Leading razor protein`.
This step is *computational expensive*, which means that it might take several
minutes to finish (depending on the size of the fasta database, evidence file,
computer power, etc)

It also requires the same reference proteome (fasta sequence database) used 
for the MaxQuant search.

*For phosphorylation*:

```
artmsProtein2SiteConversion(
  evidence_file = "/path/to/the/evidence.txt", 
  ref_proteome_file = "/path/to/the/reference_proteome.fasta", 
  output_file = "/path/to/the/output/ph-sites-evidence.txt", 
  mod_type = "PH")
```

As a result, the IDs in the "Leading razor protein" column will contain 
site/peptide-specific notation. For example:

Before: `P12345` 
After: `P12345_S23_S45`

*For ubiquitination*:

```
artmsProtein2SiteConversion(
  evidence_file = "/path/to/the/evidence.txt", 
  ref_proteome_file = "/path/to/the/reference_proteome.fasta", 
  output_file = "/path/to/the/output/ub-sites-evidence.txt", 
  mod_type = "UB")
```

*For acetylation*:

```
artmsProtein2SiteConversion(
  evidence_file = "/path/to/the/evidence.txt", 
  ref_proteome_file = "/path/to/the/reference_proteome.fasta", 
  output_file = "/path/to/the/output/ac-sites-evidence.txt", 
  mod_type = "AC")
```

*For all PTMS supported by MaxQuant (in addition to PH, AC, UB)*:

Example with PH and UB:

```
# Phosphopeptide in evidence file: `_AAGGAPS(ph)PPPPVR_`
artmsProtein2SiteConversion(
  evidence_file = "/path/to/the/evidence.txt", 
  ref_proteome_file = "/path/to/the/reference_proteome.fasta", 
  output_file = "/path/to/the/output/ac-sites-evidence.txt", 
  mod_type = "PTM:STY:(ph)")

# Ubiquitinated peptide: `_AAASK(gl)LGEFAK_`
artmsProtein2SiteConversion(
  evidence_file = "/path/to/the/evidence.txt", 
  ref_proteome_file = "/path/to/the/reference_proteome.fasta", 
  output_file = "/path/to/the/output/ac-sites-evidence.txt", 
  mod_type = "PTM:K:(gl)")
```

**Tip: How to re-annotate all the Protein columns on the same file**.

By default, `artmsProtein2SiteConversion` doesn't allow to overwrite the 
`evidence.txt` file for security reasons (you don't want to lose the 
evidence file if something goes wrong). To overwrite the evidence file the
argument `overwrite_evidence` must be turned on (`overwrite_evidence = TRUE`).

If the `column_name` argument is not used, `artmsProtein2SiteConversion`
converts the `Leading razor protein` column, which is used in the
quantification step when `protein_groups : remove` is selected (default). However,
if `protein_groups : keep` is used, `artMS` will use the `Proteins` column.
To convert the `Proteins` column to the site/peptide-specific notation, then 
add the argument `column_name = "Proteins"`.

To annotate both columns of the same file, first generate the 
"site-evidence.txt" file, and then use this same output file as the 
`evidence_file` and activate `overwrite.evidence = TRUE`. 

In summary, to annotate both the "Leading razor protein" and `Proteins` columns
follow these steps:

```
# Convert 'Leading razor protein' evidence's file column
artmsProtein2SiteConversion(
  evidence_file = "/path/to/the/evidence.txt", # ORIGINAL
  column_name = "Leading razor protein",
  ref_proteome_file = "/path/to/the/reference_proteome.fasta", 
  output_file = "/path/to/the/phsites-evidence.txt", # SITES VERSION
  mod_type = "PH")

# Convert 'Proteins' evidence's file column
artmsProtein2SiteConversion(
  evidence_file = "/path/to/the/phsites-evidence.txt", # <- USE SITES VERSION
  column_name = "Proteins",
  overwrite_evidence = TRUE, # <--- TURN ON
  ref_proteome_file = "/path/to/the/reference_proteome.fasta", 
  output_file = "/path/to/the/phsites-evidence.txt", # <- SITES VERSION
  mod_type = "PH")
```


2. Generate a new configuration file (`phsites_config.yaml` or 
`ubsites_config.yaml`) as [explained above](#configfile), but using the "new" 
`sites-evidence.txt` file instead of the original
`evidence.txt` file:

```
files:
  evidence : /path/to/the/evidence-site.txt
  keys : /path/to/the/keys.txt
  contrasts : /path/to/the/contrast.txt
  output : /path/to/the/output/results_ptmSITES/sites-results.txt # <- this one
  .
  .
  .
data:
  .
  .
  .
  filters:
    modifications : PH # <- Don't forget this one.
```

Once the new `yaml` file has been created, execute:

```
artmsQuantification(
  yaml_config_file = '/path/to/config/file/phsites_config.yaml')
```

## Output files

The files generated after succesfully running `artmsQuantification` are 
(based on
[MSstats documentation](http://msstats.org/wp-content/uploads/2017/01/MSstats_v3.7.3_manual.pdf)):

### TXT (tab delimited) files 

- **results.txt**: the results of the quantification in long format, i.e., each
line is a protein for each comparison. It includes the following columns:
    * `Protein`: Protein ID
    * `Label`: comparison (from contrast.txt)
    * `log2FC`: log2 fold change
    * `SE`: standard error
    * `Tvalue`: test statistic of the Student test
    * `DF`: degree of freedom of the Student test
    * `pvalue`: raw p-values
    * `adj.pvalue`: p-values adjusted among all the proteins in the specific 
    comparison using the approach by Benjamini and Hochberg 
    * `issue`: shows if there is any issue for inference in corresponding 
    protein and comparison, for example, OneConditionMissing or CompleteMissing.
    * `MissingPercentage`: percentage of random and censored missing 
    in the corresponding run and protein out of the total number of 
    feature in the corresponding protein.
    * `ImputationPercentage`: percentage of imputation
    * Note:  If a protein is completely missed in one of the conditions of 
    a given comparison, it would get the flag `OneConditionMission` with 
    `adj.pvalue=0` and `log2FC=Inf` or `-Inf` even though `pvalue=NA`. 
    For example, if for the comparison `Condition A - Condition B` 
    one protein is completely missed for condition B, then 
    `log2FC = Inf` and `adj.pvalue = 0`. 
    `SE`, `Tvalue`, and `pvalue` will all be `NA`.

- **results-annotated.txt**: same as `results.txt` but 3 more columns of
annotations, i.e., `Gene`, `ProteinName`, and `EntrezID`
- Different outputs from the MSstats dataProcess step (Normalizing 
and summarizing data, check 
[MSstats documentation](http://msstats.org/msstats-2/) to find out more), 
including:
    * `results_ModelQC.txt`
    * `results_RunlevelData.txt`
    * `results-mss-groupQuant.txt`
    * `results-mss-normalized.txt`
    * `results-mss-sampleQuant.txt`
    
- Sample size and power calculation:
    * `results_sampleSize.txt`
    * `results_experimentPower.txt`

### Plots (pdf)

- `results-heatmap.pdf`
- `results-peptidecounts-perBait.pdf`
- `results-peptidecounts.pdf`
- `results-sign.pdf`
- `results-volcano.pdf`




<a name="analysisquan" />

# ANALYSIS OF QUANTIFICATIONS

Comprehensive analysis of the quantifications outputs obtained from the function
[artmsQuantification()](#relquan) section to find out more). It includes:

`artmsAnalysisQuantifications()` performs a comprehensive analysis of the 
quantifications outputs obtained from the function 
[artmsQuantification()](#relquan). It includes:

- Annotations
- Summary files in different formats (xls, txt) and shapes (long, wide)
- Basic Imputation (see *Notes on Imputation* and input option **`mnbr`** below)
- Numerous summary plots
- Enrichment analysis using 
[Gprofiler](https://cran.r-project.org/web/packages/gProfileR/index.html)
- PCA of protein abundance
- PCA of quantifications
- Clustering analysis

## Inputs

It takes as input two files generated from the previous quantification step 
([artmsQuantification()](#relquan))

- `-results.txt` : MSstats quantification results
- `-results_ModelQC.txt` : MSstats normalized abundance values. 
It will be used to extract details about reproducibility.

To run this analysis:

1. Set as the working directory the folder with the results obtained from 
`artmsQuantification()`.

```
setwd('~/path/to/the/results_quantification/')
```

And then run the following function (e.g., for a protein abundance 
"AB" experiment)

```
artmsAnalysisQuantifications(log2fc_file = "ab-results.txt",
                              modelqc_file = "ab-results_ModelQC.txt",
                              species = "human",
                              output_dir = "AnalysisQuantifications")
```

A few comments on the available options for `artmsAnalysisQuantifications`:

- `isPTM`: two options:
   + `"noptm"`: use for protein abundance (`AB`), Affinity Purification-Mass 
Spectrometry (`APMS`), and global analysis of posttranslational modifications 
(`PH`, `UB`, `AC`) use the option .
   + `"ptmsites"`: use for site specific PTM analysis.
- `species`: this downstream analysis supports (for now) `"human"` and 
`"mouse"` only.
- `outliers`: outliers can be kept (default) or could be removed from the abundance data. Options:
   + `keep`: keeps the outliers 
   + `iqr`: removes any outlier outside +/- 6 x interquartile range from the mean (recommended) 
   + `std`: it removes any outliers outside +/- 6 x the standard deviation from the mean
- `enrich`: If `TRUE`, it will perform enrichment analysis using `gProfileR`
- `isBackground`. If `enrich = TRUE`, the user can provide a background 
gene list (add the file path as well)
- `mnbr`: **PARAMETER FOR NAIVE IMPUTATION**. 
Minimal number of biological replicates for "naive 
imputation" and filtering. Default: `mnbr = 2`. 
*Details*:
Intensity values for proteins/PTMs that are completely missed in one 
of the two conditions compared ("condition A"), but are found in 
at least 2 biological replicates (`mnbr = 2`) of the other "condition B", 
are imputed (values artificially assigned) and the log2FC values calculated. 
The goal is to keep those proteins/PTMs that are consistently found in 
one of the two conditions (in this case "condition B") and facilitate the 
inclusion in downstream analysis (if wished). The imputed intensity values 
are sampled from the lowest intensity values detected in the experiment, 
and (**WARNING**) the p-values are just randomly assigned between 
0.05 and 0.01 for illustration purposes (when generating a volcano 
plot with the output of `artmsAnalysisQuantifications`) or to include 
them when making a cutoff of `p-value < 0.05` for enrichment analysis or similar.
**CAUTION**: `mnbr` would also add the constraint that any protein 
must be identified in at least `nmbr` biological replicates of the 
**same** condition or it will be filtered out. That is,
if `mnbr = 2`, a protein found in two conditions but only in one
biological replicate in each of them, it would be removed.

## Outputs

**Summary file (`summary.xlsx`)**

*Reminder*: for any given relative quantification, as for example WT-Mutant:

- Proteins with positive log2fc values (i.e. `log2fc > 0`) are more abundant
in the condition on the left / numerator (WT)
- Proteins with negative log2fc values (i.e. `log2fc < 0`) are more abundant
in the condition on the right / denominator (Mutant)

The summary excel file (`results-summary.xlsx`) gathers several tabs:

- `log2fcImputed`: includes quantitative results. 
   +  Self-defining columns: *Protein*, *Gene*, *ProteinName*, *EntrezID*, *Comparison*, 
*log2FC*, *pvalue*, *adj.pvalue*. 
   + Not self-defining columns: 
      - *imputed* (`yes/no`) indicates whether the iLog2FC value has been imputed according to the `nmbr` criteria (see above)
      - *iLog2FC*, *iPvalue*: in addition to the model based log2fc and pvalues, 
these columns include imputed log2fc and pvalues (when completely missed 
in one of the conditions), followed by as many columns as the number of 
conditions, indicating the number of biological replicates in which each 
protein/ptmsite was identified.
      - *CMA* stands for "Condition Most Abundant", i.e. condition in which each protein was found more abundantly (based on the log2fc).
- `wide_iLog2fc`: log2fc values (including imputed values) in wide format, i.e., 
each row is a unique protein/ptmsite. The columns shows the values for each 
of the comparisons.
- `wide_iPvalue`: same as before, but for pvalues (including imputed)
- `enrichALL`: enrichment analysis using GProfileR for all the proteins changing 
significantly in any direction (ab(log2fc) > 0 and pvalue < 0.05)
- `enrich-MACpos`: enrichment of only the positive significant changes 
(log2fc > 1, pvalue < 0.05)
- `enirch-MACneg`: enrichment of only the negative significant changes 
(log2fc < -1, pvalue < 0.05)
- `enMACallCorum, enMACposCorum, enMACnegCorum`: same as above but only for 
protein complex enrichment analysis (based on CORUM)


**Text files**

- `results-log2fc-long.txt`: same as the `log2fcImputed` tab from the 
summary file
- `results-log2fc-wide.txt`: wide version (i.e., each row is an individual 
protein) of pvalues and adj.pvalues for each comparison


**Gene Enrichment analysis**: enrichment analysis only supported for 
human and mouse. Check the 
[GprofileR documentation](https://cran.r-project.org/web/packages/gProfileR/index.html) 
to find out more about the details:

- `results-enrich-MAC-allsignificants.txt`: all significant changes 
(abs(log2fc) > 1 & pvalue < 0.05)
- `results-enrich-MAC-positives.txt`: only positive significant changes
(log2fc > 1 & pvalue < 0.05)
- `results-enrich-MAC-negatives.txt`: all significant changes (based on p-value
only)


**Protein Complex Enrichment analysis (based on CORUM)**

- `results-enrich-MAC-allsignificants-corum.txt`
- `results-enrich-MAC-positives-corum.txt`
- `results-enrich-MAC-negatives-corum.txt`

- `results-enrich-MAC-allsignificants-corum.pdf`
- `results-enrich-MAC-negatives-corum.pdf`
- `results-enrich-MAC-positives-corum.pdf`


**Clustering**

- `results.clustering.log2fc.all-overview.pdf`
- `results.clustering.log2fc.all-zoom.pdf`
- `results.clustering.log2fcSign.all-overview.pdf`
- `results.clustering.log2fcSign.all-zoom.pdf`


- `results.log2fc-clusterheatmap-enriched.txt`
- `results.log2fc-clusterheatmap.txt`
- `results.log2fc-clusterheatmap.pdf`
- `results.log2fc-clusters.pdf`


**Correlations**

- `results.correlationConditions.pdf`
- `results.reproducibilityAbundance.pdf`
- `results.correlationQuantifications.pdf`
- `results.log2fc-corr.pdf`

**Miscellaneous**

- `results.relativeABUNDANCE.pdf`

- `results.distributions.pdf`
- `results.distributionsFil.pdf`
- `results.imputation.pdf`
- `results.TotalQuantifications.pdf`



**PCA**

***Based on relative abundance***

- `results-pca-pca01.pdf`
- `results-pca-pca02.pdf`
- `results-pca-pca03.pdf`
- `results-pca-pca04.pdf`


***Based on significant changes***

- `results.log2fc-dendro.pdf`
- `results.log2fc-individuals-pca.pdf`


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MISCELLANEOUS FUNCTIONS
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<a name="misc" />

# MISCELLANEOUS FUNCTIONS

`artMS` also provides a number of very handy functions.


## Annotate data.frame with Gene Symbol, Name, ENTREZ based on Uniprot IDs

Takes the given `columnid` (of Uniprot IDs) from the input data.frame,
and map the gene symbol, name, and entre id 
(source: [bioconductor annotation packages](https://bioconductor.org/packages/3.8/data/annotation/))

```{r, eval = FALSE}
# This example adds annotations to the evidence file available in
# artMS, based on the column 'Proteins'.

evidence_anno <- artmsAnnotationUniprot(x = artms_data_ph_evidence,
                                        columnid = 'Proteins',
                                        species = 'human')
```


## Average Intensity, RT, CR

Taking as input the evidence file, it will summarize and return back 
the average intensity, average retention time, and the average calibrated
retention time for each protein. If a list of proteins is provided, then only 
those proteins will be summarized and returned. Check `?artmsAvgIntensityRT()` 
to find out more options.


```
artmsAvgIntensityRT(evidence_file = '/path/to/the/evidence.txt)
```

## Change column name

Changes a given column name in the input data.frame

```{r, eval = FALSE}
artms_data_ph_evidence <- artmsChangeColumnName(
                               dataset = artms_data_ph_evidence,
                               oldname = "Phospho..STY.",
                               newname = "PH_STY")
```

## Individual abundance dot plots

Protein abundance dot plots for each unique uniprot id. It can take a long time

```
artmsDataPlots(input_file = "results/ab-results-mss-normalized.txt",
               output_file = "results/ab-results-mss-normalized.pdf")
```

## Enrichment analysis function

Enrichment analysis based on a data.frame with `Gene` and `Comparison`/`Label` 
protein (i.e, typical MSstats results)

```{r, eval=FALSE}
# The data must be annotated (Protein and Gene columns)
data_annotated <- artmsAnnotationUniprot(
                      x = artms_data_ph_msstats_results,
                      columnid = "Protein",
                      species = "human")
# And then the enrichment
enrich_set <- artmsEnrichLog2fc(
                   dataset = data_annotated,
                   species = "human",
                   background = unique(data_annotated$Gene), 
                   verbose = FALSE)
```

## Enrichment analysis using gProfileR

Function that simplifies enrichment analysis using gProfileR

```{r, eval=FALSE}
# annotate the MSstats results to get the Gene name
data_annotated <- artmsAnnotationUniprot(
                                     x = artms_data_ph_msstats_results,
                                     columnid = "Protein",
                                     species = "human")

# Filter the list of genes with a log2fc > 2
filtered_data <- 
unique(data_annotated$Gene[which(data_annotated$log2FC > 2)])

# And perform enrichment analysis
data_annotated_enrich <- artmsEnrichProfiler(
                                   x = filtered_data,
                                   categorySource = c('KEGG'),
                                   species = "hsapiens",
                                   background = unique(data_annotated$Gene))
```


## MaxQuant evidence file to SAINTexpress format

Converts the MaxQuant evidence file to the 3 required files by [SAINTexpress](http://saint-apms.sourceforge.net/Main.html). Choose one of the 
following quantitative MS metrics:

- MS spectral counts (use msspc)
- MS intensities (use msint)

```
artmsEvidenceToSaintExpress(evidence_file = "/path/to/evidence.txt", 
                            keys_file = "/path/to/keys.txt", 
                            ref_proteome_file = "/path/to/org.proteome.fasta")
```


## MaxQuant evidence file to SAINTq format

Converts the MaxQuant evidence file to the required files by [SAINTq](http://saint-apms.sourceforge.net/Main.html). The user can filter 
based on either peptides with spectral counts (use `msspc`) or all the peptides
(use `all`) for the analysis. The quantitative metric can be also chosen 
(either MS intensity or spectral counts)

```
artmsEvidenceToSAINTq(evidence_file = "/path/to/evidence.txt", 
                      keys_file = "/path/to/keys.txt", 
                      output_dir = "saintq_input_files")
```

## Generate Phosfate input file

It generates the Phosfate input file from the `imputedL2fcExtended.txt` file 
resulting from running the `artmsAnalysisQuantifications()` on a ph-site 
quantification (see above). Notice that the only species suported by PHOTON 
is humans.

```
artmsPhosfateOutput(inputFile = "your-imputedL2fcExtended.txt")
```

## Generate Photon input file

It generates the Photon input file from the `imputedL2fcExtended.txt` file 
resulting from running the `artmsAnalysisQuantifications()` on a ph-site 
quantification (see above). Please, notice that the only species suported by 
PHOTON is humans.

```
artmsPhotonOutput(inputFile = "your-imputedL2fcExtended.txt")
```


## Remove contaminants and empty proteins from the MaxQuant evidence file

Remove contaminants and erroneously identified 'reverse' sequences by MaxQuant,
in addition to empty protein ids

```
evidencefiltered <- artmsFilterEvidenceContaminants(x = artms_data_ph_evidence)
```


## Generate ph-site specific evidence file

Generate extended detailed ph-site file, where every line is a ph site instead 
of a peptide. Therefore, if one peptide has multiple ph sites it will be 
breaking down in multiple extra lines for each of the sites. 

```
artmsGeneratePhSiteExtended(df = dfobject, 
                            species = "mouse", 
                            ptmType = "ptmsites",
                            output_name = log2fc_file)
```



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METABOLOMICS
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<a name="metabolomics" />

# METABOLOMICS

`artMS` enables the relative quantification of untargeted polar metabolites 
using the alignment table generated by [MarkerView](https://sciex.com/products/software/markerview-software).
This means that the metabolites do not need to have an id in order to perform 
the quantification, as the m/z and retention time will be used as identifiers. 

[MarkerView](https://sciex.com/products/software/markerview-software) is an 
ABSciex software that supports the files generated by Analyst software 
(`.wiff`) used to run our specific mass 
spectrometer (ABSciex Triple TOF 5600+). 
It also supports `.t2d` files generated by the 
Applied Biosystems 4700/4800 MALDI-TOF. 

Markview is used to align mass spectrometry data from several 
samples for comparison. Using the import feature in the software, `.wiff` files 
(also `.t2d` MALDI-TOF files and tab-delimited `.txt` mass spectra data 
in mass-intensity format) are loaded for retention time alignment. 
Once the data files are selected, a series of windows will appear wherein 
peak finding, alignment, and filtering options can be entered and selected. 
These options include minimum spectral peak width, minimum retention time 
peak width, retention time and mass tolerance, and the ability to filter 
out peaks that do not appear in more than a user selected number of samples.

The alignment file is further processed and formatted to perform QC 
and relative quantification using the following `artMS` functions:

## Convert Metabolomics

Pre-process the markview `.txt` file to generate 
an "evidence-like" file by running:

```
artmsConvertMetabolomics(input_file = "markview-output.txt", 
                         out_file = "metabolomics-evidence.txt")
```

## QC Metabolomics

Perform quality control analysis on the metabolomics 
data by running: 


```
artmsQualityControlMetabolomics(evidence_file = "metabolomics-evidence.txt",
                                keys_file = "metabolomics-keys.txt")
```

It generates the following plots:

- `plotINTDIST.pdf` contains both *Box-dot plot* 
and *Jitter plot* of biological replicates based on MS (raw) 
intensity values.
- `plotREPRO.pdf` correlation dotplot for all the 
combinations of biological replicates of conditions, based on MS Intensity 
values using features (mz_rt+charge)
- `plotCORMAT.pdf`, includes up to 3 pdf files for 
technical replicates, biological replicates, and conditions. 
Each pdf file contains: 
    - *Correlation matrix* for all the biological replicates using 
MS Intensity values, 
    - *Clustering matrix* of the MS Intensities and correlation distribution 
    - *histogram* of the distribution of correlations
- `plotINTMISC.pdf` the pdf contains several pages, including 
bar plots of *Total Sum of Intensities in BioReplicates*, 
*Total Sum of Intensities in Conditions*, 
*Total Feature Counts in BioReplicates*, 
*Total Feature Counts in conditions* separated by categories 
(INT: has a intensity value NOINT: no intensity value ) 
*Box plots* of MS Intensity values per 
biological replicates and conditions; *bar plots* of total intensity 
by bioreplicates and conditions; Barplots of 
*total feature counts* by bioreplicates and conditions.


## Relative Quantification: 

The relative quantification is performed using 
`MSstats`. It requires a configuration file (`yaml` format, please check above).
A template can be generated by running:
`artmsWriteConfigYamlFile(config_file_name = "metab_config.yaml")`.
The relative quantification is performed by running:

```
artmsQuantification(yaml_config_file = "metabConfig.yaml")
```








<!--
TESTING FILES
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# TESTING FILES

The artMS package provides the following testing datasets

**Phosphoproteomics dataset**: 
example dataset consisting of two head and neck cancer cell lines 
(conditions `"Cal33"` and `"HSC6"`), 2 biological 
replicates each). The number of peptides was reduced to 1/8 due to bioconductor 
limitations on data size.

- Evidence file: `artms_data_ph_evidence`
- Keys file: `artms_data_ph_keys`
- Results file: `artms_data_ph_msstats_results`: results after running 
`artmsQuantification()` on the reduced version

The full data set (2 conditions, 4 biological replicates) can be found at the 
following urls:

- `url_evidence <- 'http://kroganlab.ucsf.edu/artms/ph/evidence.txt'`
- `url_keys <- 'http://kroganlab.ucsf.edu/artms/ph/keys.txt'`

**Protein Complexes dataset**: downloaded (2017-08-01) from 
[CORUM](http://mips.helmholtz-muenchen.de/corum/) database  
and further enriched with annotations of mouse mitochondrial complexes 
not available at CORUM. Used for complex enrichment calculations.

- `artms_data_corum_mito_database`

**Pathogens Uniprot IDs**:

- `artms_data_pathogen_LPN`: *Legionella pneumophila philadelphia* 
(downloaded 2017-07-17)
- `artms_data_pathogen_TB`: *Mycobacterium tuberculosis* 
strain ATCC 35801 / TMC 107 / Erdman (downloaded 2018-04-01)

Check the individual help pages (e.g, `?artms_data_ph_evidence`) to find out 
more about them.


<!--
HELP
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-->


# HELP

- Errors or warnings? Please, 
<a href="https://github.com/biodavidjm/artMS/issues" target="_blank">submit them as a new issue</a>
at the official Github repository
- Any other inquiries: <artms.help@gmail.com>





