---
title: "MACSQuantifyR - Step-by-step analysis"
author:
- name: Raphaël Bonnet, Jean-François Peyron
email: raphael.bonnet@unice.fr
affiliation: Université Côte d’Azur, Inserm, C3M, France
date: "`r Sys.Date()`"
output:
prettydoc::html_pretty:
theme: cayman
toc: true
css: MACSQuantifyR.css
bibliography: REFERENCES.bib
csl: clinical-pharmacology-and-therapeutics.csl
vignette: >
%\VignetteIndexEntry{MACSQuantifyR_step_by_step_analysis}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
fig.width = 8,
fig.height = 8
)
```
```{r, echo = FALSE, fig.width=10, fig.height=8,fig.asp=0.6}
library(png)
library(grid)
path_intro <- system.file("extdata", "manual.png", package = "MACSQuantifyR")
intro <- readPNG(path_intro)
grid.raster(intro)
```
# Introduction
This is an example of how to run MACSQuantifyR with combination drug analysis.
To know more about the package, have a look at the [introduction
vignette](MACSQuantifyR.html)
In this vignette we will run the MACSQuantifyR package on the example given
in the introduction of the previous vignette.
More precisely, this experiment corresponds to the screening
of the combination effects of two drugs on human cells.
Here is the plate template chosen by the user that represents drugs alone
(up part of the well plate) and combinations (low part of the well plate).
```{r, echo = FALSE}
path_img1 <- system.file("extdata", "combo.png", package = "MACSQuantifyR")
img1 <- readPNG(path_img1)
grid.raster(img1)
```
# Pipeline
Load the packages
```{r load}
library(MACSQuantifyR)
library(knitr)
library(grid)
library(gridExtra)
library(ggplot2)
library(tools)
library(readxl)
library(lattice)
library(latticeExtra)
suppressMessages(library(R.utils))
```
## Create a new object MACSQuant: new_class_MQ()
---
---
This function allows the user to create a new object of class MACSQuant.
This will allow the user to set specific options regarding the experiment and
the output of some functions before running other functions.
---
---
* Create the MACSQuant object:
```{r}
MACSQuant <- new_class_MQ(path = "output_path")
```
* Define experiment name:
```{r}
slot(MACSQuant, "experiment_name") <- "Combo drug1-drug2 HC line 101"
```
* Define output path:
`slot(MACSQuant,"param.output")$path`
## Import your data: load_MACSQuant()
Once the excel file has been generated by miltenyi
MACSQuantify software. The user can load the data with the
following function.
It will generate a variable called my_data,
necessary for the next steps of the analysis.
Load the data:
```{r load_file, include = TRUE}
filepath <- system.file("extdata", "drugs.xlsx",
package = "MACSQuantifyR")
MACSQuant <- load_MACSQuant(filepath,
sheet_name = "combo_drugs",
MACSQuant.obj = MACSQuant)
```
## Sort your replicates: on_plate_selection()
---
---
By calling the function on_plate_selection() with the number
of conditions in the experiment and the number of replicates
by conditions, the user will be asked to select sequentially
the replicates for each conditions.
Before running the function,
the user can define one of the experiment parameters called
c_names in which condition names are stored.
---
---
Experiment parameters are:
* c_names: condition names to plot
`slot(MACSQuant,"param.experiment")$c_names`
* doses: colors for the barplots
`slot(MACSQuant,"param.experiment")$doses`
```{r}
# this line is used to created c_names variable
# for this experiment according to selection
slot(MACSQuant, "param.experiment")$c_names <-
c(sprintf("Drug1_c%d", 1:4), # DRUG1 ALONE
sprintf("Drug2_c%d", 1:3), # DRUG2 ALONE
sprintf("D2[1]_D1[%d]", 1:4), # DRUG2_C1 + DRUG1_Cs
sprintf("D2[2]_D1[%d]", 1:4), # DRUG2_C2 + DRUG1_Cs
sprintf("D2[3]_D1[%d]", 1:4)) # DRUG2_C3 + DRUG1_Cs
# custom colors can be defined (with control if selected)
plt.col <- c(heat.colors(length(slot(MACSQuant, "param.experiment")$c_names)),
1)
# dose vector of concentration each condition
slot(MACSQuant, "param.experiment")$doses <-
c(1, 3, 5, 10, # DRUG1 ALONE
0, 0, 0, # DRUG2 ALONE
1, 3, 5, 10, # DRUG2_C1 ++ DRUG1_Cs
1, 3, 5, 10, # DRUG2_C2 + DRUG1_Cs
1, 3, 5, 10 # DRUG2_C3 + DRUG1_Cs
)
slot(MACSQuant, "param.experiment")$doses.alt <-
c(0, 0, 0, 0, # DRUG1 ALONE
10, 50, 100, # DRUG2 ALONE
10, 10, 10, 10, # DRUG2_C1 + DRUG1_Cs
50, 50, 50, 50, # DRUG2_C2 ++ DRUG1_Cs
100, 100, 100, 100 # DRUG2_C3 ++ DRUG1_Cs
)
```
The function is ready to run:
```{r,eval=FALSE}
MACSQuant <- on_plate_selection(MACSQuant,
number_of_replicates = 3,
number_of_conditions = 19,
control = TRUE,
save.files = TRUE)
```
Once the replicates of all conditions have been identified by the user,
the on_plate_selection function will automatically reorder the data stored
in the variable my_data into a new variable called my_data_sorted.
During the process of sorting replicates basic statistical analysis
for each condition is done (mean and standard deviation of replicates).
This will generate a new variable called statistics necessary for the
next part of the pipeline.
## 2D/3D data representation: barplot_data()
---
---
This function allows the user to generate 2D and 3D plots corresponding
to two flavours (cell counts, percentages).
Before running the function,
the user can define one of the experiment parameters
---
---
flavours are:
* `counts`: cell count
* `percent`: fluorochrome positive cell percentage
Output.parameters are:
are:
* plt.title: Barplot title
`slot(MACSQuant,"param.output")$plt.title` this will be used as
subtitle for the Word document.
* plt.labels: Barplot labels
`slot(MACSQuant,"param.output")$plt.labels`
* the user can also load specific colors:
```{r,include=F}
filepath <- system.file("extdata", "drugs.Rdata",
package = "MACSQuantifyR")
load(filepath)
```
```{r}
plt.col <-
c(
heat.colors(length(slot(MACSQuant, "param.experiment")$c_names)), 1)
plt.labels <- c(slot(MACSQuant, "param.experiment")$c_names, "Control")
slot(MACSQuant, "param.output")$plt.title <- "Custom title example"
```
The barplot_data() function is ready to run
```{r,fig.width=31.75, fig.height=15.875,fig.asp=0.5,fig.asp=0.5}
flav <- "counts"
p_counts <- barplot_data(MACSQuant,
plt.col = plt.col,
plt.flavour = flav,
plt.labels = plt.labels,
plt.combo = TRUE,
xlab = "Drug1",
ylab = "Drug2")
grid.arrange(p_counts)
flav <- "percent"
p_percent <- barplot_data(MACSQuant,
plt.col = plt.col,
plt.flavour = flav,
plt.labels = plt.labels,
plt.combo = TRUE,
xlab = "Drug1",
ylab = "Drug2")
grid.arrange(p_percent)
```
Save the generated plots else they will not appear
in the excel document:
```{r eval =FALSE}
# flav='counts'
ggsave(paste(MACSQuant@param.output$path,
"/outputMQ/barplot_", flav, ".png", sep = ""),
width = 31.75, height = 15.875,
units = "cm", p_counts)
# flav='percent'
ggsave(paste(MACSQuant@param.output$path,
"/outputMQ/barplot_", flav, ".png", sep = ""),
width = 31.75, height = 15.875,
units = "cm", p_percent)
```
## Combination index computation: combination_index()
---
---
This function allows the user to compute combination index as computed by
Chou TC, Pharmacol Rev. 2006[@chou2006theoretical].
Four plots are generated by this function:
* The dose response plot for drugs alone
* The median effect plot (log dose vs log(response/1-response))
* The normalized isobologram with synergism decision
* The 3D barplot with synergy corresponding color code
---
---
```{r}
MACSQuant <- combination_index(MACSQuant)
```
## Report generation: generate_report()
---
---
This function uses all the files saved during the analysis
(argument <span style="color:red">save.files=T</span>).
The save.files option creates a folder called outputMQ
in your current directory (default) or at specified
path and save:
* The well plate template image (plate_template.png)
* The sorted_replicates data table (sorted_table.txt)
* The statistic table (statistics.txt)
* Plots barplot_counts, barplot_percent
* Any other generated plots
Be careful to avoid overriding data, content of
existing outputMQ folder could be erased.
---
---
You can run generate_report at any steps of the pipeline to generate the
Word document named results.docx.
```{r,eval=FALSE}
generate_report(MACSQuant)
```
# Links
* You can find an example of the report generated
by the step-by-step analysis()
at this address: https://github.com/Peyronlab/MACSQuantifyR
* Also a version of this package is available as an excel
macro at this address:
https://github.com/Peyronlab/MACSQuantifyXL
# Reference