---
title: "GRmetrics: an R package for calculation and visualization of 
dose-response metrics based on growth rate inhibition"
author: "Nicholas Clark"
date: "`r doc_date()`"
output: 
  BiocStyle::html_document
vignette: >
  %\VignetteIndexEntry{GRmetrics: an R package for calculation and visualization of dose-response metrics based on growth rate inhibition}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
```{r style, echo = FALSE, results = 'asis'}
BiocStyle::markdown()
```

# Background

Quantifying drug response is the cornerstone of many pharmacological 
experiments ranging from pharmacogenomics studies to small-scale analyses of 
drug resistance. In general, cells are grown in the presence or absence of 
drugs for a few days and the endpoint cell count values (or a surrogate) is 
compared. From the relative cell count, metrics of drug sensitivity such as 
IC50 or Emax values are evaluated. In cases where the untreated control 
cells grow over the course of the assay, these traditional metrics are 
confounded by 
the number of divisions that take place over the course of an assay. In 
particular, for drugs that impact growth rate and block cell division, 
slow-growing cell lines will appear more resistant than fast-growing lines 
although the biological effect on a per-division basis may be the same.

Hafner 
et al. recently proposed alterative drug-response metrics based on growth rate 
inhibition (GR metrics) that are robust to differences in nominal division rate 
and assay duration. Using these metrics requires only to know the number of 
cells (or a surrogate) at the time of treatment; note that this value may also 
be inferred from the nominal division rate and the value of an untreated 
sample at the endpoint.

To facilitate the use of these GR metrics, we have developed 
an R package that provides functions to analyze and visualize drug response 
data with these new metrics across multiple conditions and cell lines.

# Installation

The **GRmetrics** package can be installed through Bioconductor

```r
if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("GRmetrics")
```

#References:
Hafner, M., Niepel, M. Chung, M. and Sorger, P.K., *Growth Rate Inhibition 
Metrics Correct For Confounders In Measuring Sensitivity To Cancer Drugs*. 
Nature Methods 13.6 (2016): 521-527.
(http://dx.doi.org/10.1038/nmeth.3853)

Corresponding MATLAB and Python scripts available on repo:

https://github.com/sorgerlab/gr50_tools.

Note: Most, but not all of these scripts have been reproduced in this R 
package. Namely, this package does not contain code for "Case B" of the 
MATLAB scripts nor does it contain an R script to generate the example input 
data. The python script for generating the example data can be found in the 
"inst/scripts/" directory.

Browser interface and online tools: http://www.grcalculator.org

Much of the description below has been adapted from:

https://github.com/datarail/gr_metrics/blob/master/README.md.

#Input data
The main function of the package is **GRfit**, which takes in a data frame 
containing information about concentration, cell counts over time, and 
additional grouping variables for a dose-response assay and calculates 
growth-rate inhibition (GR) metrics for each experiment in the dataset.

There are two cases of data input accepted by the **GRfit** function. 
They are described in detail below. Case "A" is the default option.

##Case A: a single file with control values assigned to treated measurements
The control values (both control and time 0 cell counts) are pre-computed by 
the user and assigned to each treatment (row) in appropriate columns in the 
input file. Control cell counts should be in a column labeled 
*cell_count\_\_ctrl* and the time 0 cell counts in a column labeled 
*cell_count\_\_time0*.

An example input data frame for "Case A", named "inputCaseA", is contained 
within the package.
To access it, use the following code:
 
```r
library(GRmetrics)
data(inputCaseA)
```

**The mandatory inputs for Case "A" are:**

- **inputData** - the name of an input data frame with the following columns 
as well as other grouping columns
    1. **concentration** - column with concentration values 
(not log transformed) of the perturbagen on which dose-response curves will be 
evaluated
    2. **cell_count** - column with the measure of cell number 
(or a surrogate of cell number) after treatment
    3. **cell_count\_\_time0** - column with initial (Time 0) cell 
    counts - the measure of cell number in untreated wells grown in parallel 
    until the time of treatment
    4. **cell_count\_\_ctrl** - column with the Control cell 
  count: the measure of cell number in control (e.g. untreated or 
  DMSO-treated) wells from the same plate

All other columns will be treated as additional keys on which the data will be 
grouped (e.g. *cell_line*, *drug*, *time*, *replicate*)

##Case C: a single file with control values stacked with treated measurements
In the most general case, the control cell counts are in the same file and 
format as the treated cell counts. Control cell counts will be averaged (using 
a 50%-trimmed mean) and automatically matched to the treated cell counts based 
on the keys (columns in the data file). The control cell count values must 
have a value of 0 for *concentration* and a value for *time* that matches the 
treated measurements. The time 0 cell count values must have value of 0 for 
*time*. If the structure of the data is complex, the provided scripts may 
inappropriately match control and treated cell counts, so users instead should 
format their data as described in case A. 

An example input data frame for "Case C", named "inputCaseC", is contained 
within the package.
To access it, use the following code:
 
```r
library(GRmetrics)
data(inputCaseC)
```

**The mandatory inputs for Case "C" are:**

- **inputData** - the name of an input data frame with the following columns 
as well as other grouping columns
    1. **concentration** - column with concentration values 
(not log transformed) of the perturbagen on which dose-response curves 
will be evaluated
    2. **cell_count** - column with the measure of cell number 
    (or a surrogate of cell number)
    3. **time** - column with the time at which a cell count is 
observed

All other columns will be treated as additional keys on which the data will be 
grouped (e.g. *cell_line*, *drug*, *replicate*)

##Functions
The package contains 3 visualization functions: **GRdrawDRC**, **GRscatter**, 
and **GRbox**.

All of these functions take in an object created by **GRfit** as well as 
additional arguments. The results can be viewed in a static ggplot image or an 
interactive plotly (turned on/off by the *plotly* parameter).

- **GRdrawDRC**   this function draws the (growth-rate inhibition) 
dose-response curve using the parameters calculated by the **GRfit** function. 
If *points* is set to TRUE, then it will also plot the points used to fit each 
curve.
- **GRscatter**   this function draws a scatterplot of a given GR metric 
(GR50, GRmax, etc.) with the *xaxis* value(s) plotted against the *yaxis* 
value(s).
- **GRbox**   this function draws boxplots of a given GR metric 
(GR50, GRmax, etc.) for values of a given grouping variable. It overlays the 
points used to make these boxplots and can color them according to another 
grouping variable.

The package also contains 4 accessor functions, which extract data from the 
SummarizedExperiment object created by **GRfit**. These functions are: 
**GRgetMetrics**, **GRgetDefs**, **GRgetValues**, and **GRgetGroupVars**.

- **GRgetMetrics**  this function returns a table of GR metrics and 
traditional metrics along with goodness of fit measures. It also identifies 
each fit as flat or sigmoidal.
- **GRgetDefs** this function returns a table containing the definition of 
each GR metric, traditional metric, and goodness of fit measure calculated.
- **GRgetValues** this function returns a table of the original data (in the 
form of "Case A") with columns for GR values and relative cell counts.
- **GRgetGroupVars**   this function returns a vector of the grouping 
variables used to create the object. These are the variables in the dataset 
that are not averaged over.

# Examples

```{r, include = FALSE}
## Case A (DRC examples)
library(GRmetrics)
```
Load example data (Case A)
```{r}
data(inputCaseA)
```

```{r, include = FALSE}
inputCaseA = as.data.frame(inputCaseA)
```

```{r}
head(inputCaseA)
```

Calculate GR values and solve for GR metrics parameters (i.e. fit curves)

```{r, include = FALSE}
drc_output = GRfit(inputCaseA, groupingVariables = c('cell_line','agent'))
```

```r
drc_output = GRfit(inputCaseA, groupingVariables = c('cell_line','agent'))
```
See overview of output data (SummarizedExperiment object)

```{r}
drc_output
```

Review output table of GR metrics parameters

```{r}
head(GRgetMetrics(drc_output))
```

View descriptions of each GR metric (or goodness of fit measure)

```
View(GRgetDefs(drc_output))
```

Review output table of GR values

```{r}
head(GRgetValues(drc_output))
```

View grouping variables used for calculation

```{r}
GRgetGroupVars(drc_output)
```
You can also export your results. Here are two examples:
```r
# Write GR metrics parameter table to tab-separated text file
write.table(GRgetMetrics(drc_output), file = "filename.tsv", quote = FALSE,
sep = "\t", row.names = FALSE)
# Write original data plus GR values to comma-separated file
write.table(GRgetValues(drc_output), file = "filename.csv", quote = FALSE,
sep = ",", row.names = FALSE)
```

#Visualizations
You can draw GR dose-response curves with plotly or with ggplot2. You can also 
specify the range of the graph.
```{r}
# Draw dose-response curves
GRdrawDRC(drc_output)
GRdrawDRC(drc_output, experiments = c('BT20 drugA', 'MCF10A drugA', 
                                      'MCF7 drugA'))
GRdrawDRC(drc_output, experiments = c('BT20 drugA', 'MCF10A drugA', 
                                      'MCF7 drugA'), 
          min = 10^(-4), max = 10^2)
GRdrawDRC(drc_output, plotly = FALSE)
```

You can also draw scatterplots and boxplots of GR metrics with plotly or 
ggplot2. 
Here is an example using example data in the format of Case C.
```{r}
## Case C (scatterplot and boxplot examples)
data(inputCaseC)
```

```{r, include = FALSE}
inputCaseC = as.data.frame(inputCaseC)
```

```{r}
head(inputCaseC)
```

```{r, include = FALSE}
output1 = GRfit(inputData = inputCaseC, groupingVariables = 
                  c('cell_line','agent', 'perturbation', 'replicate', 'time'), 
                case = "C")
```

```r
output1 = GRfit(inputData = inputCaseC, groupingVariables = 
c('cell_line','agent', 'perturbation', 'replicate', 'time'), case = "C")
```

```{r}
# Draw scatterplots
GRscatter(output1, 'GR50', 'agent', c('drugA','drugD'), 'drugB')
GRscatter(output1, 'GR50', 'agent', c('drugA','drugD'), 'drugB', 
          plotly = FALSE)

# Draw boxplots
GRbox(output1, metric ='GRinf', groupVariable = 'cell_line', 
      pointColor = 'agent')
GRbox(output1, metric ='GRinf', groupVariable = 'cell_line', 
      pointColor = 'agent',
      factors = c('BT20', 'MCF10A'))
GRbox(output1, metric ='GRinf', groupVariable = 'cell_line', 
      pointColor = 'agent',
      factors = c('BT20', 'MCF10A'), plotly = FALSE)
GRbox(output1, metric ='GR50', groupVariable = 'cell_line', 
      pointColor = 'agent', wilA = 'BT20', wilB = c('MCF7', 'MCF10A'),
      plotly = FALSE)

```

#GR metric details
We have developed scripts to calculate normalized growth rate inhibition (GR) 
values and corresponding metrics (GR50, GRmax, ...) based on cell counts 
measured in dose-response experiments. Users provide a tab-separated data file 
in which each row represents a separate treatment condition and the columns 
specify the keys that define the treatment condition (e.g. cell line, drug or 
other perturbagen, perturbagen concentration, treatment time, replicate) and 
the measured cell counts (or surrogate). The experimentally measured cell 
counts that are required for GR metric calculation are as follows: 
- measured cell counts after perturbagen treatment (*cell_count*, *x(c)*)
- measured cell counts of control (e.g. untreated or DMSO-treated) wells on 
the same plate (*cell_count\_\_ctrl*, *x_ctrl*)
- measured cell counts from an untreated sample grown in parallel until the 
time of treatment (*cell_count\_\_time0*, *x_0*)

The provided GR scripts compute over the user’s data to calculate GR values 
individually for each treatment condition (cell line, time, drug, 
concentration, ...) using the formula:

    GR(c) = 2 ^ ( log2(x(c)/x_0) / log2(x_ctrl/x_0) ) - 1

Based on a set of GR values across a range of concentrations, the data are 
fitted with a sigmoidal curve:

    GR(c) = GRinf + (1-GRinf)/(1 + (c/(GEC50))^Hill )

The following GR metrics are calculated:

- **GR50**, the concentration at which the effect reaches a GR value of 0.5 
based on interpolation of the fitted curve.

- **GRmax**, the effect at the highest tested concentration. Note that 
*GRmax* can differ from *GRinf* if the dose-response does not reach its 
plateau value. For robustness, we take this as the minimum mean GR value at 
the two highest concentrations.

- **GR_AOC**, the area over the dose-response curve, which is the integral of 
*1-GR(c)* over the range of concentrations tested, normalized by the range of 
concentration. 

- **GEC50**, the drug concentration at half-maximal effect, which reflects the 
potency of the drug.

- **GRinf**, GR(c->inf), which reflects asymptotic drug efficacy. 

- **h_GR**, The Hill coefficient of the fitted (GR) curve, which reflects how steep the dose response curve is

- **r2_GR**, The coefficient of determination - essentially how well the (GR) curve fits to the data points

- **pval_GR**, The p-value of the F-test comparing the fit of the (GR) curve to a horizontal line fit

- **flat_fit_GR**, For data that doesn't significantly fit better to a curve than a horizontal line fit (p > 0.05), the y value (GR) of the flat line

The following traditional metrics are calculated:

- **IC50**, The concentration at which relative cell count = 0.5

- **Emax**, The maximal effect of the drug (minimal relative cell count value). 
For robustness, we take this as the minimum mean relative cell count at the 
two highest concentrations.

- **AUC**, The 'Area Under the Curve' - The area below the fitted (traditional) dose response curve

- **EC50**, The concentration at half-maximal effect (not growth rate normalized)

- **Einf**, The asymptotic effect of the drug (not growth rate normalized)

- **h**, The Hill coefficient of the fitted (traditional) dose response curve, which - reflects how steep the dose response curve is

- **r2_rel_cell**, The coefficient of determination - essentially how well the (traditional) curve fits to the data points

- **pval_rel_cell**, The p-value of the F-test comparing the fit of the (traditional) curve to a horizontal line fit

- **flat_fit_rel_cell**, For data that doesn't significantly fit better to a curve than a horizontal line fit (p > 0.05), the y value (relative cell count) of the flat line

In addition to the metrics, the scripts report the r-squared of the fit and
evaluate the significance of the sigmoidal fit based on an F-test. If the fit 
is not significant (p > 0.05), the sigmoidal fit is replaced 
by a constant value (flat fit). This can be circumvented by using the "force" 
option in the *GRfit* function. Additional information and considerations 
are described in the supplemental material of the manuscript referred above.

 
