https://github.com/astrazeneca/arrayedcrisprscreener

The goal of arrayedCRISPRscreener is to simulate arrayed CRISPR screening data for the purpose of benchmarking data analysis tools as well as power calculation.

https://github.com/astrazeneca/arrayedcrisprscreener

Repository

The goal of arrayedCRISPRscreener is to simulate arrayed CRISPR screening data for the purpose of benchmarking data analysis tools as well as power calculation.

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# arrayedCRISPRscreener




The goal of arrayedCRISPRscreener is to simulate arrayed CRISPR screening data for the purpose of benchmarking data analysis tools as well as power calculation.

## Installation

You can install arrayedCRISPRscreener from [GitHub](https://github.com/AstraZeneca/arrayedCRISPRscreener) with:

```{r}
# install.packages("devtools")
# devtools::install("arrayedCRISPRscreener")
```
## Quick start

```{r}
library(arrayedCRISPRscreener)
library(ggplot2)
```

The simulation is designed to run based on a platemap layout. 
The following are the most basic steps for simulating arrayedCRISPR data using the platemap design. 
Please note that an example platemap is included in the package. 
To perform a simulation, users must specify parameters that correspond to various experimental factors. 
For example, we demonstrate a simulation that only includes the gKO effect, without any additional treatments.

```{r}
## data frame of platemap layout
plate_layout <- platemap # example platemap design
head(plate_layout)

## Number of randomly selected wells with phenotypic effect due to gKO
n_hit <- 10

## Number of gKO treatment interacting phenotypic effect with other treatment.
## In this example, we assume there is no additional treatment in the assay
n_hit_with_interaction <- 0

## Numeric value desired number of cells per well
desired_cells_per_well <- 1000

## Numeric value baseline endpoint value due to gKO
base_level <- log(100)

## variation among the negative genes
sigma_bg0 <- 0.05

## Average gKO effect size as percentage based on the baseline endpoint value
mu_bg <- 0.2

## Variation of gKO effect
sigma_bg <- 0.05

## Average effect size of interaction between gKO and other stimulated
## treatment as percentage based on the baseline endpoint value
mu_btg <- 0

## Variation of interaction effect
sigma_btg <- 0

## Coefficient of variation (sig/mu) for the variation between cells within well
cv_cell <- 0.05

## Measurement error in log scale
measure_error <- 0.3

## Spatial bias can be included in the simulated data. Example input for
## spatial biased is included in the package as follow,
head(wells_with_spatial_bias)

## Data frame specifying the systematic spatial bias
wells_with_spatial_bias$spatial_bias <-
  0.2 * wells_with_spatial_bias$spatial_bias
summary(wells_with_spatial_bias$spatial_bias)

## Simulation for single plate
sdata <- sim_single_plate(platemap,
                          n_hit,
                          n_hit_with_interaction,
                          desired_cells_per_well,
                          base_level,
                          mu_bg,
                          sigma_bg,
                          mu_btg,
                          sigma_btg,
                          cv_cell,
                          measure_error,
                          wells_with_spatial_bias,
                          sigma_bg0)

head(as.data.frame(sdata))
```

We can visualized the simulated arrayed CRISPR screen data based on a 384 well-plate in a heatmap. 

```{r, example, fig.width = 7} 
p <- ggplot(sdata, aes(x = Column, y = Row)) +
            geom_tile(aes(fill = y), color = "white") +
            scale_y_discrete(limits = rev(unique(sdata$Row))) +
            scale_fill_continuous(type = "viridis") +
            labs(fill = "endpoint.simulated") + theme_bw()

print(p)
```

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