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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
dpi = 200
)
set.seed(0)
```
# PackageBluishgreen
<!-- badges: start -->
[](https://github.com/Kevin-Haigis-Lab/PackageBluishgreen/actions)
[](https://www.tidyverse.org/lifecycle/#experimental)
<!-- badges: end -->
The goal of `PackageBluishgreen` is to package the internals for clustering cells for Olesja Popow (pronounced "po-pow").
The cells were identified using a separate algorithm which output DAPI and FITC values for each cell into a CSV.
This package maintains this data in a data structure called `tissue_slide` and manages any classification methods applied to the cells.
## Installation
You can install the released version of 'PackageBluishgreen' from [GitHub](https://github.com/Kevin-Haigis-Lab/PackageBluishgreen) with:
``` r
#> If using 'renv'
renv::install("Kevin-Haigis-Lab/PackageBluishgreen")
#> else
devtools::install_github("Kevin-Haigis-Lab/PackageBluishgreen")
```
**The full documentation can be found [here](https://kevin-haigis-lab.github.io/PackageBluishgreen/).
For examples, check out the [vignettes](https://kevin-haigis-lab.github.io/PackageBluishgreen/articles/).**
If there is a specific classification method you would like, please open an [issue](https://github.com/Kevin-Haigis-Lab/PackageBluishgreen/issues) on GitHub.
## Example usage
```{r}
library(PackageBluishgreen)
```
### The *tissue slide* data structure
The *tissue slide* class is designed to hold three things:
1. the signal intensity data from a microscopy slide
2. metadata for the samples
3. classification methods and results
A new tissue slide can be created by just passing in the slide data.
```{r}
pancreas_data <- read.csv(system.file(
"extdata",
"unmicst-OP1181_pancreas_TUNEL_01.csv",
package = "PackageBluishgreen"
))
pancreas_data <- pancreas_data[, c(1, 3:5)]
colnames(pancreas_data) <- c("cell_id", "fitc", "x", "y")
pancreas_slide <- tissue_slide(pancreas_data, metadata = list(tissue = "pancreas", mouse = "OP1181"))
```
The metadata can be easily retrieved.
```{r}
get_slide_metadata(pancreas_slide)
```
It is also very easy to plot the data.
```{r}
plot_tissue(pancreas_slide, color = log10(fitc))
```
### Manual classification
A more thorough guide can be found in the ["Manual classification"](https://kevin-haigis-lab.github.io/PackageBluishgreen/articles/manual-classification.html) vignette.
The `cluster_manually()` function should be used to apply a manual classification cutoff to the data.
```{r}
pancreas_slide <- cluster_manually(pancreas_slide, fitc, 4.3, transform = log10)
```
The results can be easily plotted.
```{r}
plot_slide_clusters(pancreas_slide, method = "manual")
```
A summary of the results can be obtained using the `summarize_cluster_results()` function.
```{r}
summarize_cluster_results(pancreas_slide)
```
---
Mistakes or questions? Open an [issue](https://github.com/Kevin-Haigis-Lab/PackageBluishgreen/issues) on Github.