---
title: "Getting started with cellspecR"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Getting started with cellspecR}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
resource_files:
- figures/logo.svg
- figures/logo-dark.svg
---
```{r, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(cellspecR)
```
`cellspecR` puts an explicit contract between segmentation and downstream
analysis. The first object to inspect is the deterministic bundled example.
```{r}
x <- cs_example()
x
summary(x)
cs_validate(x)
```
The dictionary describes every measurement, including its compartment and
statistic. Select features through dictionary fields rather than parsing a
feature name.
```{r}
cs_features(x, kind = "intensity", marker = "CD3e", statistic = "mean")
cs_feature_support(x)
```
Signal selection is explicit and recorded as a policy.
```{r}
policy <- cs_signal_policy(
marker = c("CD3e", "FOXP3"),
compartment = c("cell", "nucleus"),
statistic = "mean"
)
selected <- cs_signal_matrix(x, policy)
head(selected$signal)
```
Finally, write an integrity checked directory and verify it before handing the
object to another analysis package.
```{r}
destination <- file.path(tempdir(), "cellspec-getting-started")
cs_write(x, destination, overwrite = TRUE)
cs_verify(destination)
y <- cs_read_cellspec(destination)
identical(cs_cells(x), cs_cells(y))
```
