README.Rmd

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

# `bayesVG`

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# Installation

You can install the most recent version of `bayesVG` using:

```{r, eval=FALSE}
remotes::install_github("jr-leary7/bayesVG")
```

# Usage 

## Libraries 

```{r, eval=FALSE}
library(Seurat)
library(bayesVG)
```

## HVG detection 

### Data 

First, we load the 10X Genomics pbmc3k dataset, which is composed of 2,700 peripheral blood mononuclear cells from a single healthy donor. 

```{r, eval=FALSE}
data("seu_pbmc")
```

### Modeling 

Now we're able to model gene expression, summarize the posterior distribution of variance for each gene, and classify the top 3000 most-variable genes as HVGs. 

```{r, eval=FALSE}
seu_pbmc <- findVariableFeaturesBayes(seu_pbmc, 
                                      n.cells.subsample = 1000L, 
                                      algorithm = "meanfield",
                                      save.model = TRUE) %>% 
            classifyHVGs(n.HVG = 3000L)
```

## SVG detection 

### Data 

First, we load the 10X Genomics anterior mouse brain dataset.

```{r, eval=FALSE}
data("seu_brain")
```

Before running `bayesVG` for SVG detection it's necessary to normalize the expression data and identify a set of naive HVGs. 

```{r, eval=FALSE}
seu_brain <- SCTransform(seu_brain,
                         assay = "Spatial",
                         variable.features.n = 3000L,
                         vst.flavor = "v2",
                         return.only.var.genes = FALSE,
                         seed.use = 312,
                         verbose = FALSE)
```

### Modeling 

Now we can model gene expression with an approximate Gaussian process, summarize the spatial component of variance for each gene, and classify the top 1000 most spatially variable genes as SVGs. 

```{r, eval=FALSE}
seu_brain <- findSpatiallyVariableFeaturesBayes(seu_brain, 
                                                kernel = "matern", 
                                                kernel.smoothness = 1.5, 
                                                algorithm = "meanfield", 
                                                n.cores = 4L, 
                                                save.model = TRUE) %>% 
             classifySVGs(n.SVG = 1000L)
```

# Contact information 

This package is developed & maintained by Jack R. Leary. Feel free to reach out by [opening an issue](https://github.com/jr-leary7/bayesVG/issues) or by email (j.leary@ufl.edu) if more detailed assistance is needed.