README.Rmd

---
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-",
  fig.dev = "grDevices::png",
  dpi = 300L,
  dev.args = list(),
  fig.ext = "png",
  fig.height=8,
  fig.width=12,
  fig.retina = 2L,
  fig.align = "center"
)
```

# stana

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Metagenotyping analysis in R. Import and analyse, visualize the metagenotyping output of the software like [MIDAS](https://github.com/snayfach/MIDAS), [MIDAS2](https://github.com/czbiohub/MIDAS2), [metaSNV and metaSNV v2](https://github.com/metasnv-tool/metaSNV), and [inStrain](https://github.com/MrOlm/inStrain). In general the metagenotyping software produces the allelic count information and gene copy number tables and the package utilizes these information to analyze the intra-species diversity.

The detailed usage is available at [https://noriakis.github.io/software/stana](https://noriakis.github.io/software/stana), using `bookdown`.

## Installation

Using `devtools`:

```{r, eval=FALSE}
devtools::install_github("noriakis/stana")
```

## Pipeline

<img src="https://github.com/noriakis/software/blob/main/images/stana_pipeline.png?raw=true" width="800px">

## Examples

```{r message=FALSE, warning=FALSE, fig.width=6, fig.height=6}
## Using example data
library(stana)
library(phangorn)
load(system.file("extdata", "sysdata.rda", package = "stana"))

stana
getID(stana)

## Make example metadata
samples <- getSlot(stana, "snps")[[1]] |> colnames()
metadata <- data.frame(
    row.names=samples,
    treatment=factor(sample(1:3, length(samples), replace=TRUE)),
    marker=runif(length(samples))
)


## Set metadata
stana <- setMetadata(stana, metadata)

## Call consensus sequence
## Infer and plot tree based on metadata
stana <- stana |>
  consensusSeq(argList=list(site_prev=0.95)) |>
  inferAndPlotTree(meta=c("treatment","marker"))
getFasta(stana)[[1]]
getTree(stana)[[1]]
getTreePlot(stana)[[1]]
```

If the gene copy number table is available like in `MIDAS` series and `inStrain`, one can compare the functional implications of these gene contents. The details are described in the [documentation](https://noriakis.github.io/software/stana).


## Interactive inspection

The users can inspect metagenotyping results interactively using Shiny based on the variables such as disease conditions (`exportInteractive()`). One can publish the results in the hosting services for sharing the research findings.