---
title: "De novo Chimera Detection"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{De novo Chimera Detection}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
library(rchime)
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Overview

The `rchime()` function allows you to detect and remove chimeric sequences using the dataset as it's own reference (de novo). The denovo approach is our preferred method for removing chimeras. `rchime()` can be used with [strollur objects](https://mothur.org/strollur/reference/strollur.html) or data.frames as inputs. Let's look at examples of both data types using sequence data from [mothur's](https://mothur.org) [MiSeq_SOP](https://mothur.org/wiki/miseq_sop/) example analysis. 

### Creating *[strollur](https://mothur.org/strollur/)* objects

```{r}
fasta_data <- readRDS(rchime_example("miseq_fasta.rds"))
abundance_data <- readRDS(rchime_example("miseq_abundance.rds"))

strollur <- strollur::new_dataset("rchime de novo example")

strollur::add(strollur, table = fasta_data, type = "sequence")
strollur::assign(strollur, table = abundance_data, type = "sequence_abundance")

strollur
```

### Loading data.frames

```{r}
df <- readRDS(rchime_example("miseq_data_frame_by_sample.rds"))

str(df)
```

## Removing chimeras

When removing chimeras using the de novo method, the potential parents are chosen from more abundant sequences in your dataset. 

Before we remove the chimeras let's discuss the `dereplicate` parameter. When `dereplicate=FALSE`, if a sequence is flagged as chimeric in one sample, it is removed from all samples. Our experience suggests that this is a bit aggressive since we’ve seen rare sequences get flagged as chimeric when they’re the most abundant sequence in another sample. For a more conservative approach, we recommend using the default `dereplicate=TRUE` which will only remove sequences from the samples in which they are flagged as chimeric. Let's use the de novo method to remove the chimeras.

```{r}
rchime(strollur)
strollur

data <- rchime(df)
data
```

## Results

The `rchime()` function returns a [strollur object](https://mothur.org/strollur/reference/strollur.html). Let's take a closer look at the `chimera_report` generated by `rchime()`. The [chimera_report](https://mothur.org/rchime/articles/chimera_report.html) is a data.frame with a row for each sequence in your dataset. Let's take a look at the first 5 chimeric sequences in the report:

```{r}
chimera_report <- strollur::report(data, type = "chimera_report")

chimera_report[chimera_report$Chimeric_Status == "Y", ] |> head(n = 5)
```