---
title: "seqtrie"
output:
  html_vignette:
    keep_md: no
  rmarkdown::github_document: default
vignette: >
  %\VignetteIndexEntry{seqtrie}
  \usepackage[utf8]{inputenc}
  %\VignetteEngine{knitr::rmarkdown}
---

```{r, setup, echo=FALSE}
GITHUB_README <- Sys.getenv("GITHUB_README") != ""
CAN_IGRAPH_PLOT <- requireNamespace("igraph", quietly=TRUE) && requireNamespace("ggplot2", quietly=TRUE)
knitr::opts_chunk$set(dpi=96,fig.width=6.5)
```

### Basic usage

```{r, basic_usage, eval=FALSE}
results <- dist_search(x, y, max_distance = 2, nthreads = 1)
```

The above code will find all similar sequences/strings between `x` and `y`. 
This will generally be significantly faster than calculating pairwise distance or
pairwise alignment. 

### Background

`seqtrie` is a collection of Radix Tree algorithms. These include some classic
algorithms like prefix lookup and more bioinformatics focused algorithms such as
alignment algorithms and sequence distance calculations (Hamming and Levenshtein
distances).

A trie (aka Prefix Tree) is a data structure used in many applications.  It is 
a space efficient data structure where a collection of sequences are stored in 
a tree structure, where each leaf represents one sequence, and each node holds 
one character representing a shared prefix of all sequences descending from it.
A Radix Tree (aka Compact Prefix Tree) is an improvement on a trie, using less
memory and being generally faster. In a Radix Tree, each node is able to
represent multiple characters instead of just one.

Tries and Radix Trees have similar complexity to a hashmap. Storing a sequence
within the tree or looking it up are O(k) where k is the length of the sequence.
The main advantage of a trie is that it can be used to quickly find similar
sequences by various algorithms and metrics, whereas a hashmap does not contain
any sequence similarity information.

See also: https://en.wikipedia.org/wiki/Radix_tree

In `seqtrie`, there are two `R6` classes:

`RadixTree` is the primary class in this package. There are three main methods.
The `$insert()` method is used to store sequences on the tree, `$erase()` for
erasing sequences from the tree and `$search()` for finding similar sequences
stored on the tree.

The second `R6` class is `RadixForest`, a derivative data structure where
separate trees are constructed for each sequence length. This data structure has
advantages and disadvantages, discussed later.

Finally, there's a simple convienence function `dist_search` to find similar 
sequences using a `RadixTree` or `RadixForest` object. This is a wrapper around
the `$new`, `$insert` and `$search()` methods.

### Install

`install.packages("seqtrie")`

### More examples

Below is a simple example where we insert some sequences (strings), erase one and 
then plot out the tree.

```{r, basic_plot, eval=!GITHUB_README && CAN_IGRAPH_PLOT, out.width=400}
library(seqtrie)
tree <- RadixTree$new()
tree$insert(c("cargo", "cart", "carburetor", "carbuncle", "bar", "zebra"))
tree$erase("zebra")
# tree$graph requires igraph package
set.seed(1); tree$graph()
```

```{r, basic_plot_output, eval=GITHUB_README && CAN_IGRAPH_PLOT, echo=FALSE, message=FALSE, results='hide'}
library(seqtrie)
tree <- RadixTree$new()
tree$insert(c("cargo", "cart", "carburetor", "carbuncle", "bar", "zebra"))
tree$erase("zebra")
png("simple_tree.png", width = 400*1.5, height = 300*1.5, res = 96)
set.seed(1); tree$graph()
dev.off()
```

```{r, basic_plot_github, eval=GITHUB_README, echo=FALSE, results='asis'}
cat('![](vignettes/simple_tree.png "simple_tree")')
```

### Levenshtein "edit distance" search

Below is an example using COVID19 T-cell data from Adaptive Biotechnologies.
(doi: 10.21203/rs.3.rs-51964/v1. This data is licensed under CC 4.0.)

Here, we find highly similar sequences within a fixed edit distance. For the
purpose of this vignette, we sample a small selection of sequences. 

On the full dataset, if you tried to calculate an edit distance matrix, it would
take a pretty long time, not to mention requiring a lot of memory. A trie-based 
method could be used to find similar sequences in a fraction of the time. 
Approximate running times on the full dataset using 8 threads are listed in the 
comments.

```{r, small_cdr3_ex}
# 130,000 "CDR3" sequences
set.seed(1)
data(covid_cdr3)
covid_cdr3 <- sample(covid_cdr3, 1000)
tree <- RadixTree$new()
tree$insert(covid_cdr3)
# Full data: 1 min
results <- tree$search(covid_cdr3, max_distance=2, mode="levenshtein", nthreads=2)

# Alternatively, instead of using the RadixTree object directly, you can use the
# dist_search function, which is a wrapper around the RadixTree object.
results <- dist_search(covid_cdr3, covid_cdr3, max_distance=2)

# The output is a data.frame mapping query (search sequences)
# and target (sequences inserted into the tree).
dplyr::filter(results, query != target)
```

### Search parameters

The `$search()` function contains two mutually exclusive parameters:
`max_distance` and `max_fraction`.The former parameter sets an absolute
threshold that limits the distance between pairs of sequences in the output, and
the latter parameter sets a threshold relative to the query sequence length.

The search time is monotonically increasing with the distance threshold,
logarithmically increasing with tree size and linearly increasing with the
number of query sequences and the length of each sequence. Overall, the
algorithm is significantly faster than a pairwise/matrix edit distance
calculation for finding similar sequences. However, _care still needs to be
taken when setting parameters for searching a large number of sequences
(~100,000+)._

**Some additional examples using the max_fraction parameter.**
```{r, lv_search}
# Full data: several seconds
results <- tree$search(covid_cdr3, max_fraction=0.035, mode="levenshtein", nthreads=2)
# Full data: 1 minute
results <- tree$search(covid_cdr3, max_fraction=0.06, mode="levenshtein", nthreads=2)
# Full data: 15-20 minutes
results <- tree$search(covid_cdr3, max_fraction=0.15, mode="levenshtein", nthreads=2)
```

### Hamming distance search

Hamming distance is similar to Levenshtein distance, but does not allow
insertions or deletions. Sequences must be the same length.
Because of this restriction, Hamming distance is generally a lot faster.
```{r, hm_search}
# Full data: 1 second
results <- tree$search(covid_cdr3, max_fraction=0.035, mode="hamming", nthreads=2)
# Full data: several seconds
results <- tree$search(covid_cdr3, max_fraction=0.06, mode="hamming", nthreads=2)
# Full data: 1.5 minutes
results <- tree$search(covid_cdr3, max_fraction=0.15, mode="hamming", nthreads=2)
```

### Anchored alignment searches

An anchored alignment is a form of semi-global alignment, where the query
sequence is "anchored" (global) to the beginning of both the query and target
sequences, but is semi-global in that the end of the either the query sequence
or target sequence (but not both) can be unaligned. This type of alignment is
sometimes called an "extension" alignment in literature.

```{r, anchored_search}
tree <- RadixTree$new()
tree$insert("CARTON")
tree$insert("CAR")
tree$insert("CARBON")
tree$search("CART", max_distance = 0, mode = "anchored")
```

Because the alignment is semi-global at the end of the alignment, the query of
"CART" finds "CAR" and "CARTON" but not "CARBON" given a max distance of 0.
Additionally, the output of an anchored search also returns the position of the
query and target at the ends. Either the query or the target must fully align,
so at least one of the end positions will be the full length of the sequence.
This type of alignment is frequently useful in biology e.g. if you are trying to
align multiple reads that are variable in length but start at the same genomic
position or primer site.

### Custom distance searches and affine gap alignment

`seqtrie` supports custom alignment parameters, including an affine gap
parameter. The interface is similar to the `Biostrings::pairwiseAlignment`.
Note: we are calculating distance (higher is worse) and not alignment score
(higher is better).

```{r, custom_search}
tree <- RadixTree$new()
tree$insert(covid_cdr3)
# define a custom distance matrix - generate_cost_matrix is a convienence function
# gap and gap_open can be defined directly in the cost_matrix or as search method parameters
cost_mat <- generate_cost_matrix("ACGT", match=0, mismatch=5, gap=2, gap_open=1)
print(cost_mat)
# Perform a search. "Mode" can be either global or anchored.
results <- tree$search(covid_cdr3, max_distance=8, cost_matrix=cost_mat, mode="global", nthreads=2)
dplyr::filter(results, query != target)
```

### Radix Forest for faster Levenshtein searches

The `RadixForest` class is a data structure holding a collection of Radix Trees,
where a separate tree is constructed for each sequence length. The primary
advantage of `RadixForest` is significantly faster Levenshtein searches, because
you can know sequence length up front. The disadvantages are higher memory
usage, and no support for custom distance searches.

Below is a brief comparison:

```{r radix_forest}
# RadixTree, full data: 45 seconds
tree <- RadixTree$new()
tree$insert(covid_cdr3)
results_tree <- tree$search(covid_cdr3, max_distance=2, mode="levenshtein", nthreads=2)
# RadixForest, full data: 19 seconds
frst <- RadixForest$new()
frst$insert(covid_cdr3)
results_frst <- frst$search(covid_cdr3, max_distance=2, mode="levenshtein", nthreads=2)
# The results are the same, but order is not guaranteed
identical(
  dplyr::arrange(results_tree, query, target),
  dplyr::arrange(results_frst, query, target) )
```

### Finding strings that start with a pattern

The `$find_prefix()` function can be used to find similar sequences that start
with a pattern. This is one of the classic use cases of trie data structures,
for use as a database lookup and predictive text.

```{r, prefix_search}
tree <- RadixTree$new()
tree$insert(c("cargo", "cart", "carburetor", "carbuncle", "bar"))
tree$prefix_search("car")
```

### Why not just use Bowtie2, BWA or other fast alignment software?

There are no apples-to-apples comparisons. With NGS alignment software, you are 
looking for alignments of reads (queries) _within_ a genome reference (target). 
Here, we're looking for alignments from the query to the _full_ target. However, 
many NGS aligners do use Tries and similar data structures. 

Compared to pairwise alignment packages, calculating all alignment pairs takes
much longer, but on the other hand gives you more information.

### References and literature

- "Fast string correction with Levenshtein automata" (2002) \<doi:10.1007/s10032-002-0082-8>
- "A survey of sequence alignment algorithms for next-generation sequencing" (2010) \<doi:10.1093/bib/bbq015>
- "Fast and Easy Levenshtein distance using a Trie" (2011) \<http://stevehanov.ca/blog/index.php?id=114>
- "Spell Checker Application Based on Levenshtein Automaton" (2021) \<doi:10.1007/978-3-030-91608-4_5>
