clustur

Background

clustur was developed to be similar to mothur’s cluster function that was written in C++. In order to cluster your data, users need to provide their own sparse or phylip-formatted distance matrix. They also need to provide a count table that either comes from mothur or that they create in R. Once these objects are built users can call the cluster() function. We currently support 5 methods: opticlust (default) and furthest, nearest, weighted, and average neighbor. The opticlust method is cluster() and mothur’s default. The speed of the methods implemented in {clustur} and mothur are comparable; {clustur} may even be faster! Below we will show you how to create your sparse matrix and count table. If you do not have a count table, clustur can produce one from you, but it will assume the abundance of each sequence is one and it will only cluster the sequences in the distance matrix. The output of running clustur() includes what is typically provided in a mothur-formatted shared file.

Starting Up

For the official release from CRAN you can use the standard install.packages() function:

# install via cran
install.packages("clustur")

For the developmental version, you can use the install_github() function from the {devtools} package

# install via github
devtools::install_github("SchlossLab/clustur")

Because {clustur}’s functions make use of a random number generator, users are strongly encouraged to set the seed.

library(clustur)
set.seed(19760620)

Read count files

clustur will produce the same output using either a sparse (default) or full count table

full_count_table <- read_count(example_path("amazon.full.count_table"))
sparse_count_table <- read_count(example_path("amazon.sparse.count_table"))

Read distance matrix file

clustur will read both mothur’s column/sparse distance matrix and Phylip-formatted distance matrix formats.

column_distance <- read_dist(example_path("amazon_column.dist"), full_count_table, cutoff = 0.03)

phylip_distance <- read_dist(example_path("amazon_phylip.dist"), full_count_table, cutoff = 0.03)

The return value of distance_data will be a memory address. If you want a data frame version of the distances, you can use get_distance_df(distance_data).

get_distance_df(column_distance)
#>    FirstName SecondName Distance
#> 1     U68638     U68618 0.020396
#> 2     U68638     U68620 0.020396
#> 3     U68638     U68658 0.027067
#> 4     U68618     U68620 0.000000
#> 5     U68618     U68658 0.022512
#> 6     U68620     U68658 0.022512
#> 7     U68641     U68667 0.000000
#> 8     U68641     U68673 0.018238
#> 9     U68667     U68673 0.018238
#> 10    U68636     U68631 0.006024
#> 11    U68680     U68615 0.003141
#> 12    U68679     U68663 0.020354
#> 13    U68679     U68665 0.017144
#> 14    U68679     U68688 0.009987
#> 15    U68665     U68663 0.012295
#> 16    U68665     U68688 0.008059
#> 17    U68663     U68688 0.020272
get_distance_df(phylip_distance)
#>    FirstName SecondName Distance
#> 1     U68615     U68680 0.003141
#> 2     U68618     U68620 0.000000
#> 3     U68618     U68638 0.020396
#> 4     U68618     U68658 0.022512
#> 5     U68620     U68638 0.020396
#> 6     U68620     U68658 0.022512
#> 7     U68631     U68636 0.006024
#> 8     U68638     U68658 0.027067
#> 9     U68641     U68667 0.000000
#> 10    U68641     U68673 0.018238
#> 11    U68663     U68665 0.012295
#> 12    U68663     U68679 0.020354
#> 13    U68663     U68688 0.020272
#> 14    U68665     U68679 0.017144
#> 15    U68665     U68688 0.008059
#> 16    U68667     U68673 0.018238
#> 17    U68679     U68688 0.009987

Clustering the data

The default method for clustering in cluster is “opticlust”

cutoff <- 0.03
cluster_data <- cluster(column_distance, cutoff)

Selecting different clustering methods


cluster_data <- cluster(column_distance, cutoff, method = "furthest")
cluster_data <- cluster(column_distance, cutoff, method = "nearest")
cluster_data <- cluster(column_distance, cutoff, method = "average")
cluster_data <- cluster(column_distance, cutoff, method = "weighted")

Output data from clustering

edit this paragraph further…

All methods produce a list object with an indicator of the cutoff that was used (label), as well as cluster composition (cluster) and shared (abundance) data frames. The clusters data frame shows which OTU (Operation Taxonomic Unit) each sequence was assigned to. The abundance data frame contains columns indicating the OTU and sample identifiers and the abundance of each OTU in each sample. The OptiClust method also includes the metrics data frame, which describe the optimization value for each iteration in the fitting process; the data in clusters and shared are taken from the last iteration. clustur provides getter functions, get_label(), get_clusters(), get_shared(), and get_metrics(), which will be demonstrated below.

clusters <- cluster(column_distance, cutoff, method = "opticlust")
get_cutoff(clusters)
#> [1] 0.03
get_bins(clusters)
#>   feature  bin
#> 1  U68630 bin1
#> 2  U68595 bin2
#> 3  U68600 bin3
#> 4  U68591 bin4
#> 5  U68647 bin5
#> 6  U68661 bin6
#> 7  U68605 bin7
#>  [ reached 'max' / getOption("max.print") -- omitted 81 rows ]
get_abundance(clusters)
#>   samples  otu abundance
#> 1 pasture otu1         0
#> 2  forest otu1         1
#> 3 pasture otu2         0
#> 4  forest otu2         1
#> 5 pasture otu3         0
#>  [ reached 'max' / getOption("max.print") -- omitted 171 rows ]
get_metrics(clusters)
#> $metrics
#>      label   cutoff specificity      ppv       ttp  f1score          tn
#> 1 0.030000 0.030000    1.000000 1.000000 17.000000 1.000000 4736.000000
#>        mcc       fn       fp sensitivity      npv      fdr accuracy
#> 1 1.000000 0.000000 0.000000    1.000000 1.000000 1.000000 1.000000
#> 
#> $iteration_metrics
#>      iter time label num_otus cutoff tp tn fp fn sensitivity specificity ppv
#>      npv fdr accuracy mcc f1score
#>  [ reached 'max' / getOption("max.print") -- omitted 3 rows ]