From bfac338c82af794e4a7891a777d873ad51fdc909 Mon Sep 17 00:00:00 2001 From: GregJohnsonJr Date: Mon, 11 Nov 2024 15:44:31 -0500 Subject: [PATCH] More Spell Checking --- DESCRIPTION | 2 +- man/clustur-package.Rd | 2 +- vignettes/clustur.Rmd | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 4a2073b..b593600 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -11,7 +11,7 @@ Authors@R: c( comment = c(ORCID = "0000-0002-6935-4275")) ) Maintainer: Patrick Schloss -Description: A tool that implements the clustering algorithms from mothur (Schloss PD et al. (2009) ). clustur make use of the 'cluster' and 'make.shared' command from mothur. The cluster command has five different algorithms implemented: 'opticlust', 'furthest', 'nearest', 'average', and 'weighted'. Opticlust is an optimized OTU clustering algorithm, and you can learn more here, (Westcott SL, Schloss PD (2017) ). The 'make.shared' command is always applied at the end of the clustering command. This functionality allows us to generate and create clustering and abundance data efficiently. +Description: A tool that implements the clustering algorithms from mothur (Schloss PD et al. (2009) ). clustur make use of the 'cluster' and 'make.shared' command from mothur. The cluster command has five different algorithms implemented: 'opticlust', 'furthest', 'nearest', 'average', and 'weighted'. OptiClust is an optimized clustering method for Operational Taxonomic Units, and you can learn more here, (Westcott SL, Schloss PD (2017) ). The 'make.shared' command is always applied at the end of the clustering command. This functionality allows us to generate and create clustering and abundance data efficiently. License: MIT + file LICENSE Encoding: UTF-8 Imports: diff --git a/man/clustur-package.Rd b/man/clustur-package.Rd index d4c0e10..04f2dd3 100644 --- a/man/clustur-package.Rd +++ b/man/clustur-package.Rd @@ -6,7 +6,7 @@ \alias{clustur-package} \title{clustur: Clustering} \description{ -A tool that implements the clustering algorithms from mothur (Schloss PD et al. (2009) \doi{10.1128/AEM.01541-09}). clustur make use of the 'cluster' and 'make.shared' command from mothur. The cluster command has five different algorithms implemented: 'opticlust', 'furthest', 'nearest', 'average', and 'weighted'. Opticlust is an optimized OTU clustering algorithm, and you can learn more here, (Westcott SL, Schloss PD (2017) \doi{10.1128/mspheredirect.00073-17}). The 'make.shared' command is always applied at the end of the clustering command. This functionality allows us to generate and create clustering and abundance data efficiently. +A tool that implements the clustering algorithms from mothur (Schloss PD et al. (2009) \doi{10.1128/AEM.01541-09}). clustur make use of the 'cluster' and 'make.shared' command from mothur. The cluster command has five different algorithms implemented: 'opticlust', 'furthest', 'nearest', 'average', and 'weighted'. OptiClust is an optimized clustering method for Operational Taxonomic Units, and you can learn more here, (Westcott SL, Schloss PD (2017) \doi{10.1128/mspheredirect.00073-17}). The 'make.shared' command is always applied at the end of the clustering command. This functionality allows us to generate and create clustering and abundance data efficiently. } \seealso{ Useful links: diff --git a/vignettes/clustur.Rmd b/vignettes/clustur.Rmd index 7ebd647..a712dd1 100644 --- a/vignettes/clustur.Rmd +++ b/vignettes/clustur.Rmd @@ -116,7 +116,7 @@ cluster_data <- cluster(column_distance, cutoff, method = "weighted") #### 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 how which OTU each sequence was assigned to. The `abundance` data frame +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