README.md

In aplantin/pldist: Paired and Longitudinal Ecological Dissimilarities

pldist: Paired and Longitudinal Ecological Dissimilarities

Author: Anna Plantinga

Introduction

pldist allows distance-based analysis of paired and longitudinal microbiome data. In particular, the package supports both paired and longitudinal versions of unweighted UniFrac, generalized UniFrac, Bray-Curtis, Jaccard, Gower, and Kulczynski distances or dissimilarities. Functions implementing the transformations that underlie these distances are also provided so that transformed OTU data may be included in analyses beyond distance-based methods. The code can handle paired data, balanced longitudinal data, and unbalanced longitudinal data, although use for highly unbalanced designs is not recommended.

Issues

If you encounter any bugs or have any specific feature requests, please file an issue.

Installation

You may install pldist from GitHub using the following code:

```{r install}

install.packages("devtools") # run this line if devtools is not installed

devtools::install_github(repo = "aplantin/pldist")

## Example

This example demonstrates usage of `pldist` in a simple setting with simulated data. For more examples and details, please see the [vignette](https://github.com/aplantin/pldist/blob/master/vignettes/introduction.Rmd). 

```{r example} 
library(pldist)
library(ape) 
data(sim.tree)
data(paired.otus)
data(paired.meta)
data(bal.long.otus) 
data(bal.long.meta) 

# Look at the OTU data: 
# row names are sample IDs, column names are OTU IDs 
paired.otus[1:4,1:4] 

# Look at the metadata: 
# columns are subjID, sampID, time 
# One row per sample 
paired.meta[1:4, ]

# Gower distance, paired quantitative transformation, with/without CLR 
pldist(paired.otus, paired.meta, paired = TRUE, binary = FALSE, clr = FALSE, method = "gower")$D
pldist(paired.otus, paired.meta, paired = TRUE, binary = FALSE, clr = TRUE, method = "gower")$D

# Gower distance, paired qualitative/binary transformation 
pldist(paired.otus, paired.meta, paired = TRUE, binary = TRUE, method = "gower")$D

# Gower distance, longitudinal quantitative transformation 
pldist(bal.long.otus, bal.long.meta, paired = FALSE, binary = FALSE, method = "gower")$D

# Gower distance, longitudinal qualitative/binary transformation 
pldist(bal.long.otus, bal.long.meta, paired = FALSE, binary = TRUE, method = "gower")$D

# Other distances 
pldist(paired.otus, paired.meta, paired = TRUE, binary = FALSE, method = "bray")$D
pldist(paired.otus, paired.meta, paired = TRUE, binary = FALSE, method = "kulczynski")$D
pldist(paired.otus, paired.meta, paired = TRUE, binary = FALSE, method = "jaccard")$D

# UniFrac also requires a phylogenetic tree and gamma values 
# (Gamma controls weight placed on abundant lineages) 
pldist(paired.otus, paired.meta, paired = TRUE, binary = FALSE, 
    method = "unifrac", tree = sim.tree, gam = c(0, 0.5, 1))$D 

pldist(paired.otus, paired.meta, paired = TRUE, binary = FALSE, clr = TRUE, 
    method = "unifrac", tree = sim.tree, gam = c(0, 0.5, 1))$D 

aplantin/pldist documentation built on Feb. 26, 2021, 2:19 p.m.