NMDS shiny

App for fast NMDS plots of sample sites based on microbial OTUs relative abundance.

Table of contents

• Where to try app
• How to upload data
• Settings
• References

Where to try app

• online web app hosted on labenvmicro.shinyapps.io, this option is limited to 1GB of RAM memory. Therefore bigger data may take some time to analyze. For huge tables beyond memory limit the online app gives error.

• or start your local installation of R language and paste following code which automatically downloads prerequisties and starts app. Better option for big data, the only limit is local computer hardware:

install.packages(c("shiny", "shinythemes", "openxlsx", "tidyverse", "vegan", "shinycssloaders", "ggrepel", "RColorBrewer"))
library(shiny)
runGitHub("NMDS_shiny", "Vojczech") 

Data upload

Two excel files/sheets are necessary:

1. OTU table (example) with values in percents. The first column contains OTU label, other columns contanin abundance values in separate samples. After upload of the file, call the correct sheet by its name.

OTU_label	sample1	sample2	sample3	sample4	sample5
CL01	1.5	5.4	10.5	8.5	7.2
CL02	2.3	4.6	9.2	2.5	9.5
CL03	4.5	4.9	1.1	1.0	0.3

2. Sample list (example) with sample names (same names as in OTU table) and further environmental variables which are used for grouping in NMDS and envfit function. Again, choose sheet by its name.

sample_name	age_class	sampled_org	year
sample1	1	fagus	2013
sample2	2	fagus	2008
sample3	1	picea	2013
sample4	2	spruce	2008
sample5	3	spruce	1997

You should now see the preview of the uploaded tables.

Settings

1. After upload of the tables (.xlsx) it is necessary to choose the correct excel sheet by its name.

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2. It is possible to filter OTUs for NMDS construction by abundance treshold in certain number of samples.

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3. Colours of points in NMDS

• For colour coding of different groups of samples choose appropriate grouping factor (i.e. column with environmental variable in the sample list) and check "Factor". By checking Display ellipses ggplot will calculate ellipses for each group of points.

or

• For gradient colour of sample sites according to environmental metadata choose "Values".

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4. Label points by sample ID or sample type or by other variable. Label positions are iteratively found with great package ggrepel by @slowkow.

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5. Hellinger distance or Bray-Curtis dissimilarity are two optional dissimilarity measures which can be used. Default is Hellinger distance based on [Legendre, 2013][1].

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6. By selecting columns from your sample list, you can fit several environmental variables into NMDS using envfit function. Avoid missing values in environmental variables

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7. You can download

• Excel file with NMDS points coordinates and environmental variables scores. This is useful if you want to plot ordination in external program.

• final pdf file with rendered ggplot

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Memory on the shinyapps.io is limited, if server disconnects after upload try to reduce excel file size by deleting of unnecessary OTUs (singletons and rare ones) or use your own local R installation as described in Where to try app

References

[1]Legendre P, De Cáceres M. Beta diversity as the variance of community data: Dissimilarity coefficients and partitioning. Ecol Lett 2013;16:951–63.

This app is driven by following awesome packages.

• base (R Core Team 2019a)
• dplyr (Wickham et al. 2019)
• ggrepel (Slowikowski 2019)
• openxlsx (Walker 2019)
• RColorBrewer (Neuwirth 2014)
• readxl (Wickham and Bryan 2019)
• shiny (Chang et al. 2019)
• shinycssloaders (Sali 2017)
• shinythemes (Chang 2018)
• tibble (Müller and Wickham 2019)
• tidyverse (Wickham 2017)
• tools (R Core Team 2019b)
• vegan (Oksanen et al. 2019)

Chang, Winston. 2018. Shinythemes: Themes for Shiny. https://CRAN.R-project.org/package=shinythemes.

Chang, Winston, Joe Cheng, JJ Allaire, Yihui Xie, and Jonathan McPherson. 2019. Shiny: Web Application Framework for R. https://CRAN.R-project.org/package=shiny.

Müller, Kirill, and Hadley Wickham. 2019. Tibble: Simple Data Frames. https://CRAN.R-project.org/package=tibble.

Neuwirth, Erich. 2014. RColorBrewer: ColorBrewer Palettes. https://CRAN.R-project.org/package=RColorBrewer.

Oksanen, Jari, F. Guillaume Blanchet, Michael Friendly, Roeland Kindt, Pierre Legendre, Dan McGlinn, Peter R. Minchin, et al. 2019. Vegan: Community Ecology Package. https://CRAN.R-project.org/package=vegan.

R Core Team. 2019ab. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Sali, Andras. 2017. Shinycssloaders: Add Css Loading Animations to ’Shiny’ Outputs. https://CRAN.R-project.org/package=shinycssloaders.

Slowikowski, Kamil. 2019. Ggrepel: Automatically Position Non-Overlapping Text Labels with ’Ggplot2’. https://CRAN.R-project.org/package=ggrepel.

Walker, Alexander. 2019. Openxlsx: Read, Write and Edit Xlsx Files. https://CRAN.R-project.org/package=openxlsx.

Wickham, Hadley. 2017. Tidyverse: Easily Install and Load the ’Tidyverse’. https://CRAN.R-project.org/package=tidyverse.

Wickham, Hadley, and Jennifer Bryan. 2019. Readxl: Read Excel Files. https://CRAN.R-project.org/package=readxl.

Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2019. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.