Beta diversity ordinations

Beta diversity ordinations


First, let's load some packages including HTSSIP.


Also let's get an overview of the phyloseq object that we're going to use.


Parsing the dataset

See HTSSIP introduction vignette for a description on why dataset parsing is needed.

Let's the parameters for parsing the dataset into individual treatment-control comparisons.

params = get_treatment_params(physeq_S2D2, c('Substrate', 'Day'))

We just need the parameters for each treatment, so let's filter out the controls. In this case, the controls are all '12C-Con'.

params = dplyr::filter(params, Substrate!='12C-Con')

Now, we will use an expression that will subset the phyloseq object into the comparisons that we want to make.

ex is the expression that will be used for pruning the phyloseq object

ex = "(Substrate=='12C-Con' & Day=='${Day}') | (Substrate=='${Substrate}' & Day == '${Day}')"
physeq_S2D2_l = phyloseq_subset(physeq_S2D2, params, ex)

Calculating ordinations

Now, let's actually make ordinations of each treatment compared to the control. This will return a data.frame object for plotting.

# running in parallel
physeq_S2D2_l_df = SIP_betaDiv_ord(physeq_S2D2_l, parallel=TRUE)
physeq_S2D2_l_df %>% head(n=3)

Each specific phyloseq subset (treatment-control comparison) is delimited with the "phyloseq_subset" column.

physeq_S2D2_l_df %>% .$phyloseq_subset %>% unique

For clarity, I'm going edit these long strings to make them more readable.

physeq_S2D2_l_df = physeq_S2D2_l_df %>%
  dplyr::mutate(phyloseq_subset = gsub(' \\| ', '\n', phyloseq_subset),
                phyloseq_subset = gsub('\'3\'', '\'03\'', phyloseq_subset))
physeq_S2D2_l_df %>% .$phyloseq_subset %>% unique

OK, let's plot the data!


As you can see, the 'heavy' gradient fraction 'communities' for the labeled-treatments tend to diverge from the unlabeled gradient fraction communities, but the amount of divergence in dependent on substrate and time point.

Session info


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HTSSIP documentation built on May 15, 2018, 9:03 a.m.