`library("ampvis")`

`data(DNAext_1.0)`

All samples are subset to 25.000 reads and then only OTUs which are seen at least 10 / 25000 times in a single sample is kept for further ordination analysis.

```
time <- subset_samples(V13, Exp.time == "YES") %>%
rarefy_even_depth(sample.size = 25000, rngseed = 712) %>%
filter_taxa(function(x) max(x) >= 10, TRUE)
```

PCA with square root transformed OTU abundances. The effect of sampling from different tanks is tested using the envfit function in vegan (permutation test).

```
pca <- amp_ordinate(data = time,
plot.color = "Date",
plot.point.size = 3,
plot.theme = "clean",
envfit.factor = "Date",
envfit.show = F,
output = "complete")
```

Plot the data. It looks like there is significant groupings.

```
pca$plot +
theme(legend.position = "none")
```

`ggsave("plots/S1A.eps", width = 55, height = 55, units = "mm")`

The model reports a p-value of 0.001, hence there is an effect of time.

`pca$eff.model`

```
##
## ***FACTORS:
##
## Centroids:
## PC1 PC2
## Date2012-10-17 -2.5868 -1.7040
## Date2012-10-31 -2.2921 -1.0687
## Date2012-11-14 -1.5441 0.0266
## Date2012-12-12 -0.7073 2.4142
## Date2013-01-16 2.7550 4.0816
## Date2013-03-25 4.3753 -3.7496
##
## Goodness of fit:
## r2 Pr(>r)
## Date 0.9875 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999
```

The Bray-Curtis dissimilarity index is used as an alternative method to test for significant groupings in the dataset.

```
beta <- amp_test_cluster(data = time,
group = "Date",
method = "bray",
plot.color = "Date",
plot.label = "Date",
plot.theme = "clean")
```

Using adonis we also find a significant effect of time as the p-value is 0.001.

`beta$adonis`

```
##
## Call:
## adonis(formula = test_formula, data = sample)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Date 5 0.26872 0.053744 8.1263 0.772 0.001 ***
## Residuals 12 0.07936 0.006614 0.228
## Total 17 0.34808 1.000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
```

Clustering the data show a very nice grouping by time. Except the two first time-points two weeks appart all time-points cluster separately.

```
beta$plot_cluster +
theme(legend.position = "none")
```

`ggsave("plots/S1B.eps", width = 60, height = 55, units = "mm")`

```
time_weeks <- subset_samples(time, Date %in% c("2012-10-17", "2012-10-31", "2012-11-14")) %>%
filter_taxa(function(x) max(x) >= 10, TRUE)
```

```
pca_weeks <- amp_ordinate(data = time_weeks,
plot.color = "Date",
plot.point.size = 3,
plot.theme = "clean",
envfit.factor = "Date",
envfit.show = F,
output = "complete"
)
```

Plot the data. It looks like there is significant groupings even when looking at a time-scale of weeks.

`pca_weeks$plot`

The model reports a p-value of 0.006, showing there is an significant differnce between samples taken only weeks appart.

`pca_weeks$eff.model`

```
##
## ***FACTORS:
##
## Centroids:
## PC1 PC2
## Date2012-10-17 3.1830 -0.0726
## Date2012-10-31 -0.5669 1.9927
## Date2012-11-14 -2.6161 -1.9201
##
## Goodness of fit:
## r2 Pr(>r)
## Date 0.6672 0.006 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999
```

The Bray-Curtis dissimilarity index is used as an alternative method to test for significant groupings in the dataset.

```
beta_weeks <- amp_test_cluster(data = time_weeks,
group = "Date",
method = "bray",
plot.color = "Date",
plot.label = "Date",
plot.theme = "clean")
```

Using adonis we also find a significant effect of weekly sampling as the p-value is 0.006.

`beta_weeks$adonis`

```
##
## Call:
## adonis(formula = test_formula, data = sample)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Date 2 0.029387 0.0146934 2.2223 0.42554 0.006 **
## Residuals 6 0.039670 0.0066117 0.57446
## Total 8 0.069057 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
```

Looking at the clustering it seems like the we canâ€™t distinguish between samples from the two first timepoints (2012-10-17 and 2012-10-31). However, they are quite different from the timpoint 2 weeks later.

`beta_weeks$plot_cluster`

```
time_plant <- subset_samples(V13, Exp.time == "YES" | Plant == "AAE") %>%
rarefy_even_depth(sample.size = 25000, rngseed = 712) %>%
filter_taxa(function(x) max(x) >= 10, TRUE)
```

The major difference is plants and the second axis explain the differences related to the time-series.

```
amp_ordinate(data = time_plant,
plot.color = "Date",
plot.shape = "Plant",
plot.point.size = 3,
plot.theme = "clean"
)
```

The Bray-Curtis dissimilarity index is used as an alternative method to test for significant groupings in the dataset.

```
beta_plant <- amp_test_cluster(data = time_plant,
group = "Date",
method = "bray",
plot.color = "Date",
plot.label = c("Date","Plant"),
plot.theme = "clean")
```

`beta_plant$plot_cluster`