The first step is to read in your table(s) of operational taxonomic unit (OTU) counts and the clinical data for your subjects. The `tidyMicro`

package contains 4 OTU tables from premature infants who required mechanical ventilation and had tracheal aspirates samples collected at 7, 14, and 21 days of age (+/- 48 hours). Each infant had bronchopulmonary dysplasia severity classified as mild, moderate, or severe. The OTU tables come from the phylum, class, order, and family level classifications. The corresponding clinical data is also included. The data are published here.

```
## Loading OTU tables
data(phy, package = "tidyMicro")
data(cla, package = "tidyMicro")
data(ord, package = "tidyMicro")
data(fam, package = "tidyMicro")
## Loading meta data to merge with OTU table
data(clin, package = "tidyMicro")
```

The **clinical data** must have a column of *unique* sequencing libraries named “Lib” that match the column names of your OTU table(s).

The **OTU tables** should be formatted as we see below: 1. The first column contains the OTU names with taxonomic rank delineated by “/” 2. The following column names are the *unique* sequencing libraries 3. The rest of the table is populated with sequencing counts.

OTU_Name | T0003 | T0004 | T0007 | T0008 | T0011 | |
---|---|---|---|---|---|---|

2 | Bacteria | 0 | 1 | 10 | 0 | 14323 |

3 | Acidobacteria/Acidobacteria | 0 | 0 | 0 | 0 | 0 |

4 | Actinobacteria/Acidimicrobiia | 0 | 0 | 0 | 0 | 0 |

5 | Actinobacteria/Actinobacteria | 6 | 48 | 0 | 6 | 1 |

6 | Actinobacteria/Coriobacteriia | 5 | 0 | 0 | 0 | 2 |

7 | Actinobacteria/Nitriliruptoria | 0 | 0 | 0 | 0 | 0 |

Once the OTU table is formatted in this way the `tidy_micro`

function is fairly simple to use. There are three possible ways to merge your OTU table(s) and clinical data into a “*micro_set*”.

- Provide a single OTU table and the name separately
- Provide an unnamed list of your OTU tables and provide the OTU names separately
- Names provided will be the “Table” column in your final
*micro_set*- Order of names and OTU tables should match up

- Names provided will be the “Table” column in your final
- Provide a named list of your OTU tables
- Names of OTU tables will be the “Table” column in your final
*micro_set*

- Names of OTU tables will be the “Table” column in your final

```
## 1. Single OTU table
micro.set <- tidy_micro(otu_tabs = cla, ## OTU Table
tab_names = "Class", ## OTU Names (Ranks)
clinical = clin) ## Clinical Data
## 2. Unnamed List
otu_tabs <- list(phy, cla, ord, fam)
tab_names <- c("Phylum", "Class", "Order", "Family")
micro.set <- tidy_micro(otu_tabs = otu_tabs, ## OTU Table
tab_names = tab_names, ## OTU Names (Ranks)
clinical = clin) ## Clinical Data
## 3. Named List
otu_tabs <- list(Phylum = phy, Class = cla, Order = ord, Family = fam)
micro.set <- tidy_micro(otu_tabs = otu_tabs, ## OTU Table
clinical = clin) ## Clinical Data
```

The default for this function is to keep only the libraries found in each of the OTU tables’ column names and the sequencing library column of your clinical data. You will receive a warning if some libraries are excluded during the merge. This option can be turned off by setting the option `complete_clinical = FALSE`

.

Below are the first 6 rows and 12 columns of the data frame we created with `tidy_micro`

. The first 4 columns are the OTU table names, the subject/library names, the taxa names, and the sequencing depths (Total). Columns 4-8 are calculated from the given OTU table: `bin`

is a binary variable for presence or absence of taxa in that subject/library, `cts`

is the original count given, `clr`

is centered log-ratio transformed counts, and `ra`

is the relative abundance, \(RA = \frac{Taxa\ Count}{Seq\ Depth}\), of the taxa. A small amount (1/sequencing depth) is added to each library’s taxa count before the clr transformation in order to avoid \(log(0) = -\infty\). The following columns contain the supplied clinical data.

Table | Lib | Taxa | Total | bin | cts | clr | ra | study_id | weight | gender | gestational_age |
---|---|---|---|---|---|---|---|---|---|---|---|

Phylum | T0003 | Bacteria | 47490 | 0 | 0 | -10.8545090 | 0.0000000 | 0127Y | 0.7 | M | 25 |

Phylum | T0003 | Acidobacteria | 47490 | 0 | 0 | -10.8545090 | 0.0000000 | 0127Y | 0.7 | M | 25 |

Phylum | T0003 | Actinobacteria | 47490 | 1 | 11 | 2.3116626 | 0.0231628 | 0127Y | 0.7 | M | 25 |

Phylum | T0003 | Bacteroidetes | 47490 | 1 | 57 | 3.9568171 | 0.1200253 | 0127Y | 0.7 | M | 25 |

Phylum | T0003 | Candidate-division-SR1 | 47490 | 0 | 0 | -10.8545090 | 0.0000000 | 0127Y | 0.7 | M | 25 |

Phylum | T0003 | Candidate-division-TM7 | 47490 | 1 | 1 | -0.0862135 | 0.0021057 | 0127Y | 0.7 | M | 25 |

All of the following functions rely on this format of the data set, so this must be the first step for any use of this pipeline.

For simplicity, we will continue using only the sequencing information from day 7.

Several standard plots useful for data exploration are available within tidy.micro.

The function `taxa_summary`

will supply a table of useful descriptive statistics. It will output a table containing group counts, the percent of subjects with \(RA=0\), the RA mean, RA median, RA standard deviation, RA IQR, and several RA percentiles. You can control the taxa information summarized using the `obj`

argument. You can stratify by categorical variables using the `...`

argument. If the `table`

argument is left to `NULL`

, `taxa_summary`

will summarize all tables within your *micro_set*.

Table | Taxa | n | Percent_0 | Mean | SD | Median | IQR | Percentile_5th | Percentile_10th | Percentile_25th | Percentile_75th | Percentile_90th | Percentile_95th |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Phylum | Acidobacteria | 24 | 100.00 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |

Phylum | Actinobacteria | 24 | 0.00 | 0.3341367 | 1.0115688 | 0.0257123 | 0.0765905 | 0.00 | 0.00 | 0.01 | 0.09 | 0.45 | 1.65 |

Phylum | Bacteria | 24 | 33.33 | 4.4180972 | 20.2317256 | 0.0048883 | 0.0160987 | 0.00 | 0.00 | 0.00 | 0.02 | 0.07 | 5.64 |

Phylum | Bacteroidetes | 24 | 0.00 | 0.1990559 | 0.1534557 | 0.1731676 | 0.1453758 | 0.01 | 0.02 | 0.10 | 0.24 | 0.43 | 0.48 |

Phylum | Candidate-division-SR1 | 24 | 91.67 | 0.0001979 | 0.0007042 | 0.0000000 | 0.0000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |

Phylum | Candidate-division-TM7 | 24 | 41.67 | 0.0040695 | 0.0058673 | 0.0029175 | 0.0042201 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.02 |

Phylum | Cyanobacteria | 24 | 87.50 | 0.0006640 | 0.0020834 | 0.0000000 | 0.0000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |

Phylum | Deinococcus-Thermus | 24 | 100.00 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |

Phylum | Firmicutes | 24 | 0.00 | 70.9380828 | 39.5968844 | 96.6334919 | 56.0562921 | 0.42 | 2.49 | 43.34 | 99.39 | 99.67 | 99.71 |

Phylum | Fusobacteria | 24 | 4.17 | 0.0311693 | 0.0276454 | 0.0194823 | 0.0274130 | 0.01 | 0.01 | 0.01 | 0.04 | 0.07 | 0.08 |

Phylum | Proteobacteria | 24 | 0.00 | 5.1187023 | 20.4554034 | 0.1416012 | 0.1792101 | 0.01 | 0.02 | 0.04 | 0.22 | 0.88 | 16.65 |

Phylum | Spirochaetae | 24 | 87.50 | 0.0008745 | 0.0026564 | 0.0000000 | 0.0000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 |

Phylum | Synergistetes | 24 | 100.00 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |

Phylum | Tenericutes | 24 | 4.17 | 18.9420583 | 34.3059209 | 0.0268098 | 17.2474957 | 0.00 | 0.00 | 0.01 | 17.26 | 83.10 | 92.16 |

Phylum | Unclassified | 24 | 20.83 | 0.0128916 | 0.0177126 | 0.0061030 | 0.0094733 | 0.00 | 0.00 | 0.00 | 0.01 | 0.04 | 0.05 |

`micro_pca`

will calculate principle components on the centered log ratio transformation of the taxa counts using the `prcomp`

function from the `stats`

package. Scaling the taxa counts to a unit variance is the default option, and recommended, but this can be changed using `scaled = F`

. The components are then plotted using the `ggbiplot`

function.

We need to specify the OTU table we’d like to work with using the `table`

argument. This is consistent throughout the pipeline. Different tables can contain different the taxonomic ranks for analysis as it does in this walk through (e.g. phylum, family, or genus level data), or the different tables could also reflect other important differences such as sampling site (e.g. skin, nasal, gut, etc).

```
micro.set %>% micro_pca(table = "Family", ## Taxonomic table of interest
grp_var = bpd1, ## A factor variable for colors
legend_title = "BPD Severity")
```

Principle components calculated from a dissimilarity matrix are called “principle coordinates”. If we supply a dissimilarity or distance matrix, e.g. a beta diversity, `micro_pca`

will output a principal coordinate plot. Principle coordinate analysis (PCoA) and principle component analysis (PCA) are both excellent exploratory plots, and are the same process mathematically. However, PCoA is more appropriate than PCA when data are missing or when there are fewer subjects than there are dimensions of the feature space, as is often the case in microbiome (or any omics) analysis.

```
bray_beta <- micro.set %>% ## Calculating dissimilarity
beta_div(table = "Family")
micro.set %>%
micro_pca(dist = bray_beta, ## Beta diversity
grp_var = bpd1, ## A factor variable for colors
legend_title = "BPD Severity")
#> PCoA plot created
```

The following methods were established in microbiome data by K. Williamson and is based off of the Tucker3 loss function. These are tools for dealing with repeated measures within the ordination framework.

```
long_micro <- tidy_micro(otu_tabs = otu_tabs, ## OTU tables
clinical = clin) ## clinical Data
#> Contains 74 libraries from OTU files.
#> Summary of sequencing depth:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 8851 24938 33314 36650 43590 97408
```

Three Mode PCA is a way of creating Principle component plots while controlling for correlations between observations. We can interpret them the same as we would any PCA plot. Unfortunately we aren’t seeing much separation between our groups.

The function requires your *micro_set* to have a column that indicates your subject names and a column that indicates your time points. The function also requires a grouping variable.

```
long_micro %>%
three_mode(table = "Family", group = bpd1, subject = study_id,
time_var = day, main = "ThreeMode PCA",
subtitle = "3 Time Points", legend_title = "BPD Severity")
#> Warning: Subjects are not consistent across time points.
#> Only complete cases will be used.
#> Found 3 time points and 15 subjects with complete cases.
#> Rational ORTHONORMALIZED start
#> Tucker3 function value at start is 9.09494701772928e-13
#> Tucker3 function value is 9.09494701772928e-13 after 3 iterations
#> Fit percentage is 100 %
#> Procedure used 0.03 seconds
```

Notice the warning message that pops up. The function will automatically pull out the subjects that are in every time point and only make plots with their information.

These 3D plots collapse over a chosen dimension of variation (e.g. time, subject, or taxa) and perform principle component / coordinate analyses on the remaining two dimensions.

In order to make the function work we need to supply a subject, time, and the number of time points as in the `three_mode`

function.

```
long_micro %>%
pca_3d(table = "Family", time_var = day, subject = study_id,
modes = "AC", type = "PCoA")
#> Warning: Subjects are not consistent across time points.
#> Only complete cases will be used.
#> Found 3 time points and 15 subjects with complete cases.
#> Tucker3 function value at start is 10716.5212840935
#> Tucker3 function value is 0 after 2 iterations
#> Fit percentage is 100 %
#> Procedure used 0.01 seconds
```

You can change which axis to collapse over by changing the “modes” to either “BA” or “CB”.

```
long_micro %>%
pca_3d(table = "Family", time_var = day, subject = study_id,
modes = "CB", type = "PCoA")
#> Warning: Subjects are not consistent across time points.
#> Only complete cases will be used.
#> Found 3 time points and 15 subjects with complete cases.
#> Tucker3 function value at start is 10716.5212840935
#> Tucker3 function value is 0 after 2 iterations
#> Fit percentage is 100 %
#> Procedure used 0.01 seconds
```

Stacked bar charts of taxa RA are very standard visualizations for microbiome research. We can create bar charts of the raw RA stratified by categorical variable(s) of interest. As before, we need to specify the OTU table we want to plot.

```
ra_bars(micro.set, ## Dataset
table = "Phylum", ## Table we want
bpd1, ## Variable of interest
ylab = "% RA",
xlab = "BPD",
main = "Stacked Bar Charts")
```

As you can see, several of these phylum are very low abundance within the cohort, not to mention how quickly our plot legend would overflow if we tried to plot all of our taxa orders or families. Several of our functions include options to aggregate low taxa counts together in order to clean up these plots. 1. Specify how many taxa will be named and used in the stacked bar charts using `top_taxa`

. This option will take \(X\) taxa with the highest average RA and aggregate all others into an “Other” category. 2. Use the `RA`

option to aggregate all taxa with RA below your cutoff into an “Other” category.

Only one of these can be used at a time. If a particular taxa is of interest you can use `specific_taxa`

to pull any taxa out of the “Other” category if it doesn’t meet the bar set by either `top_taxa`

or `RA`

.

```
ra_bars(micro.set, ## Dataset
table = "Phylum", ## Table we want
bpd1, ## Variable of interest
top_taxa = 3,
RA = 0,
specific_taxa = c("Actinobacteria", "Bacteroidetes"),
ylab = "% RA", xlab = "BPD", main = "Stacked Bar Charts")
```

`ra_bars`

is flexible enough to include multiple factors in the `...`

argument.

```
ra_bars(micro.set, ## Dataset
table = "Phylum", ## Table we want
bpd1, gender, ## Variables of interest
top_taxa = 6,
ylab = "% RA", xlab = "BPD by Sex",
main = "Stacked Bar Charts") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

This function can also create subject level bar charts by using “Lib” as your grouping variable.

```
ra_bars(micro.set, ## Dataset
table = "Phylum", ## Table we want
Lib, ## Variable of interest
top_taxa = 6,
ylab = "% RA", xlab = "Library", main = "Stacked Bar Charts") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
```

Or just create a single stacked bar chart for the entire cohort by ignoring the `...`

argument.

```
ra_bars(micro.set, ## Dataset
table = "Phylum", ## Table we want
top_taxa = 6,
ylab = "% RA", main = "Stacked Bar Charts")
```

Note that you can manipulate the output plots by adding on `geom`

s as we did in `ra_bars`

. Most plotting functions in this pipeline will return a `ggplot`

that you can manipulate through additional `geom`

just like any other ggplot.

```
ra_bars(micro.set, ## Dataset
table = "Phylum", ## Table we want
top_taxa = 6,
main = "Manipulated Stacked Bar Charts") +
## Additional geoms
theme_dark() +
coord_flip() +
theme(legend.title = element_text(color = "blue", size = 20),
legend.text = element_text(color = "red"))
```

We can make box plots of taxa relative abundance stratified by some categorical variable using the function `taxa_boxplot`

.

```
staph <- "Firmicutes/Bacilli/Bacillales/Staphylococcaceae"
taxa_boxplot(micro.set, ## Our dataset
taxa = staph, ## Taxa we are interested in
bpd1, ## Variable of interest
xlab = "BPD",
ylab = "Relative Abundance",
main = "Box Plot")
```

We can plot other taxa information besides the relative abundance (such as the raw counts) by specifying the `y`

argument. This function also allows for multiple variables of stratification, and will give you the interaction of all categorical variables given. There is an apparent trend here, but be aware that raw counts are rarely the information of interest for analysis. Relative abundance or the centered log ratio is almost always more meaningful.

```
taxa_boxplot(micro.set, ## Our dataset
taxa = staph, ## Taxa we are interested in
y = clr, ## Making Boxplot of CLR
bpd1, gender, ## Variables of interest
ylab = "Staphylococcaceae CLR",
main = "Box plot", subtitle = "Subtitle") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

Correlations between taxa RA and continuous variables can be useful descriptives during data exploration. The functions `cor_heatmap`

and `cor_rocky_mtn`

will calculate correlation between the selected variable(s) and taxa information. The taxa information (counts, CLR, or RA) can be controlled through the `y`

argument, and the type of correlation can be controlled through the `cor_type`

argument. We recommend using either CLR transformed counts with Spearman’s rank based correlation or taxa RA with Kendall’s rank based correlation. Since the large number of 0 counts often present will create many ties Kendall’s correlation more appropriate for taxa RA.

Correlations for each taxa within the selected table are plotted in a heat map for one or several continuous variables. This style of non-symmetric heat map is sometimes called a “lasagna plot.”

The rocky mountain plot shows the correlation for each taxa within the selected table displayed along the horizontal axis. They will be color coded by the phylum they belong to (characters before the first “/” in the taxa name), and taxa with a correlation magnitude greater than or equal to a specified cutoff (`cor_label`

) will be labeled. This function uses the same options for `cor_type`

and `y`

as `cor_heatmap`

.

`alpha_div`

calculates Good’s Coverage and the following alpha diversities through rarefied bootstrap samples and attaches them as columns to the given *micro_set*:

- Richness: \(S_{obs}\) and Chao1
- Evenness: Shannon’s and Simpsons E
- Diversity: Shannon’s H and Simpsons D

It will either calculate the alpha diversity based on the specified `table`

and attach it within each table of your *micro_set*, or calculate the alpha diversity of each `table`

if no table is specified. Either way the alpha diversities will be attached as if they were a part of your clinical data. If your *micro_set* contains OTU tables of different taxonomic ranks, we recommend only calculating and analyzing the alpha diversity of the lowest rank.

This step can be computationally intensive. Lowering the number of bootstrap iterations with the `iter`

argument can speed this up. We recommend removing subjects with poor sequencing as well (for all analyses). There are two options for removing subjects with poor sequencing:

`min_depth`

: Remove libraries with sequencing depths (Total) below min_depth.`min_goods`

: Remove libraries with Good’s Coverage below min_goods.

```
micro_alpha <- alpha_div(micro.set,
table = "Family", ## Table of interest
min_depth = 5000, ## Requires a Seq Depth of 5000 to be included
min_goods = 80) ## Requires a Good's coverage of %80
#> Warning in alpha_div(micro.set, table = "Family", min_depth = 5000, min_goods = 80): The minimum Library size is 8851.
#> No libraries dropped based on sequencing depth or Good's coverage.
```

Once alpha diversities are calculated, standard regression can be used to analyse alpha diversities. `micro_alpha_reg`

is a simple wrapper function that will run linear regression on each alpha diversity with your specified covariate pattern within the specified rank. Covariates included in the function will be added together in your covariate structure. For instance, if we include *Group, Age, Sex*, the function will fit \[\hat{alpha} \sim Group + Age + Sex.\] You can also include an interaction by typing *Group * Age*.

The function will output a summary table for every model containing a column for the alpha diversity measure, the model coefficients, the \(\beta\) estimates, the standard errors of that estimate, the test statistic, the p-value, and a 95% confidence interval for the \(\beta\) estimates.

```
#> Alpha_Div Coef Beta std.error t.stat p.value CI_95
#> 1 Goods (Intercept) 99.98 0.01 10736.8720 0.0000 (99.9604, 99.9992)
#> 2 Goods bpd1Moderate 0.00 0.01 0.0102 0.9920 (-0.0222, 0.0224)
#> 3 Goods bpd1Severe -0.01 0.01 -0.7824 0.4431 (-0.0321, 0.0146)
#> 4 Goods genderM 0.01 0.01 0.7455 0.4646 (-0.0104, 0.022)
#> 5 Sobs (Intercept) 17.66 2.80 6.3000 0.0000 (11.811, 23.5039)
#> 6 Sobs bpd1Moderate -1.91 3.21 -0.5944 0.5589 (-8.6098, 4.7911)
#> 7 Sobs bpd1Severe -0.26 3.37 -0.0771 0.9393 (-7.2915, 6.772)
#> 8 Sobs genderM -1.12 2.33 -0.4798 0.6366 (-5.9886, 3.749)
#> 9 Chao1 (Intercept) 22.43 3.40 6.5938 0.0000 (15.3315, 29.5204)
#> 10 Chao1 bpd1Moderate -1.27 3.90 -0.3268 0.7472 (-9.4047, 6.8569)
#> 11 Chao1 bpd1Severe 0.15 4.09 0.0377 0.9703 (-8.3784, 8.6872)
#> 12 Chao1 genderM -1.83 2.83 -0.6465 0.5253 (-7.7393, 4.0769)
#> 13 ShannonE (Intercept) 0.10 0.06 1.6759 0.1093 (-0.0239, 0.2197)
#> 14 ShannonE bpd1Moderate 0.01 0.07 0.1388 0.8910 (-0.1303, 0.1489)
#> 15 ShannonE bpd1Severe 0.04 0.07 0.6153 0.5453 (-0.1033, 0.1897)
#> 16 ShannonE genderM -0.06 0.05 -1.1632 0.2584 (-0.158, 0.0449)
#> 17 ShannonH (Intercept) 0.41 0.24 1.6985 0.1049 (-0.0925, 0.9037)
#> 18 ShannonH bpd1Moderate 0.03 0.27 0.1002 0.9211 (-0.5435, 0.5983)
#> 19 ShannonH bpd1Severe 0.18 0.29 0.6154 0.5452 (-0.4224, 0.7759)
#> 20 ShannonH genderM -0.24 0.20 -1.1997 0.2443 (-0.6534, 0.1763)
#> 21 SimpsonD (Intercept) 1.17 0.22 5.3648 0.0000 (0.7144, 1.6234)
#> 22 SimpsonD bpd1Moderate 0.13 0.25 0.5036 0.6200 (-0.3951, 0.6466)
#> 23 SimpsonD bpd1Severe 0.27 0.26 1.0363 0.3124 (-0.2751, 0.8182)
#> 24 SimpsonD genderM -0.22 0.18 -1.2253 0.2347 (-0.6008, 0.1562)
```

`beta_div`

is a simple wrapper around the `vegdist`

function. It requires you to specify the OTU table and the method of beta diversity you want calculated.

The `beta_heatmap`

function can be used to create a heat map of your calculated beta diversities ordered by some categorical variable of interest.

The `vegan`

package contains a useful function called `adonis2`

for running a PERMANOVA test. PERMANOVA is a permutation based ANOVA using a distance matrix as your response. `micro_PERMANOVA`

is simply a wrapper function to make working within your *micro_set* easy. The output is analogous to that of a standard ANOVA. Please note that the F statistic given is a “pseudo-F” statistic from a “pseudo-F test” since it is based off of a permutation distribution not a true F distribution.

```
micro_PERMANOVA(micro.set, ## micro_set to pull covariates from
bray, ## Beta diversity matrix (or any distance matrix)
method = "bray", ## method used to calculate the beta diversity
bpd1, mom_ethncty_2) ## Covariates
#> Permutation test for adonis under reduced model
#> Terms added sequentially (first to last)
#> Permutation: free
#> Number of permutations: 999
#>
#> vegan::adonis2(formula = f, data = micro_set, permutations = nperm, method = method)
#> Df SumOfSqs R2 F Pr(>F)
#> bpd1 2 0.2157 0.04301 0.5041 0.842
#> mom_ethncty_2 1 0.5207 0.10381 2.4335 0.059 .
#> Residual 20 4.2797 0.85318
#> Total 23 5.0162 1.00000
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
```

There are several reasons you might want to aggregate rare taxa counts into an “Other” category. Some plots and figures (e.g. `corHeatmap`

, `corRockyMtn`

, ) will look nicer without all of the extra names cluttering the legend, some taxa might not be fully classified, or their models might be unstable and therefore a waste of computation time. We can use the `otu_filter`

function to apply filtering requirements to each table within the *micro_set*. There are three functions built in to filter out taxa with low counts.

`prev_cutoff`

is a prevalence cutoff where \(X%\) of subjects must have this taxa present or it will be included in the “Other” category.`ra_cutoff`

is a relative abundance (RA) cutoff where at least one subject must have a RA above the cutoff or the taxa will be included in the “Other” category.`exclude_taxa`

can be used to specify any taxa that you would like to make sure are included in the “Other” category. For instance, our OTU tables have taxa that are named “Unclassified” that we’d like to exclude. This can be a character string of any length.- Please note that the taxa specified with
`exclude_taxa`

will be filtered out of every OTU table given. To avoid this, you can make a subset of the data before filtering.

- Please note that the taxa specified with

```
## Taxa names "Bacteria" are essentially unclassified
exclude_taxa <- c("Unclassified", "Bacteria")
micro.filt <- micro.set %>%
otu_filter(prev_cutoff = 1, ## Prevalence cutoff
ra_cutoff = 0.1, ## Relative abundance cutoff
exclude_taxa = exclude_taxa) ## Uninteresting taxa
#> Filter for Class counts
#> Found 'Unclassified' category in input data.
#> Created new 'Other' category.
#> Found 'Bacteria' category in input data.
#> Found 34 OTUs.
#> Collapsed 2 OTUs into 'Other' in OTU table.
#> Converted 61662 counts to 'Other' otu category.
#> Remaining OTUs: 33 (Including 'Other').
#> Prevalence cutoff: 1% (i.e., at least 1% of libaries must be represented to keep OTU)
#> Found 33 OTUs.
#> Found 'Other' category in input data.
#> Collapsed 12 OTUs into 'Other' in OTU table.
#> Converted 0 counts to 'Other' in otu category.
#> Remaining OTUs: 21 (Including 'Other').
#> Relative abundance cutoff: 0.1% (i.e., at least one library must have RA > 0.1% to keep OTU).
#> Found 21 OTUs.
#> Found 'Other' category in input data.
#> Collapsed 12 OTUs into 'Other' in OTU table.
#> Converted 337 counts to 'Other' otu category.
#> Remaining OTUs: 9 (Including 'Other').
#> Filter for Family counts
#> Found 'Unclassified' category in input data.
#> Created new 'Other' category.
#> Found 'Bacteria' category in input data.
#> Found 116 OTUs.
#> Collapsed 2 OTUs into 'Other' in OTU table.
#> Converted 61662 counts to 'Other' otu category.
#> Remaining OTUs: 115 (Including 'Other').
#> Prevalence cutoff: 1% (i.e., at least 1% of libaries must be represented to keep OTU)
#> Found 115 OTUs.
#> Found 'Other' category in input data.
#> Collapsed 45 OTUs into 'Other' in OTU table.
#> Converted 0 counts to 'Other' in otu category.
#> Remaining OTUs: 70 (Including 'Other').
#> Relative abundance cutoff: 0.1% (i.e., at least one library must have RA > 0.1% to keep OTU).
#> Found 70 OTUs.
#> Found 'Other' category in input data.
#> Collapsed 50 OTUs into 'Other' in OTU table.
#> Converted 751 counts to 'Other' otu category.
#> Remaining OTUs: 20 (Including 'Other').
#> Filter for Order counts
#> Found 'Unclassified' category in input data.
#> Created new 'Other' category.
#> Found 'Bacteria' category in input data.
#> Found 62 OTUs.
#> Collapsed 2 OTUs into 'Other' in OTU table.
#> Converted 61662 counts to 'Other' otu category.
#> Remaining OTUs: 61 (Including 'Other').
#> Prevalence cutoff: 1% (i.e., at least 1% of libaries must be represented to keep OTU)
#> Found 61 OTUs.
#> Found 'Other' category in input data.
#> Collapsed 21 OTUs into 'Other' in OTU table.
#> Converted 0 counts to 'Other' in otu category.
#> Remaining OTUs: 40 (Including 'Other').
#> Relative abundance cutoff: 0.1% (i.e., at least one library must have RA > 0.1% to keep OTU).
#> Found 40 OTUs.
#> Found 'Other' category in input data.
#> Collapsed 24 OTUs into 'Other' in OTU table.
#> Converted 525 counts to 'Other' otu category.
#> Remaining OTUs: 16 (Including 'Other').
#> Filter for Phylum counts
#> Found 'Unclassified' category in input data.
#> Created new 'Other' category.
#> Found 'Bacteria' category in input data.
#> Found 15 OTUs.
#> Collapsed 2 OTUs into 'Other' in OTU table.
#> Converted 61662 counts to 'Other' otu category.
#> Remaining OTUs: 14 (Including 'Other').
#> Prevalence cutoff: 1% (i.e., at least 1% of libaries must be represented to keep OTU)
#> Found 14 OTUs.
#> Found 'Other' category in input data.
#> Collapsed 3 OTUs into 'Other' in OTU table.
#> Converted 0 counts to 'Other' in otu category.
#> Remaining OTUs: 11 (Including 'Other').
#> Relative abundance cutoff: 0.1% (i.e., at least one library must have RA > 0.1% to keep OTU).
#> Found 11 OTUs.
#> Found 'Other' category in input data.
#> Collapsed 4 OTUs into 'Other' in OTU table.
#> Converted 50 counts to 'Other' otu category.
#> Remaining OTUs: 7 (Including 'Other').
```

Please note that we did not filter before calculating our diversity measures! If we aggregate rare taxa into a single category, this will bias all of our diversity measures. Our Sobs and Choa1 in particular will be greatly biased. If you do not wish to calculate alpha or beta diversities, these filtering options are also available in the initial tidy_micro read in step.

```
## Named List
otu_tabs <- list(Phylum = phy, Class = cla, Ord = ord, Family = fam)
tidy.filt <- tidy_micro(otu_tabs = otu_tabs, ## OTU Table
clinical = clin, ## Clinical Data
prev_cutoff = 1, ## Prevalence cutoff
ra_cutoff = 1, ## Relative abundance cutoff
exclude_taxa = exclude_taxa) ## Uninteresting taxa
```

It is a standard practice to model the relative abundance of each taxa using a negative binomial distribution. The function `nb_mods`

will fit negative binomial models for each taxa within a specified table using the observed counts and the total sequencing counts as an offset. It uses `glm.nb`

from the `MASS`

package to fit these models; the profile likelihood confidence intervals are also calculated through the `MASS`

package using the `confint`

function.

As in micro_alpha_reg and bb_mods, `nb_mods`

will take each variable you specify as a new term to add into the model. For instance, if we include *Group, Age, Sex* the function will run the model \[log(\hat{cts}) \sim \beta_0 + \beta_1 Group + \beta_2 Age + \beta_3 Sex + log(Total).\] The offset of log(Total) will be included automatically and is recommended, however it is possible to remove this term with `Offset = FALSE`

```
nb_fam <- micro.filt %>% ## micro_set
nb_mods(table = "Family", ## Rank of taxa we want to model
bpd1) ## The covariate in our model
#>
#> 18 taxa converged
#> 2 taxa did not converge
```

As in micro_alpha_reg and bb_mods, you can also include interaction terms (such as *Age*Sex*) as you can with any other model.

```
## If we wanted the covariates to be bpd1+gender+bpd1*gender we just need input bpd1*gender.
nb_int <- micro.filt %>%
nb_mods(table = "Class", bpd1*gender)
```

Notice that the function will tell you how many different models converged and how many did not. To explore the convergent models we can access the **Convergent_Summary** from `nb_mods`

. The convergent summary table gives the taxa name, the model coefficients, the estimated \(\beta s\), profile likelihood confidence limits, the Z-score, the p-value from a Wald test, an FDR adjusted p-value, and finally an Anova test from a likelihood-ratio test. Below is the summary information from the first two convergent taxa.

Taxa | Coef | Beta | CI | Z | P_val | FDR_Pval | LRT |
---|---|---|---|---|---|---|---|

Actinobacteria/Actinobacteria/Actinomycetales/Actinomycetaceae | (Intercept) | -8.2921218 | (-9.5266, -6.4186) | -11.1387 | 0.0000000 | 0.0000 | NA |

Actinobacteria/Actinobacteria/Actinomycetales/Actinomycetaceae | bpd1Moderate | -0.4842894 | (-2.5126, 1.1272) | -0.5550 | 0.5789104 | 0.7105 | 0.5330622 |

Actinobacteria/Actinobacteria/Actinomycetales/Actinomycetaceae | bpd1Severe | -0.9958385 | (-3.0842, 0.7387) | -1.0918 | 0.2749094 | 0.4241 | 0.5330622 |

Actinobacteria/Actinobacteria/Corynebacteriales/Corynebacteriaceae | (Intercept) | -5.3792759 | (-7.6366, 1.9534) | -2.9617 | 0.0030599 | 0.0083 | NA |

Actinobacteria/Actinobacteria/Corynebacteriales/Corynebacteriaceae | bpd1Moderate | -2.2474168 | (-9.7445, 1.7989) | -1.0590 | 0.2896069 | 0.4344 | 0.0000000 |

Actinobacteria/Actinobacteria/Corynebacteriales/Corynebacteriaceae | bpd1Severe | 0.1151231 | (-7.4138, 4.5979) | 0.0527 | 0.9579410 | 0.9599 | 0.0000000 |

`nb_mods`

also provides an **Estimate_Summary** table that contains a table of model summaries for convergent taxa that is more readily exportable into publications. It gives the taxa name, the model coefficients, the exponentiated beta coefficients for the rate ratio, a Wald confidence interval, the Z-score, and FDR_Pvalue. For interaction terms, the rate ratios are calculated taking their main effects into account. That is, rate ratios are the exponentiation sum of interaction and main effect \(\beta\) coefficients, and confidence intervals are for the exponentiation sum of the \(\beta\) coefficients. The FDR p-values are for the individual \(\beta\) coefficients. Below is the summary information from the first two convergent taxa.

Taxa | Coef | RR | CI_95 | Z | FDR_Pval |
---|---|---|---|---|---|

Actinobacteria/Actinobacteria/Actinomycetales/Actinomycetaceae | bpd1Moderate | 0.6161 | (0.1114, 3.4078) | -0.5550 | 0.7105 |

Actinobacteria/Actinobacteria/Actinomycetales/Actinomycetaceae | bpd1Severe | 0.3694 | (0.0618, 2.2075) | -1.0918 | 0.4241 |

Actinobacteria/Actinobacteria/Corynebacteriales/Corynebacteriaceae | bpd1Moderate | 0.1057 | (0.0016, 6.7678) | -1.0590 | 0.4344 |

Actinobacteria/Actinobacteria/Corynebacteriales/Corynebacteriaceae | bpd1Severe | 1.1220 | (0.0156, 80.9383) | 0.0527 | 0.9599 |

For the non-convergent taxa we can explore their summary information within the **RA_Summary** from `nb_mods`

. This is a table of summary measures for all taxa (not just the non-convergent taxa) that is stratified by the categorical variables in the models. It includes the counts (n), percent of subjects with counts of 0, the average, median, standard deviation, IQR, percentiles of taxa RA, and finally an indicator for whether or not this taxa’s negative binomial model converged.

Another standard practice is to model taxa abundance using a beta binomial distribution. The function `bb_mods`

will fit beta binomial model to each taxa within a specified table using the `vglm`

function from the `VGAM`

package. Confidence intervals are fit using the `confintvglm`

function. The current default for confidence intervals around individual \(\beta\) parameters are Wald intervals, although this can be change to profile likelihood confidence intervals using `CI_method = "profile"`

. Profile likelihoods are much more computationally intensive.

```
bb_fam <- micro.filt %>% ## micro_set
bb_mods(table = "Phylum", ## Table we want to model
bpd1) ## The covariate in our model
```

As in micro_alpha_reg and nb_mods, `bb_mods`

will take each variable you specify as a new term to add into the model. For instance, if we include *Group, Age, Sex* the function will run the model \[logit(\hat{RA}) \sim \beta_0 + \beta_1 Group + \beta_2 Age + \beta_3 Sex\]

Again, as in micro_alpha_reg and nb_mods, you can also include interaction terms (such as *Age*Sex*) as you can with any other model.

The model output from nb_mods and bb_mods can easily cause some information overload. `tidyMicro`

includes several useful functions to visualize the results of all convergent models. They function the same way for both model types, so we will only show examples from our nb_mods output.

We have created functions that will calculate the estimated RA of all taxa based on the convergent models. This gives us the ability to visualize stacked bar charts of taxa RA while controlling for other variables in the model. For instance, if our model is \[log(\hat{cts}) \sim \beta_0 + \beta_1 Group + \beta_2 Age + \beta_3 Sex + log(Total),\] we can visualize the estimated differences in RA among different groups while holding Age and Sex constant.

The functions nb_mods and bb_mods will create these stacked bar charts based on the output of from `nb_mods`

and `bb_mods`

, respectively. They requires the name of a covariate in your model and can create plots based on main effects or interactions. If a continuous variable is one of the supplied covariates there are two options for visualization available through the `quant_style`

argument. You can either visualize two points as two separate bars (`quant_style = "discrete"`

) or visualize the continuous change (`quant_style = "continuous"`

). By default the functions will use the first and third quartiles of the continuous variable as your endpoints, but this can be changed through the `range`

option. `nb_bars`

and `bb_bars`

also give you the ability to aggregate estimated RA into and “Other” category just like the ra_bars function.

```
nb_fam %>% nb_bars(bpd1, ## Covariate of interest
top_taxa = 5, ## How many named taxa we want
xlab = "Group",
xaxis = c("1","2","3")) ## Labels
```

Including every taxonomic level in your legend often looks cluttered. The nb_bars and bb_bars functions use the `Model_Coef`

data frame from `nb_mods`

and `bb_mods`

output to create their bar charts. You can manipulate the taxa names here to avoid confusion further up in the pipeline.

```
nb_fam$Model_Coef$Taxa %<>%
stringr::str_split("/") %>% ## Splitting by "/" in taxa names
lapply(function(x) x[length(x)]) %>% ## selecting piece after the last "/"
unlist ## Unlisting to put back into data frame
## Reordering to put "Other" at the bottom of the legend
## our functions usually do this automatically, but we'll need to do
## this externally since we are messing with the output
non.other <- nb_fam$Model_Coef %>%
filter(Taxa != "Other") %>%
arrange(Taxa) %>%
distinct(Taxa) %>%
pull(Taxa)
nb_fam$Model_Coef$Taxa <- factor(nb_fam$Model_Coef$Taxa,
levels = c(non.other, "Other"))
nb_fam %>% nb_bars(bpd1, ## Covariate of interest
top_taxa = 5, ## How many named taxa we want
xlab = "BPD Severity", main = "Cleaner Legend") ## Labels
```

The function `micro_rocky_mtn`

displays magnitude of the log FDR adjusted p-values for each of the taxa in `nb_mods`

as vertical bars next to each other along the x-axis. The direction of the bars will be determined by the direction of the estimated relationship. The taxa will be color coded by the phylum they belong to, and taxa that have FRD adjusted p-values for the specified covariate below your desired significance cutoff will be labeled. This significance cutoff is 0.05 by default and can be changed through the `alpha`

argument. You can also turn off the labels with `sig_text = FALSE`

.

```
## Order level models
nb_ord <- micro.filt %>% ## micro_set
nb_mods(table = "Order", ## Rank of taxa we want to model
bpd1) ## The covariate in our model
#>
#> 15 taxa converged
#> 1 taxa did not converge
nb_ord %>%
micro_rocky_mtn(bpd1, ## Covariate of interest
xlab = "Taxa", main = "Rocky Mountain Plot",
subtitle = "Direction of bar indicates direction of relationship", facet_labels = c("Moderate", "Severe"))
```

`micro_forest`

will create forest plots for a specified covariate from each taxa’s nb_mod or bb_mod. Forest plots display the estimated \(\beta\) coefficients with their 95% confidence intervals.

```
## Class level models
nb_cla <- micro.filt %>% ## micro_set
nb_mods(table = "Class", ## Rank of taxa we want to model
bpd1) ## The covariate in our model
#>
#> 9 taxa converged
#> 0 taxa did not converge
nb_cla %>%
micro_forest(bpd1, ## Covariate of interest
main = "Forest Plot for BPD Severity")
```