---
title: "Microbial dIversity and Network Analysis with *MINA*"
author: "Rui Guan"
date: "`r Sys.Date()`"
abstract: >
    With the help of rapidly developing sequencing technologies, an increasing
    number of microbiome datasets are generated and analysed. At present,
    diversity analysis of taxonomic profiling data is mainly conducted using
    composition-based methods, which ignores the interactions between community
    members and limits the study of community dynamics.The goal of *MINA* is to
    provide a framework for microbial community analysis based on higher order
    community structures to better understand the principles that govern the
    establishment of those communities. In addition, a bootstrap-permutation
    based network comparison method was developed to compare global and local
    ecological networks and to statistically assess their dissimilarity.
    MINA package version: `r packageVersion("mina")`
output:
    rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Microbial dIversity and Network Analysis with MINA}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
  %\usepackage[utf8]{inputenc}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# Overview

The package is used for microbial profiling data analysis, including community
similarity / dissimilarity / distance calculation and network comparison.

# Input data
*MINA* package expects count data (e.g. OTU table or ASV table) to represent
community profiling results and a descriptive table which indicates the
information of each sample. In the quantitative table, each row contains one
composition in the community and each column represents one profiled sample. In
the descriptive table, same samples as rows in quantitative table should be
included in the column 'Sample\_ID**'.

## Import data
Using `new()` to create a new object and then import data into the object.
The new object could be generated and slots could be imported simultaneously:
```{r importData, eval = TRUE}
    library(mina)
    # maize_asv2 and maize_des2 are subset of maize_asv and maize_des
    maize <- new("mina", tab = maize_asv2, des = maize_des2)
```
Please be aware that the descriptive table have to contain a column called
"**Sample_ID**" which includes the same samples indicated in the quantitative
tables. See an example here:
```{r showData, eval = TRUE}
    head(maize_des)
```
For the quantitative table, each column correspond to one sample indicated in
the descriptive table and each row represent one composition in the community.
```{r showData_2, eval = TRUE}
    maize_asv[1:6, 1:6]
```

## Check data format and tidy up
For the format of data, one could take a look at the data included in the
package as indicated as before. After checking, if there is mismatch between
quantitative and descriptive tables, `fit_tabs()` could be implied.
```{r fitTabs, eval = TRUE}
    maize <- fit_tabs(maize)
```

# Diversity analysis of the community
Typically the analysis of microbial community data includes estimating within
and between sample diversities (alpha- and beta-diversity) based on
compositions. By counting the number of observed compositions and evaluating
the evenness of their distribution, alpha diversity of each community is
quantified. Distance or dissimilarity between samples calculated from counts
differentiation of compositions is used to indicate the beta diversity of
community.

## Data normalization
Due to the varied sequencing depth, it is essential to normalize the data before
the analysis of the diversity.
Rarefaction and normalization by total sum are available here. For rarefaction,
to reduce the random effect, multiple times bootstrap is recommended. The
normalized table will be stored in the same *mina* object automatically when
it were given as input.
```{r rarefData, eval = TRUE}
    # check available normalization methods
    ? norm_tab_method_list
    # normalized by total sum
    maize <- norm_tab(maize, method = "total")
    # normalized by rarefaction
    maize <- norm_tab(maize, method = "raref", depth = 5000)
    # normalized by rarefaction and bootstrap 9 times
    maize <- norm_tab(maize, method = "raref", depth = 5000, multi = 9)
```
When given a matrix for normalization, the normalized matrix will be returned.
```{r rarefData_2, eval = TRUE}
    # normalized by total sum
    maize_asv_norm <- norm_tab(maize_asv2, method = "total")
    # normalized by rarefaction
    maize_asv_norm <- norm_tab(maize_asv2, method = "raref", depth = 5000)
    # normalized by rarefaction and bootstrap 99 times
    maize_asv_norm <- norm_tab(maize_asv2, method = "raref", depth = 5000,
                               multi = 9)
```

## Community diversity
Based on the normalized quantitative table, distance / dissimilarity could be
calculated between pairwise samples and used for beta-diversity analysis.
```{r comDis, eval = TRUE}
    # check available dissimilarity parameters
    ? com_dis_list
    # tidy the norm tab, intial tab and des tab
    maize <- fit_tabs(maize)
    # community dissimilarity calculation, Bray-Curtis used in example
    maize <- com_dis(maize, method = "bray")
    # TINA dissimilarity in Schmidt_et_al_2016
    # maize <- com_dis(maize, method = "tina")
```
For *TINA* dissimilarity in `com_dis()` function, *Spearman* correlation and
weighted Jaccard was used by default, to calculate *TINA* with other options,
use function `tina()`.

```{r tina, eval = FALSE}
    # get the TINA dissimilarity of normalized quantitative table
    maize_tina <- tina(maize_asv_norm, cor_method = "spearman", sim_method =
                       "w_ja", threads = 80, nblocks = 400)
```

## Unexplained variance of community diversity
To evaluate the biological meaningful variance and noise ratio, the ratio of
variance that could not be explained by any factors was calculated.
```{r getR2, eval = TRUE}
    # get the unexplained variance ratio of quantitative table according to the
    # group information indicated in descriptive table.
    com_r2(maize, group = c("Compartment", "Soil", "Host_genotype"))
    # use tables as input
    maize_dis <- dis(maize)
    get_r2(maize_dis, maize_des, group = c("Compartment", "Soil", "Host_genotype"))
```

## Community beta-diversity visualization
PCoA (Principle Coordinate Analysis) is usually used for the visualization of
beta-diversity of microbial community data. By using different color and shape,
samples from different conditions are compared.
```{r pcoa, eval = TRUE}
    # dimensionality reduction
    maize <- dmr(maize)
    # plot the community beta-diversity
    # separate samples from different conditions by color, plot PCo1 and PCo2
    p1 <- com_plot(maize, match = "Sample_ID", color = "Compartment")
    # plot PCo3 and PCo4
    p2 <- com_plot(maize, match = "Sample_ID", d1 = 3, d2 = 4, color =
                    "Compartment")
    # in addition, separate samples from different soil type by shape
    p3 <- com_plot(maize, match = "Sample_ID", color = "Compartment", shape =
                    "Soil")
    # plot PCo1 and PCo4
    p4 <- com_plot(maize, match = "Sample_ID", d1 = 1, d2 = 4, color =
                    "Compartment", shape = "Soil")
```
When using dissimilarity matrix as input, the `dmr()` function is used to reduce
the dimension of data and `pcoa_plot()` is used for plotting.
```{r pcoa_2, eval = TRUE}
    maize_dmr <- dmr(maize_dis, k = 4)
    maize_des <- maize_des[maize_des$Sample_ID %in% rownames(maize_dis), ]
    p <- pcoa_plot(maize_dmr, maize_des, match = "Sample_ID", d1 = 3, d2 = 4,
                   color = "Host_genotype")
```

# Network inference and clustering
For the microbial community research, diversity analysis capture only static
features and co-occurrence networks are typically inferred to indicate dynamics
of the system.

## Correlation coefficient adjacency matrix
Correlation will be calculated according to the covariance of compositions
across samples. When significance test is applied, `rcorr()` from `Hmisc`.
```{r adj, eval = TRUE}
    # check available adjacency matrix
    ? adj_method_list
    # Pearson and Spearman correlation
    maize <- adj(maize, method = "pearson")
    # Pearson and Spearman correlation with significance test
    maize <- adj(maize, method = "spearman", sig = TRUE)
```
Also the function could be applied to matrix directly, the correlation between
pairwise rows will be calculated.
```{r adj_2, eval = TRUE}
    # Pearson and Spearman correlation
    asv_adj <- adj(maize_asv_norm, method = "pearson")
```

## Network clustering
By removing the non-significant(waiting for update) and weak correlations, the
network of each adjacency matrix is generated and closely related nodes will be
inferred by clustering methods. In the package, Markov Cluster Algorithm (MCL,
Dongen, 2000) and Affinity Propagation (AP, Frey _et al_, 2007) are integrated
for network clustering.
```{r cls, eval = TRUE}
    # check available network clustering methods
    ? net_cls_list
    # network clustering by MCL
    maize <- net_cls(maize, method = "mcl", cutoff = 0.6)
    # network clustering by AP
    maize <- net_cls(maize, method = "ap", cutoff = 0.6, neg = FALSE)
```
Also it is possible to give a adjacency matrix directly and got the generated
cluster data frame.
```{r cls_2, eval = TRUE}
    # filter the weak correlation by cutoff and cluster by MCL
    asv_cls <- net_cls(asv_adj, method = "mcl", cutoff = 0.6)
```

# Higher-order feature based diversity analysis
By accumulating the relative abundance of compositions belong to the same
network clusters, the higher-order feature quantitative table is obtained and
could be used for further diversity analysis. Besides, compositions belong to
the same phylogenetic group could also be grouped together as new quantitative
table.

## Higher-order quantitative table
According to the network cluster assignments, compositions belong to the same
higher order level group are accumulated by summing up their relative
abundances.
```{r cls_tab, eval = TRUE}
    # get the cluster table by summing up compositions of the same cluster
    maize <- net_cls_tab(maize)
```

## Community diversity analysis and comparison
Same diversity analysis could be applied to cluster table and compared with
composition based table.
```{r cls_diversity, eval = TRUE}
    # dissimilarity between samples based on cluster table
    maize_cls_tab <- cls_tab(maize)
    maize_cls_dis <- com_dis(maize_cls_tab, method = "bray")
    get_r2(maize_cls_dis, maize_des, group = c("Compartment", "Soil",
                                               "Host_genotype"))
```

# Network comparison and statistical test
To compare the network of communities, pairwise distance between adjacency
matrix, which present all connection information, are calculated. By substrate
adjacency matrix (**A**) by the degree matrix (**D**), Laplacian matrix is obtained and
the corresponding eigenvector and eigenvalues are calculated. Spectral distance
then defined as the Euclidean distance between first *k* eigenvalues.
Alternatively, Jaccard distance between matrix is implemented as dividing the
sum of matrix contrast by the sum of larger absolute value between two adjacency
matrices.

## Bootstrap-permutation based network construction
To be able to test the significance of distances between matrices, a
bootstrap-permutation based method is developed. By subsampling and bootstrap,
true correlation adjacency matrices were constructed from subset of original
data. Then the metadata of samples is randomly swapped as permutated datasets,
from which the pseudo correlation coefficient is calculated. By comparing the
true adjacency matrices with the pseudo ones, the significance of distance is
obtained.
```{r bs_pm, eval = FALSE}
    # compare the networks from different compartments
    maize <- fit_tabs(maize)
    maize <- bs_pm(maize, group = "Compartment")
    # only get the distance, no significance test
    maize <- bs_pm(maize, group = "Compartment", sig = FALSE)
```
When the compsition number is big, the bootstrap-permutation could take very
long time, thus pre-filtering is needed. `g_size` is the minimum number of
samples for groups defined by `group`. Conditions with less than `g_size` would
be removed for later analysis and this is set as 88 by default. `s_size` is the
subsampling size for bootstrap and permutation, 30 by default. `s_size` should
definitely smaller than `g_size` and recommendly smaller than half of it. Also
compositions appear in less than specific percentage of samples could be
filtered by setting the occupancy threshold `per` and `rm`. By default, the
compositions which present in less than 10% samples would be filtered.
When the quantitative matrix is too big, one could choose to output the
bootstrap and permutation results separately for each comparison.
```{r bs_pm2, eval = FALSE}
    # set the size of group to remove consitions with less sample
    # also larger s_size will lead to more stable results but will consume more
    # computation and time resource
    maize <- bs_pm(maize, group = "Compartment", g_size = 200, s_size = 80)
    # remove the compositions appear in less than 20% of samples
    maize <- bs_pm(maize, group = "Compartment", per = 0.2)
    
    # set the bootstrap and permutation times. Again the more times bootstrap
    # and permutation, the more reliable the significance, with increased
    # computation and time resource.
    maize <- bs_pm(maize, group = "Compartment", bs = 11, pm = 11)

    # output the comparison separately to the defined directory
    bs_pm(maize, group = "Compartment", bs = 6, pm = 6,
    individual = TRUE, out_dir = out_dir)
```
## Network distance calculation and significance test
After getting the true and pseudo adjacency matrices, Spectral and Jaccard
distance defined before is then calculated and p value is obtained by comparing
the F (the real distance) and Fp (the pseudo distance) following the formula:
p = $\frac { C_{F_p > F} + 1  }{ N_{dis} + 1 }$
For the individual generated network comparison results, the distance
calculation is implemented by the function `net_dis_indi()`. Same methods are
available.
```{r sig, eval = FALSE}
    # check the available methods
    ? net_dis_method_list
    # calculate the distances between matrices
    maize <- net_dis(maize, method = "spectra")
    maize <- net_dis(maize, method = "Jaccard")
    # check the ditance results and significance (if applicable)
    dis_stat(maize)
    # the comparison stored separately in previous step
    ja <- net_dis_indi(out_dir, method = "Jaccard")
    dis_stat(ja)
    spectra <- net_dis_indi(out_dir, method = "spectra")
    dis_stat(spectra)
```
