--- title: "Introduction to BAnOCC (Bayesian Analaysis Of Compositional Covariance)" author: "Emma Schwager" date: "`r Sys.Date()`" output: BiocStyle::html_document: toc: yes toc_depth: 4 bibliography: BAnOCC_Bibliography.bib vignette: > %\VignetteIndexEntry{BAnOCC (Bayesian Analysis of Compositional Covariance)} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ## Introduction Compositional data occur in many disciplines: geology, nutrition, economics, and ecology, to name a few. Data are compositional when each sample is sum-constrained. For example, mineral compositions describe a mineral in terms of the weight percentage coming from various elements; or taxonomic compositions break down a community by the fraction of community memebers that come from a particular species. In ecology in particular, the covariance between features is often of interest to determine which species possibly interact with each other. However, the sum constraint of compositional data makes naive measures inappropriate. BAnOCC is a package for analyzing compositional covariance while accounting for the compositional structure. Briefly, the model assumes that the unobserved counts are log-normally distributed and then infers the correlation matrix of the log-basis (see the [The Model] section for a more detailed explanation). The inference is made using No U-Turn Sampling for Hamiltonian Monte Carlo [@HoffmanAndGelman2014] as implemented in the `rstan` R package [@StanSoftware2015]. ## How To Install There are three options for installing BAnOCC: * Within R * Using compressed file from bitbucket * Directly from bitbucket ### From Within R **This is not yet available** ```{r, eval=FALSE, echo=FALSE} if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("BAnOCC") ``` ### From Bitbucket (Compressed File) **This is not yet available** ### From Bitbucket (Directly) Clone the repository using `git clone`, which downloads the package as its own directory called `banocc`. ```bash git clone https://@bitbucket.org/biobakery/banocc.git ``` Then, install BAnOCC's dependencies. If these are already installed on your machine, this step can be skipped. ```bash Rscript -e "install.packages(c('rstan', 'mvtnorm', 'coda', 'stringr'))" ``` Lastly, install BAnOCC using `R CMD INSTALL`. Note that this _will not_ automatically install the dependencies, so they must be installed first. ```bash R CMD INSTALL banocc ``` ## How To Run ### Loading We first need to load the package: ```{r load, eval=TRUE} library(banocc) ``` ### Package Features The BAnOCC package contains four things: * `banocc_model`, which is the BAnOCC model in the `rstan` format * `run_banocc`, a wrapper function for `rstan::sampling` that samples from the model and returns a list with various useful elements * `get_banocc_output`, which takes as input a `stanfit` object outputted by `run_banocc`, and outputs various statistics from the chains. * Several test datasets which are included both as counts and as the corresponding compositions: Dataset Description | Counts | Composition ----------------------------- | ------------------ | ------------ No correlations in the counts | `counts_null` | `compositions_null` No correlations in the counts | `counts_hard_null` | `compositions_hard_null` Positive corr. in the counts | `counts_pos_spike` | `compositions_pos_spike` Negative corr. in the counts | `counts_neg_spike` | `compositions_neg_spike` ### Data and Prior Input For a full and complete description of the possible parameters for `run_banocc` and `get_banocc_output`, their default values, and the output, see ```{r run-banocc-help, eval=FALSE} ?run_banocc ?get_banocc_output ``` #### Required Input There are only two required inputs to `run_banocc`: 1. The dataset `C`. This is assumed to be $N \times P$, with $N$ samples and $P$ features. The row sums are therefore required to be less than one for all samples. 1. The compiled stan model `compiled_banocc_model`. The compiled model is required so that `run_banocc` doesn't need to waste time compiling the model every time it is called. To compile, use `rstan::stan_model(model_code=banocc::banocc_model)`. The simplest way to run the model is to load a test dataset, compile the model, sample from it (this gives a warning because the default number of iterations is low), and get the output: ```{r rerun, cache=TRUE, echo=FALSE} rerun <- 0 ``` ```{r basic-run-banocc, eval=TRUE, cache=TRUE, dependson=c('rerun'), results="hide"} data(compositions_null) compiled_banocc_model <- rstan::stan_model(model_code = banocc::banocc_model) b_fit <- banocc::run_banocc(C = compositions_null, compiled_banocc_model=compiled_banocc_model) b_output <- banocc::get_banocc_output(banoccfit=b_fit) ``` #### Hyperparameters The hyperparameter values can be specified as input to `run_banocc`. Their names correspond to the parameters in the plate diagram figure (see section [The Model]). For example, ```{r input-hyperparameters, eval=TRUE, cache=TRUE, dependson=c('rerun'), results="hide"} p <- ncol(compositions_null) b_fit_hp <- banocc::run_banocc(C = compositions_null, compiled_banocc_model = compiled_banocc_model, n = rep(0, p), L = 10 * diag(p), a = 0.5, b = 0.01) ``` ### Sampling Control There are several options to control the behavior of the HMC sampler within `run_banocc`. This is simply a call to `rstan::sampling`, and so many of the parameters are the same. #### General Sampling Control The number of chains, iterations, and warmup iterations as well as the rate of thinning for `run_banocc` can be specified using the same parameters as for `rstan::sampling` and `rstan::stan`. For example, the following code gives a total of three iterations from each of two chains. These parameters are used only for brevity and are NOT recommended in practice. ```{r sampling-sampling, eval=TRUE, cache=TRUE, dependson=c('rerun'), results="hide"} b_fit_sampling <- banocc::run_banocc(C = compositions_null, compiled_banocc_model = compiled_banocc_model, chains = 2, iter = 11, warmup = 5, thin = 2) ``` #### Number of Cores The number of cores used for sampling on a multi-processor machine can also be specified, which allows chains to run in parallel and therefore decreases computation time. Since its purpose is running chains in parallel, computation time will decrease as cores are added up to when the number of cores and the number of chains are equal. ```{r sampling-cores, eval=FALSE} # This code is not run b_fit_cores <- banocc::run_banocc(C = compositions_null, compiled_banocc_model = compiled_banocc_model, chains = 2, cores = 2) ``` #### Initial Values By default, the initial values for **$m$** and $\lambda$ are sampled from the priors and the initial values for **$O$** are set to the identity matrix of dimension $P$. Setting the initial values for **$O$** to the identity helps ensure a parsimonious model fit. The initial values can also be set to a particular value by using a list whose length is the number of chains and whose elements are lists of initial values for each parameter: ```{r sampling-init, eval=TRUE, cache=TRUE, dependson=c('rerun'), results="hide"} init <- list(list(m = rep(0, p), O = diag(p), lambda = 0.02), list(m = runif(p), O = 10 * diag(p), lambda = runif(1, 0.1, 2))) b_fit_init <- banocc::run_banocc(C = compositions_null, compiled_banocc_model = compiled_banocc_model, chains = 2, init = init) ``` More specific control of the sampler's behavior comes from the `control` argument to `rstan::sampling`. Details about this argument can be found in the help for the `rstan::stan` function: ```{r, eval=FALSE} ?stan ``` ### Output Control There are several parameters that control the type of output which is returned by `get_banocc_output`. #### Credible Interval Width The width of the returned credible intervals is controlled by `conf_alpha`. A $100\% * (1-\alpha_\text{conf})$ credible interval is returned: ```{r output-ci, eval=TRUE, cache=TRUE, dependson=c('rerun'), results="hide"} # Get 90% credible intervals b_out_90 <- banocc::get_banocc_output(banoccfit=b_fit, conf_alpha = 0.1) # Get 99% credible intervals b_out_99 <- banocc::get_banocc_output(banoccfit=b_fit, conf_alpha = 0.01) ``` #### Checking Convergence Convergence is evaluated automatically, and in this case the credible intervals, estimates, and any additional output in section [Additional Output] is missing. This behavior can be turned off using the `eval_convergence` option. But be careful! ```{r eval-convergence, eval=TRUE, cache=TRUE, dependson=c('rerun'), results="hide"} # Default is to evaluate convergence b_out_ec <- banocc::get_banocc_output(banoccfit=b_fit) # This can be turned off using `eval_convergence` b_out_nec <- banocc::get_banocc_output(banoccfit=b_fit, eval_convergence = FALSE) ``` ```{r show-eval-convergence, eval=TRUE} # Iterations are too few, so estimates are missing b_out_ec$Estimates.median # Convergence was not evaluated, so estimates are not missing b_out_nec$Estimates.median ``` #### Additional Output Two types of output can be requested for each correlation that are not included by default: 1. The smallest credible interval width that includes zero 1. The scaled neighborhood criterion, or SNC [@LiAndLin2010] ```{r output-extra, eval=TRUE, cache=TRUE, dependson=c('rerun'), results="hide"} # Get the smallest credible interval width that includes zero b_out_min_width <- banocc::get_banocc_output(banoccfit=b_fit, get_min_width = TRUE) # Get the scaled neighborhood criterion b_out_snc <- banocc::get_banocc_output(banoccfit=b_fit, calc_snc = TRUE) ``` Detailed statements about the function's execution can also be printed using the `verbose` argument. The relative indentation of the verbose output indicates the nesting level of the function. The starting indentation can be set with `num_level`. ## Assessing Convergence There are many ways of assessing convergence, but the two most easily implemented using BAnOCC are: 1. Traceplots of parameters, which show visually what values of a parameter have been sampled across all iterations. At convergence, the sampler should be moving rapidly across the space, and the chains should overlap well. In other words, it should look like grass. 1. The Rhat statistic [@GelmanAndRubin1992], which measures agreement between all the chains. It should be close to one at convergence. ### Traceplots Traceplots can be directly accessed using the `traceplot` function in the `rstan` package, which creates a `ggplot2` object that can be further maniuplated to 'prettify' the plot. The traceplots so generated are for the samples drawn _after_ the warmup period. For example, we could plot the traceplots for the inverse covariances of feature 1 with all other features. There is overlap between some of the chains, but not all and so we conclude that we need more samples from the posterior to be confident of convergence. ```{r traceplot, eval=TRUE, cache=TRUE} # The inverse covariances of feature 1 with all other features rstan::traceplot(b_fit$Fit, pars=paste0("O[1,", 2:9, "]")) ``` We could also see the warmup period samples by using `inc_warmup=TRUE`. This shows that some of the chains have moved from very different starting points to a similar distribution, which is a good sign of convergence. ```{r traceplot-warmup, eval=TRUE, cache=TRUE} # The inverse covariances of feature 1 with all other features, including warmup rstan::traceplot(b_fit$Fit, pars=paste0("O[1,", 2:9, "]"), inc_warmup=TRUE) ``` ### Rhat Statistics The Rhat values can also be directly accessed using the `summary` function in the `rstan` package. It measures the degree of agreement between all the chains. At convergence, the Rhat statistics should be approximately one for all parameters. For example, the Rhat values for the correlation between feature 1 and all other features (the same as those plotted above), agree with the traceplots that convergence has not yet been reached. ```{r Rhat, eval=TRUE, cache=TRUE} # This returns a named vector with the Rhat values for all parameters rhat_all <- rstan::summary(b_fit$Fit)$summary[, "Rhat"] # To see the Rhat values for the inverse covariances of feature 1 rhat_all[paste0("O[1,", 2:9, "]")] ``` ## Choosing Priors The hyperparameters for the model (see section [The Model]) need to be chosen appropriately. ### Log-Basis Precision Matrix The prior on the precision matrix **$O$** is a GLASSO prior from [@Wang2012] with parameter $\lambda$ [see also section [The Model]]. As $\lambda$ decreases, the degree of shrinkage correspondingly increases. ![lambda behavior](lambda_behavior_figure.png) ```{r, eval=FALSE, echo=FALSE} # The above plot is from Dropbox/hutlab/Emma/paper1/writeup/figures/supplemental/lambbda_behavior.png ``` ### Log-Basis Mean We recommend using an uninformative prior for the log-basis mean: centered at zero and with large variance. ### GLASSO Shrinkage Parameter We recommend using a prior with large probability mass close to zero; because $\lambda$ has a gamma prior, this means that the shape parameter $a$ should be less than one. The rate parameter $b$ determines the variability; in cases with either small (order of 10) or very large ($p > n$) numbers of features $b$ should be large so that the variance of the gamma distribution, $a / b^2$, is small. Otherwise, a small value of $b$ will make the prior more uninformative. ## The Model A pictoral representation of the model is shown below. Briefly, the basis (or unobserved, unrestricted counts) for each sample is assumed to be a lognormal distribution with parameters **$m$** and **$S$**. The prior on **$m$** is a normal distribution parametrized by mean **$n$** and variance-covariance matrix **$L$**. Since we are using a graphical LASSO prior, we parametrize the model with precision matrix **$O$**. The prior on **$O$** is a graphical LASSO prior [@Wang2012] with shrinkage parameter $\lambda$. To circumvent the necessity of choosing $\lambda$, a gamma hyperprior is placed on $\lambda$, with parameters $a$ and $b$. ![plate-diagram](Figure3.png) If we print the model, we can actually see the code. It is written in the format required by the `rstan` package, since `banocc` uses this package to sample from the model. See [@StanManual2015] for more detailed information on this format. ```{r model, eval=FALSE} # This code is not run cat(banocc::banocc_model) ``` ## References