Fixing remaining BiocCheck and readme

R/cbea_internals.R

@@ -163,21 +163,23 @@ get_diagnostics <- function(env = caller_env()){
 
     req_objs <- c("output", "distr", "parametric", "raw_scores", "perm_scores", "adj")
     obj_names <- env_names(env)
-    sapply(req_objs, function(x){
+    vapply(req_objs, function(x){
         if(!x %in% obj_names){
             stop(x, " not found")
         }
-    })
+        return(0)
+    }, FUN.VALUE = 0)
     if (env$parametric == TRUE){
         add_objs <- c("final_distr", "perm_distr")
         if (env$adj == TRUE){
             add_objs <- c(add_objs, "unperm_distr")
         }
-        sapply(add_objs, function(x){
+        vapply(add_objs, function(x){
             if(!x %in% obj_names){
                 stop(x, " not found")
             }
-        })
+            return(0)
+        }, FUN.VALUE = 0)
 
     }
     if (env$output == "raw" | env$parametric == FALSE){
@@ -544,7 +546,8 @@ get_adj_mnorm <- function(perm, unperm, verbose = FALSE, fix_comp = "none") {
         mu = perm$mu, mean = perm_mean
     )
     if (verbose == TRUE) {
-        print(paste("Total sd is", unperm_sd, "and estimated sd is", estim_sd))
+        message("Total sd is ", unperm_sd, " and estimated sd is ",
+                estim_sd)
     }
     param <- list(mu = perm$mu, sigma = est_sig, lambda = perm$lambda)
     return(param)

R/utils.R

@@ -37,7 +37,7 @@ pmnorm <- function(q, mu, sigma, lambda, log = FALSE, verbose = FALSE) {
     q <- as.vector(q)
     n_components <- length(sigma)
     if (verbose == TRUE) {
-        message(paste(n_components, "components!", "\n"))
+        message(n_components, " components!", "\n")
     }
     comp <- vector(mode = "list", length = n_components)
     for (i in seq_len(n_components)) {
@@ -53,7 +53,7 @@ dmnorm <- function(x, mu, sigma, lambda, log = FALSE, verbose = FALSE) {
     x <- as.vector(x)
     n_components <- length(sigma)
     if (verbose == TRUE) {
-        message(paste(n_components, "components!", "\n"))
+        message(n_components, "components!", "\n")
     }
     comp <- vector(mode = "list", length = n_components)
     for (i in seq_len(n_components)) {

README.Rmd

@@ -18,15 +18,13 @@ knitr::opts_chunk$set(
 [![Codecov test coverage](https://codecov.io/gh/qpmnguyen/CBEA/branch/master/graph/badge.svg)](https://codecov.io/gh/qpmnguyen/CBEA?branch=master)
 [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
 [![R-CMD-check](https://github.com/qpmnguyen/CBEA/workflows/R-CMD-check-bioc/badge.svg)](https://github.com/qpmnguyen/CBEA/actions)
-[![BioC status](http://www.bioconductor.org/shields/build/release/bioc/CBEA.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/CBEA)
+<!-- [![BioC status](http://www.bioconductor.org/shields/build/release/bioc/CBEA.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/CBEA) -->
 <!-- badges: end -->
 
 ### Quang Nguyen
 
 The `CBEA` package provides basic functionality to perform taxonomic enrichment analysis in R. This package mainly supports the `CBEA` method, and provides additional support for generating sets for analyses using approaches commonly used in the gene set testing literature. 
 
-**This package is under ongoing development and might not be stable at the moment. Only install the development version if R CMD CHECK badge is green (passed) or if the error is due to dependency installation.**  
-
 ### Installation  
 
 And the development version from [GitHub](https://github.com/) with:
@@ -38,13 +36,12 @@ devtools::install_github("qpmnguyen/CBEA")
 
 ### Features  
 
-This package supports the implementation of the CBEA approach (formerly known as cILR) for taxonomic enrichment analysis.   
-
-### Dependency Graph  
+This package implements the CBEA approach for performing set-based enrichment analysis for microbiome relative abundance data. A preprint of the package can be found [on bioXriv](https://www.biorxiv.org/content/10.1101/2021.09.07.459294v1.full). In summary, CBEA (Competitive Balances for taxonomic Enrichment Analysis) provides an estimate of the activity of a set by transforming an input taxa-by-sample data matrix into a corresponding set-by-sample data matrix. The resulting output can be used for additional downstream analyses such as differential abundance, classification, clustering, etc. using set-based features instead of the original units.  
 
-```{r, include = TRUE, echo=FALSE}
-#dd <- deepdep::deepdep(package = "CBEA", local = TRUE, depth = 2, dependency_type = "Imports")
-#ggplot2::ggsave(plot = deepdep::plot_dependencies(dd), filename = "man/figures/dep_new.png")
+The transformation that CBEA applies is based on the isometric log ratio transformation:  
+$$
+CBEA_{i,\mathbb{S}} = \sqrt{\frac{|\mathbb{S}||\mathbb{S_c}|}{|\mathbb{S}| + |\mathbb{S_c}|}} \ln \frac{g(X_{i,j | j\in \mathbb{S}})}{g(X_{i,j | j \notin \mathbb{S}})}
+$$
+Where $\mathbb{S}$ is the set of interest, $\mathbb{S}_C$ is it's complement, $g()$ is the geometric mean operation, and $X$ is the original data matrix where $i$ is the index representing samples and $j$ is the index representing variables (or taxa).   
 
-knitr::include_graphics("man/figures/dep_new.png")
-```
+The inference procedure is performed through estimating the null distribution of the test statistic. This can be done either via permutations or a parametric fit of a distributional form on the permuted scores. Users can also adjust for variance inflation due to inter-taxa correlation. Please refer to the main manuscript for any additional details.  

README.md

@@ -11,8 +11,7 @@ coverage](https://codecov.io/gh/qpmnguyen/CBEA/branch/master/graph/badge.svg)](h
 state and is being actively
 developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
 [![R-CMD-check](https://github.com/qpmnguyen/CBEA/workflows/R-CMD-check-bioc/badge.svg)](https://github.com/qpmnguyen/CBEA/actions)
-[![BioC
-status](http://www.bioconductor.org/shields/build/release/bioc/CBEA.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/CBEA)
+<!-- [![BioC status](http://www.bioconductor.org/shields/build/release/bioc/CBEA.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/CBEA) -->
 <!-- badges: end -->
 
 ### Quang Nguyen
@@ -22,10 +21,6 @@ enrichment analysis in R. This package mainly supports the `CBEA`
 method, and provides additional support for generating sets for analyses
 using approaches commonly used in the gene set testing literature.
 
-**This package is under ongoing development and might not be stable at
-the moment. Only install the development version if R CMD CHECK badge is
-green (passed) or if the error is due to dependency installation.**
-
 ### Installation
 
 And the development version from [GitHub](https://github.com/) with:
@@ -37,9 +32,43 @@ devtools::install_github("qpmnguyen/CBEA")
 
 ### Features
 
-This package supports the implementation of the CBEA approach (formerly
-known as cILR) for taxonomic enrichment analysis.
+This package implements the CBEA approach for performing set-based
+enrichment analysis for microbiome relative abundance data. A preprint
+of the package can be found [on
+bioXriv](https://www.biorxiv.org/content/10.1101/2021.09.07.459294v1.full).
+In summary, CBEA (Competitive Balances for taxonomic Enrichment
+Analysis) provides an estimate of the activity of a set by transforming
+an input taxa-by-sample data matrix into a corresponding set-by-sample
+data matrix. The resulting output can be used for additional downstream
+analyses such as differential abundance, classification, clustering,
+etc. using set-based features instead of the original units.
+
+The transformation that CBEA applies is based on the isometric log ratio
+transformation:  
+
+![
+CBEA\_{i,\\mathbb{S}} = \\sqrt{\\frac{\|\\mathbb{S}\|\|\\mathbb{S\_c}\|}{\|\\mathbb{S}\| + \|\\mathbb{S\_c}\|}} \\ln \\frac{g(X\_{i,j \| j\\in \\mathbb{S}})}{g(X\_{i,j \| j \\notin \\mathbb{S}})}
+](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%0ACBEA_%7Bi%2C%5Cmathbb%7BS%7D%7D%20%3D%20%5Csqrt%7B%5Cfrac%7B%7C%5Cmathbb%7BS%7D%7C%7C%5Cmathbb%7BS_c%7D%7C%7D%7B%7C%5Cmathbb%7BS%7D%7C%20%2B%20%7C%5Cmathbb%7BS_c%7D%7C%7D%7D%20%5Cln%20%5Cfrac%7Bg%28X_%7Bi%2Cj%20%7C%20j%5Cin%20%5Cmathbb%7BS%7D%7D%29%7D%7Bg%28X_%7Bi%2Cj%20%7C%20j%20%5Cnotin%20%5Cmathbb%7BS%7D%7D%29%7D%0A "
+CBEA_{i,\mathbb{S}} = \sqrt{\frac{|\mathbb{S}||\mathbb{S_c}|}{|\mathbb{S}| + |\mathbb{S_c}|}} \ln \frac{g(X_{i,j | j\in \mathbb{S}})}{g(X_{i,j | j \notin \mathbb{S}})}
+")
 
-### Dependency Graph
+Where
+![\\mathbb{S}](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Cmathbb%7BS%7D "\mathbb{S}")
+is the set of interest,
+![\\mathbb{S}\_C](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Cmathbb%7BS%7D_C "\mathbb{S}_C")
+is it’s complement,
+![g()](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;g%28%29 "g()")
+is the geometric mean operation, and
+![X](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;X "X")
+is the original data matrix where
+![i](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;i "i")
+is the index representing samples and
+![j](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;j "j")
+is the index representing variables (or taxa).
 
-<img src="man/figures/dep_new.png" width="100%" />
+The inference procedure is performed through estimating the null
+distribution of the test statistic. This can be done either via
+permutations or a parametric fit of a distributional form on the
+permuted scores. Users can also adjust for variance inflation due to
+inter-taxa correlation. Please refer to the main manuscript for any
+additional details.

vignettes/basic_usage.Rmd

@@ -102,6 +102,7 @@ If there are any issues with the installation procedure or package features, the
 library("CBEA")
 library(BiocSet)
 library(tidyverse)
+set.seed(1020)
 ```
 
 ## Loading sample data