updated Readme and vignettes link

.Rbuildignore

@@ -9,3 +9,6 @@ LICENSE
 ^pkgdown$
 ^cran-comments\.md$
 ^\.github$
+^vignettes/pbmc\.rds$
+^vignettes/liu\.rds$
+^vignettes/hannum\.rds$

.github/workflows/pkgdown.yaml

@@ -0,0 +1,48 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+    branches: [main, master]
+  release:
+    types: [published]
+  workflow_dispatch:
+
+name: pkgdown
+
+jobs:
+  pkgdown:
+    runs-on: ubuntu-latest
+    # Only restrict concurrency for non-PR jobs
+    concurrency:
+      group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::pkgdown, local::.
+          needs: website
+
+      - name: Build site
+        run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
+        shell: Rscript {0}
+
+      - name: Deploy to GitHub pages 🚀
+        if: github.event_name != 'pull_request'
+        uses: JamesIves/[email protected]
+        with:
+          clean: false
+          branch: gh-pages
+          folder: docs

.gitignore

@@ -1,3 +1,4 @@
+**/.DS_Store
 .Rproj.user
 .Rhistory
 .RData

DESCRIPTION

@@ -44,3 +44,4 @@ Config/testthat/edition: 3
 Encoding: UTF-8
 LazyData: true
 RoxygenNote: 7.2.3
+URL: https://cozygene.github.io/Unico/

R/Unico.r

@@ -55,7 +55,7 @@
 #' \item{sigmas_hat_list}{A list tracking, for each feature, the computed estimates of the variance-covariance matrix at each iteration of the variance-covariance optimization (activated only if \code{debug == TRUE}).}
 #'
 #' @examples
-#' data = simulate_data(n = 100, m = 2, k = 3, p1 = 1, p2 = 1, taus_std = 0)
+#' data = simulate_data(n = 100, m = 2, k = 3, p1 = 1, p2 = 1, taus_std = 0, log_file = NULL)
 #' res = list()
 #' res$params.hat = Unico(data$X, data$W, data$C1, data$C2, parallel=FALSE, log_file=NULL)
 #'
@@ -256,7 +256,7 @@ Unico = function(X, W, C1, C2, fit_tau = FALSE,
 #' @return A \code{k} by \code{m} by \code{n} array with the estimated source-specific values. The first axis/dimension in the array corresponds to the different sources.
 #'
 #' @examples
-#' data = simulate_data(n = 100, m = 2, k = 3, p1 = 1, p2 = 1, taus_std = 0)
+#' data = simulate_data(n = 100, m = 2, k = 3, p1 = 1, p2 = 1, taus_std = 0, log_file = NULL)
 #' res = list()
 #' res$params.hat = Unico(data$X, data$W, data$C1, data$C2, parallel=FALSE, log_file=NULL)
 #' res$Z = tensor(data$X, data$W, data$C1, data$C2, res$params.hat, parallel=FALSE, log_file=NULL)
@@ -370,7 +370,7 @@ tensor = function(X, W, C1, C2, Unico.mdl, parallel = TRUE, num_cores = NULL,
 #' \item{phi_hat_pvals}{An \code{m} by \code{k+p1*k+p2} matrix containing the p-values associated with \code{phi_hat} for each feature (activated only if \code{debug == TRUE}).}
 #'
 #' @examples
-#' data = simulate_data(n = 100, m = 2, k = 3, p1 = 1, p2 = 1, taus_std = 0)
+#' data = simulate_data(n = 100, m = 2, k = 3, p1 = 1, p2 = 1, taus_std = 0, log_file = NULL)
 #' res = list()
 #' res$params.hat = Unico(data$X, data$W, data$C1, data$C2, parallel=FALSE, log_file=NULL)
 #' res$params.hat = association_parametric(data$X, res$params.hat, parallel=FALSE, log_file=NULL)
@@ -608,7 +608,7 @@ association_parametric = function(X, Unico.mdl, slot_name = "parametric",
 #' \item{phi_hat_pvals}{An \code{m} by \code{k+p1*k+p2} matrix containing the p-values associated with \code{phi_hat} for each feature (activated only if \code{debug == TRUE}).}
 #'
 #' @examples
-#' data = simulate_data(n = 100, m = 2, k = 3, p1 = 1, p2 = 1, taus_std = 0)
+#' data = simulate_data(n = 100, m = 2, k = 3, p1 = 1, p2 = 1, taus_std = 0, log_file = NULL)
 #' res = list()
 #' res$params.hat = Unico(data$X, data$W, data$C1, data$C2, parallel=FALSE, log_file=NULL)
 #' res$params.hat = association_asymptotic(data$X, res$params.hat, parallel=FALSE, log_file=NULL)
@@ -840,7 +840,7 @@ association_asymptotic = function(X, Unico.mdl, slot_name = "asymptotic",
 
 #'
 #' @examples
-#' sim.data = simulate_data(n = 100, m = 5, k = 5, p1 = 1, p2 = 2)
+#' sim.data = simulate_data(n = 100, m = 5, k = 5, p1 = 1, p2 = 2, log_file = NULL)
 #'
 #' @export simulate_data
 

README.md

@@ -2,63 +2,26 @@
   <!-- badges: start -->
   [![R-CMD-check](https://github.com/cozygene/Unico/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/cozygene/Unico/actions/workflows/R-CMD-check.yaml)
   <!-- badges: end -->
-  
+
 We present Unico, a unified cross-omics method designed to deconvolve standard 2-dimensional bulk matrices of samples by features into a 3-dimensional tensors representing samples by features by cell types. Unico stands out as the first principled model-based deconvolution method that is theoretically justified for any heterogeneous genomic data.
 
 All necessary scripts used for the analyses reported in the manuscript can be found under ./Rscripts
 
 Unico will be available soon on CRAN as an R package
 
-### Installation Instructions
-Please install the following packages in R, which should finish within an hour.
+### Installation from github
 ```
-install.packages("pbapply")
-install.packages("config")
-install.packages("data.table")
-install.packages("matrixStats")
-install.packages("matrixcalc")
-install.packages("mgcv")
-install.packages("nloptr")
-install.packages("testit")
-install.packages("compositions")
-install.packages("MASS")
-install.packages("pracma")
+if (!require("devtools", quietly = TRUE))
+    install.packages("devtools")
+devtools::install_github("https://github.com/cozygene/Unico")
 ```
+
 ### Version info
 Our algorithm is tested on both R 3.6.1 and R 4.1.0, on both Linux and MacOS based machines.
 The entirety of the system environment is also included at the end of the tutorial notebook.
 
-
-### Running Unico
-```
-Please provide (1) Bulk matrix of normalized counts or methylation beta values as input: X (features by samples),
-               (2) The (estimated) sample specific cell-type proportions matrix: W (samples by cell types).
-                   This matrix should be normalized such that proportions on each row/individual sum up to 1
-               (3) Optionally, the model can also model cell-type level covariates: C1 (samples by number of cell-type-level covariates)
-               (4) Optionally, the model can also model tissue-level covariates: C2 (samples by number of tissue-level covariates)
-We encourage users to provide all matrices with explicit and meaningful row and column names when possible to avoid confusion regarding the dimension of the output object.
-```
-##### First step is always to perform parameter estimation.
-```
-#Unico will learn cell-type specific means, variances and covariances along with cell-type-level and tissue-level effect sizes under a generalized method of moment (GMM) framework
-unico.mdl = list()
-unico.mdl$params.hat <- Unico(X, W, C1, C2, parallel = TRUE)
-```
-##### Once parameters are estimated we can further estimate cell-type level profiles: cell types by features by samples (denoted as Z.hat)
-```
-unico.mdl$Z.hat = tensor(X, W = W, C1, C2, unico.mdl$params.hat)
-```
-##### Alternatively, we can perform association study
-```
-#parametric version (under normality assumption) without explicitly deriving the underlying cell-type levels:
-Unico.mdl$params.hat = add_C1_C2_pvals_parametric(X = X, Unico.mdl = Unico.mdl$params.hat, slot_name = "parametric")
-```
-```
-#Non-parametric version (p-values are derived based on asymptotics):
-Unico.mdl$params.hat = add_C1_C2_pvals_asymptotic(X = X, Unico.mdl = Unico.mdl$params.hat, slot_name = "asymptotic")
-```
 ### Tutorial
-Please head to "vignettes" folder for a more involved step by step tutorial on deconvolving a simulated PBMC pseudo-bulk expression dataset and association testing on methylation data.
+Please head to "vignettes" folder for a more involved step by step tutorial on deconvolving a simulated PBMC pseudo-bulk expression dataset and association testing on subsets of publicly available methylation data.
 
 ### Author
 

vignettes/.gitignore

@@ -1,2 +1,2 @@
 *.html
-*.R
+

vignettes/Unico-Tutorial.Rmd

@@ -17,7 +17,7 @@ knitr::opts_chunk$set(
 ```{r eval=F, setup}
 library(Unico)
 library(matrixStats)
-source("vignetts.utils.r")
+source("https://github.com/cozygene/Unico/raw/main/vignettes/vignetts.utils.r")
 ```
 
 
@@ -27,7 +27,7 @@ Let's first load a small simulated pseudo-bulk PBMC expression profiles using da
 ```{r eval=F}
 data_path <- "./"
 if(!file.exists(file.path(data_path, "pbmc.rds"))){
-	download.file("github.com/cozygene/Unico/blob/master/vignettes/pbmc.rds?raw=true", file.path(data_path,"pbmc.rds"))
+	download.file("https://github.com/cozygene/Unico/raw/main/vignettes/pbmc.rds", file.path(data_path,"pbmc.rds"))
 }
 sim.data = readRDS(file.path(data_path,"pbmc.rds"))
 ```
@@ -74,10 +74,10 @@ Unico also offers association testing at cell-type resolution. Let's look at two
 ```{r eval=F, echo = T, results = 'hide'}
 data_path <- "./"
 if(!file.exists(file.path(data_path, "liu.rds"))){
-	download.file("github.com/cozygene/Unico/blob/master/vignettes/liu.rds?raw=true", file.path(data_path,"liu.rds"))
+	download.file("https://github.com/cozygene/Unico/raw/main/vignettes/liu.rds", file.path(data_path,"liu.rds"))
 }
 if(!file.exists(file.path(data_path, "hannum.rds"))){
-	download.file("github.com/cozygene/Unico/blob/master/vignettes/hannum.rds?raw=true", file.path(data_path,"hannum.rds"))
+	download.file("https://github.com/cozygene/Unico/raw/main/vignettes/hannum.rds", file.path(data_path,"hannum.rds"))
 }
 liu    = readRDS(file.path(data_path,"liu.rds"))
 hannum = readRDS(file.path(data_path,"hannum.rds"))