update readme

Author: yulijia

README.md

@@ -6,27 +6,29 @@ Single-cell technologies have revolutionized our understanding of cellular dynam
 
 ## Installation
 
+The iDAS package is still under development to meet Bioconductor standards. If you have any questions, please don't hesitate to open an issue.
 
 ```{r}
+## install from github
 devtools::install_github("SydneyBioX/iDAS")
 ```
 
-
 ## Example command
 
 ```{r}
 # Example using two factors
 result_twoway <- iDAS(Z = my_feature_matrix, factor1 = group1, factor2 = group2)
 
 # Example using three factors
-result_threeway <- iDAS(Z = my_feature_matrix, factor1 = group1, factor2 = group2, factor3 = timepoint)
+result_threeway <- iDAS(Z = my_feature_matrix, factor1 = group1, 
+                        factor2 = group2, factor3 = timepoint)
 ```
 
 ### Simulation example
 
+Here, we provide some simple simulation example to show how to use iDAS function.
+
 ```{r}
-## Not run: 
-# Generate sample data
 set.seed(123)
 Z <- matrix(rnorm(1000), ncol = 10)
 colnames(Z)=paste0("gene",1:10)
@@ -52,12 +54,15 @@ result <- threefactors(
   p_adjust_method = "BH"
 )
 
+```
+
+Results include three table, the p-value (or adjusted p-value) table, F-statistics table, and the gene associated groups.
+
+```{r}
 # Inspect results
 head(result$pval_matrix)
 head(result$stat_matrix)
 head(result$class_df)
-
-## End(Not run)
 ```
 
-The iDAS package is still under development to meet Bioconductor standards. If you have any questions, please don't hesitate to open an issue.
+

vignettes/iDAS.Rmd

@@ -1,10 +1,52 @@
 ---
-title: "iDAS"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{iDAS}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
+title: "iDAS: Interpretable Differential Abundance Signature"
+date: "`r Sys.Date()`"
+author:
+- name: Lijia Yu
+  affiliation:
+  - School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia;
+  - Sydney Precision Data Science Centre, University of Sydney, NSW 2006, Australia
+  - Charles Perkins Centre, The University of Sydney, NSW 2006, Australia
+  - Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, University of Sydney, NSW 2145, Australia
+- name: Yingxin Lin
+  affiliation:
+  - Department of Biostatistics, Yale University, New Haven, CT 208034, USA
+- name: Xiangnan Xu
+  affiliation:
+  - School of Business and Economics, Humboldt-Universität zu Berlin, Berlin 10099, Germany
+- name: Pengyi Yang
+  affiliation:
+  - Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, University of Sydney, NSW 2145, Australia;
+  - School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia;
+  - Sydney Precision Data Science Centre, University of Sydney, NSW 2006, Australia;
+  - Charles Perkins Centre, The University of Sydney, NSW 2006, Australia;
+  - Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China.
+- name: Jean Yang
+  affiliation:
+  - School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia;
+  - Sydney Precision Data Science Centre, University of Sydney, NSW 2006, Australia;
+  - Charles Perkins Centre, The University of Sydney, NSW 2006, Australia;
+  - Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China.
+output:
+  BiocStyle::html_document:
+    toc_float: true
+    toc_depth: 3
+    theme: united
+    highlight: tango
+    code-fold: false
+    code-tools: true
+    embed-resources: true
+    number_sections: false
+  BiocStyle::pdf_document: 
+    number_sections: false
+package: iDAS
+vignette: |
+    %\VignetteIndexEntry{iDAS tutorial: simulation}
+    %\VignetteEncoding{UTF-8}
+    %\VignetteEngine{knitr::rmarkdown}
+editor_options: 
+  markdown: 
+    wrap: 80
 ---
 
 ```{r, include = FALSE}
@@ -18,8 +60,37 @@ knitr::opts_chunk$set(
 library(iDAS)
 ```
 
+just a test, haven't finished.
 
 ```{r}
+set.seed(123)
+Z <- matrix(rnorm(1000), ncol = 10)
+colnames(Z)=paste0("gene",1:10)
+factor1 <- as.factor(rep(1:2, each = 5))
+factor2 <- as.factor(rep(1:2, times = 5))
+factor3 <- as.factor(rep(1:2, length.out = 10))
 
+# Run the differential analysis using iDAS
+result <- threefactors(
+  Z, factor1, factor2, factor3,
+  model_fit_function = "lm",
+  test_function = "anova_test",
+  pval_quantile_cutoff = 0.02,
+  pval_cutoff_full = 0.05,
+  pval_cutoff_interaction = 0.01,
+  pval_cutoff_factor1 = 0.01,
+  pval_cutoff_factor2 = 0.01,
+  pval_cutoff_factor3 = 0.01,
+  pval_cutoff_int12 = 0.01,
+  pval_cutoff_int13 = 0.01,
+  pval_cutoff_int23 = 0.01,
+  pval_cutoff_int123 = 0.01,
+  p_adjust_method = "BH"
+)
+
+# Inspect results
+head(result$pval_matrix)
+head(result$stat_matrix)
+head(result$class_df)
 
 ```