Set up vignette and wrote examples for DGE_analysis

.gitignore

@@ -52,3 +52,5 @@ inst/markdown/test.Rmd
 codecov.yml
 !.github/codecov.yml
 docs/
+/doc/
+/Meta/

vignettes/getting_started.Rmd

@@ -1,5 +1,7 @@
 ---
-title: "Getting Started"
+title: "Get Started" 
+author: "<h4>Authors: <i>Salman Fawad, Yunning Yuan, Alan Murphy, Nathan Skene</i></h4>"
+date: "<h4>Vignette updated: <i>`r format( Sys.Date(), '%b-%d-%Y')`</i></h4>"
 output: rmarkdown::html_vignette
 vignette: >
   %\VignetteIndexEntry{getting_started}
@@ -13,21 +15,61 @@ knitr::opts_chunk$set(
   comment = "#>"
 )
 ```
-***
-# Introduction
 
-The *poweranalysis* R package is designed to run robust power analysis for differential gene expression in scRNA-seq studies, addressing the challenge of low statistical power due to small sample sizes. It provides tools to estimate the optimal number of samples and cells needed to achieve reliable power levels.
-<br>
-<br>
-Using *poweranalysis* involves four steps: <br>
-**1. Differential Expression (DE) Analysis:** Import your SCE object and perform DE analysis using a pseudobulking approach, allowing for the robust identification of differentially expressed genes (DEGs) across conditions or groups. <br>
-**2. Correlation Analysis:** Assess the consistency of DEG effect sizes across random permutations, subsets of your data, and different studies by computing correlation matrices. <br>
-**3. Downsampling and Power Analysis:** Test the robustness of your findings by downsampling both individuals and cells, then analyze the effect on DEG detection power. <br>
-**4. Visualisation:** Generate comprehensive power plots and visual summaries of the power analysis results, including DEG detection rates, false discovery rates, and mean LogFC correlation between down-sampled subsets. 
-<br>
+```{r, echo=FALSE, include=FALSE}
+pkg <- read.dcf("../DESCRIPTION", fields = "Package")[1]
+library(pkg, character.only = TRUE)
+```
 
-# Setup
+The *poweranalysis* R package is designed to run robust power analysis for differential gene expression in scRNA-seq studies and provides tools to estimate the optimal number of samples and cells needed to achieve reliable power levels.
 
+## Setup
 ```{r setup}
 library(poweranalysis)
 ```
+
+## Differential Gene Expression (DGE) Analysis
+Import an SCE object and perform differential expression analysis using a pseudobulking approach, enabling the robust identification of differentially expressed genes (DEGs) across conditions or groups from single-cell data. <br>
+**Example Usage**: <br>
+To run the DGE analysis, first load your own SingleCellExperiment (SCE) object.
+
+```{r, message=FALSE, warning=FALSE}
+# Load your SCE object (replace with actual file path)
+library(qs)
+library(SingleCellExperiment)
+
+SCE <- qs::qread("./data/sce.qs")
+```
+
+To use the `DGE_analysis` function, specify the formula for comparison along with the pseudobulk ID and celltype ID. The function requires the following key arguments:
+
+· **`design`**: A formula that defines the variables included in the model for differential expression analysis. This determines how gene expression is compared across different groups.
+
+· **`coef`**: A character string that specifies which group in the `design` formula you want to investigate for differential expression. <br>
+
+For example, to validate the differential expression (DEG) analysis approach, you can run a comparison between sexes using the <i>formula = ~ sex. </i>. This will assess how gene expression differs between male and female groups. 
+
+```{r}
+# Run the DGE_analysis function for a sex comparison
+DGE_analysis.sex <- DGE_analysis(
+  SCE,
+  design = ~ sex,
+  pseudobulk_ID = "sample_id",
+  celltype_ID = "cluster_celltype",
+  coef = "M"
+)
+```
+
+If you want to compare disease and control conditions, specify the disease status in the formula and the disease group of interest in the coef.
+
+```{r}
+# Run the DGE_analysis function for a disease vs. control comparison
+DGE_analysis.AD_sex <- DGE_analysis(
+  SCE,
+  design = ~ pathological_diagnosis,
+  pseudobulk_ID = "sample_id",
+  celltype_ID = "cluster_celltype",
+  coef = "AD"
+)
+```
+