diff --git a/.Rbuildignore b/.Rbuildignore
new file mode 100644
index 0000000..91114bf
--- /dev/null
+++ b/.Rbuildignore
@@ -0,0 +1,2 @@
+^.*\.Rproj$
+^\.Rproj\.user$
diff --git a/DESCRIPTION b/DESCRIPTION
new file mode 100644
index 0000000..1886c0b
--- /dev/null
+++ b/DESCRIPTION
@@ -0,0 +1,32 @@
+Package: PathwayEmbed
+Title: Tools for Pathway-Level Embedding and Visualization in Single-Cell Data
+Version: 0.0.0.9000
+Authors@R: 
+    person("Yaqing", "Huang", email = "yaqing.huang@yale.edu", role = c("aut", "cre"))
+Description: Provides tools for analyzing and visualizing pathway-level activity 
+    in single-cell RNA-seq data. Includes functions for computing cell-wise pathway scores, 
+    visualizing transduction states, calculating activation percentages, 
+    and integrating pathway data with Seurat objects.
+License: MIT + file LICENSE
+Encoding: UTF-8
+Roxygen: list(markdown = TRUE)
+RoxygenNote: 7.3.2
+Depends: 
+    R (>= 3.5)
+Imports:
+    readxl,
+    Seurat,
+    RColorBrewer,
+    ggplot2,
+    cowplot,
+    dplyr,
+    matrixStats,
+    viridis,
+    stats,
+    effsize,
+    rlang
+Suggests: 
+    testthat (>= 3.0.0)
+Config/testthat/edition: 3
+LazyData: true
+
diff --git a/LICENSE b/LICENSE
index 62584c7..ef97142 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2024 Raredon Lab
+Copyright (c) 2025 Raredon Lab
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/NAMESPACE b/NAMESPACE
new file mode 100644
index 0000000..7d09a63
--- /dev/null
+++ b/NAMESPACE
@@ -0,0 +1,22 @@
+# Generated by roxygen2: do not edit by hand
+
+export(CalculatePercentage)
+export(ComputeCellData)
+export(LoadPathway)
+export(PathwayMaxMin)
+export(PlotPathway)
+export(PreparePlotData)
+import(RColorBrewer)
+import(Seurat)
+import(cowplot)
+import(ggplot2)
+import(matrixStats)
+import(readxl)
+import(stats)
+import(tidyverse)
+import(viridis)
+importFrom(dplyr,bind_rows)
+importFrom(dplyr,filter)
+importFrom(dplyr,pull)
+importFrom(effsize,cohen.d)
+importFrom(rlang,sym)
diff --git a/PathwayEmbed.Rproj b/PathwayEmbed.Rproj
new file mode 100644
index 0000000..ac761db
--- /dev/null
+++ b/PathwayEmbed.Rproj
@@ -0,0 +1,23 @@
+Version: 1.0
+ProjectId: 0c111876-39b0-460f-a888-db107bec1084
+
+RestoreWorkspace: No
+SaveWorkspace: No
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
+
+AutoAppendNewline: Yes
+StripTrailingWhitespace: Yes
+LineEndingConversion: Posix
+
+BuildType: Package
+PackageUseDevtools: Yes
+PackageInstallArgs: --no-multiarch --with-keep.source
+PackageRoxygenize: rd,collate,namespace
diff --git a/R/CalculatePercentage.R b/R/CalculatePercentage.R
new file mode 100644
index 0000000..374e6c8
--- /dev/null
+++ b/R/CalculatePercentage.R
@@ -0,0 +1,46 @@
+#' Calculate the percentage of cells in activation status
+#' @name CalculatePercentage
+#' @importFrom dplyr filter pull bind_rows
+#' @importFrom rlang sym
+#' @importFrom effsize cohen.d
+#' @param to.plot A data frame containing at least a `scale` column and a grouping column.
+#' @param group_var A string specifying the grouping variable (e.g., "genotype", "treatment").
+#' @return A data frame with the percentage of ON/OFF cells and Cohen's d (if applicable).
+#' @examples
+#' data(fake_to_plot)
+#' CalculatePercentage(fake_to_plot, "genotype")
+#' @export
+CalculatePercentage <- function(to.plot, group_var){
+  stopifnot("scale" %in% names(to.plot))
+
+  group_sym <- sym(group_var)
+  groups <- unique(na.omit(to.plot[[group_var]]))
+  results <- list()
+
+  for (g in groups) {
+    subset_data <- dplyr::filter(to.plot, !!group_sym == g)
+    total <- nrow(subset_data)
+
+    on <- sum(subset_data[["scale"]] > 0, na.rm = TRUE)
+    off <- sum(subset_data[["scale"]] < 0, na.rm = TRUE)
+
+    results[[as.character(g)]] <- list(
+      percentage_on = round(100 * on / total, 2),
+      percentage_off = round(100 * off / total, 2)
+    )
+  }
+
+  if (length(groups) == 2) {
+    g1 <- groups[1]
+    g2 <- groups[2]
+    vec1 <- pull(dplyr::filter(to.plot, !!group_sym == g1), scale)
+    vec2 <- pull(dplyr::filter(to.plot, !!group_sym == g2), scale)
+    cohens_d_val <- cohen.d(vec1, vec2)$estimate
+
+    results[[as.character(g1)]]$cohens_d <- cohens_d_val
+    results[[as.character(g2)]]$cohens_d <- cohens_d_val
+  }
+
+  df <- bind_rows(results, .id = "group")
+  return(df)
+}
diff --git a/R/ComputeCellData.R b/R/ComputeCellData.R
new file mode 100644
index 0000000..a742664
--- /dev/null
+++ b/R/ComputeCellData.R
@@ -0,0 +1,145 @@
+#' A function for scRNA sequencing pathway analysis
+#'
+#' This function computes cell status for a given pathway in single-cell RNA-seq data,
+#' based on the distance between genes in a specified pathway. The distance is computed
+#' for each batch of cells, and classical multidimensional scaling (MDS) is used to
+#' visualize the pathway expression across cells.
+#'
+#' @name ComputeCellData
+#' @import Seurat
+#' @import tidyverse
+#' @import viridis
+#' @import matrixStats
+#' @import stats
+#' @param x A `Seurat` object containing single-cell RNA sequencing data.
+#' @param pathway A `character` string specifying the pathway name.
+#' @param distance.method A `character` string specifying the distance method.
+#' Options include: "manhattan", "euclidean", "canberra", "binary", "minkowski".
+#' @return A data frame representing the multidimensional scaling (MDS) results
+#' for the cells based on the pathway expression.
+#' @examples
+#' data(fake_test_object) # load the fake test data
+#' ComputeCellData(fake_test_object, "Wnt", "manhattan")
+#' @export
+ComputeCellData <- function(x, pathway, distance.method){
+
+  # Get pathway data
+  pathwaydata <- LoadPathway(pathway)
+  names <- c(pathwaydata[[1]])
+
+  # Ensure only valid genes are used
+  valid_names <- intersect(names, rownames(x))
+  if (length(valid_names) == 0) {
+    stop("No matching genes found in the Seurat object for the given pathway.")
+  }
+
+  # Pathway max and min
+  pathway.stat <- PathwayMaxMin(x, pathway)
+
+  # Gel all cells
+  all_cells <- Cells(x)
+
+  # Define batch size
+  batch_size <-1000
+  # test batch_size = 1 store the output, -> identical or not?
+
+  # Determine the number of iterations
+  num_batches <- ceiling(length(all_cells) / batch_size)
+  # Initialize list to store results
+  batch_results <- list()
+
+  # Loop through batches of 500 cells
+  for (i in seq_len(num_batches)) {
+
+    # Ensure there are remaining cells to sample
+    if (length(all_cells) == 0) break
+
+    # Sample cells
+    sample_cells <- sample(all_cells, min(batch_size, length(all_cells)))
+    if (length(sample_cells) == 0) next  # Avoid errors if no cells left
+
+    # Subset Seurat object
+    x_batch <- subset(x, cells = sample_cells)
+    DefaultAssay(x_batch) <- "RNA"  # Ensure correct assay
+
+    # Extract expression data
+    temp.data.batch <- x_batch[valid_names, ] # when n= 1, it is a vecor
+    # if temp.data.batch > 2 more rows
+    # if temp.data.batch = 1 row
+    # if temp.data.batch = 0, stop
+    # Convert to data frame to avoid vector issues when n = 1
+    if (is.vector(temp.data.batch)) {
+      temp.data.batch <- as.data.frame(t(temp.data.batch))
+    } else {
+      temp.data.batch <- as.data.frame(temp.data.batch@assays[["RNA"]]$data)
+    }
+
+    # Check if temp.data.batch is empty
+    if (nrow(temp.data.batch) == 0) {
+      warning("Batch", i, "has no valid data. Skipping...")
+      next
+    }
+
+    # Merge pathway stats with expression data
+    # Ensure they have the same columes
+    common_rows <- intersect(rownames(pathway.stat), rownames(temp.data.batch))
+    pathway.stat <- pathway.stat[common_rows, , drop = FALSE]
+    temp.data.batch <- temp.data.batch[common_rows, , drop = FALSE]
+
+    pathwaytempdata <- cbind(pathway.stat, temp.data.batch)
+
+    # Ensure there are at least two columns for distance computation
+    if (ncol(pathwaytempdata) < 2) {
+      warning("Batch", i, "does not have enough features for distance calculation. Skipping...")
+      next
+    }
+
+    # Compute Manhattan distance
+    # distance.method <- 'manhattan'
+    message("Computing distance...")
+    d <- dist(t(pathwaytempdata), method = distance.method) # should we use scaled data?
+    # "manhattan" is sum of absolute differences (city block distance), good for sparse data (gene expression)
+    # "euclidean" is stratight-line distance, is useful for PCA clustering
+    # "canberra" is weighted distance, is also good for sparse data and when values have very different scales
+    # "binary" is distance based on presence/absence (0/1)
+    # "minkowski" is generalization of euclidean & manhattan, tunable using p parameter
+    # choose "manhattan" as it works well for high-dimensional data and less sensitive to large outliers than euclidean distance
+
+    # Perform classical multidimensional scaling (MDS)
+    message("running mds ...")
+    fit <- cmdscale(d, eig = TRUE, k = 1)
+    message("mds finished")
+
+
+    # Transform to data frame
+    temp.data.mds <- as.data.frame(fit$points)
+    colnames(temp.data.mds) <- "V1"
+
+    # Normalize the MDS data safely
+    V1_min <- min(temp.data.mds$V1, na.rm = TRUE)
+    V1_max <- max(temp.data.mds$V1, na.rm = TRUE)
+
+    if (V1_max == V1_min) {
+      temp.data.mds$normalized <- 0  # Avoid division by zero
+    } else {
+      temp.data.mds$normalized <- (temp.data.mds$V1 - V1_min) / (V1_max - V1_min)
+    }
+
+    # Store MDS results for each batch
+    batch_results[[i]] <- temp.data.mds
+
+    # Print progress
+    cat("Batch", i, "processed with", length(sample_cells), "cells\n")
+
+    # Remove used cells to avoid duplication in the next iteration
+    all_cells <- setdiff(all_cells, sample_cells)
+  }
+  final_mds <- do.call(rbind, batch_results)  # Merge all batch MDS results
+
+  return(final_mds)
+}
+
+# we need to re-scale
+# help function -> documentation
+# clear all R environment -> test_script see if works
+# document()
diff --git a/R/LoadPathway.R b/R/LoadPathway.R
new file mode 100644
index 0000000..1375938
--- /dev/null
+++ b/R/LoadPathway.R
@@ -0,0 +1,26 @@
+## Pathway Data Extraction from Exceldataset
+#'
+#' This function reads pathway data from the package's built-in Excel file.
+#' @name LoadPathway
+#' @param pathway The name of the pathway interested.
+#' @return A data frame with pathway data.
+#' @examples
+#' LoadPathway("Wnt")
+#' @import readxl
+#' @export
+LoadPathway <- function(pathway) {
+  file_path <- system.file("extdata", "Pathway_Embedding.xlsx", package = "PathwayEmbed")
+
+  if (file_path == "") {
+    stop("Pathway data file not found. Ensure the package is installed correctly.")
+  }
+
+  # Read the specified sheet
+  data <- readxl::read_excel(file_path, sheet = pathway)
+  # extract the molecules in the pathway
+  pathway.molecules <- c(data[["Molecules"]])
+  # extract the coefficients of the molecules in the pathway
+  pathway.coefficients <- as.numeric(c(data[["Coefficients"]]))
+
+  return(data)
+}
diff --git a/R/PathwayMaxMin.R b/R/PathwayMaxMin.R
new file mode 100644
index 0000000..7043cac
--- /dev/null
+++ b/R/PathwayMaxMin.R
@@ -0,0 +1,69 @@
+#' A function for scRNA sequencing pathway analysis
+#' @name PathwayMaxMin
+#' @import Seurat
+#' @import tidyverse
+#' @import viridis
+#' @import matrixStats
+#'
+#' @param x A Seurat Object.
+#' @param pathway The name of the pathway.
+#' @return The value for Pathway on and off (max and min value for features)
+#' @examples
+#' data(fake_test_object) # load the fake test data
+#' PathwayMaxMin(fake_test_object, "Wnt")
+#' @export
+PathwayMaxMin <- function(x, pathway){
+
+  # Define pathway parameters using LoadPathway
+  pathwaydata <- LoadPathway(pathway) # load pathway data
+  names <- c(pathwaydata[[1]]) # molecule names
+  pathway.on <- as.numeric(c(pathwaydata[[2]])) # coefficients
+  names(pathway.on) <- names
+  pathway.off <- -pathway.on # define off status
+
+  # Extract normalized RNA expression data for the pathway genes
+  temp.data <- x[names, ]
+  data.temp <- as.data.frame(temp.data@assays[["RNA"]]$data) # Seurat version
+  # "sometimes is counts not data
+
+  # Max and min value for genes in the pathway
+  # Compute row-wise min and max values
+  ranges <- cbind(
+    rowMins(as.matrix(data.temp), na.rm = FALSE),
+    rowMaxs(as.matrix(data.temp), na.rm = FALSE)
+  )
+
+  # Scale the ON/OFF states to the extrema of these ranges for each features
+  for (i in seq_along(pathway.on)) {  # safer than 1:length(pathway.on)
+    feature_name <- names(pathway.on[i])
+
+    if (!feature_name %in% rownames(ranges)) {
+      warning(paste("Feature", feature_name, "not found in ranges!"))
+      next  # Skip iteration if feature is missing
+    }
+    if (pathway.on[i] < 0) {
+      pathway.on[i] <- ranges[feature_name, 1]  # min for ON
+    } else {
+      pathway.on[i] <- ranges[feature_name, 2]  # max for ON
+    }
+  }
+  for (i in seq_along(pathway.off)) {  # Safer indexing
+    feature_name <- names(pathway.off[i])  # Get feature name
+
+    if (!feature_name %in% rownames(ranges)) {  # Check if feature exists in ranges
+      warning(paste("Feature", feature_name, "not found in ranges! Skipping..."))
+      next  # Skip to the next iteration if missing
+    }
+
+    # Assign min or max based on value
+    pathway.off[i] <- ifelse(pathway.off[i] < 0,
+                             ranges[feature_name, 1],  # Min for OFF
+                             ranges[feature_name, 2])  # Max for OFF
+  }
+
+
+  # Bind on and off states
+  pathway.stat <- data.frame(pathway.on,pathway.off)
+
+  return(pathway.stat)
+}
diff --git a/R/PlotPathway.R b/R/PlotPathway.R
new file mode 100644
index 0000000..2bcc2c1
--- /dev/null
+++ b/R/PlotPathway.R
@@ -0,0 +1,43 @@
+#' A function to plot the Pathway activation status
+#' @name PlotPathway
+#' @import Seurat
+#' @import RColorBrewer
+#' @import ggplot2
+#' @import cowplot
+#' @import tidyverse
+#' @import viridis
+#'
+#' @param to.plot A dataframe.
+#' @param pathway A name of the pathway.
+#' @param group Ident of the plot.
+#' @param color Colors for the group.
+#' @return A plot.
+#' @examples
+#' data(fake_to_plot)
+#' PlotPathway(fake_to_plot, "Wnt", "genotype", c("#ae282c","#2066a8"))
+#' @export
+PlotPathway <- function (to.plot, pathway, group, color){
+  # get rid of NA columns (those pathway on and off values)
+  to.plot_clean <- to.plot[complete.cases(to.plot), ]
+
+  #color has to be assigned
+  plot.total <- ggplot(data=to.plot_clean,
+                             aes(x=scale,
+                                 group = .data[[group]],
+                                 fill= .data[[group]],
+                                 color= .data[[group]]))+
+
+    geom_density(alpha = 0.5) +  # Example: Density plot
+    labs(title = paste(pathway, "Pathway"),
+         x = "Transduction State",
+         y = "Population Density") +
+    scale_fill_manual(values = color) +  # Set fixed colors
+    scale_color_manual(values = color) +
+    theme_classic() +
+    geom_vline(xintercept=0, linetype="dotted",
+               color = "black", size=0.5)
+
+  return(plot.total)
+}
+
+# x=scale or normalized can use True or False
diff --git a/R/PreparePlotData.R b/R/PreparePlotData.R
new file mode 100644
index 0000000..2eca276
--- /dev/null
+++ b/R/PreparePlotData.R
@@ -0,0 +1,43 @@
+#' A function to prepare the signal transduction dataframe for plotting
+#' @name PreparePlotData
+#' @import Seurat
+#' @import RColorBrewer
+#' @import ggplot2
+#' @import cowplot
+#' @import tidyverse
+#' @import viridis
+#' @import matrixStats
+#'
+#' @param x A `Seurat` object containing single-cell RNA sequencing data.
+#' @param final_mds A 'dataframe' output from ComputeCellData.
+#' @param group group for the comparision
+#' @return data for plotting
+#' @examples
+#' data(fake_test_object)
+#' data(fake_final_mds)
+#' PreparePlotData(fake_test_object, fake_final_mds, "genotype")
+#' @export
+PreparePlotData <- function(x, final_mds, group){
+
+  # make a data frame from final_mds
+  to.plot <- as.data.frame(final_mds)
+
+  # the rownames changed in last step, to make them consistent with meta.data
+  rownames(to.plot) <- gsub("\\.", "-", rownames(to.plot))
+
+  # add group into the dataframe and assign group
+  to.plot[[group]] <- NA
+  meta.data <- x@meta.data
+  to.plot[rownames(meta.data),][[group]] <- as.character(meta.data[[group]])
+
+  # Scale
+  to.plot$scale <- scale(to.plot$normalized,center = T)[,1]
+
+  # Get ride of non-cell rows
+  # to.plot <- to.plot[!is.na(to.plot[[group]]), ]
+
+  return(to.plot)
+}
+
+
+
diff --git a/R/data_documentation.R b/R/data_documentation.R
new file mode 100644
index 0000000..e5f14f9
--- /dev/null
+++ b/R/data_documentation.R
@@ -0,0 +1,55 @@
+#' Example Seurat Object for Testing
+#'
+#' A simulated Seurat object with fake gene expression data for the Wnt signaling pathway.
+#' This Seurat object contains gene expression data from simulated cells with Wnt positive
+#' and negative gene expression values.
+#'
+#' @format A Seurat object. The object contains:
+#' \describe{
+#'   \item{assays}{List of assays used for data storage. Includes RNA expression data.}
+#'   \item{meta.data}{Metadata associated with the cells. Contains information about the groups (e.g., WT vs. Mutant).}
+#'   \item{features}{Gene features (including Wnt pathway genes) used in the analysis.}
+#'   \item{cells}{Cell names, labeled as Cell1, Cell2, ..., CellN.}
+#' }
+#' @source Simulated for demonstration purposes.
+#' @usage data(fake_test_object)
+"fake_test_object"
+
+
+#' Example Cell Status and Normalized Data
+#'
+#' A dataset generated by `ComputeCellData` applied to `fake_test_object`.
+#' Contains cell status and normalized values.
+#'
+#' @format A data frame with the following columns:
+#' \describe{
+#'   \item{V1}{A numerical value presenting the status of the cell}
+#'   \item{normalized}{Numerical value representing normalized data}
+#' }
+#' @usage data(fake_final_mds)
+#' @examples
+#' data(fake_final_mds)
+#' head(fake_final_mds)
+#' @keywords datasets
+"fake_final_mds"
+
+#' Example Processed Data for Plotting
+#'
+#' A dataset generated by running `PreparePlotData` on `fake_test_object` and `fake_final_mds`.
+#' Contains processed data ready for visualization, with the following features:
+#'
+#' @format A data frame with the following columns:
+#' \describe{
+#'   \item{V1}{A numerical or categorical value depending on the specific analysis}
+#'   \item{normalized}{Normalized numerical value representing the cell's data}
+#'   \item{genotype}{Group classification of the cell (e.g., "WT", "Mutant")}
+#'   \item{scale}{Scaled data for visualization purposes}
+#' }
+#' @usage data(fake_to_plot)
+#' @examples
+#' data(fake_to_plot)
+#' head(fake_to_plot)
+#' @keywords datasets
+"fake_to_plot"
+
+
diff --git a/README.md b/README.md
index f94ac27..b3137bc 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# PathwayEmbedding
+# PathwayEmbed
 This is a repository for work with Yaqing Huang.
 
 ## We are focusing on 1-D embeddings of pathway state.
diff --git a/data/fake_final_mds.rda b/data/fake_final_mds.rda
new file mode 100644
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diff --git a/data/fake_test_object.rda b/data/fake_test_object.rda
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diff --git a/data/fake_to_plot.rda b/data/fake_to_plot.rda
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diff --git a/inst/extdata/Pathway_Embedding.xlsx b/inst/extdata/Pathway_Embedding.xlsx
new file mode 100644
index 0000000..b2c8852
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diff --git a/man/CalculatePercentage.Rd b/man/CalculatePercentage.Rd
new file mode 100644
index 0000000..4fd9955
--- /dev/null
+++ b/man/CalculatePercentage.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/CalculatePercentage.R
+\name{CalculatePercentage}
+\alias{CalculatePercentage}
+\title{Calculate the percentage of cells in activation status}
+\usage{
+CalculatePercentage(to.plot, group_var)
+}
+\arguments{
+\item{to.plot}{A data frame containing at least a \code{scale} column and a grouping column.}
+
+\item{group_var}{A string specifying the grouping variable (e.g., "genotype", "treatment").}
+}
+\value{
+A data frame with the percentage of ON/OFF cells and Cohen's d (if applicable).
+}
+\description{
+Calculate the percentage of cells in activation status
+}
+\examples{
+data(fake_to_plot)
+CalculatePercentage(fake_to_plot, "genotype")
+}
diff --git a/man/ComputeCellData.Rd b/man/ComputeCellData.Rd
new file mode 100644
index 0000000..11bcb40
--- /dev/null
+++ b/man/ComputeCellData.Rd
@@ -0,0 +1,30 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ComputeCellData.R
+\name{ComputeCellData}
+\alias{ComputeCellData}
+\title{A function for scRNA sequencing pathway analysis}
+\usage{
+ComputeCellData(x, pathway, distance.method)
+}
+\arguments{
+\item{x}{A \code{Seurat} object containing single-cell RNA sequencing data.}
+
+\item{pathway}{A \code{character} string specifying the pathway name.}
+
+\item{distance.method}{A \code{character} string specifying the distance method.
+Options include: "manhattan", "euclidean", "canberra", "binary", "minkowski".}
+}
+\value{
+A data frame representing the multidimensional scaling (MDS) results
+for the cells based on the pathway expression.
+}
+\description{
+This function computes cell status for a given pathway in single-cell RNA-seq data,
+based on the distance between genes in a specified pathway. The distance is computed
+for each batch of cells, and classical multidimensional scaling (MDS) is used to
+visualize the pathway expression across cells.
+}
+\examples{
+data(fake_test_object) # load the fake test data
+ComputeCellData(fake_test_object, "Wnt", "manhattan")
+}
diff --git a/man/LoadPathway.Rd b/man/LoadPathway.Rd
new file mode 100644
index 0000000..dcb4e8d
--- /dev/null
+++ b/man/LoadPathway.Rd
@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/LoadPathway.R
+\name{LoadPathway}
+\alias{LoadPathway}
+\title{This function reads pathway data from the package's built-in Excel file.}
+\usage{
+LoadPathway(pathway)
+}
+\arguments{
+\item{pathway}{The name of the pathway interested.}
+}
+\value{
+A data frame with pathway data.
+}
+\description{
+This function reads pathway data from the package's built-in Excel file.
+}
+\examples{
+LoadPathway("Wnt")
+}
diff --git a/man/PathwayMaxMin.Rd b/man/PathwayMaxMin.Rd
new file mode 100644
index 0000000..6e56736
--- /dev/null
+++ b/man/PathwayMaxMin.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/PathwayMaxMin.R
+\name{PathwayMaxMin}
+\alias{PathwayMaxMin}
+\title{A function for scRNA sequencing pathway analysis}
+\usage{
+PathwayMaxMin(x, pathway)
+}
+\arguments{
+\item{x}{A Seurat Object.}
+
+\item{pathway}{The name of the pathway.}
+}
+\value{
+The value for Pathway on and off (max and min value for features)
+}
+\description{
+A function for scRNA sequencing pathway analysis
+}
+\examples{
+data(fake_test_object) # load the fake test data
+PathwayMaxMin(fake_test_object, "Wnt")
+}
diff --git a/man/PlotPathway.Rd b/man/PlotPathway.Rd
new file mode 100644
index 0000000..c616bdf
--- /dev/null
+++ b/man/PlotPathway.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/PlotPathway.R
+\name{PlotPathway}
+\alias{PlotPathway}
+\title{A function to plot the Pathway activation status}
+\usage{
+PlotPathway(to.plot, pathway, group, color)
+}
+\arguments{
+\item{to.plot}{A dataframe.}
+
+\item{pathway}{A name of the pathway.}
+
+\item{group}{Ident of the plot.}
+
+\item{color}{Colors for the group.}
+}
+\value{
+A plot.
+}
+\description{
+A function to plot the Pathway activation status
+}
+\examples{
+data(fake_to_plot)
+PlotPathway(fake_to_plot, "Wnt", "genotype", c("#ae282c","#2066a8"))
+}
diff --git a/man/PreparePlotData.Rd b/man/PreparePlotData.Rd
new file mode 100644
index 0000000..b480950
--- /dev/null
+++ b/man/PreparePlotData.Rd
@@ -0,0 +1,26 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/PreparePlotData.R
+\name{PreparePlotData}
+\alias{PreparePlotData}
+\title{A function to prepare the signal transduction dataframe for plotting}
+\usage{
+PreparePlotData(x, final_mds, group)
+}
+\arguments{
+\item{x}{A \code{Seurat} object containing single-cell RNA sequencing data.}
+
+\item{final_mds}{A 'dataframe' output from ComputeCellData.}
+
+\item{group}{group for the comparision}
+}
+\value{
+data for plotting
+}
+\description{
+A function to prepare the signal transduction dataframe for plotting
+}
+\examples{
+data(fake_test_object)
+data(fake_final_mds)
+PreparePlotData(fake_test_object, fake_final_mds, "genotype")
+}
diff --git a/man/fake_final_mds.Rd b/man/fake_final_mds.Rd
new file mode 100644
index 0000000..4dff275
--- /dev/null
+++ b/man/fake_final_mds.Rd
@@ -0,0 +1,25 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data_documentation.R
+\docType{data}
+\name{fake_final_mds}
+\alias{fake_final_mds}
+\title{Example Cell Status and Normalized Data}
+\format{
+A data frame with the following columns:
+\describe{
+\item{V1}{A numerical value presenting the status of the cell}
+\item{normalized}{Numerical value representing normalized data}
+}
+}
+\usage{
+data(fake_final_mds)
+}
+\description{
+A dataset generated by \code{ComputeCellData} applied to \code{fake_test_object}.
+Contains cell status and normalized values.
+}
+\examples{
+data(fake_final_mds)
+head(fake_final_mds)
+}
+\keyword{datasets}
diff --git a/man/fake_test_object.Rd b/man/fake_test_object.Rd
new file mode 100644
index 0000000..5879575
--- /dev/null
+++ b/man/fake_test_object.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data_documentation.R
+\docType{data}
+\name{fake_test_object}
+\alias{fake_test_object}
+\title{Example Seurat Object for Testing}
+\format{
+A Seurat object. The object contains:
+\describe{
+\item{assays}{List of assays used for data storage. Includes RNA expression data.}
+\item{meta.data}{Metadata associated with the cells. Contains information about the groups (e.g., WT vs. Mutant).}
+\item{features}{Gene features (including Wnt pathway genes) used in the analysis.}
+\item{cells}{Cell names, labeled as Cell1, Cell2, ..., CellN.}
+}
+}
+\source{
+Simulated for demonstration purposes.
+}
+\usage{
+data(fake_test_object)
+}
+\description{
+A simulated Seurat object with fake gene expression data for the Wnt signaling pathway.
+This Seurat object contains gene expression data from simulated cells with Wnt positive
+and negative gene expression values.
+}
+\keyword{datasets}
diff --git a/man/fake_to_plot.Rd b/man/fake_to_plot.Rd
new file mode 100644
index 0000000..59a4c55
--- /dev/null
+++ b/man/fake_to_plot.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data_documentation.R
+\docType{data}
+\name{fake_to_plot}
+\alias{fake_to_plot}
+\title{Example Processed Data for Plotting}
+\format{
+A data frame with the following columns:
+\describe{
+\item{V1}{A numerical or categorical value depending on the specific analysis}
+\item{normalized}{Normalized numerical value representing the cell's data}
+\item{genotype}{Group classification of the cell (e.g., "WT", "Mutant")}
+\item{scale}{Scaled data for visualization purposes}
+}
+}
+\usage{
+data(fake_to_plot)
+}
+\description{
+A dataset generated by running \code{PreparePlotData} on \code{fake_test_object} and \code{fake_final_mds}.
+Contains processed data ready for visualization, with the following features:
+}
+\examples{
+data(fake_to_plot)
+head(fake_to_plot)
+}
+\keyword{datasets}