updated docs

docs/analysis/cell_idx/step1-cell_SGE.md

@@ -1,4 +1,4 @@
-# Step1. Prepare Cell-indexed Spatial Digital Gene Expression Matrix
+# Step1. Prepare Cell-indexed Spatial Digital Gene Expression (SGE) Matrix
 
 ## Set Up Computing Environment
 
@@ -33,7 +33,7 @@ prefix=<output_prefix>                                  ## Replace with your out
 
 ## Step 1.1 Prepare Histology-based Cell Segmentation Mask Matrix
 
-To construct a cell-indexed spatial digital gene expression matrix (SGE), begin by executing histology-based cell segmentation using external methodologies, such as [Watershed](https://imagej.net/imaging/watershed) or [Cellpose](https://github.com/MouseLand/cellpose). Details for performing histology-based cell segmentation using [Watershed](https://imagej.net/imaging/watershed) and [Cellpose](https://github.com/MouseLand/cellpose) are provided in the [NovaScope Protocol paper](../../index.md#references). 
+To construct a cell-indexed spatial digital gene expression (SGE) matrix, begin by executing histology-based cell segmentation using external methodologies, such as [Watershed](https://imagej.net/imaging/watershed) or [Cellpose](https://github.com/MouseLand/cellpose). Details for performing histology-based cell segmentation using [Watershed](https://imagej.net/imaging/watershed) and [Cellpose](https://github.com/MouseLand/cellpose) are provided in the [NovaScope Protocol paper](../../index.md#references). 
 
 !!! note
 
@@ -69,9 +69,9 @@ Examples:
 ### Cellpose
 Cellpose produces an `npy` file that serves as the segmentation mask matrix in `NumPy` array format. No additional action is required with NEDA.
 
-## Step1.2 Create cell-indexed spatial digital gene expression matrix
+## Step1.2 Create cell-indexed SGE matrix
 
-Use the histology-based cell segmentation mask matrix file from [Step1.1](#step-11-prepare-histology-based-cell-segmentation-mask-matrix) to aggregate spatial transcriptomic data at the cellular level. NEDA’s `make_sge_from_npy.py` script is utilized here. Note that the npy file from [Watershed](https://imagej.net/imaging/watershed) and [Cellpose](https://github.com/MouseLand/cellpose) differs, so the script requires specifying the `--approach`. This step creates a cell-indexed SGE in 10x genomics format.
+Use the histology-based cell segmentation mask matrix file from [Step1.1](#step-11-prepare-histology-based-cell-segmentation-mask-matrix) to aggregate the input SGE matrix at the cellular level. NEDA’s `make_sge_from_npy.py` script is utilized here. Note that the `npy` file from [Watershed](https://imagej.net/imaging/watershed) and [Cellpose](https://github.com/MouseLand/cellpose) differs, so the script requires specifying the `--approach`. This step creates a cell-indexed SGE in 10X Genomics format.
 
 ### Watershed
 

docs/analysis/cell_idx/step2-Seurat-clustering.md

@@ -30,7 +30,6 @@ Parameters:
 * `--Y_col`: Specify which part of the hexagon ID corresponds to the Y coordinate. As the Y coordinate is the second component in the example case, it should set to 2.  Default: 4.
 * `--nFeature_RNA_cutoff`: Cutoff value for filtering hexagons by nFeature_RNA. Since this cell-indexed SGE is derived from histology files, `nFeature_RNA_cutoff` is set to be 0.
 
-
 Commands:
 ```bash
 Rscript ${neda}/scripts/seurat_analysis.R \
@@ -40,5 +39,4 @@ Rscript ${neda}/scripts/seurat_analysis.R \
     --X_col 1 \
     --Y_col 2 \
     --nFeature_RNA_cutoff 0 
-```
-
+```

docs/analysis/hex_idx/intro.md

@@ -33,4 +33,4 @@ Each step contains detailed instructions for:
 
 ## An Overview
 ![overview_brief](./ST_overview.png)
-**Figure 1: A Brief Overview of the Inputs, Outputs, and Process Steps for Pixel-level Analysis.** 
+**Figure 1: A Brief Overview of the Inputs, Outputs, and Process Steps for Pixel-level Analysis.**

docs/analysis/hex_idx/job_config.md

@@ -20,7 +20,7 @@ If you wish to customize these defaults, refer to the `AUXILIARY PARAMS` section
 # Mandatory Fields
 #=========================
 ## Input files
-input_transcripts=/path/to/the/transcripts/file                     ## Path to the input spatial digital gene expression matrix (SGE) in FICTURE-compatible TSV format.
+input_transcripts=/path/to/the/transcripts/file                     ## Path to the input spatial digital gene expression (SGE) matrix in FICTURE-compatible TSV format.
 input_features=/path/to/the/feature/file                            ## Path to the input feature file.
 input_xyrange=/path/to/the/xyrange                                  ## Path to the input meta file with minimum and maximum X Y coordinates.
 

docs/analysis/hex_idx/prepare_data.md

@@ -1,13 +1,13 @@
 # Preparing Input Dataset
 
-The input spatial transcriptomics data can be generated using [NovaScope](https://github.com/seqscope/NovaScope/tree/main). 
+The input spatial digital gene expression (SGE) matrix can be generated using [NovaScope](https://github.com/seqscope/NovaScope/tree/main). 
 
 ## Input Files:
 The following files are essential and can be prepared using NovaScope:
 
 ### (1) A Spatial Digital Gene Expression (SGE) Matrix in TSV format
 
-* Description: A Spatial Digital Gene Expression (SGE) matrix in **FICTURE-compatible TSV format**, containing information of spatial barcode, gene, and UMI count for each genomic feature by barcode and gene.
+* Description: A SGE matrix in **FICTURE-compatible TSV format**, containing information of spatial barcode, gene, and UMI count for each genomic feature by barcode and gene.
 * Preparation: NovaScope facilitates the preparation of a raw SGE matrix via [Rule sdgeAR_reformat](https://seqscope.github.io/NovaScope/fulldoc/rules/sdgeAR_reformat) and a filtered SGE matrix via [Rule sdgeAR_polygonfilter](https://seqscope.github.io/NovaScope/fulldoc/rules/sdgeAR_polygonfilter). Both can serve as input files for NEDA. The filtered SGE matrix undergoes gene filtering and density-based polygon filtering in this format. Users can select the option that best suits their requirements. Our example uses the filtered SGE matrix as input.
 
 ### (2) A Tab-Delimited Feature File
@@ -29,4 +29,4 @@ The following files are essential and can be prepared using NovaScope:
     * For Seurat+FICTURE analysis, supply a hexagon-indexed SGE matrix in 10x Genomics format. This file can be generated using Rule [sdgeAR_segment_10x](https://seqscope.github.io/NovaScope/fulldoc/rules/c04_sdgeAR_segment_10x) in NovaScope.
 
 ## Example Datasets
-Alternatively, NEDA offers three example datasets, each suitable for input in spatial transcriptomic analysis within NEDA. For detailed information on these datasets and instructions on how to download them, see [Accessing Example Datasets](../../installation/example_data.md#input-for-spatial-transcriptomic-analysis).
+Alternatively, NEDA offers three example datasets for this pixel-level analysis. For detailed information on these datasets and instructions on how to download them, see [Accessing Example Datasets](../../installation/example_data.md#input-for-spatial-transcriptomic-analysis).

docs/analysis/hex_idx/step4-decode.md

@@ -11,7 +11,6 @@ decode_prefix="${train_prefix}.decode.prj_${fit_width}.r_${anchor_dist}_${neighb
 * `neighbor_radius`: represents the radius (um) of each anchor point's territory. By default, `neighbor_radius = anchor_dist + 1`.
 * Other variables applied above are in the [Job Configuration](./job_config.md).
 
-
 ## Step 4.1 pixel-level Decoding. 
 Decode the model matrix on individual pixels, which returns a tab-delimited file of the posterior count of factors on individual pixels.
 

docs/home/documentation_overview.md

@@ -27,5 +27,5 @@ The current documentation include the following sections:
 
 * [Introduction](../analysis/cell_idx/intro.md): An Overview of the prelimary single-cell analysis.
 * [Preparing Input Data](../analysis/cell_idx/prepare_data.md): Details of required input files.
-* [Create Cell-indexed SGE](../analysis/cell_idx/step1-cell_SGE.md): Computing environment setup and preparation of a cell-indexed spatial digital gene expression matrix.
+* [Create Cell-indexed SGE](../analysis/cell_idx/step1-cell_SGE.md): Computing environment setup and preparation of a cell-indexed spatial digital gene expression (SGE) matrix.
 * [Seurat Clustering](../analysis/cell_idx/step2-Seurat-clustering.md): Application of multi-dimensional clustering with `Seurat` for cell type factor inference.

docs/index.md

@@ -3,11 +3,11 @@
 ## Introduction
 This document serves as a guide for exemplary downstream analysis of spatial transcriptomics data generated from [NovaScope](https://github.com/seqscope/NovaScope/tree/main). The main functionalities include:
 
-1) **[Pixel-level Analysis](./analysis/hex_idx/intro.md)**: 
-   This feature enables the identification of spatial factors at a pixel-level resolution using a hexagon-indexed spatial digital gene expression matrix (SGE).
+1) **[Pixel-level Analysis](./analysis/hex_idx/intro.md)**:
+   This feature enables the identification of spatial factors at a pixel-level resolution using a hexagon-indexed spatial digital gene expression (SGE) matrix.
 
-2) **[Cell Segmentation-based Analysis](./analysis/cell_idx/intro.md)**: 
-    This feature facilitates the aggregation of spatial transcriptomics data at the cellular level based on histology files and supports the analysis of cell type clusters using the cell-indexed SGE.
+2) **[Cell Segmentation-based Analysis](./analysis/cell_idx/intro.md)**:
+    This feature facilitates the aggregation of SGE matrix at the cellular level based on histology files and supports the analysis of cell type clusters using the cell-indexed SGE.
 
 ## References
 For additional information, please refer to the following links:

docs/installation/installation.md

@@ -38,7 +38,7 @@ ls -hlt $neda_dir/submodules/ficture
 
 #### 2.2.2 Create a Python Environment
 
-Set up a Python environment for [FICTURE](https://github.com/seqscope/ficture/tree/protocol) as per the [requirement file](https://github.com/seqscope/ficture/blob/8ceb419618c1181bb673255427b53198c4887cfa/requirements.txt). The requirement file is included in the FICTURE repository.
+Set up a Python environment for FICTURE as per the [requirement file](https://github.com/seqscope/ficture/blob/8ceb419618c1181bb673255427b53198c4887cfa/requirements.txt). The requirement file is included in the FICTURE repository.
 
 First, ensure the requirements file is accessible:
 
@@ -90,7 +90,7 @@ To enable Seurat analysis, install the following required R packages:
 * RColorBrewer
 
 ```R
-## install all required packages in R
+## install all required packages in R (need to be done only once)
 install.packages(c( "Seurat", "optparse", "patchwork", "dplyr", "tidyverse", "stringr", 
                     "ggplot2", "cowplot", "RColorBrewer"))
 ```

site/analysis/cell_idx/step1-cell_SGE/index.html

@@ -76,7 +76,7 @@
     <div data-md-component="skip">
 
 
-        <a href="#step1-prepare-cell-indexed-spatial-digital-gene-expression-matrix" class="md-skip">
+        <a href="#step1-prepare-cell-indexed-spatial-digital-gene-expression-sge-matrix" class="md-skip">
           Skip to content
         </a>
 
@@ -1108,13 +1108,13 @@
 </li>
 
         <li class="md-nav__item">
-  <a href="#step12-create-cell-indexed-spatial-digital-gene-expression-matrix" class="md-nav__link">
+  <a href="#step12-create-cell-indexed-sge-matrix" class="md-nav__link">
     <span class="md-ellipsis">
-      Step1.2 Create cell-indexed spatial digital gene expression matrix
+      Step1.2 Create cell-indexed SGE matrix
     </span>
   </a>
 
-    <nav class="md-nav" aria-label="Step1.2 Create cell-indexed spatial digital gene expression matrix">
+    <nav class="md-nav" aria-label="Step1.2 Create cell-indexed SGE matrix">
       <ul class="md-nav__list">
 
           <li class="md-nav__item">
@@ -1254,13 +1254,13 @@
 </li>
 
         <li class="md-nav__item">
-  <a href="#step12-create-cell-indexed-spatial-digital-gene-expression-matrix" class="md-nav__link">
+  <a href="#step12-create-cell-indexed-sge-matrix" class="md-nav__link">
     <span class="md-ellipsis">
-      Step1.2 Create cell-indexed spatial digital gene expression matrix
+      Step1.2 Create cell-indexed SGE matrix
     </span>
   </a>
 
-    <nav class="md-nav" aria-label="Step1.2 Create cell-indexed spatial digital gene expression matrix">
+    <nav class="md-nav" aria-label="Step1.2 Create cell-indexed SGE matrix">
       <ul class="md-nav__list">
 
           <li class="md-nav__item">
@@ -1304,7 +1304,7 @@
 
 
 
-<h1 id="step1-prepare-cell-indexed-spatial-digital-gene-expression-matrix">Step1. Prepare Cell-indexed Spatial Digital Gene Expression Matrix<a class="headerlink" href="#step1-prepare-cell-indexed-spatial-digital-gene-expression-matrix" title="Permanent link">&para;</a></h1>
+<h1 id="step1-prepare-cell-indexed-spatial-digital-gene-expression-sge-matrix">Step1. Prepare Cell-indexed Spatial Digital Gene Expression (SGE) Matrix<a class="headerlink" href="#step1-prepare-cell-indexed-spatial-digital-gene-expression-sge-matrix" title="Permanent link">&para;</a></h1>
 <h2 id="set-up-computing-environment">Set Up Computing Environment<a class="headerlink" href="#set-up-computing-environment" title="Permanent link">&para;</a></h2>
 <p>Ensure that your computing environment is properly configured before <strong>each</strong> step.</p>
 <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 1</span>
@@ -1354,7 +1354,7 @@ <h2 id="set-up-computing-environment">Set Up Computing Environment<a class="head
 <span class="nv">prefix</span><span class="o">=</span>&lt;output_prefix&gt;<span class="w">                                  </span><span class="c1">## Replace with your output prefix, e.g., watershed</span>
 </code></pre></div></td></tr></table></div>
 <h2 id="step-11-prepare-histology-based-cell-segmentation-mask-matrix">Step 1.1 Prepare Histology-based Cell Segmentation Mask Matrix<a class="headerlink" href="#step-11-prepare-histology-based-cell-segmentation-mask-matrix" title="Permanent link">&para;</a></h2>
-<p>To construct a cell-indexed spatial digital gene expression matrix (SGE), begin by executing histology-based cell segmentation using external methodologies, such as <a href="https://imagej.net/imaging/watershed">Watershed</a> or <a href="https://github.com/MouseLand/cellpose">Cellpose</a>. Details for performing histology-based cell segmentation using <a href="https://imagej.net/imaging/watershed">Watershed</a> and <a href="https://github.com/MouseLand/cellpose">Cellpose</a> are provided in the <a href="../../../#references">NovaScope Protocol paper</a>. </p>
+<p>To construct a cell-indexed spatial digital gene expression (SGE) matrix, begin by executing histology-based cell segmentation using external methodologies, such as <a href="https://imagej.net/imaging/watershed">Watershed</a> or <a href="https://github.com/MouseLand/cellpose">Cellpose</a>. Details for performing histology-based cell segmentation using <a href="https://imagej.net/imaging/watershed">Watershed</a> and <a href="https://github.com/MouseLand/cellpose">Cellpose</a> are provided in the <a href="../../../#references">NovaScope Protocol paper</a>. </p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>Irrespective of the segmentation technique used, ensure that the input histology image is <strong>aligned</strong> with the input SGE. </p>
@@ -1387,8 +1387,8 @@ <h3 id="watershed">Watershed<a class="headerlink" href="#watershed" title="Perma
    <strong>Figure 3: A black-and-white cell segmentation TIFF image from <a href="https://imagej.net/imaging/watershed">Watershed</a>.</strong> </p>
 <h3 id="cellpose">Cellpose<a class="headerlink" href="#cellpose" title="Permanent link">&para;</a></h3>
 <p>Cellpose produces an <code>npy</code> file that serves as the segmentation mask matrix in <code>NumPy</code> array format. No additional action is required with NEDA.</p>
-<h2 id="step12-create-cell-indexed-spatial-digital-gene-expression-matrix">Step1.2 Create cell-indexed spatial digital gene expression matrix<a class="headerlink" href="#step12-create-cell-indexed-spatial-digital-gene-expression-matrix" title="Permanent link">&para;</a></h2>
-<p>Use the histology-based cell segmentation mask matrix file from <a href="#step-11-prepare-histology-based-cell-segmentation-mask-matrix">Step1.1</a> to aggregate spatial transcriptomic data at the cellular level. NEDA’s <code>make_sge_from_npy.py</code> script is utilized here. Note that the npy file from <a href="https://imagej.net/imaging/watershed">Watershed</a> and <a href="https://github.com/MouseLand/cellpose">Cellpose</a> differs, so the script requires specifying the <code>--approach</code>. This step creates a cell-indexed SGE in 10x genomics format.</p>
+<h2 id="step12-create-cell-indexed-sge-matrix">Step1.2 Create cell-indexed SGE matrix<a class="headerlink" href="#step12-create-cell-indexed-sge-matrix" title="Permanent link">&para;</a></h2>
+<p>Use the histology-based cell segmentation mask matrix file from <a href="#step-11-prepare-histology-based-cell-segmentation-mask-matrix">Step1.1</a> to aggregate the input SGE matrix at the cellular level. NEDA’s <code>make_sge_from_npy.py</code> script is utilized here. Note that the <code>npy</code> file from <a href="https://imagej.net/imaging/watershed">Watershed</a> and <a href="https://github.com/MouseLand/cellpose">Cellpose</a> differs, so the script requires specifying the <code>--approach</code>. This step creates a cell-indexed SGE in 10X Genomics format.</p>
 <h3 id="watershed_1">Watershed<a class="headerlink" href="#watershed_1" title="Permanent link">&para;</a></h3>
 <p>Input &amp; Output
 <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1</span>

site/analysis/hex_idx/intro/index.html

@@ -1242,7 +1242,7 @@ <h2 id="a-step-by-step-procedure">A Step-by-Step Procedure<a class="headerlink"
 </ul>
 <h2 id="an-overview">An Overview<a class="headerlink" href="#an-overview" title="Permanent link">&para;</a></h2>
 <p><img alt="overview_brief" src="../ST_overview.png" />
-<strong>Figure 1: A Brief Overview of the Inputs, Outputs, and Process Steps for Pixel-level Analysis.</strong> </p>
+<strong>Figure 1: A Brief Overview of the Inputs, Outputs, and Process Steps for Pixel-level Analysis.</strong></p>
 
 
 

site/analysis/hex_idx/job_config/index.html

@@ -1250,7 +1250,7 @@ <h2 id="an-input-configuration-template">An Input Configuration Template<a class
 # Mandatory Fields
 #=========================
 ## Input files
-input_transcripts=/path/to/the/transcripts/file                     ## Path to the input spatial digital gene expression matrix (SGE) in FICTURE-compatible TSV format.
+input_transcripts=/path/to/the/transcripts/file                     ## Path to the input spatial digital gene expression (SGE) matrix in FICTURE-compatible TSV format.
 input_features=/path/to/the/feature/file                            ## Path to the input feature file.
 input_xyrange=/path/to/the/xyrange                                  ## Path to the input meta file with minimum and maximum X Y coordinates.