diff --git a/docs/analysis/cell_idx/step1-cell_SGE.md b/docs/analysis/cell_idx/step1-cell_SGE.md index e019540..aecac1e 100644 --- a/docs/analysis/cell_idx/step1-cell_SGE.md +++ b/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= ## 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 diff --git a/docs/analysis/cell_idx/step2-Seurat-clustering.md b/docs/analysis/cell_idx/step2-Seurat-clustering.md index dcf3b3b..2a06dfb 100644 --- a/docs/analysis/cell_idx/step2-Seurat-clustering.md +++ b/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 -``` - +``` \ No newline at end of file diff --git a/docs/analysis/hex_idx/intro.md b/docs/analysis/hex_idx/intro.md index 289fe04..c0e2c15 100644 --- a/docs/analysis/hex_idx/intro.md +++ b/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.** diff --git a/docs/analysis/hex_idx/job_config.md b/docs/analysis/hex_idx/job_config.md index c00d38e..715151f 100644 --- a/docs/analysis/hex_idx/job_config.md +++ b/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. diff --git a/docs/analysis/hex_idx/prepare_data.md b/docs/analysis/hex_idx/prepare_data.md index 4a4e701..d450639 100644 --- a/docs/analysis/hex_idx/prepare_data.md +++ b/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). \ No newline at end of file +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). \ No newline at end of file diff --git a/docs/analysis/hex_idx/step4-decode.md b/docs/analysis/hex_idx/step4-decode.md index 9dccd08..0a752db 100644 --- a/docs/analysis/hex_idx/step4-decode.md +++ b/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. diff --git a/docs/home/documentation_overview.md b/docs/home/documentation_overview.md index 24a799d..84dffd3 100644 --- a/docs/home/documentation_overview.md +++ b/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. diff --git a/docs/index.md b/docs/index.md index 952cbd8..fc55a31 100644 --- a/docs/index.md +++ b/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: diff --git a/docs/installation/installation.md b/docs/installation/installation.md index f8f355f..8a96022 100644 --- a/docs/installation/installation.md +++ b/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")) ``` \ No newline at end of file diff --git a/site/analysis/cell_idx/step1-cell_SGE/index.html b/site/analysis/cell_idx/step1-cell_SGE/index.html index 34e5954..3dd9c8b 100644 --- a/site/analysis/cell_idx/step1-cell_SGE/index.html +++ b/site/analysis/cell_idx/step1-cell_SGE/index.html @@ -76,7 +76,7 @@
- + Skip to content @@ -1108,13 +1108,13 @@
  • - + - Step1.2 Create cell-indexed spatial digital gene expression matrix + Step1.2 Create cell-indexed SGE matrix -