1. StereoHub:

1.1 StereoHub Architecture:

StereoHub: a comprehensive cloud platform for Stereo-Seq analysis, Single-Cell visualization, and CT segments annotation.

1. Github Team: https://github.com/StereoHub/

2. Source Repository: https://github.com/StereoHub/StereoHub

3. Documents: https://stereohub.github.io

4. Cloud Platform: http://43.242.96.52:5002

1.2 StereoHub Features:

1.2.1 Stereo-Seq Analysis

Pipelined single-slice analysis and time-based multi-slice analysis.

1.2.2 Single-Cell Seurat

Pipelined analysis and visualization of Single-Cell expression data based on Seurat.

1.2.3 Computed Tomography Slicer

Supports online labeling and feature extraction of DICOM data from CT and NMR.

1.3 StereoHub Develop or Deploy:

1.3.1 Installing

# 1. git clone repository
git clone https://github.com/StereoHub/StereoHub.git
cd StereoHub

# 2. Install Environment
# 2.1 For Windows (Recommended)
.\env-win.bat

# OR

# 2.2 For Linux (Recommended)
bash env-linux.sh

# OR

# 2.3 Install Steps (Recommended)
# 2.3.1 Mamba Env Create and Activate
mamba create -n stereohub python=3.8
mamba activate stereohub

# 2.3.2 Shiny
mamba install -c conda-forge shiny=1.0.0 shinywidgets=0.3.2 IPython=8.12.2 ipywidgets=8.1.3

# 2.3.3 Utils
mamba install -c conda-forge numpy=1.23.5 pandas=1.5.3 matplotlib=3.7.1 faicons=0.2.2

# 2.3.4 Stereopy
mamba install stereopy=1.3.1 -c stereopy -c grst -c numba -c conda-forge -c bioconda -c fastai -c defaults

# OR

# 2.4 Conda Env Export and Create (Not Recommended)
conda env create -f stereohub.yml
conda activate stereohub

1.3.2 Run, develop and debug

# 1. For Windows
.\start-win.bat

# 2. For Linux
bash start-linux.sh

# 3. All Terminals
python -m shiny run                         --host 127.0.0.1                         --port 5000                         --reload                         --reload-includes "*.py,*.css,*.js,*.html,*.md"                         --reload-excludes "*.png,*.pdf"                         --log-level info                         --app-dir "."                         --launch-browser                         --dev-mode                         app.py

1.3.3 Deploy: Docker (Recommended)

# 1. Build, Run, Push
# 1.1 Setting services name, build, tag, port, volume, etc.
vim docker-compose.yml

# 1.2 Build stereohub and run container
docker compose up stereohub -d --build

# 1.3 Push stereohub
docker push omicsdocker/stereohub:1.1.0

# 2. Pull from DockerHub
# 2.1 Pull image with tag
docker pull omicsdocker/stereohub:1.1.0

# 2.2 Run service with port and name
docker run -d -p 3838:3838 --name stereohub omicsdocker/stereohub:1.1.0

2. Stereo-Seq:

2.1 Stereo-Seq Resources:

1. STomics Stereo-Seq: https://stomics.tech

2. STomics Cloud: https://cloud.stomics.tech

3. STOmics Database: https://db.cngb.org/stomics/

4. ImageStudio, StereoMap: https://stomics.tech/products/BioinfoTools/OfflineSoftware

5. STomics Github: https://github.com/STOmics/

6. Stereopy: https://github.com/STOmics/Stereopy/

2.2 Stereo-Seq Paper:

Ao Chen, Sha Liao, Mengnan Cheng, Longqi Liu, Xun Xu, Jian Wang. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell, 2022, doi: https://doi.org/10.1016/j.cell.2022.04.003

Figure 1. Graphical Abstract:

Figure 2. Stereo-Seq Technology:

Version Update:

1. GEF (Gene Expression Format) Information

2. SED (Stereo Expression Data) Information

Cells x Genes Number:

Bin Type:

Bin Size:

Cell Attributes:

Cell Names:

Gene Attributes:

Gene Names:

Parameters

1. Stereo Formats:

2. GEF Path:

3. Bin Type:

4. Bin Size:

5. Threads Number:

1. Quality Control Violin

total_counts: the total counts per cell;

n_genes_by_counts: the number of genes expressed in count matrix;

pct_counts_mt: the percentage of counts in mitochondrial genes.

Parameters

1. Dot Size:

2. X Label Angle:

3. Multi Panel:

4. Color Palette:

5. Image DPI:

6. Download PDF / PNG QC Violin QC Violin

1. Quality Control Spatial

total_counts: the total counts per cell;

n_genes_by_counts: the number of genes expressed in count matrix;

pct_counts_mt: the percentage of counts in mitochondrial genes.

Parameters

1. Dot Size:

2. X Label:

3. Y Label:

4. Color Palette:

5. Image DPI:

6. Download PDF / PNG QC Spatial QC Spatial

1. Filter Cells Genes

1. Before Filter Cells & Genes

2. After Filter Cells & Genes

Parameters

1. Dot Size:

2. Dot Color:

3. Image DPI:

4. Min Gene Counts:

5. Max Gene Counts:

6. Min Genes:

7. Max Genes:

8. Percent MT:

9. Download PDF / PNG Filter Cells Filter Cells

1. Normalization

1. Normalize Total

Satija, R., Farrell, J., Gennert, D. et al. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33, 495–502 (2015). https://doi.org/10.1038/nbt.3192

2. Log1(pvalue)

Booeshaghi, A. S., & Pachter, L. (2021). Normalization of single-cell RNA-seq counts by log(x + 1) or log(1 + x). Bioinformatics, 37(15), 2223–2224. https://doi.org/10.1093/bioinformatics/btab085

3. Single-Cell Transform

Hafemeister, C., Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol 20, 296 (2019). https://doi.org/10.1186/s13059-019-1874-1

1. Principal Component Analysis

Parameters

1. PCs Number:

2. Use Highly Variable Genes:

3. SVD Solver:

4. Image DPI:

5. Download PDF / PNG PCA Elbow PCA Elbow

1. Neighbor Graph

Parameters

1. Neighbor Method:

2. Neighbor Metric:

3. Neighbors Number:

4. K-Nearest Neighbors:

5. Threads Number:

1. Multipule Samples:

2. Multipule Samples Statistics:

3. First Sample Statistics:

Parameters

1. Stereo Formats:

2. H5AD Dir Path:

3. Flavor:

4. Bin Type:

5. Bin Size:

1. Quality Control Violin

total_counts: the total counts per cell;

n_genes_by_counts: the number of genes expressed in count matrix;

pct_counts_mt: the percentage of counts in mitochondrial genes.

Parameters

1. Dot Size:

2. X Label Angle:

3. Multi Panel:

4. Color Palette:

5. Image DPI:

6. Download PDF / PNG QC Violin QC Violin

1. Quality Control Spatial

total_counts: the total counts per cell;

n_genes_by_counts: the number of genes expressed in count matrix;

pct_counts_mt: the percentage of counts in mitochondrial genes.

Parameters

1. Dot Size:

2. X Label:

3. Y Label:

4. Color Palette:

5. Scale:

6. Coordinate:

7. Image DPI:

8. Download PDF / PNG QC Spatial QC Spatial

1. Filter Cells Genes

1. Before Filter Cells & Genes

2. After Filter Cells & Genes

Parameters

1. Dot Size:

2. Dot Color:

3. Image DPI:

4. Min Gene Counts:

5. Max Gene Counts:

6. Min Genes:

7. Max Genes:

8. Percent MT:

9. Download PDF / PNG Filter Cells Filter Cells

1. Normalization

1. Normalize Total

Satija, R., Farrell, J., Gennert, D. et al. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33, 495–502 (2015). https://doi.org/10.1038/nbt.3192

2. Log1(pvalue)

Booeshaghi, A. S., & Pachter, L. (2021). Normalization of single-cell RNA-seq counts by log(x + 1) or log(1 + x). Bioinformatics, 37(15), 2223–2224. https://doi.org/10.1093/bioinformatics/btab085

3. Single-Cell Transform

Hafemeister, C., Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol 20, 296 (2019). https://doi.org/10.1186/s13059-019-1874-1

1. Principal Component Analysis

Parameters

1. PCs Number:

2. Use Highly Variable Genes:

3. SVD Solver:

4. Image DPI:

5. Download PDF / PNG PCA Elbow PCA Elbow

1. Neighbor Graph

Parameters

1. Neighbor Method:

2. Neighbor Metric:

3. Neighbors Number:

4. K-Nearest Neighbors:

5. Threads Number:

1. UMAP Visualization

1. StereoHub:

1.1 StereoHub Introduction:

StereoHub: An interactive cloud platform for downstream analytics at Stereo-Seq. The StereoHub user interface is designed and developed based on the Shiny v1.0.0 framework of Python v3.8.10, providing rich web components for data manipulation, function parameters, and result display, making data analytics parameters as completely intuitive as possible for scientists. The functions of StereoHub for Stereo-Seq spatiotemics data analytics are realized by important functional modules such as stereopy v3.1.3, anndata v0.9.2, biopython v1.83.0, hdf5 v1.14.0, panel v0.14.4, bokeh v2.4.3, plotly v5.23.0, etc. Thanks to these excellent open-source modules.

1.2 StereoHub Resources:

1. Github Team: https://github.com/StereoHub/

2. Github Source Code Repository: https://github.com/StereoHub/StereoHub

3. Documents: https://stereohub.github.io

4. Cloud Platform: https://hiplot.com.cn/stereohub

2. Our Team:

Benben Miao:

Ph.D. candidate at Xiamen University. Dedicated to research in marine biology genomics and evolution, and focus on cutting-edge exploration and development in bioinformatics.

Github: https://github.com/benben-miao

The founder of Bioinformatics-Omics WeChat official account. Representative works: 1. Hiplot bioinformatics cloud platform (https://hiplot.com.cn); 2. OmicsSuite multi-omics visulization desktop suite (https://github.com/omicssuite/); 3. TOmicsVis transcriptomics visualization R package (https://github.com/benben-miao/TOmicsVis/); 4. MolluskTaxoDB Mollusk taxonomy database (https://github.com/benben-miao/MolluskTaxonomy); 5. StereoHub Stereo-Seq analysis cloud (https://github.com/StereoHub/). Published 6 first-author papers in journals such as iMeta, Horticulture Research, Briefings in Bioinformatics, Animals, Genes, Journal of Applied Oceanography, Frontiers in Marine Science, etc,.

Wei Dong:

Postdoctoral researcher at Guangzhou Women and Children's Medical Center. At present, research focuses on utilizing single-cell multi-omics, spatial multi-omics, and 3D genomics technologies to investigate the mechanisms of complex genetic diseases and tumor immunity, along with bioinformatics development.

Github: https://github.com/dongwei1220

The core member of Hiplot platform, and an editor for the Bioinfomics WeChat official account. Contributions include the development of the Hiplot bioinformatics analysis cloud platform (https://hiplot.com.cn), the OmicsSuite multi-omics desktop suite (https://github.com/omicssuite/), the TOmicsVis transcriptomics visualization R package (https://github.com/benben-miao/TOmicsVis/), and the MicroWorldOmics microbial and viral metagenomics desktop suite (https://github.com/hzaurzli/MicroWorldOmics). Published over 10 SCI papers as first or co-first author in journals such as Briefings in Bioinformatics, Genomics Proteomics & Bioinformatics, iMeta, and Horticulture Research. Additionally, co-authored nine papers in internationally renowned journals like Nature, Cell, and the Journal of Clinical Investigation, with over 1,000 citations on Google Scholar. Participates in peer review for several SCI journals and serves as a junior editorial board member for iMeta.

Mingjie Wang:

Attending physician in the Department of Gastroenterology at Shanghai Ruijin Hospital. Focuses on the application of multi-modal omics data in clinical diagnosis and treatment, as well as software development.

Github: https://github.com/mingjiewang

The founder of the Hiplot platform, and a co-founder of the 科研猫 WeChat official account. Published and co-authored 35 papers, including 15 first-author and corresponding-author SCI papers in journals such as Briefings in Bioinformatics and Emerging Microbes & Infection, with a cumulative impact factor of 98 and an H-index of 13. Holding one software copyright, leads a project funded by the 2021 Shanghai "扬帆计划" serves as a reviewer for several SCI journals, is a junior editorial board member for iMeta, and an editor for a special issue in Frontiers in Genetics.

2. Software and Cloud:

Hiplot Pro Cloud:

https://hiplot.com.cn

Hiplot Pro is an open, advanced one-stop biomedical visualization and analysis platform with various modules. In Hiplot, you can even publish your personal needs in the cloud-market, or share your own code with the world's researchers. Hiplot-Focus on Science, Care for Health.

OmicsSuite Desktop:

https://omicssuite.github.io

OmicsSuite (https://omicssuite.github.io), original name BioSciTools, a desktop program developed based on Java-v11.0.0 and R-v4.2.2, aims to make new exploration and contribution to the development of bioinformatics, and realize data analysis and visualization in the fields of statistics, algorithm, sequence analysis, multi-omics (transcriptomics, genomics, proteomics, metabolomics, single cell), microbiology, clinical, etc.

TOmicsVis R Package:

https://benben-miao.github.io/TOmicsVis/

TOmicsVis is a R package for (https://benben-miao.github.io/TOmicsVis/) transcriptome visualization, from sample trait statistics to gene expression analysis. Six categories include "Samples Statistics", "Traits Analysis", "Differential Expression Analysis", "Advanced Analysis", "GO and KEGG Enrichment", "Tables Operations", with complete sample data.

Hiplot Community:

https://hiplot.cn

Hiplot (https://hiplot.org), with concise and top-quality data visualization applications for the life sciences and biomedical fields. This web service improve the efficiency in use and development of its equipped 240+ biomedical data visualization functions, involving basic statistics,multi-omics, regression, clustering, dimensional plugins.