STew

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Uncover spatially informed variations underlying single-cell spatial transcriptomics with STew.

STew 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# STew <img width="28%" align = "right" src="https://github.com/fanzhanglab/STew/blob/main/STewlogo.png">

[![R-CMD-check](https://github.com/fanzhanglab/STew/actions/workflows/check-standard.yaml/badge.svg)](https://github.com/fanzhanglab/STew/actions/workflows/check-standard.yaml)
![Visitors](https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fgithub.com%2Ffanzhanglab%2FSTew&label=%23Visits&labelColor=%23000000&countColor=%2337d67a&style=plastic)

We introduce STew, a Spatial
Transcriptomic multi-viEW
representation learning method, or STew, to jointly characterize the
gene expression variation and spatial information in the shared
low-dimenion space in a scalable manner. STew will output distinct
spatially informed cell gradients, robust clusters, and statistical
goodness of model fit to reveal significant genes that reflect subtle
spatial niches in complex tissues.

## Installation

You can install the STew Package from
[GitHub](https://github.com/fanzhanglab/STew/) using the devtools as
follows:

``` r
# install.packages("devtools")
devtools::install_github("fanzhanglab/STew")
```

(OR)

``` r
remotes::install_github("fanzhanglab/STew")
```

### Dependencies / Other required packages

- R (\>= 4.2)
- MERINGUE (\>= 1.0)
- Matrix (\>= 1.5.4)
- ggplot2 (\>= 3.4.2)
- ggpubr (\>= 0.6.0)
- Seurat (\>= 4.3.0)
- future.apply (\>= 1.10.0)
- RANN (\>= 2.6.1)
- sctransform
- tibble

## Tutorials

**Step-by-step notebook** of applying STew on identifying spatially
informed low-dimensional embeddings and spatially aware clusters on the
10X Visium Human Brain Data (DLPFC):

- <a href="https://htmlpreview.github.io/?https://github.com/fanzhanglab/STew/blob/main/vignettes/Tutorial_STew_DLPFC.html">
 Tutorial of applying STew on DLPFC data </a>
- <a href = 'https://htmlpreview.github.io/?https://github.com/fanzhanglab/STew/blob/main/vignettes/count_modeling_tutorial_dlpfc.html'>
 Tutorial of count data modelling </a>

#### Below are several major steps of running STew:

``` r
# Create a new STew object for the loaded spatial transcriptomic data
STew = STew_Obj(count = dlpfc$count_exp,
                  spatial = dlpfc$spatial)
```

… (skip several preprocessing steps) …

``` r
# permute optimal penalty parameters
STew <- parallel_cca_permute(x = STew$exp_adj_matrix, z = STew$adj_matrix, obj = STew, nperms=50, niter=3)
```

``` r
# Perform sparse CCA based on the optimal penalty parameters
STew <- cca_main(x = STew$exp_adj_matrix, z = STew$adj_matrix, obj = STew, K=20, penaltyx=STew$bestpenaltyx, penaltyz=STew$bestpenaltyz, v=STew$v.init)
```

``` r
gradient_plot <- spatial_gradient(STew)
gradient_plot[1:5]
```

``` r
cluster_plot <- plot_cluster(coordis = spatial, label = cluster$res_0.30, colors = colors, t="Cell clusters based on STew")
cluster_plot
```

``` r
# Save the main results into the STew object
saveRDS(STew, file="STew_10x_human_dlpfc_no.rds")
```

#### Benchmarcking STew with other algorithms:

## Citations

Guo, N., Vargas, J., Reynoso, S., Fritz, D., Krishna, R., Wang, C., Zhang, F., Uncover spatially informed variations for single-cell spatial transcriptomics with STew, Bioinformatics Advances, https://doi.org/10.1093/bioadv/vbae064, 2024

## Help, Suggestion and Contribution

Using github [**issues**](https://github.com/fanzhanglab/STew/issues)
section, if you have any question, comments, suggestions, or to report
coding related issues of STew is highly encouranged than sending emails.

- Please **check the GitHub
  [issues](https://github.com/fanzhanglab/STew/issues)** for similar
  issues that has been reported and resolved. This helps the team to
  focus on adding new features and working on cool projects instead of
  resolving the same issues!
- **Examples** are required when filing a GitHub issue. In certain
  cases, please share your STew object and related codes to understand
  the issues.

## Contact

Please contact [fanzhanglab@gmail.com](fanzhanglab@gmail.com) for
further questions or protential collaborative opportunities!

STew

STew 최신버젼 다운로드

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