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README.Rmd
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---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# STEAM
<img width="30%" align = "right" src="https://github.com/fanzhanglab/STEAM/blob/main/media/STEAM_logo.png?raw=true">
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<p align = "justify">
One common challenge in evaluating the robustness of identified cell type clusters in spatial omics is the lack of ground truth cell type labels in real-world data from disease conditions. To address this, we introduce STEAM, a Spatial Transcriptomics Evaluation Algorithm and Metric for clustering performance, developed to evaluate the consistency of clustering algorithms and the reliability of cell annotations in spatial omics data. Our hypothesis is that if clusters are robust and consistent across tissue regions, selecting a subset of cells or spots within a cluster should enable accurate prediction of cell type annotations for the remaining cells within that cluster, due to spatial proximity and gene expression covarying patterns.
STEAM incorporates various machine learning models, including Random Forest, XGBoost, and SVM, to assess the prediction accuracy and consistency of clusters, along with statistical metrics like Kappa score, F1 score, ARI (Adjusted Rand Index), etc. We demonstrated the capability of STEAM on multi-cell and single-cell resolution spatial transcriptomics and proteomics. Notably, STEAM supports multi-sample training, enabling the evaluation of cross-replicate clustering consistency. Furthermore, we used STEAM to evaluate the performance of spatial-aware and spatial-ignorant clustering methods, offering researchers a valuable tool for more informed result interpretation.
</p>
<img width="100%" align = "center" src="https://github.com/fanzhanglab/STEAM/blob/main/media/Figure1.png?raw=true">
<!-- <img width="100%" align = "center" src="https://github.com/fanzhanglab/STEAM/blob/main/man/figures/Figure1.png"> -->
</br>
## Installation
You can install the STEAM Package from
[GitHub](https://github.com/fanzhanglab/STEAM/) using the devtools as
follows:
``` r
# install.packages("devtools")
devtools::install_github("fanzhanglab/STEAM")
```
(OR)
``` r
remotes::install_github("fanzhanglab/STEAM")
```
<br/>
### Dependencies / Other required packages
``` r
- R (\>= 4.2)
- ggplot2 (\>= 3.4.2)
- caret
- randomForest
- e1071
- scales
- gridExtra
- grid
- reshape2
- viridis
```
<br/>
## Tutorials
**Step-by-step notebook** of applying STEAM on 10X Visium Human Brain Data (DLPFC):
- <a href="https://htmlpreview.github.io/?https://github.com/fanzhanglab/STEAM/blob/main/tutorials/STEAM_DLPFC_tutorial.html"> Tutorial of applying STEAM on DLPFC data </a>
#### Below are several major steps of running STEAM:
``` r
# Create a new STEAM object for the loaded spatial transcriptomic data
STEAM.Obj <- LoadSTEAM(count_exp = matrix, spatial = coordinates, labels = labels, Seurat.obj = NULL)
```
``` r
STEAM.Obj <- RunSTEAM(STEAM.obj, train.ratio = 0.8, n.size = 5, seed = 123, cv.folds = 10, cv.repeats = 3, trainval.ratio = 0.8, model = "rf", n.tree = 500, kernel = 'linear', train.folder.name = 'train.out', allowParallel = FALSE)
```
<br/>
## Citations
Reynoso, S., Schiebout, C., Krishna, R., Zhang, F. STEAM: Spatial Transcriptomics Evaluation Algorithm and Metric for clustering performance, [*bioRxiv*](link), 2025
<br/>
## Help, Suggestion and Contribution
Using github [**issues**](https://github.com/fanzhanglab/STEAM/issues)
section, if you have any question, comments, suggestions, or to report
coding related issues of STEAM is highly encouraged than sending
emails.
- Please **check the GitHub
[issues](https://github.com/fanzhanglab/STEAM/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 STEAM object and related codes to understand
the issues.
<br/>
## Contact
Please contact [fanzhanglab@gmail.com](fanzhanglab@gmail.com) for further questions or protential collaborative opportunities!