-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathREADME.Rmd
75 lines (53 loc) · 3.87 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# MCube: Identifying cell-type-specific spatially variable genes with the Mixture of Mixed Models <a href="https://github.com/YangLabHKUST/MCube"><img src="inst/figures/sticker.png" height="128" align="right" alt="Sticker" /></a>
<!-- badges: start -->
[](https://hits.seeyoufarm.com)
<!-- [](https://github.com/YangLabHKUST/MCube/actions/workflows/R-CMD-check.yaml) -->
<!-- badges: end -->
## Introduction
The R package `MCube` implements the methods in the **MMM** paper.
**MMM**, standing for the **Mixture of Mixed Models**, is a unified framework for statistical identification of cell-type-specific spatially variable genes in spatial transcriptomic (ST) studies.
<!-- Beginning with the raw count data, **MMM** uses a log-mixture structure to account for cell type composition while simultaneously correcting for the spot and platform effects between ST and scRNA-seq data. -->
<!-- The mixed-effects model decomposes the cell-type-specific gene expression in ST data into three components: the average gene expression of the same cell type obtained from scRNA-seq data, spatial variations, and non-spatial variations, enabling a statistically rigorous way to examine the significance of the spatial variations. -->
<!-- The statistical significance of spatial variations is then examined using a powerful non-parametric test capable of detecting diverse spatial patterns. -->
<figure>
<img src="inst/figures/pipeline.png" style="width:95.0%"
alt="Pipeline" />
</figure>
## Installation
You can install the development version of `MCube` from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("YangLabHKUST/MCube")
```
## Real data analysis
The code for reproducing the real data analysis results presented in our paper are available at the tutorial website (<https://mcube-tutorial.readthedocs.io/>):
* [Visium human dorsolateral prefrontal cortex dataset](https://mcube-tutorial.readthedocs.io/en/latest/analysis/DLPFC/)
* [Multiple adult mouse brain datasets from different sources](https://mcube-tutorial.readthedocs.io/en/latest/analysis/mouse_brain/)
* [Xenium human breast cancer dataset](https://mcube-tutorial.readthedocs.io/en/latest/analysis/breast_cancer/)
* [3D *Drosophila* embryo model constructed from Stereo-seq dataset](https://mcube-tutorial.readthedocs.io/en/latest/analysis/Drosophila_embryo/)
## Reference
If you find the `MCube` package or any of the source code in this repository useful for your work, please cite:
> A unified framework for identification of cell-type-specific spatially variable genes in spatial transcriptomic studies.
Zhiwei Wang, Yeqin Zeng, Ziyue Tan, Yuheng Chen, Xinrui Huang, Hongyu Zhao, Zhixiang Lin, and Can Yang.
2025.
## Development
The R package `MCube` is developed by [Zhiwei Wang](https://sites.google.com/view/statwangz).
## Contact
Please feel free to contact [Zhiwei Wang](mailto:zhiwei.wang@connect.ust.hk) or [Prof. Can Yang](mailto:macyang@ust.hk) if any inquiries.