Introduction to SRTsim
Welcome
Welcome to the SRTsim project! It is composed of:
- a shiny web application that we are hosting at https://jiaqiangzhu.shinyapps.io/srtsim that can handle a limited set of concurrent users,
- a R package at CRAN that lets you generate the spatially resolved transcriptomic (SRT) data and run a local version of our web application.
The web application allows you to design spatial pattern and generate SRT data with patterns of interest.
Install SRTsim
R is an open-source statistical environment which can be
easily modified to enhance its functionality via packages. SRTsim is a
R package available via CRAN. R can be
installed on any operating system from CRAN after which you can install
SRTsim
by using the following commands in your R session:
Run Reference-Based Simulation
To get started, please load the SRTsim package.
Once you have installed the package, we can perform reference-based Tissue-wise simulation with the example data.
## explore example SRT data
str(exampleLIBD)
#> List of 2
#> $ count:Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
#> .. ..@ i : int [1:241030] 1 2 8 9 10 11 13 14 15 16 ...
#> .. ..@ p : int [1:3612] 0 67 122 182 252 322 392 462 534 609 ...
#> .. ..@ Dim : int [1:2] 80 3611
#> .. ..@ Dimnames:List of 2
#> .. .. ..$ : chr [1:80] "ENSG00000175130" "ENSG00000159176" "ENSG00000168314" "ENSG00000080822" ...
#> .. .. ..$ : chr [1:3611] "AAACAAGTATCTCCCA-1" "AAACAATCTACTAGCA-1" "AAACACCAATAACTGC-1" "AAACAGAGCGACTCCT-1" ...
#> .. ..@ x : num [1:241030] 1 1 1 7 10 1 5 2 1 1 ...
#> .. ..@ factors : list()
#> $ info :'data.frame': 3611 obs. of 6 variables:
#> ..$ row : int [1:3611] 50 3 59 14 43 47 73 61 45 42 ...
#> ..$ col : int [1:3611] 102 43 19 94 9 13 43 97 115 28 ...
#> ..$ imagerow: num [1:3611] 381 126 428 187 341 ...
#> ..$ imagecol: num [1:3611] 441 260 183 417 153 ...
#> ..$ tissue : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
#> ..$ layer : chr [1:3611] "Layer3" "Layer1" "WM" "Layer3" ...
example_count <- exampleLIBD$count
example_loc <- exampleLIBD$info[,c("imagecol","imagerow","layer")]
colnames(example_loc) <- c("x","y","label")
## create a SRT object
simSRT <- createSRT(count_in=example_count,loc_in =example_loc)
## Set a seed for reproducible simulation
set.seed(1)
## Estimate model parameters for data generation
simSRT1 <- srtsim_fit(simSRT,sim_schem="tissue")
## Generate synthetic data with estimated parameters
simSRT1 <- srtsim_count(simSRT1)
## Explore the synthetic data
simCounts(simSRT1)[1:5,1:5]
#> 5 x 5 sparse Matrix of class "dgCMatrix"
#> AAACAAGTATCTCCCA-1 AAACAATCTACTAGCA-1 AAACACCAATAACTGC-1
#> ENSG00000175130 . . 10
#> ENSG00000159176 1 3 5
#> ENSG00000168314 1 . 6
#> ENSG00000080822 . . 3
#> ENSG00000091513 . . 5
#> AAACAGAGCGACTCCT-1 AAACAGCTTTCAGAAG-1
#> ENSG00000175130 . 2
#> ENSG00000159176 . 1
#> ENSG00000168314 2 1
#> ENSG00000080822 1 .
#> ENSG00000091513 1 3
simcolData(simSRT1)
#> DataFrame with 3611 rows and 3 columns
#> x y label
#> <numeric> <numeric> <character>
#> AAACAAGTATCTCCCA-1 440.639 381.098 Layer3
#> AAACAATCTACTAGCA-1 259.631 126.328 Layer1
#> AAACACCAATAACTGC-1 183.078 427.768 WM
#> AAACAGAGCGACTCCT-1 417.237 186.814 Layer3
#> AAACAGCTTTCAGAAG-1 152.700 341.269 Layer5
#> ... ... ... ...
#> TTGTTTCACATCCAGG-1 254.410 422.862 WM
#> TTGTTTCATTAGTCTA-1 217.147 433.393 WM
#> TTGTTTCCATACAACT-1 208.416 352.430 Layer6
#> TTGTTTGTATTACACG-1 250.720 503.735 WM
#> TTGTTTGTGTAAATTC-1 284.293 148.110 Layer2We can perform reference-based Domain-specific simulation with the example data.
## Set a seed for reproducible simulation
set.seed(1)
## Estimate model parameters for data generation
simSRT2 <- srtsim_fit(simSRT,sim_scheme='domain')
## Generate synthetic data with estimated parameters
simSRT2 <- srtsim_count(simSRT2)
## Explore the synthetic data
simCounts(simSRT2)[1:5,1:5]
#> 5 x 5 sparse Matrix of class "dgCMatrix"
#> AAACAAGTATCTCCCA-1 AAACAATCTACTAGCA-1 AAACACCAATAACTGC-1
#> ENSG00000175130 . . 11
#> ENSG00000159176 1 2 7
#> ENSG00000168314 1 . 7
#> ENSG00000080822 . . 3
#> ENSG00000091513 . . 6
#> AAACAGAGCGACTCCT-1 AAACAGCTTTCAGAAG-1
#> ENSG00000175130 . 2
#> ENSG00000159176 . 1
#> ENSG00000168314 2 1
#> ENSG00000080822 1 .
#> ENSG00000091513 2 3Comparison Between Reference Data and Synthetic Data
Summarized Metrics
After data generation, we can compare metrics of reference data and synthetic data
Reproducibility
The SRTsim package was made possible thanks to:
- R (R Core Team, 2024)
- BiocStyle (Oleś, 2024)
- knitr (Xie, 2014)
- Matrix (Bates, Maechler, and Jagan, 2024)
- RefManageR (McLean, 2017)
- rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, and Iannone, 2024)
- S4Vectors (Pagès, Lawrence, and Aboyoun, 2024)
- sessioninfo (Wickham, Chang, Flight, Müller, and Hester, 2021)
Code for creating the vignette
## Create the vignette
library("rmarkdown")
system.time(render("SRTsim.Rmd"))
## Extract the R code
library("knitr")
knit("SRTsim.Rmd", tangle = TRUE)Date the vignette was generated.
#> [1] "2024-11-20 06:38:48 UTC"Wallclock time spent generating the vignette.
#> Time difference of 13.598 secsR session information.
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#> setting value
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#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────Bibliography
This vignette was generated using BiocStyle (Oleś, 2024), knitr (Xie, 2014) and rmarkdown (Allaire, Xie, Dervieux et al., 2024) running behind the scenes.
Citations made with RefManageR (McLean, 2017).
[1] J. Allaire, Y. Xie, C. Dervieux, et al. rmarkdown: Dynamic Documents for R. R package version 2.29. 2024. URL: https://github.com/rstudio/rmarkdown.
[2] D. Bates, M. Maechler, and M. Jagan. Matrix: Sparse and Dense Matrix Classes and Methods. R package version 1.7-1. 2024. URL: https://Matrix.R-forge.R-project.org.
[3] M. W. McLean. “RefManageR: Import and Manage BibTeX and BibLaTeX References in R”. In: The Journal of Open Source Software (2017). DOI: 10.21105/joss.00338.
[4] A. Oleś. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.35.0. 2024. URL: https://github.com/Bioconductor/BiocStyle.
[5] H. Pagès, M. Lawrence, and P. Aboyoun. S4Vectors: Foundation of vector-like and list-like containers in Bioconductor. R package version 0.45.2. 2024. URL: https://bioconductor.org/packages/S4Vectors.
[6] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, 2024. URL: https://www.R-project.org/.
[7] H. Wickham, W. Chang, R. Flight, et al. sessioninfo: R Session Information. R package version 1.2.2, https://r-lib.github.io/sessioninfo/. 2021. URL: https://github.com/r-lib/sessioninfo#readme.
[8] Y. Xie. “knitr: A Comprehensive Tool for Reproducible Research in R”. In: Implementing Reproducible Computational Research. Ed. by V. Stodden, F. Leisch and R. D. Peng. Chapman and Hall/CRC, 2014. ISBN: 978-1466561595. URL: https://www.routledge.com/Implementing-Reproducible-Research/Stodden-Leisch-Peng/p/book/9781466561595.