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1 Welcome
2 Install SRTsim
3 Run Reference-Based Simulation
4 Comparison Between Reference Data and Synthetic Data
- 4.1 Summarized Metrics
- 4.2 Expression Patterns For Genes of Interest
5 Reproducibility
6 Bibliography

1 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.

2 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:

 install.packages("SRTsim")

3 Run Reference-Based Simulation

To get started, please load the SRTsim package.

library("SRTsim")

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      Layer2

We 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                  3

4 Comparison Between Reference Data and Synthetic Data

4.1 Summarized Metrics

After data generation, we can compare metrics of reference data and synthetic data


## Compute metrics 
simSRT1   <- compareSRT(simSRT1)

## Visualize Metrics
visualize_metrics(simSRT1)

4.2 Expression Patterns For Genes of Interest

visualize_gene(simsrt=simSRT1,plotgn = "ENSG00000183036",rev_y=TRUE)

visualize_gene(simsrt=simSRT2,plotgn = "ENSG00000168314",rev_y=TRUE)

This work was done by Jiaqiang Zhu, Lulu Shang and Xiang Zhou.

5 Reproducibility

The SRTsim package was made possible thanks to:

R (R Core Team, 2022)
AnnotationHub
BiocStyle (Oleś, 2022)
GenomicRanges
knitr (Xie, 2014)
Matrix (Bates, Maechler, and Jagan, 2022)
RefManageR (McLean, 2017)
rmarkdown (Allaire, Xie, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, and Iannone, 2022)
rtracklayer
S4Vectors (Pagès, Lawrence, and Aboyoun, 2022)
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] "2023-01-02 18:00:00 EST"

Wallclock time spent generating the vignette.

#> Time difference of 18.865 secs

R session information.

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#>  language (EN)
#>  collate  C
#>  ctype    en_US.UTF-8
#>  tz       America/New_York
#>  date     2023-01-02
#>  pandoc   2.18 @ /usr/local/bin/ (via rmarkdown)
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6 Bibliography

This vignette was generated using BiocStyle (Oleś, 2022), knitr (Xie, 2014) and rmarkdown (Allaire, Xie, McPherson, et al., 2022) running behind the scenes.

Citations made with RefManageR (McLean, 2017).

[1] J. Allaire, Y. Xie, J. McPherson, et al. rmarkdown: Dynamic Documents for R. R package version 2.14. 2022. 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 1.4-1. 2022. URL: https://CRAN.R-project.org/package=Matrix.

[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 2.24.0. 2022. 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 0.34.0. 2022. 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, 2022. 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. 2021. URL: https://CRAN.R-project.org/package=sessioninfo.

[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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.

Introduction to SRTsim

2 January 2023

Package

Contents

1 Welcome

2 Install `SRTsim`

3 Run Reference-Based Simulation

4 Comparison Between Reference Data and Synthetic Data

4.1 Summarized Metrics

4.2 Expression Patterns For Genes of Interest

5 Reproducibility

6 Bibliography

Introduction to SRTsim

2 January 2023

Package

Contents

1 Welcome

2 Install SRTsim

3 Run Reference-Based Simulation

4 Comparison Between Reference Data and Synthetic Data

4.1 Summarized Metrics

4.2 Expression Patterns For Genes of Interest

5 Reproducibility

6 Bibliography

2 Install `SRTsim`