Introduction to SRTsim
23 June 2022
Package
SRTsim 0.99.2
Contents
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 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 34 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
- BiocFileCache (Shepherd and Morgan, 2022)
- 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] "2022-06-23 15:13:09 EDT"Wallclock time spent generating the vignette.
#> Time difference of 18.585 secsR session information.
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#> ui X11
#> language (EN)
#> collate C
#> ctype en_US.UTF-8
#> tz America/Detroit
#> date 2022-06-23
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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.
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