Last updated: 2022-03-10
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In this tutorial, we introduce the workflow and core functions of BASS to help you get familiar with the BASS package and start analyzing your own datasets. Please check the Real data analyses sections for examples on how we analyze spatial transcriptomic datasets.
Install BASS R package maintained in github through the “devtools” package.
if(!require(devtools))
install.packages(devtools)
devtools::install_github("zhengli09/BASS")library(BASS)load("data/starmap_mpfc.RData") # starmap_cnts, starmap_infoTo facilitate all steps of the spatial transcriptomic analysis, we wrap up the raw data, processed data, model hyper-parameters, MCMC controlling parameters, and model results into a S4 object (BASS-class). Create a BASS object to start your analysis with the function createBASSObject. There are four required arguments: an L-list of gene expression count matrices for L tissue sections (X), an L-list of spatial coordinates matrices for L tissue sections (xy), the number of cell types (C) and the number of spatial domains (R). By default, we fix the cell-cell interaction parameter \(\beta\) to the value specified by beta if the user has already had a good sense about the value of this parameter. However, BASS can also accurately infer the cell-cell interaction parameter \(\beta\) based on the data at hand at the cost of a slightly longer computational time. Check the documentation of createBASSObject for further details.
# gene expression count data
cnts <- starmap_cnts
# spatial coordinates
xy <- lapply(starmap_info, function(info.i){
as.matrix(info.i[, c("x", "y")])
})
BASS <- createBASSObject(cnts, xy, C = 15, R = 4,
beta_est_approach = "ACCUR_EST")***************************************
INPUT INFO:
- Number of tissue sections: 3
- Number of cells/spots: 1049 1053 1088
- Number of genes: 166
- Potts interaction parameter estimation approach: ACCUR_EST
- Variance-covariance structure of gene expression features: EEE
To list all hyper-parameters, Type listAllHyper(BASS_object)
***************************************You can check all the hyper-parameters that BASS uses with the function listAllHyper.
listAllHyper(BASS)***************************************
Please refer to the paper for details of the paramters
ALL HYPER-PARAMETERS:
- Number of cell types C: 15
- Number of spatial domains R: 4
- Initialization method: kmeans
- Covariance structure: EEE
- Scale matrix of the inverse-Wishart prior W0: 1I
- Degrees of freedom of the inverse-Wishart prior n0: 1
- Number of MCMC burn-in iterations: 10000
- Number of MCMC posterior samples: 10000
- Potts interaction parameter estimation approach: ACCUR_EST
- Number of burn-in interations in Potts sampling: 100
- Number of Potts samples to approximate the partition ratio: 100
- Step size of a uniform random walk epsilon: 0.1
- Threshold of convergence for beta: 0.1
- Upper bound for beta: 4
- Concentration parameter of the Dirichlet prior alpha0: 1
- Minimum number of neighbors for each cell/spot based on the Euclidean distance: 4
***************************************We pre-process the raw gene expression data stored at the BASS@X slot and store the processed data at the BASS@X_run slot for running the following BASS algorithm. We have provided a function BASS.preprocess to automate the pre-processing steps, where we combine cells/spots across all L tissue sections, conduct library size normalization followed by a log2-transformation (after adding a pseudo-count of 1), select feature genes, perform dimension reduction with PCA on the normalized expression matrix to extract J low-dimensional expression features, and perform batch effect adjustment with Harmony to align expression data from different tissue sections. Users can also pre-process the gene expression data by themselves and store the processed data to the BASS@X_run slot. Check the documentation of BASS.preprocess for further details and various other controls.
BASS <- BASS.preprocess(BASS)Run the Gibbs sampling algorithm in combination with the Metroplis-Hastings algorithm to sample all the parameters specified in the BASS model. Posterior samples of all the parameters are stored in the BASS@res slot and you can find a detailed description of what has been stored in BASS@res in the documentation of the BASS object (?`BASS-class`). Check the documentation of BASS.run for further details.
BASS <- BASS.run(BASS)Post-process the posterior sampling results with the function BASS.postprocess. There, we address the label switching issue associated with the sampling of cell type labels and spatial domain labels based on the iterative version 1 of the equivalence class representation (ECR) algorithm implemented in the label.switching package. In addition, we summarize posterior samples and obtain final estimates of cell type labels, spatial domain labels, and cell type composition in each spatial domain. The resulting estimates are stored in the BASS@res_postprocess slot. Check the documentation of BASS.postprocess for further details.
BASS <- BASS.postprocess(BASS)
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.6 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] BASS_1.0 mclust_5.4.9 GIGrvg_0.5 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8 bslib_0.3.1 compiler_4.1.2 pillar_1.7.0
[5] later_1.1.0.1 git2r_0.28.0 jquerylib_0.1.4 tools_4.1.2
[9] getPass_0.2-2 digest_0.6.29 jsonlite_1.8.0 evaluate_0.15
[13] tibble_3.1.6 lifecycle_1.0.1 pkgconfig_2.0.3 rlang_1.0.1
[17] cli_3.2.0 rstudioapi_0.13 yaml_2.3.5 xfun_0.29
[21] fastmap_1.1.0 httr_1.4.2 stringr_1.4.0 knitr_1.37
[25] sass_0.4.0 fs_1.5.2 vctrs_0.3.8 rprojroot_2.0.2
[29] glue_1.6.2 R6_2.5.1 processx_3.5.2 fansi_1.0.2
[33] rmarkdown_2.12.1 callr_3.7.0 magrittr_2.0.2 whisker_0.4
[37] ps_1.6.0 promises_1.1.1 htmltools_0.5.2 ellipsis_0.3.2
[41] httpuv_1.5.4 utf8_1.2.2 stringi_1.7.6 crayon_1.5.0