Last updated: 2024-06-21
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diff_expression_spatial_linear_models/
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The spatialGE software includes the STdiff
function to test for differentially expressed genes using spatial
covariance structures. The differential expression tests in the manuscript
associated with this website were performed in an HPC environment.
Nonetheless, the STdiff algorithm can be run on a laptop computer.
Here an example to run the STdiff function is presented
on a small subset of genes to reduce computational time. This vignette
assumes that an STlist object has been already created (click
here
for a tutorial to create an STlist object). The STlist
used here will be the same as in the main vignette generating the
figures for the manuscript.
Users are also encouraged to take a look at the vignette
in the spatialGE package.
Load libraries
library('spatialGE')
library('tidyverse')── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.0 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.0
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsLoad STlist containing the CosMx-SMI lung cancer data set used in the manuscript.
stlist_obj = readRDS('code/diff_expr_hpcrun_spamm/data/smi_stlist_w_clusters_lungcancer.RDS')In this small example, the STdiff fuction will be used
to test for differentially expressed genes in tumor cells (“tumor_5”)
and epithelial cells in the FOV “lung5rep3_fov28”. Only the 100 top most
variable will be tested using non-spatial models. Then, 10% of
the differentially expressed genes (i.e., p < 0.05), will be tested
using spatial models.
Counts were already transfomed with the transform_data
function. The STdiff function can be directly called on the
STlist object:
degs = STdiff(stlist_obj, # STlist object
annot='annots', # Name of the column in stlist_obj@spatial_meta[['lung5rep3_fov28']] containing cell types
samples='lung5rep3_fov28', # Name of the sample (i.e., FOV)
topgenes=100, # Top variable genes to run non-spatial models
sp_topgenes=0.1, # THe percentage of non-spatial DE tests to re-run using spatial models
pairwise=T, # Do pairwise comparisons
clusters=c('epithelial', 'tumor_5')) # Specific cell types to testTesting metadata: annots...
Running non-spatial mixed models...Registered S3 methods overwritten by 'registry':
method from
print.registry_field proxy
print.registry_entry proxy Completed non-spatial mixed models (0.17 min).
Running spatial tests...Using paralellisation might be useful. See help("setNBThreads")Using paralellisation might be useful. See help("setNBThreads")
Using paralellisation might be useful. See help("setNBThreads")
Using paralellisation might be useful. See help("setNBThreads")
Using paralellisation might be useful. See help("setNBThreads")
Using paralellisation might be useful. See help("setNBThreads")
Completed spatial mixed models (5.39 min).
STdiff completed in 5.66 min.The “exp_adj_p_val” column contains the FDR p-values from the spatial tests. The “adj_p_val” contains the FDR p-values from the non-spatial tests. The results cna be observed like so:
degs[['lung5rep3_fov28']] %>%
filter(!is.na(exp_p_val)) # Show only genes with spatial tests # A tibble: 6 × 10
sample gene avg_log2fc cluster_1 cluster_2 mm_p_val adj_p_val exp_p_val
<chr> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
1 lung5rep3_f… OLFM4 4.62 tumor_5 epitheli… 0 0 0
2 lung5rep3_f… CEAC… 3.36 tumor_5 epitheli… 0 0 0
3 lung5rep3_f… CXCL5 2.51 tumor_5 epitheli… 0 0 0
4 lung5rep3_f… KRT19 2.20 tumor_5 epitheli… 0 0 0
5 lung5rep3_f… KRT17 2.16 tumor_5 epitheli… 0 0 0
6 lung5rep3_f… DMBT1 3.03 tumor_5 epitheli… 0 0 1.01e-13
# ℹ 2 more variables: exp_adj_p_val <dbl>, comments <chr>
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/New_York
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[5] purrr_1.0.2 readr_2.1.4 tidyr_1.3.0 tibble_3.2.1
[9] ggplot2_3.5.0 tidyverse_2.0.0 spatialGE_1.2.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] dotCall64_1.1-1 gtable_0.3.4 spam_2.10-0
[4] xfun_0.41 bslib_0.6.1 ggpolypath_0.3.0
[7] processx_3.8.3 lattice_0.21-9 numDeriv_2016.8-1.1
[10] callr_3.7.3 tzdb_0.4.0 vctrs_0.6.5
[13] tools_4.3.2 ps_1.7.5 generics_0.1.3
[16] parallel_4.3.2 proxy_0.4-27 fansi_1.0.6
[19] pkgconfig_2.0.3 Matrix_1.6-4 checkmate_2.3.1
[22] lifecycle_1.0.4 compiler_4.3.2 git2r_0.33.0
[25] fields_15.2 munsell_0.5.0 getPass_0.2-4
[28] httpuv_1.6.13 htmltools_0.5.7 maps_3.4.1.1
[31] sass_0.4.8 yaml_2.3.8 nloptr_2.0.3
[34] crayon_1.5.2 later_1.3.2 pillar_1.9.0
[37] jquerylib_0.1.4 whisker_0.4.1 MASS_7.3-60
[40] cachem_1.0.8 boot_1.3-28.1 nlme_3.1-163
[43] spaMM_4.4.0 tidyselect_1.2.0 digest_0.6.33
[46] slam_0.1-50 stringi_1.8.3 rprojroot_2.0.4
[49] fastmap_1.1.1 grid_4.3.2 colorspace_2.1-0
[52] cli_3.6.2 magrittr_2.0.3 utf8_1.2.4
[55] withr_2.5.2 backports_1.4.1 scales_1.3.0
[58] promises_1.2.1 registry_0.5-1 timechange_0.2.0
[61] rmarkdown_2.25 httr_1.4.7 hms_1.1.3
[64] ROI_1.0-1 pbapply_1.7-2 evaluate_0.23
[67] knitr_1.45 viridisLite_0.4.2 rlang_1.1.2
[70] Rcpp_1.0.11 glue_1.6.2 minqa_1.2.6
[73] rstudioapi_0.15.0 jsonlite_1.8.8 R6_2.5.1
[76] fs_1.6.3