FindSpatiallyVariableFeatures in HD slides

[torsteintengs]

Hi!

I am looking at data from 3' HD slides, and I am trying to run FindSpatiallyVariableFeatures. However, none of the examples for this command I have found seem to work. I have been trying to resolve the various errors I am getting, but keep running into new ones... Is this command even possible to run on HD data?

[anashen]

Hi @torsteintengs,

Can you please let us know the following:

• more info about your object / data
• the calls to FindSpatiallyVariableFeatures you are running and the exact errors received
• result of running sessionInfo()

This will be helpful in understanding the issue. Thanks!

[torsteintengs]

Sure!

The data are from Atlantic salmon and the input generated using spaceranger version 4.0.1.

I am not a very experienced Seurat user, but I have tried calls like:

seurat_spatial_filtered<- FindSpatiallyVariableFeatures(
seurat_spatial_filtered,
assay = "Spatial.016um",
selection.method = "moransi",
features = VariableFeatures(head1)[1:100], # Run on top HVGs to save time
layer = "Spatial.016um"
)

That gives me a few warnings and one error:

Warning: No layers found matching search pattern provided
Warning: No layers found matching search pattern provided
Error in colnames<-(x = *tmp*, value = paste0("MoransI_", colnames(x = svf.info))) :
attempt to set 'colnames' on an object with less than two dimensions
In addition: Warning message:
Layer ‘scale.data’ is empty

In order to reduce the amont of memory needed, I have played around with filtering out low count data and only running the analyse on the most variable features (I have seen that for other calls using HD data, this is an issue), but that does not seem to affect anything.

sessionInfo() gives me:

R version 4.5.2 (2025-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26200)

Matrix products: default
LAPACK version 3.12.1

locale:
[1] C

time zone: Europe/Oslo
tzcode source: internal

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] hdf5r_1.3.12 arrow_23.0.1.1 dplyr_1.2.0 patchwork_1.3.2
[5] ggplot2_4.0.2 SeuratData_0.2.2.9002 Seurat_5.4.0 SeuratObject_5.3.0
[9] sp_2.2-1

loaded via a namespace (and not attached):
[1] deldir_2.0-4 pbapply_1.7-4 gridExtra_2.3
[4] rlang_1.1.7 magrittr_2.0.4 RcppAnnoy_0.0.23
[7] otel_0.2.0 matrixStats_1.5.0 ggridges_0.5.7
[10] compiler_4.5.2 spatstat.geom_3.7-0 png_0.1-9
[13] vctrs_0.7.1 reshape2_1.4.5 stringr_1.6.0
[16] crayon_1.5.3 pkgconfig_2.0.3 fastmap_1.2.0
[19] promises_1.5.0 tzdb_0.5.0 bit_4.6.0
[22] purrr_1.2.1 jsonlite_2.0.0 goftest_1.2-3
[25] later_1.4.8 spatstat.utils_3.2-2 irlba_2.3.7
[28] parallel_4.5.2 cluster_2.1.8.2 R6_2.6.1
[31] ica_1.0-3 stringi_1.8.7 RColorBrewer_1.1-3
[34] spatstat.data_3.1-9 reticulate_1.45.0 parallelly_1.46.1
[37] spatstat.univar_3.1-6 lmtest_0.9-40 scattermore_1.2
[40] assertthat_0.2.1 Rcpp_1.1.1 tensor_1.5.1
[43] future.apply_1.20.2 zoo_1.8-15 sctransform_0.4.3
[46] httpuv_1.6.16 Matrix_1.7-4 splines_4.5.2
[49] igraph_2.2.2 tidyselect_1.2.1 rstudioapi_0.18.0
[52] abind_1.4-8 spatstat.random_3.4-4 codetools_0.2-20
[55] miniUI_0.1.2 spatstat.explore_3.7-0 listenv_0.10.1
[58] lattice_0.22-9 tibble_3.3.1 plyr_1.8.9
[61] withr_3.0.2 shiny_1.13.0 S7_0.2.1
[64] ROCR_1.0-12 Rtsne_0.17 future_1.70.0
[67] fastDummies_1.7.5 survival_3.8-6 polyclip_1.10-7
[70] fitdistrplus_1.2-6 pillar_1.11.1 KernSmooth_2.23-26
[73] plotly_4.12.0 generics_0.1.4 RcppHNSW_0.6.0
[76] scales_1.4.0 globals_0.19.1 xtable_1.8-8
[79] glue_1.8.0 lazyeval_0.2.2 tools_4.5.2
[82] data.table_1.18.2.1 RSpectra_0.16-2 RANN_2.6.2
[85] dotCall64_1.2 cowplot_1.2.0 grid_4.5.2
[88] tidyr_1.3.2 nlme_3.1-168 cli_3.6.5
[91] rappdirs_0.3.4 spatstat.sparse_3.1-0 spam_2.11-3
[94] viridisLite_0.4.3 uwot_0.2.4 gtable_0.3.6
[97] digest_0.6.39 progressr_0.18.0 ggrepel_0.9.8
[100] htmlwidgets_1.6.4 farver_2.1.2 htmltools_0.5.9
[103] lifecycle_1.0.5 httr_1.4.8 mime_0.13
[106] bit64_4.6.0-1 MASS_7.3-65

Thank you!

-Torstein

[anashen]

Thanks for the info @torsteintengs!

I don't think you are passing the correct layer to the function, hence the warnings "Warning: No layers found matching search pattern provided" and "In addition: Warning message:
Layer ‘scale.data’ is empty". FindSpatiallyVariableFeatures runs on the scale.data layer by default. You should do preprocessing (Normalize etc or SCT) before running FindSpatiallyVariableFeatures. Note that if you use SCT, you should also pass assay = "SCT" in your call.

I'm able to run this function successfully with Seurat v5.4 on a HD 3' dataset from Space Ranger 4.0.1.

[torsteintengs]

Thanks! It looks like I am making some progress based on your suggestions :-)

However, if I try the call:

seurat_spatial_filtered <- FindSpatiallyVariableFeatures(
seurat_spatial_filtered,
assay = "Spatial.016um",
selection.method = "moransi",
layer = "scale.data"
)

...I get:

Computing Moran's I
Error in seq.int(s.1, s2, s.1) : 'to' must be a finite number

I suspected that this might have something to do with the UMI count numbers, but as far as I can tell, they are OK.

Thank you,

-Torstein