Spatial Analysis (Vignette Error)

[SCHansmann]

Hi,

I am currently in the process of learning ST analysis and was following the vignette however, the FindSpatiallyVariableFeatures function returns the following error: In dist(x = pos) : NAs introduced by coercion.

Continuing with the vignette returns the following error: Error in xtfrm.data.frame(x) : cannot xtfrm data frames

I tried troubleshooting but cannot seem to find a solution and was wondering if this is due to changes from Seurat 3 to 5, rendering some functions deprecated.

Any help to resolve this would be appreciated!!

Thanks in advance!

[smzt]

Same problem here. @SCHansmann did you solved the issue?
Thanks in advance.

[YoukaiFromAccounting]

Hello @SCHansmann and @smzt, thank you for reporting this issue.

Are you encountering the Error in xtfrm.data.frame(x) : cannot xtfrm data frames when running the first FindSpatiallyVariableFeatures call in the vignette? (the stxBrain object):

brain <- FindSpatiallyVariableFeatures(brain, assay = "SCT", features = VariableFeatures(brain)[1:1000],
    selection.method = "moransi")

Would you also mind providing your sessionInfo() when encountering the error?

[INCLIVA-bioinformatics]

Hi @YoukaiFromAccounting,
Yes, I'm facing this issue after running the vignette with the same example dataset.

My sessionInfo():

R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
[6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

time zone: Europe/Madrid
tzcode source: system (glibc)

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

other attached packages:
[1] stxBrain.SeuratData_0.1.2 ifnb.SeuratData_3.1.0 SeuratData_0.2.2.9002 plotrix_3.8-4 cutoff.scATOMIC_0.1.0
[6] agrmt_1.42.12 caret_7.0-1 lattice_0.22-6 data.table_1.15.4 plyr_1.8.9
[11] scATOMIC_2.0.3 copykat_1.1.0 randomForest_4.7-1.2 Rmagic_2.0.3 Matrix_1.7-0
[16] reticulate_1.42.0 HGNChelper_0.8.15 Seurat.utils_2.8.5 lubridate_1.9.4 forcats_1.0.0
[21] stringr_1.5.1 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[26] tidyverse_2.0.0 magrittr_2.0.3 ggExpress_0.9.5 MarkdownHelpers_1.1.1 ggpubr_0.6.0
[31] CodeAndRoll2_2.7.0 Stringendo_0.7.0 biomaRt_2.60.1 anndataR_0.99.0 SeuratDisk_0.0.0.9021
[36] Banksy_1.3.0 SeuratWrappers_0.4.0 spacexr_2.2.1 clustree_0.5.1 ggraph_2.2.1
[41] dplyr_1.1.4 patchwork_1.2.0 ggplot2_3.5.1 hdf5r_1.3.11 Seurat_5.3.0
[46] SeuratObject_5.0.2 sp_2.2-0 knitr_1.48

loaded via a namespace (and not attached):
[1] igraph_2.0.3 ica_1.0-3 plotly_4.10.4 Formula_1.2-5
[5] devtools_2.4.5 zlibbioc_1.50.0 tidyselect_1.2.1 bit_4.0.5
[9] doParallel_1.0.17 rjson_0.2.21 EnhancedVolcano_1.22.0 urlchecker_1.0.1
[13] blob_1.2.4 S4Arrays_1.4.1 tictoc_1.2.1 Biobase_2.64.0
[17] png_0.1-8 cli_3.6.4 goftest_1.2-3 glmGamPoi_1.16.0
[21] basilisk.utils_1.12.1 uwot_0.2.2 curl_5.2.1 mime_0.12
[25] evaluate_0.24.0 stringi_1.8.4 desc_1.4.3 pROC_1.18.5
[29] backports_1.5.0 parallelDist_0.2.6 collapsibleTree_0.1.8 ggVennDiagram_1.5.2
[33] httpuv_1.6.15 AnnotationDbi_1.66.0 rappdirs_0.3.3 splines_4.4.1
[37] mclust_6.1.1 prodlim_2024.06.25 RApiSerialize_0.1.4 DT_0.29
[41] sctransform_0.4.1 ggbeeswarm_0.7.2 sessioninfo_1.2.2 DBI_1.2.3
[45] withr_3.0.0 class_7.3-22 lmtest_0.9-40 tidygraph_1.3.1
[49] formatR_1.14 BiocManager_1.30.23 htmlwidgets_1.6.4 fs_1.6.4
[53] S4Vectors_0.42.1 SingleCellExperiment_1.26.0 ggrepel_0.9.5 princurve_2.1.6
[57] labeling_0.4.3 SparseArray_1.4.8 MatrixGenerics_1.16.0 zoo_1.8-12
[61] XVector_0.44.0 UCSC.utils_1.0.0 BiocGenerics_0.50.0 timechange_0.3.0
[65] foreach_1.5.2 fansi_1.0.6 caTools_1.18.2 grid_4.4.1
[69] timeDate_4041.110 R.oo_1.26.0 RSpectra_0.16-1 irlba_2.3.5.1
[73] clipr_0.8.0 ggrastr_1.0.2 ellipsis_0.3.2 fastDummies_1.7.3
[77] lazyeval_0.2.2 yaml_2.3.9 survival_3.7-0 SpatialExperiment_1.14.0
[81] scattermore_1.2 crayon_1.5.3 RcppAnnoy_0.0.22 RColorBrewer_1.1-3
[85] progressr_0.14.0 tweenr_2.0.3 later_1.3.2 profvis_0.3.8
[89] ggridges_0.5.6 codetools_0.2-20 KEGGREST_1.44.1 sccore_1.0.6
[93] Rtsne_0.17 limma_3.60.3 filelock_1.0.3 data.tree_1.1.0
[97] splitstackshape_1.4.8 leidenAlg_1.1.5 pkgconfig_2.0.3 xml2_1.3.6
[101] GenomicRanges_1.56.1 IRanges_2.38.1 spatstat.sparse_3.1-0 ape_5.8
[105] viridisLite_0.4.2 xtable_1.8-4 car_3.1-3 httr_1.4.7
[109] tools_4.4.1 globals_0.16.3 pkgbuild_1.4.4 hardhat_1.4.1
[113] Rfast_2.1.5.1 beeswarm_0.4.0 broom_1.0.7 checkmate_2.3.2
[117] nlme_3.1-165 futile.logger_1.4.3 lambda.r_1.2.4 dbplyr_2.5.0
[121] digest_0.6.36 dir.expiry_1.8.0 farver_2.1.2 tzdb_0.5.0
[125] reshape2_1.4.4 ModelMetrics_1.2.2.2 viridis_0.6.5 rpart_4.1.23
[129] glue_1.7.0 cachem_1.1.0 VennDiagram_1.7.3 BiocFileCache_2.12.0
[133] polyclip_1.10-6 generics_0.1.3 Biostrings_2.72.1 stats4_4.4.1
[137] presto_1.0.0 amap_0.8-20 parallelly_1.37.1 pkgload_1.4.0
[141] statmod_1.5.0 Rnanoflann_0.0.3 RcppHNSW_0.6.0 carData_3.0-5
[145] pbapply_1.7-2 httr2_1.0.2 SummarizedExperiment_1.34.0 spam_2.10-0
[149] spatialDE_1.10.0 utf8_1.2.4 gower_1.0.2 basilisk_1.12.1
[153] graphlayouts_1.1.1 gtools_3.9.5 ggsignif_0.6.4 gridExtra_2.3
[157] shiny_1.8.1.1 lava_1.8.1 GenomeInfoDbData_1.2.12 R.utils_2.12.3
[161] RCurl_1.98-1.16 memoise_2.0.1 rmarkdown_2.27 pheatmap_1.0.12
[165] scales_1.3.0 R.methodsS3_1.8.2 future_1.33.2 RANN_2.6.1
[169] stringfish_0.16.0 spatstat.data_3.1-2 rstudioapi_0.16.0 cluster_2.1.6
[173] Rfast2_0.1.5.4 spatstat.utils_3.0-5 hms_1.1.3 fitdistrplus_1.2-1
[177] munsell_0.5.1 vioplot_0.5.1 ggcorrplot_0.1.4.1 cowplot_1.1.3
[181] colorspace_2.1-0 rlang_1.1.5 GenomeInfoDb_1.40.1 DelayedMatrixStats_1.26.0
[185] sparseMatrixStats_1.16.0 ipred_0.9-15 dotCall64_1.1-1 ggforce_0.4.2
[189] aricode_1.0.3 dbscan_1.2.2 xfun_0.45 remotes_2.5.0
[193] recipes_1.2.1 iterators_1.0.14 matrixStats_1.1.0 abind_1.4-5
[197] ps_1.8.0 futile.options_1.0.1 bitops_1.0-7 promises_1.3.0
[201] RSQLite_2.3.7 openxlsx_4.2.8 DelayedArray_0.30.1 harmony_1.2.0
[205] ReadWriter_1.6.0 compiler_4.4.1 colorRamps_2.3.4 prettyunits_1.2.0
[209] RcppHungarian_0.3 listenv_0.9.1 Rcpp_1.0.14 tensor_1.5
[213] usethis_3.0.0 qs_0.27.3 MASS_7.3-61 progress_1.2.3
[217] spatstat.random_3.2-3 R6_2.5.1 fastmap_1.2.0 rstatix_0.7.2
[221] vipor_0.4.7 ROCR_1.0-11 rsvd_1.0.5 nnet_7.3-19
[225] gtable_0.3.5 KernSmooth_2.23-24 miniUI_0.1.1.1 deldir_2.0-4
[229] htmltools_0.5.8.1 RcppParallel_5.1.9 bit64_4.0.5 spatstat.explore_3.2-7
[233] lifecycle_1.0.4 zigg_0.0.2 processx_3.8.4 zip_2.3.1
[237] callr_3.7.6 MarkdownReports_4.8.0 vctrs_0.6.5 sm_2.2-6.0
[241] spatstat.geom_3.2-9 future.apply_1.11.2 pillar_1.9.0 gplots_3.1.3.1
[245] magick_2.8.5 dlm_1.1-5 jsonlite_1.8.8

Thanks for your help.

Regards,

Sheila

[YoukaiFromAccounting]

Hello @INCLIVA-bioinformatics , thank you for your message. I see from your Session Info that you are running Seurat V5.3.0; would you mind retrieving the latest version of Seurat (V5.3.1), which includes some changes to the logic behind LeverageScore(), and see if you encounter the same issue?

You can install the latest version of Seurat in RStudio via:

devtools::install_github("satijalab/seurat", ref = "main")

Alternatively, you can clone the repository in Command Line and install it from a local directory:

git clone https://github.com/satijalab/seurat.git
cd seurat
git checkout main

devtools::install_local("path/to/seurat")

[INCLIVA-bioinformatics]

Hi @YoukaiFromAccounting,
I installed Seurat version 5.3.1.000 and obtained the same error message. Looking at the warning messages I decided to update the SeuratObject package and the problem was solved.

Here is what I saw:

brain <- LoadData("stxBrain", type = "anterior1")
Validating object structure
Updating object slots
Ensuring keys are in the proper structure
Ensuring keys are in the proper structure
Ensuring feature names don't have underscores or pipes
Updating slots in Spatial
Updating slots in anterior1
Validating object structure for Assay5 ‘Spatial’
Validating object structure for VisiumV2 ‘anterior1’
Object representation is consistent with the most current Seurat version
brain <- SCTransform(brain, assay = "Spatial", verbose = FALSE)
brain <- RunPCA(brain, assay = "SCT", verbose = FALSE)
brain <- FindNeighbors(brain, reduction = "pca", dims = 1:30)
Computing nearest neighbor graph
Computing SNN
brain <- FindClusters(brain, verbose = FALSE)
brain <- RunUMAP(brain, reduction = "pca", dims = 1:30)
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
07:48:10 UMAP embedding parameters a = 0.9922 b = 1.112
07:48:10 Read 2696 rows and found 30 numeric columns
07:48:10 Using Annoy for neighbor search, n_neighbors = 30
07:48:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:48:10 Writing NN index file to temp file /tmp/RtmpJ85SqA/file29d7ef7690a871
07:48:10 Searching Annoy index using 1 thread, search_k = 3000
07:48:11 Annoy recall = 100%
07:48:12 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
07:48:14 Initializing from normalized Laplacian + noise (using RSpectra)
07:48:14 Commencing optimization for 500 epochs, with 105144 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:48:18 Optimization finished
p1 <- DimPlot(brain, reduction = "umap", label = TRUE)
p2 <- SpatialDimPlot(brain, label = TRUE, label.size = 3)
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.
p1 + p2

brain <- FindSpatiallyVariableFeatures(brain, assay = "SCT", features = VariableFeatures(brain)[1:1000], selection.method = "moransi")
Computing Moran's I
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 26s
Warning message:
In dist(x = pos) : NAs introduced by coercion

top.features <- head(SpatiallyVariableFeatures(brain, selection.method = "moransi"), 6)
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'x' in selecting a method for function 'head': cannot xtfrm data frames
In addition: Warning message:
The selection.method argument of SpatiallyVariableFeatures() is deprecated as of SeuratObject 5.0.0.
ℹ Please use the method argument instead.
This warning is displayed once every 8 hours.
Call lifecycle::last_lifecycle_warnings() to see where this warning was generated.

lifecycle::last_lifecycle_warnings()
[[1]]
<warning/lifecycle_warning_deprecated>
Warning:
The selection.method argument of SpatiallyVariableFeatures() is deprecated as of SeuratObject 5.0.0.
ℹ Please use the method argument instead.

---

Backtrace:
▆

1. ├─utils::head(...)
2. ├─SeuratObject::SpatiallyVariableFeatures(brain, selection.method = "moransi")
3. └─SeuratObject:::SpatiallyVariableFeatures.Seurat(brain, selection.method = "moransi")
4. └─SeuratObject::.Deprecate(...)

Thanks for your help.

Regards,

Sheila

[YoukaiFromAccounting]

@INCLIVA-bioinformatics Great, thanks for confirming that deprecated SeuratObject functions were the cause of the issue. @SCHansmann would you be able to try updating your Seurat and SeuratObject and see if that solves the problem?

devtools::install_github("satijalab/seurat-object", ref = "main", force = TRUE)
devtools::install_github("satijalab/seurat", ref = "main", force = TRUE)

Be sure to restart your R session to ensure that no existing package installations conflict with the updated packages.