pbmc_multimodal.h5seurat Error: Given reference.assay (SCT) has not been processed with SCTransform

[lalamotlhabi]

Similar but now a closed Issue : https://github.com/satijalab/seurat/issues/4062

Error: Given reference.assay (SCT) has not been processed with SCTransform. Please either run SCTransform or set normalization.method = 'LogNormalize'. library(SeuratDisk) system("https://atlas.fredhutch.org/data/nygc/multimodal/pbmc_multimodal.h5seurat") reference <- LoadH5Seurat("pbmc_multimodal.h5seurat")

DefaultAssay(reference) <- 'SCT'

DefaultAssay(gex) <- 'SCT'

anchors <- FindTransferAnchors( reference = reference, query = gex, normalization.method = "SCT", reference.reduction = "spca", dims = 1:50,recompute.residuals = FALSE ) I loaded the "https://atlas.fredhutch.org/data/nygc/multimodal/pbmc_multimodal.h5seurat" But keep getting the error message that the "Given reference.assay (SCT) has not been processed with SCTransform"

sessionInfo() R version 4.0.0 (2020-04-24) Platform: x86_64-pc-linux-gnu (64-bit) Running under: CentOS Linux 7 (Core)
Matrix products: default BLAS: /apps/user/R/R_4.0.0_Bioc_3.11/R-4.0.0-Bioc-3.11-prd-20201019/lib64/R/lib/libRblas.so LAPACK: /apps/user/R/R_4.0.0_Bioc_3.11/R-4.0.0-Bioc-3.11-prd-20201019/lib64/R/lib/libRlapack.so
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 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages: [1] stats graphics grDevices utils datasets methods base
other attached packages: [1] SeuratDisk_0.0.0.9013 SeuratObject_4.0.2 Seurat_4.0.3
loaded via a namespace (and not attached):
[1] nlme_3.1-147 matrixStats_0.56.0 spatstat.sparse_2.0-0 bit64_4.0.5 RcppAnnoy_0.0.18 RColorBrewer_1.1-2 httr_1.4.2 [8] sctransform_0.3.2 tools_4.0.0 R6_2.4.1 irlba_2.3.3 rpart_4.1-15 KernSmooth_2.23-16 uwot_0.1.10 [15] mgcv_1.8-31 DBI_1.1.0 lazyeval_0.2.2 colorspace_1.4-1 withr_2.2.0 tidyselect_1.1.0 gridExtra_2.3 [22] bit_4.0.4 compiler_4.0.0 cli_2.1.0 hdf5r_1.3.3 plotly_4.9.2.1 scales_1.1.1 lmtest_0.9-37 [29] spatstat.data_2.1-0 ggridges_0.5.2 pbapply_1.4-3 rappdirs_0.3.1 goftest_1.2-2 stringr_1.4.0 digest_0.6.26 [36] spatstat.utils_2.1-0 pkgconfig_2.0.3 htmltools_0.5.1.1 fastmap_1.0.1 htmlwidgets_1.5.1 rlang_0.4.11 rstudioapi_0.11 [43] shiny_1.5.0 generics_0.0.2 zoo_1.8-8 jsonlite_1.7.0 ica_1.0-2 dplyr_1.0.5 magrittr_1.5 [50] patchwork_1.0.1 Matrix_1.3-4 fansi_0.4.1 Rcpp_1.0.5 munsell_0.5.0 abind_1.4-5 reticulate_1.16 [57] lifecycle_1.0.0 stringi_1.5.3 yaml_2.2.1 MASS_7.3-51.5 Rtsne_0.15 plyr_1.8.6 grid_4.0.0 [64] blob_1.2.1 parallel_4.0.0 listenv_0.8.0 promises_1.1.1 ggrepel_0.8.2 crayon_1.3.4 miniUI_0.1.1.1 [71] deldir_0.1-28 lattice_0.20-41 cowplot_1.0.0 splines_4.0.0 tensor_1.5 pillar_1.4.6 igraph_1.2.5 [78] spatstat.geom_2.1-0 future.apply_1.6.0 reshape2_1.4.4 codetools_0.2-16 leiden_0.3.3 glue_1.4.2 data.table_1.13.0 [85] png_0.1-7 vctrs_0.3.8 httpuv_1.5.4 gtable_0.3.0 RANN_2.6.1 purrr_0.3.4 spatstat.core_2.1-2 [92] polyclip_1.10-0 tidyr_1.1.2 scattermore_0.7 future_1.18.0 assertthat_0.2.1 ggplot2_3.3.2 mime_0.9 [99] xtable_1.8-4 later_1.1.0.1 survival_3.1-12 viridisLite_0.3.0 tibble_3.0.4 cluster_2.1.0 globals_0.12.5 [106] fitdistrplus_1.1-1 ellipsis_0.3.1 ROCR_1.0-11

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[yuhanH]

We updated our reference object in Feb. Is your reference the latest one? If so, could you show reference[['SCT']]?

[no-response[bot]]

This issue has been automatically closed because there has been no response to our request for more information from the original author. With only the information that is currently in the issue, we don't have enough information to take action. Please reach out if you have or find the answers we need so that we can investigate further.

[kh49]

Hi, I am having a similar issue. I downloaded the reference yesterday from the link in the tutorial. I also tried downloading from the link referenced in #4062

The output of reference[['SCT']] is the same for both:

Assay data with 20729 features for 161764 cells
Top 10 variable features:
 S100A9, GNLY, S100A8, LYZ, IGKC, NKG7, IGLC2, IGHM, PPBP, CCL5 

It seems like the reference still lacks the SCTModels from the latest versions.

[yuhanH]

HI @kh49 If you downloaded it yesterday, it should be the latest one. Have you updated seurat and SeuratObject?

[kh49]

Yes. I am running Seurat 4.0.3 and SeuratObject 4.0.2.

I have another dataset that was processed with SCTransform and integrated. When I inspect that with myintegrateddata[["SCT"]]

I get the following:

SCTAssay data with 23965 features for 179278 cells, and 38 SCTModel(s) 
First 10 features:
 FO538757.2, AP006222.2, RP4-669L17.10, RP11-206L10.9, LINC00115, FAM41C, RP11-54O7.1, SAMD11, NOC2L, KLHL17 

I still get the same output as above for the reference data loaded as described in the tutorial using LoadH5Seurat().

I also calculated the MD5 sum using md5sum in bash for the downloaded reference file:

kai@FP-Kai-9600K:/mnt/e/SC_SLE_Sarc$ md5sum pbmc_multimodal.h5seurat
82421ea2c16fe30fba15559efbcf9094  pbmc_multimodal.h5seurat

[yuhanH]

HI, @kh49 It is weird... Could you post the link you used? I will have a double-check. Thanks.

[kh49]

@yuhanH Sure, here is the link: https://atlas.fredhutch.org/data/nygc/multimodal/pbmc_multimodal.h5seurat

[yuhanH]

Hi @kh49 The reference object is correct. I think your need to update your SeuratDisk package, because loading SCTAssay was not installed in the old version. https://github.com/mojaveazure/seurat-disk The current version is SeuratDisk_0.0.0.9019.

[kh49]

@yuhanH Thank you so much! That fixed it. I had no idea it would ignore part of the file. There also doesn't appear to be a changelog on the SeuratDisk distro.

[yuhanH]

@kh49 Thanks for reporting this. We will add some notifications about SeuratDisk.