RunTFIDF: Error in vapply(X = keyed.objects, FUN = function(x)

[Telogen]

Hi,

When I do RunTFIDF()

pbmc.ATAC <- RunTFIDF(pbmc.ATAC)

I receive this:

Performing TF-IDF normalization Error in vapply(X = keyed.objects, FUN = function(x) { : values must be length 1, but FUN(X[[1]]) result is length 0 In addition: Warning message: In RunTFIDF.default(object = GetAssayData(object = object, slot = "counts"), : Some features contain 0 total counts

Thanks a lot for your help!

[timoast]

Can you show the output of sessionInfo()?

[Telogen]

sure.

sessionInfo() R version 4.1.0 (2021-05-18) Platform: x86_64-conda-linux-gnu (64-bit) Running under: CentOS Linux 7 (Core)

Matrix products: default BLAS/LAPACK: /local/txm/anaconda3/envs/R4.1/lib/libopenblasp-r0.3.17.so

locale: [1] LC_CTYPE=zh_CN.UTF-8 LC_NUMERIC=C [3] LC_TIME=zh_CN.UTF-8 LC_COLLATE=zh_CN.UTF-8 [5] LC_MONETARY=zh_CN.UTF-8 LC_MESSAGES=zh_CN.UTF-8 [7] LC_PAPER=zh_CN.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=zh_CN.UTF-8 LC_IDENTIFICATION=C

attached base packages: [1] stats4 stats graphics grDevices utils datasets methods [8] base

other attached packages: [1] EnsDb.Mmusculus.v79_2.99.0 ensembldb_2.18.2 [3] AnnotationFilter_1.18.0 GenomicFeatures_1.46.1 [5] AnnotationDbi_1.56.2 Biobase_2.54.0 [7] GenomicRanges_1.46.0 GenomeInfoDb_1.30.0 [9] IRanges_2.28.0 S4Vectors_0.32.2 [11] BiocGenerics_0.40.0 ggplot2_3.3.5 [13] SeuratObject_4.0.3 Seurat_4.0.5 [15] Signac_1.4.0 devtools_2.4.2 [17] usethis_2.1.3

loaded via a namespace (and not attached): [1] utf8_1.2.2 reticulate_1.22 [3] tidyselect_1.1.1 RSQLite_2.2.8 [5] htmlwidgets_1.5.4 grid_4.1.0 [7] docopt_0.7.1 BiocParallel_1.28.0 [9] Rtsne_0.15 munsell_0.5.0 [11] codetools_0.2-18 ica_1.0-2 [13] future_1.23.0 miniUI_0.1.1.1 [15] withr_2.4.2 colorspace_2.0-2 [17] filelock_1.0.2 ROCR_1.0-11 [19] tensor_1.5 listenv_0.8.0 [21] MatrixGenerics_1.6.0 slam_0.1-49 [23] GenomeInfoDbData_1.2.7 polyclip_1.10-0 [25] bit64_4.0.5 farver_2.1.0 [27] rprojroot_2.0.2 parallelly_1.28.1 [29] vctrs_0.3.8 generics_0.1.1 [31] BiocFileCache_2.2.0 lsa_0.73.2 [33] ggseqlogo_0.1 R6_2.5.1 [35] DelayedArray_0.20.0 bitops_1.0-7 [37] spatstat.utils_2.2-0 cachem_1.0.6 [39] assertthat_0.2.1 promises_1.2.0.1 [41] BiocIO_1.4.0 scales_1.1.1 [43] gtable_0.3.0 globals_0.14.0 [45] processx_3.5.2 goftest_1.2-3 [47] rlang_0.4.12 RcppRoll_0.3.0 [49] splines_4.1.0 rtracklayer_1.54.0 [51] lazyeval_0.2.2 spatstat.geom_2.3-0 [53] yaml_2.2.1 BiocManager_1.30.16 [55] reshape2_1.4.4 abind_1.4-5 [57] httpuv_1.6.3 tools_4.1.0 [59] ellipsis_0.3.2 spatstat.core_2.3-1 [61] RColorBrewer_1.1-2 sessioninfo_1.2.1 [63] ggridges_0.5.3 Rcpp_1.0.7 [65] plyr_1.8.6 progress_1.2.2 [73] deldir_1.0-6 pbapply_1.5-0 [75] cowplot_1.1.1 zoo_1.8-9 [77] SummarizedExperiment_1.24.0 ggrepel_0.9.1 [79] cluster_2.1.2 fs_1.5.0 [81] magrittr_2.0.1 data.table_1.14.2 [83] scattermore_0.7 lmtest_0.9-39 [85] RANN_2.6.1 SnowballC_0.7.0 [87] ProtGenerics_1.26.0 fitdistrplus_1.1-6 [89] matrixStats_0.61.0 pkgload_1.2.3 [91] hms_1.1.1 patchwork_1.1.1 [93] mime_0.12 xtable_1.8-4 [95] XML_3.99-0.8 sparsesvd_0.2 [97] gridExtra_2.3 testthat_3.1.0 [99] compiler_4.1.0 biomaRt_2.50.0 [101] tibble_3.1.6 KernSmooth_2.23-20 [103] crayon_1.4.2 htmltools_0.5.2 [105] mgcv_1.8-38 later_1.3.0 [107] tidyr_1.1.4 DBI_1.1.1 [109] tweenr_1.0.2 dbplyr_2.1.1 [111] MASS_7.3-54 rappdirs_0.3.3 [113] Matrix_1.3-4 cli_3.1.0 [115] parallel_4.1.0 igraph_1.2.8 [117] pkgconfig_2.0.3 GenomicAlignments_1.30.0 [119] plotly_4.10.0 spatstat.sparse_2.0-0 [121] xml2_1.3.2 XVector_0.34.0 [123] stringr_1.4.0 callr_3.7.0 [125] digest_0.6.28 sctransform_0.3.2 [127] RcppAnnoy_0.0.19 spatstat.data_2.1-0 [129] Biostrings_2.62.0 leiden_0.3.9 [131] fastmatch_1.1-3 uwot_0.1.10 [133] restfulr_0.0.13 curl_4.3.2 [135] shiny_1.7.1 Rsamtools_2.10.0 [137] rjson_0.2.20 lifecycle_1.0.1 [139] nlme_3.1-153 jsonlite_1.7.2 [141] desc_1.4.0 viridisLite_0.4.0 [143] fansi_0.5.0 pillar_1.6.4 [145] lattice_0.20-45 KEGGREST_1.34.0 [147] fastmap_1.1.0 httr_1.4.2 [149] pkgbuild_1.2.0 survival_3.2-13 [151] glue_1.5.0 remotes_2.4.1 [153] qlcMatrix_0.9.7 png_0.1-7 [155] bit_4.0.4 ggforce_0.3.3 [157] stringi_1.7.5 blob_1.2.2 [159] memoise_2.0.0 dplyr_1.0.7 [161] irlba_2.3.3 future.apply_1.8.1

[timoast]

This could be an issue related to SeuratObject v4.0.3, can you try installing the previous SeuratObject version (4.0.2) and see if you still have this issue:

remotes::install_version(package = 'SeuratObject', version = package_version('4.0.2'))

Make sure to restart R after installing the SeuratObject package

[Telogen]

That pretty cool bro. Thanks a lot for your help!

[FloWuenne]

@timoast Thank you for pointing out this error here!

I am writing Galaxy wrappers for Signac at the moment and I ran into exactly this error when trying to add Annotations to a seurat object in Signac.

Error in vapply(X = keyed.objects, FUN = function(x) { : 
  values must be length 1,
 but FUN(X[[1]]) result is length 0
Calls: Annotation<- ... Annotation<-.Seurat -> [[<- -> [[<- -> Key -> Key.Seurat -> vapply
Execution halted

The problem I had, is that the dependency for SeuratObject in the conda package for Signac seems to be 4.0.3, while my local R installation had 4.0.2 installed. Since Galaxy uses conda for building environments, SeuratObject 4.0.3 was installed in my Galaxy environment the background, creating this issue.

I would suggest changing the conda dependency of Signac to SeuratObject 4.0.2 as long as this issue remains?!

[timoast]

Hi @FloWuenne

I don't actually maintain the r-signac conda build, but it looks like the version dependency is correctly set as SeuratObject >=4.0.0 (https://github.com/bioconda/bioconda-recipes/blob/master/recipes/r-signac/meta.yaml#L36). My guess is that it simply builds using the latest release that meets the dependency requirements, which is 4.0.3.

R doesn't allow setting version dependencies lower than a certain version, only above, so it's not possible to set the dependency in Signac to SeuratObject<4.0.3 in the R package. I believe you could pin the conda package to 4.0.2, but there should be a patch release of SeuratObject coming very soon that will resolve this so I'm not sure it's worth it.

As a workaround you can create a conda environment with SeuratObject 4.0.2 and Signac by specifying the versions like this:

conda create -n testenv -c bioconda r-seuratobject==4.0.2 r-signac

[FloWuenne]

Thanks for the quick reply @timoast!

I did in fact fix this it by specifying the exact version number of SeuratObject in the requirements of the Galaxy tool. It just took my a really long time to find what the problem was ^^.

If this error will be fixed with a new release of SeuratObject you are right and it might not actually be worth doing anything on your end. I just wanted anybody using the conda package to be aware that this error might occur :).

[timoast]

Yeah it's a good point and currently anyone using the latest SeuratObject release is going to run into this error. I pinned this issue to try to make the solution easier to find but I'm sure it's still costing people time. Hoping we can get the SeuratObject release out soon

[timoast]

SeuratObject 4.0.4 is now out on CRAN and should solve these issues

[drewmard]

Hi, I am running into similar issues, even after updating to SeuratObject 4.0.4. Any help would be appreciated! Please see some example outputs below:

> packageVersion("Seurat")
[1] '4.0.5'
> packageVersion("Signac")
[1] '1.4.0'
> packageVersion("SeuratObject")                           
[1] '4.0.4'
> dfseurat[[i]]
An object of class Seurat 
155523 features across 4215 samples within 1 assay 
Active assay: ATAC (155523 features, 0 variable features)
> FindTopFeatures(dfseurat[[i]][["ATAC"]], min.cutoff = 10)
ChromatinAssay data with 155523 features for 4215 cells
Variable features: 67495 
Genome: 
Annotation present: TRUE 
Motifs present: FALSE 
Fragment files: 1 
>   dfseurat[[i]][["ATAC"]] <- FindTopFeatures(dfseurat[[i]][["ATAC"]], min.cutoff = 10)
Error in vapply(X = keyed.objects, FUN = function(x) { : 
  values must be length 1,
 but FUN(X[[1]]) result is length 0
> FindTopFeatures(dfseurat[[i]], min.cutoff = 10,assay="ATAC")
Error in vapply(X = keyed.objects, FUN = function(x) { : 
  values must be length 1,
 but FUN(X[[1]]) result is length 0
>   dfseurat[[i]] <- FindTopFeatures(dfseurat[[i]], min.cutoff = 10,assay="ATAC")
Error in vapply(X = keyed.objects, FUN = function(x) { : 
  values must be length 1,
 but FUN(X[[1]]) result is length 0

Same errors occur for the RunTFIDF function. Thank you in advance! -Andrew

[genecell]

SeuratObject 4.0.4 is now out on CRAN and should solve these issues

Great! I have just tested SeuratObject 4.0.4 and it solved the above issues.

[timoast]

@drewmard you might need to re-construct the Seurat object using SeuratObject 4.0.4

[drewmard]

@timo

@drewmard you might need to re-construct the Seurat object using SeuratObject 4.0.4

Yep, running from the top and re-constructing using 4.0.4 did the trick. thank you!

[ProphetDarkland]

Hi, I've also encountered this issue, I have upgraded SeuratObject to 4.0.4, and restart R to rerun the pipeline. But the issue still exists. I would be really appreciated if you can help me solve this.

The error is as follows:

> library(SeuratData)
> library(Seurat)
> library(Signac)
> library(EnsDb.Hsapiens.v86)
> library(ggplot2)
> library(cowplot)
> pbmc.rna <- LoadData("pbmcMultiome", "pbmc.rna")
There were 36 warnings (use warnings() to see them)
> pbmc.atac <- LoadData("pbmcMultiome", "pbmc.atac")
There were 36 warnings (use warnings() to see them)
> pbmc.rna <- subset(pbmc.rna, seurat_annotations != "filtered")
> pbmc.atac <- subset(pbmc.atac, seurat_annotations != "filtered")
Error in vapply(X = keyed.objects, FUN = function(x) { :
  values must be length 1,
 but FUN(X[[1]]) result is length 0

And the sessionInfo:

> sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux Server 7.6 (Maipo)

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] pbmcMultiome.SeuratData_0.1.2 Signac_1.4.0
[3] SeuratObject_4.0.4            Seurat_4.0.5
[5] SeuratData_0.2.1

loaded via a namespace (and not attached):
  [1] fastmatch_1.1-3        plyr_1.8.6             igraph_1.2.8
  [4] lazyeval_0.2.2         splines_4.1.0          BiocParallel_1.26.2
  [7] listenv_0.8.0          scattermore_0.7        SnowballC_0.7.0
 [10] usethis_2.1.3          GenomeInfoDb_1.28.4    ggplot2_3.3.5
 [13] digest_0.6.28          htmltools_0.5.2        fansi_0.5.0
 [16] magrittr_2.0.1         memoise_2.0.0          tensor_1.5
 [19] cluster_2.1.2          ROCR_1.0-11            remotes_2.4.1
 [22] globals_0.14.0         Biostrings_2.60.2      matrixStats_0.61.0
 [25] docopt_0.7.1           spatstat.sparse_2.0-0  prettyunits_1.1.1
 [28] colorspace_2.0-2       rappdirs_0.3.3         ggrepel_0.9.1
 [31] dplyr_1.0.7            callr_3.7.0            sparsesvd_0.2
 [34] crayon_1.4.2           RCurl_1.98-1.5         jsonlite_1.7.2
 [37] spatstat.data_2.1-0    survival_3.2-13        zoo_1.8-9
 [40] glue_1.5.1             polyclip_1.10-0        gtable_0.3.0
 [43] zlibbioc_1.38.0        XVector_0.32.0         leiden_0.3.9
 [46] pkgbuild_1.2.0         future.apply_1.8.1     BiocGenerics_0.38.0
 [49] abind_1.4-5            scales_1.1.1           DBI_1.1.1
 [52] miniUI_0.1.1.1         Rcpp_1.0.7             viridisLite_0.4.0
 [55] xtable_1.8-4           reticulate_1.22        spatstat.core_2.3-1
 [58] stats4_4.1.0           htmlwidgets_1.5.4      httr_1.4.2
 [61] RColorBrewer_1.1-2     ellipsis_0.3.2         ica_1.0-2
 [64] pkgconfig_2.0.3        farver_2.1.0           ggseqlogo_0.1
 [67] uwot_0.1.10            deldir_1.0-6           utf8_1.2.2
 [70] tidyselect_1.1.1       rlang_0.4.12           reshape2_1.4.4
 [73] later_1.3.0            munsell_0.5.0          tools_4.1.0
 [76] cachem_1.0.6           cli_3.1.0              generics_0.1.1
 [79] devtools_2.4.2         ggridges_0.5.3         stringr_1.4.0
 [82] fastmap_1.1.0          goftest_1.2-3          processx_3.5.2
 [85] fs_1.5.0               fitdistrplus_1.1-6     purrr_0.3.4
 [88] RANN_2.6.1             pbapply_1.5-0          future_1.23.0
 [91] nlme_3.1-153           mime_0.12              slam_0.1-49
 [94] RcppRoll_0.3.0         compiler_4.1.0         curl_4.3.2
 [97] plotly_4.10.0          png_0.1-7              testthat_3.1.0
[100] spatstat.utils_2.2-0   tibble_3.1.6           tweenr_1.0.2
[103] stringi_1.7.5          ps_1.6.0               desc_1.4.0
[106] lattice_0.20-45        Matrix_1.3-4           vctrs_0.3.8
[109] pillar_1.6.4           lifecycle_1.0.1        spatstat.geom_2.3-0
[112] lmtest_0.9-39          RcppAnnoy_0.0.19       data.table_1.14.2
[115] cowplot_1.1.1          bitops_1.0-7           irlba_2.3.3
[118] httpuv_1.6.3           patchwork_1.1.1        GenomicRanges_1.44.0
[121] R6_2.5.1               promises_1.2.0.1       KernSmooth_2.23-20
[124] gridExtra_2.3          lsa_0.73.2             IRanges_2.26.0
[127] parallelly_1.28.1      sessioninfo_1.2.1      codetools_0.2-18
[130] pkgload_1.2.3          MASS_7.3-54            assertthat_0.2.1
[133] rprojroot_2.0.2        withr_2.4.2            qlcMatrix_0.9.7
[136] sctransform_0.3.2      Rsamtools_2.8.0        S4Vectors_0.30.2
[139] GenomeInfoDbData_1.2.6 mgcv_1.8-38            parallel_4.1.0
[142] grid_4.1.0             rpart_4.1-15           tidyr_1.1.4
[145] Rtsne_0.15             pbdZMQ_0.3-6           ggforce_0.3.3
[148] shiny_1.7.1

[timoast]

The pbmcMultiome data package in SeuratData needs to be updated using SeuratObject 4.0.4 (@mojaveazure)

As a workaround, you can set the key for the ATAC assay and it should solve this issue: pbmc.atac@assays$ATAC@key <- "atac_"

[ProphetDarkland]

Thanks very much! It solved my issues!