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Error in getClassDef(x@superClass, package = packageSlot(x))@virtual : no applicable method for @ applied to an object of class "NULL" #184
Description
Hi,
Thanks for this package, I am getting this error with the MAST::zlm function. I am scratching my head and can't find a solution. Any pointer is hugely appreciated.
> zlmCond <- MAST::zlm(
formula = model_formula,
sca = sca,
exprs_value = 'logcounts',
method = "glmer", # note: glmer requires a random effects var
ebayes = F,
parallel = F,
fitArgsD = list(nAGQ = 0)
)
Error in getClassDef(x@superClass, package = packageSlot(x))@virtual :
no applicable method for `@` applied to an object of class "NULL"
> traceback()
22: isVirtualExt(exti)
21: FUN(X[[i]], ...)
20: vapply(ext, F, NA, USE.NAMES = FALSE)
19: .selectSuperClasses(list(dsyMatrix = new("SClassExtension", subClass = structure("dpoMatrix", package = "Matrix"),
superClass = structure("dsyMatrix", package = "Matrix"),
package = "Matrix", coerce = function (from, strict = TRUE)
{
class(from) <- "dsyMatrix"
from
}, test = function (object)
TRUE, replace = function (from, to, value)
{
for (what in c("Dim", "Dimnames", "x", "uplo", "factors"
)) slot(from, what) <- slot(value, what)
from
}, simple = TRUE, by = character(0), dataPart = FALSE, distance = 1),
unpackedMatrix = new("SClassExtension", subClass = structure("dpoMatrix", package = "Matrix"),
superClass = structure("unpackedMatrix", package = "Matrix"),
package = "Matrix", coerce = function (from, strict = TRUE)
{
from <- {
class(from) <- "dsyMatrix"
from
...
18: diag(from, names = FALSE)
17: diag(from, names = FALSE)
16: asMethod(object)
15: as(rr, "corMatrix")
14: vcov.merMod(object@fitC)
13: vcov(object@fitC)
12: vcov(object@fitC)
11: .local(object, ...)
10: vcov(object, "C")
9: vcov(object, "C")
8: withCallingHandlers(expr, warning = function(w) {
if (str_detect(conditionMessage(w), regexp))
invokeRestart("muffleWarning")
})
7: hushWarning(list(coefC = coef(object, which = "C"), vcovC = vcov(object,
"C"), deviance = rowm(deviance(object@fitC), deviance(object@fitD)),
df.null = rowm(nobs(object@fitC), nobs(object@fitD)), dispersion = rowm(sigma(object@fitC),
NA), coefD = coef(object, which = "D"), vcovD = vcov(object,
"D"), loglik = torowm(logLik(object)), converged = torowm(object@fitted)),
"nobs")
6: summarize(obj)
5: summarize(obj)
4: FUN(X[[i]], ...)
3: lapply(listEE, .fitGeneSet)
2: lapply(listEE, .fitGeneSet)
1: MAST::zlm(formula = model_formula, sca = sca, exprs_value = "logcounts",
method = fargs$mast_method, ebayes = fargs$ebayes, parallel = fargs$parallel,
fitArgsD = fit_args_D)
> sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.6 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
locale:
[1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8
[4] LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
[7] LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
time zone: Europe/London
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] data.table_1.14.8 MAST_1.27.1 Matrix_1.6-5
[4] SummarizedExperiment_1.32.0 SingleCellExperiment_1.24.0 IRanges_2.36.0
[7] GenomicRanges_1.54.1 S4Vectors_0.40.1
loaded via a namespace (and not attached):
[1] R.methodsS3_1.8.2 progress_1.2.2 DirichletReg_0.7-1
[4] DT_0.30 goftest_1.2-3 Biostrings_2.70.1
[7] HDF5Array_1.30.0 vctrs_0.6.4 spatstat.random_3.2-1
[10] RApiSerialize_0.1.2 digest_0.6.33 png_0.1-8
[13] ggrepel_0.9.4 deldir_1.0-9 parallelly_1.36.0
[16] MASS_7.3-60 reshape2_1.4.4 httpuv_1.6.12
[19] foreach_1.5.2 BiocGenerics_0.48.0 xfun_0.41
[22] ggfun_0.1.3 ggpubr_0.6.0 ellipsis_0.3.2
[25] survival_3.5-7 doRNG_1.8.6 memoise_2.0.1
[28] ggbeeswarm_0.7.2 systemfonts_1.0.5 tidytree_0.4.5
[31] zoo_1.8-12 pbapply_1.7-2 R.oo_1.25.0
[34] Formula_1.2-5 prettyunits_1.2.0 rematch2_2.1.2
[37] KEGGREST_1.42.0 promises_1.2.1 httr_1.4.7
[40] rstatix_0.7.2 globals_0.16.2 fitdistrplus_1.1-11
[43] rhdf5filters_1.14.0 stringfish_0.15.8 rhdf5_2.46.0
[46] rstudioapi_0.15.0 miniUI_0.1.1.1 generics_0.1.3
[49] base64enc_0.1-3 babelgene_22.9 curl_5.1.0
[52] zlibbioc_1.48.0 ScaledMatrix_1.10.0 polyclip_1.10-6
[55] GenomeInfoDbData_1.2.11 ExperimentHub_2.10.0 SparseArray_1.2.0
[58] threejs_0.3.3 interactiveDisplayBase_1.40.0 xtable_1.8-4
[61] stringr_1.5.0 doParallel_1.0.17 evaluate_0.23
[64] S4Arrays_1.2.0 BiocFileCache_2.10.1 preprocessCore_1.64.0
[67] hms_1.1.3 irlba_2.3.5.1 qs_0.25.5
[70] colorspace_2.1-0 filelock_1.0.2 hdf5r_1.3.8
[73] apcluster_1.4.11 ROCR_1.0-11 reticulate_1.34.0
[76] spatstat.data_3.0-3 magrittr_2.0.3 lmtest_0.9-40
[79] readr_2.1.4 later_1.3.1 viridis_0.6.4
[82] ggtree_3.10.0 lattice_0.22-5 spatstat.geom_3.2-7
[85] future.apply_1.11.0 scattermore_1.2 XML_3.99-0.14
[88] scuttle_1.12.0 cowplot_1.1.1 matrixStats_1.1.0
[91] RcppAnnoy_0.0.21 pillar_1.9.0 nlme_3.1-163
[94] iterators_1.0.14 compiler_4.3.2 beachmat_2.18.0
[97] stringi_1.7.12 minqa_1.2.6 tensor_1.5
[100] plyr_1.8.9 crayon_1.5.2 abind_1.4-5
[103] scater_1.30.0 ggdendro_0.1.23 gridGraphics_0.5-1
[106] RNOmni_1.0.1.2 locfit_1.5-9.8 bib2df_1.1.2.0
[109] sp_2.1-1 terra_1.7-55 bit_4.0.5
[112] UpSetR_1.4.0 sandwich_3.0-2 dplyr_1.1.3
[115] whisker_0.4.1 codetools_0.2-19 BiocSingular_1.18.0
[118] crosstalk_1.2.0 monocle3_1.3.4 leaflet_2.2.0
[121] paletteer_1.5.0 plotly_4.10.3 mime_0.12
[124] splines_4.3.2 Rcpp_1.0.11 EWCE_1.10.2
[127] dbplyr_2.4.0 sparseMatrixStats_1.14.0 maxLik_1.5-2
[130] knitr_1.45 grr_0.9.5 blob_1.2.4
[133] utf8_1.2.4 BiocVersion_3.18.0 lme4_1.1-35.1
[136] WriteXLS_6.4.0 fs_1.6.3 listenv_0.9.0
[139] DelayedMatrixStats_1.24.0 HGNChelper_0.8.1 english_1.2-6
[142] orthogene_1.8.0 ggsignif_0.6.4 ggplotify_0.1.2
[145] tibble_3.2.1 statmod_1.5.0 tzdb_0.4.0
[148] svglite_2.1.2 pkgconfig_2.0.3 tools_4.3.2
[151] cachem_1.0.8 RSQLite_2.3.2 viridisLite_0.4.2
[154] DBI_1.1.3 rmarkdown_2.25 fastmap_1.1.1
[157] scales_1.2.1 grid_4.3.2 ica_1.0-3
[160] gprofiler2_0.2.2 Seurat_4.4.0 broom_1.0.5
[163] AnnotationHub_3.10.0 FNN_1.1.3.2 patchwork_1.1.3
[166] BiocManager_1.30.22 kBET_0.99.6 dotCall64_1.1-0
[169] carData_3.0-5 RANN_2.6.1 scFlow_0.7.2
[172] yaml_2.3.7 MatrixGenerics_1.14.0 cli_3.6.1
[175] purrr_1.0.2 stats4_4.3.2 leiden_0.4.3
[178] lifecycle_1.0.4 uwot_0.1.16 Biobase_2.62.0
[181] homologene_1.4.68.19.3.27 backports_1.4.1 DropletUtils_1.22.0
[184] BiocParallel_1.36.0 gtable_0.3.4 rjson_0.2.21
[187] ggridges_0.5.4 progressr_0.14.0 parallel_4.3.2
[190] ape_5.7-1 limma_3.58.1 jsonlite_1.8.7
[193] edgeR_4.0.1 miscTools_0.6-28 bitops_1.0-7
[196] ggplot2_3.4.4 bit64_4.0.5 assertthat_0.2.1
[199] WebGestaltR_0.4.6 Rtsne_0.16 yulab.utils_0.1.0
[202] spatstat.utils_3.0-4 BiocNeighbors_1.20.0 SeuratObject_4.1.4
[205] RcppParallel_5.1.7 riverplot_0.10 formattable_0.2.1
[208] dqrng_0.3.1 enrichR_3.2 R.utils_2.12.2
[211] lazyeval_0.2.2 shiny_1.7.5.1 htmltools_0.5.7
[214] sctransform_0.4.1 rappdirs_0.3.3 glue_1.6.2
[217] ewceData_1.10.0 rliger_1.0.0 spam_2.10-0
[220] XVector_0.42.0 RCurl_1.98-1.12 treeio_1.26.0
[223] mclust_6.0.0 gridExtra_2.3 boot_1.3-28.1
[226] humaniformat_0.6.0 igraph_1.5.1 R6_2.5.1
[229] tidyr_1.3.0 forcats_1.0.0 DoubletFinder_2.0.3
[232] cluster_2.1.6 rngtools_1.5.2 Rhdf5lib_1.24.0
[235] aplot_0.2.2 GenomeInfoDb_1.38.0 nloptr_2.0.3
[238] DelayedArray_0.28.0 tidyselect_1.2.0 vipor_0.4.5
[241] xml2_1.3.5 car_3.1-2 AnnotationDbi_1.64.0
[244] future_1.33.0 rsvd_1.0.5 munsell_0.5.0
[247] KernSmooth_2.23-22 htmlwidgets_1.6.2 RColorBrewer_1.1-3
[250] biomaRt_2.58.0 rlang_1.1.2 spatstat.sparse_3.0-3
[253] spatstat.explore_3.2-5 fansi_1.0.5 beeswarm_0.4.0
The file was a little to big to attach here so I uploaded the reproducible data here.
Activity
amcdavid commentedon Jan 22, 2024
Hi,
Thanks the detailed report. What model are you fitting in
model_formula?nfancy commentedon Feb 13, 2024
Apologies, for some reason, I didn't get a notification of your response. The model formula was
~cngenseon+(1|manifest) + diagnosisnfancy commentedon Feb 14, 2024
Here's a response I got from Matrix developer.
https://r-forge.r-project.org/tracker/?func=detail&atid=294&aid=6835&group_id=61
However, it did not solve the issue. Thanks.
amcdavid commentedon Feb 14, 2024
@nfancy I think this is a binary compatibility issue that might be resolved by recompiling (and/or reinstalling?) some packages. Using the data you provided and a bioconductor 3.17 docker image with installed MAST I could run your model successfully. I'm sorry that I can't provide more guidance than "it works on my machine". The sessionInfo of this container is listed below -- It does have MAST 1.28.0 (which shouldn't matter), but otherwise the same versions of Matrix and lme4 as you reported above. I do see that you are on an ubuntu 18.xx versus ubuntu 20.xx in my container, which could cause some subtle issues with compiled code, I suppose.
Details
> sessionInfo() R version 4.3.1 (2023-06-16) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 22.04.3 LTSMatrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 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
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] MAST_1.28.0 SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0 Biobase_2.60.0
[5] GenomicRanges_1.54.1 GenomeInfoDb_1.38.5 IRanges_2.34.1 S4Vectors_0.38.2
[9] BiocGenerics_0.46.0 MatrixGenerics_1.12.3 matrixStats_1.1.0
loaded via a namespace (and not attached):
[1] utf8_1.2.3 generics_0.1.3 SparseArray_1.2.3 bitops_1.0-7 stringi_1.7.12
[6] lattice_0.21-8 hms_1.1.3 lme4_1.1-35.1 magrittr_2.0.3 grid_4.3.1
[11] plyr_1.8.9 Matrix_1.6-5 progress_1.2.3 purrr_1.0.2 fansi_1.0.4
[16] scales_1.3.0 abind_1.4-5 cli_3.6.1 rlang_1.1.1 crayon_1.5.2
[21] XVector_0.42.0 splines_4.3.1 munsell_0.5.0 DelayedArray_0.28.0 S4Arrays_1.2.0
[26] tools_4.3.1 reshape2_1.4.4 nloptr_2.0.3 minqa_1.2.6 dplyr_1.1.4
[31] colorspace_2.1-0 ggplot2_3.4.4 GenomeInfoDbData_1.2.11 boot_1.3-28.1 vctrs_0.6.4
[36] R6_2.5.1 lifecycle_1.0.3 zlibbioc_1.46.0 stringr_1.5.0 MASS_7.3-60
[41] pkgconfig_2.0.3 pillar_1.9.0 gtable_0.3.4 glue_1.6.2 data.table_1.14.8
[46] Rcpp_1.0.11 tibble_3.2.1 tidyselect_1.2.0 rstudioapi_0.15.0 nlme_3.1-163
[51] compiler_4.3.1 prettyunits_1.1.1 RCurl_1.98-1.13
nfancy commentedon Feb 14, 2024
Hi, Thanks for checking. Can you think of any dependencies for MAST which depends on Matrix?
amcdavid commentedon Feb 14, 2024
lme4 depends on matrix, and is being used in the example you provided. Did you reinstall lme4 from source? I sometimes find these issues require deleting packages manually from the R package library and reinstalling, which has its own pitfalls. Otherwise, you could work in one of the docker containers I linked to above (kind of annoying I realize).
nfancy commentedon Feb 14, 2024
Thanks. We have incorporated MAST in our own package scFlow, hence it's mandatory that we sort this out. :(
nfancy commentedon Feb 14, 2024
You are right, the culprit is lme4 since with method = "glm" it runs file. Unfortunately deleting and reinstalling lme4 with type = "source" did not help.
amcdavid commentedon Feb 14, 2024
Oh nice! Since it's nextflow-based, I suppose you could just run the process that references MAST in a docker image, though that would probably constrain your users too much. What environments are you getting the failure? Just on local workstations, or in CI?
nfancy commentedon Feb 15, 2024
We have both an R package with MAST-wrapper and nextflow-based pipeline, hence, we first need to find a fix within our system. The worst case scenario, we may need to wait until the next version release of lme4.