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MAST v.1.21.3 -- Error: $ operator is invalid for atomic vectors #170

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akhst7 opened this issue Dec 21, 2021 · 7 comments
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MAST v.1.21.3 -- Error: $ operator is invalid for atomic vectors #170

akhst7 opened this issue Dec 21, 2021 · 7 comments

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@akhst7
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akhst7 commented Dec 21, 2021

I am using Seurat FindMarkers with MAST as follows;

FindMarkers(aml, ident.1 = "10", test.use = "MAST")

and I get an error, Error: $ operator is invalid for atomic vectors

I am using a following recently published Seurat obj but I get the same error with other Seurat obj;

aml <- readRDS(url("https://ndownloader.figshare.com/files/28403676"))

I really appreciate any pointers to resolve this.

> BiocManager::valid()
'getOption("repos")' replaces Bioconductor standard repositories, see '?repositories' for details

replacement repositories:
    CRAN: https://cran.rstudio.com/


* sessionInfo()

R version 4.1.2 (2021-11-01)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] MAST_1.21.3                 SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0 Biobase_2.54.0             
 [5] GenomicRanges_1.46.1        GenomeInfoDb_1.30.0         IRanges_2.28.0              S4Vectors_0.32.3           
 [9] BiocGenerics_0.40.0         MatrixGenerics_1.6.0        matrixStats_0.61.0          data.table_1.14.2          
[13] Seurat_4.0.6                SeuratObject_4.0.4         

loaded via a namespace (and not attached):
  [1] plyr_1.8.6             igraph_1.2.10          lazyeval_0.2.2         splines_4.1.2          BiocParallel_1.28.3   
  [6] listenv_0.8.0          scattermore_0.7        ggplot2_3.3.5          digest_0.6.29          htmltools_0.5.2       
 [11] fansi_0.5.0            magrittr_2.0.1         memoise_2.0.1          tensor_1.5             cluster_2.1.2         
 [16] ROCR_1.0-11            limma_3.48.3           globals_0.14.0         Biostrings_2.62.0      annotate_1.72.0       
 [21] spatstat.sparse_2.1-0  prettyunits_1.1.1      colorspace_2.0-2       blob_1.2.2             ggrepel_0.9.1         
 [26] lobstr_1.1.1.9000      dplyr_1.0.7            crayon_1.4.2           RCurl_1.98-1.5         jsonlite_1.7.2        
 [31] genefilter_1.74.1      spatstat.data_2.1-2    survival_3.2-13        zoo_1.8-9              glue_1.6.0            
 [36] polyclip_1.10-0        gtable_0.3.0           zlibbioc_1.40.0        XVector_0.34.0         leiden_0.3.9          
 [41] DelayedArray_0.20.0    future.apply_1.8.1     abind_1.4-5            scales_1.1.1           DBI_1.1.2             
 [46] miniUI_0.1.1.1         Rcpp_1.0.7             viridisLite_0.4.0      xtable_1.8-4           progress_1.2.2        
 [51] reticulate_1.22        spatstat.core_2.3-2    bit_4.0.4              htmlwidgets_1.5.4      httr_1.4.2            
 [56] RColorBrewer_1.1-2     ellipsis_0.3.2         ica_1.0-2              pkgconfig_2.0.3        XML_3.99-0.8          
 [61] farver_2.1.0           uwot_0.1.11            deldir_1.0-6           locfit_1.5-9.4         utf8_1.2.2            
 [66] tidyselect_1.1.1       labeling_0.4.2         rlang_0.4.12           reshape2_1.4.4         later_1.3.0           
 [71] AnnotationDbi_1.56.2   munsell_0.5.0          tools_4.1.2            cachem_1.0.6           generics_0.1.1        
 [76] RSQLite_2.2.9          ggridges_0.5.3         stringr_1.4.0          fastmap_1.1.0          goftest_1.2-3         
 [81] bit64_4.0.5            fitdistrplus_1.1-6     purrr_0.3.4            RANN_2.6.1             KEGGREST_1.34.0       
 [86] pbapply_1.5-0          future_1.23.0          nlme_3.1-153           mime_0.12              compiler_4.1.2        
 [91] plotly_4.10.0          png_0.1-7              spatstat.utils_2.3-0   tibble_3.1.6           geneplotter_1.70.0    
 [96] stringi_1.7.6          lattice_0.20-45        Matrix_1.4-0           vctrs_0.3.8            pillar_1.6.4          
[101] lifecycle_1.0.1        BiocManager_1.30.16    spatstat.geom_2.3-1    lmtest_0.9-39          RcppAnnoy_0.0.19      
[106] cowplot_1.1.1          bitops_1.0-7           irlba_2.3.5            httpuv_1.6.4           patchwork_1.1.1       
[111] R6_2.5.1               promises_1.2.0.1       KernSmooth_2.23-20     gridExtra_2.3          parallelly_1.30.0     
[116] codetools_0.2-18       MASS_7.3-54            DESeq2_1.35.0          sctransform_0.3.2      GenomeInfoDbData_1.2.7
[121] mgcv_1.8-38            parallel_4.1.2         hms_1.1.1              grid_4.1.2             rpart_4.1-15          
[126] tidyr_1.1.4            Rtsne_0.15             shiny_1.7.1           

Bioconductor version '3.13'

  * 0 packages out-of-date
  * 50 packages too new

create a valid installation with

  BiocManager::install(c(
    "annotate", "AnnotationDbi", "beachmat", "Biobase", "BiocGenerics", "BiocIO", "BiocNeighbors", "BiocParallel",
    "BiocSingular", "Biostrings", "BSgenome", "CNEr", "ComplexHeatmap", "cpp11", "DelayedArray", "DelayedMatrixStats",
    "DESeq2", "DirichletMultinomial", "fst", "GenomeInfoDb", "GenomeInfoDbData", "GenomicAlignments", "GenomicRanges",
    "ggVennDiagram", "GO.db", "graph", "IRanges", "KEGGgraph", "KEGGREST", "lobstr", "MAST", "MatrixGenerics",
    "org.Hs.eg.db", "pathfindR", "Rgraphviz", "Rhtslib", "Rsamtools", "rtracklayer", "S4Vectors", "ScaledMatrix",
    "scater", "scuttle", "seqLogo", "seriation", "SingleCellExperiment", "sparseMatrixStats", "SummarizedExperiment",
    "TFBSTools", "XVector", "zlibbioc"
  ), update = TRUE, ask = FALSE)

more details: BiocManager::valid()$too_new, BiocManager::valid()$out_of_date

Warning message:
0 packages out-of-date; 50 packages too new 
>
@amcdavid
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amcdavid commented Dec 21, 2021 via email

@akhst7
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akhst7 commented Dec 22, 2021

@amcdavid

There are 50 new packages and I am not ready to downgrade those 50 packages for a variety of reasons.

A following is the traceback. As usual, traceback is a full of Seurat garbage and not quite useful;

traceback()
8: MASTDETest(data.use = data.use, cells.1 = cells.1, cells.2 = cells.2, 
       latent.vars = latent.vars, verbose = verbose, ...)
7: PerformDE(object = object, cells.1 = cells.1, cells.2 = cells.2, 
       features = features, test.use = test.use, verbose = verbose, 
       min.cells.feature = min.cells.feature, latent.vars = latent.vars, 
       densify = densify, ...)
6: FindMarkers.default(object = data.use, slot = data.slot, counts = counts, 
       cells.1 = cells.1, cells.2 = cells.2, features = features, 
       logfc.threshold = logfc.threshold, test.use = test.use, min.pct = min.pct, 
       min.diff.pct = min.diff.pct, verbose = verbose, only.pos = only.pos, 
       max.cells.per.ident = max.cells.per.ident, random.seed = random.seed, 
       latent.vars = latent.vars, min.cells.feature = min.cells.feature, 
       min.cells.group = min.cells.group, pseudocount.use = pseudocount.use, 
       fc.results = fc.results, densify = densify, ...)
5: FindMarkers(object = data.use, slot = data.slot, counts = counts, 
       cells.1 = cells.1, cells.2 = cells.2, features = features, 
       logfc.threshold = logfc.threshold, test.use = test.use, min.pct = min.pct, 
       min.diff.pct = min.diff.pct, verbose = verbose, only.pos = only.pos, 
       max.cells.per.ident = max.cells.per.ident, random.seed = random.seed, 
       latent.vars = latent.vars, min.cells.feature = min.cells.feature, 
       min.cells.group = min.cells.group, pseudocount.use = pseudocount.use, 
       fc.results = fc.results, densify = densify, ...)
4: FindMarkers.Assay(object = data.use, slot = slot, cells.1 = cells$cells.1, 
       cells.2 = cells$cells.2, features = features, logfc.threshold = logfc.threshold, 
       test.use = test.use, min.pct = min.pct, min.diff.pct = min.diff.pct, 
       verbose = verbose, only.pos = only.pos, max.cells.per.ident = max.cells.per.ident, 
       random.seed = random.seed, latent.vars = latent.vars, min.cells.feature = min.cells.feature, 
       min.cells.group = min.cells.group, pseudocount.use = pseudocount.use, 
       mean.fxn = mean.fxn, base = base, fc.name = fc.name, densify = densify, 
       ...)
3: FindMarkers(object = data.use, slot = slot, cells.1 = cells$cells.1, 
       cells.2 = cells$cells.2, features = features, logfc.threshold = logfc.threshold, 
       test.use = test.use, min.pct = min.pct, min.diff.pct = min.diff.pct, 
       verbose = verbose, only.pos = only.pos, max.cells.per.ident = max.cells.per.ident, 
       random.seed = random.seed, latent.vars = latent.vars, min.cells.feature = min.cells.feature, 
       min.cells.group = min.cells.group, pseudocount.use = pseudocount.use, 
       mean.fxn = mean.fxn, base = base, fc.name = fc.name, densify = densify, 
       ...)
2: FindMarkers.Seurat(aml, ident.1 = "10", test.use = "MAST")
1: FindMarkers(aml, ident.1 = "10", test.use = "MAST")

At this point, I will cover this Seurat obj to SingleCellExiperiment and feed it into MAST manually and see what happens.

@lauren-fish
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lauren-fish commented Dec 23, 2021 •
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Hi @amcdavid, I reinstalled Bioconductor a couple of days ago to update several packages and I am also having the same error. Which versions of MAST and Bioconductor should I make sure I have installed in order to get everything working again? I have Bioconductor 3.14 and MAST 1.20.0, and I'm using Seurat 4.0.6. The FindMarkers command works fine when I use DESeq2 or the default option so it does seem to be a MAST issue?
When I try BiocManager::valid() it returns TRUE. I can provide my traceback if it would be useful.
Thank you!

@akhst7
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akhst7 commented Dec 23, 2021

I run MAST manually by feeding SingleCellAssay obj converted from Seurat obj into zlm as follows.

options(mc.cores = parallel::detectCores())
aml <- readRDS(url("https://ndownloader.figshare.com/files/28403676")) #Seurat Ojb
sce<-as.SingleCellExperiment(aml, assay = "RNA")
sca<-SceToSingleCellAssay(sce)
sca.fit<-zlm(~ seurat_clusters, sca)
> sca.fit
Fitted zlm on 461 genes and 31586 cells.
 Using BayesGLMlike ~ seurat_clusters 
> str(sca.fit, max.level = 2)
Formal class 'ZlmFit' [package "MAST"] with 17 slots
  ..@ coefC             : num [1:461, 1:35] 3.29 2.03 3.27 2.13 2.21 ...
  .. ..- attr(*, "dimnames")=List of 2
  ..@ coefD             : num [1:461, 1:35] 2.49 -4.57 2.37 -2.24 -5.64 ...
  .. ..- attr(*, "dimnames")=List of 2
  ..@ vcovC             : num [1:35, 1:35, 1:461] 9.59e-05 -9.59e-05 -9.59e-05 -9.59e-05 -9.59e-05 ...
  .. ..- attr(*, "dimnames")=List of 3
  ..@ vcovD             : num [1:35, 1:35, 1:461] 0.00336 -0.00335 -0.00324 -0.00307 -0.00334 ...
  .. ..- attr(*, "dimnames")=List of 3
  ..@ LMlike            :Formal class 'BayesGLMlike' [package "MAST"] with 16 slots
  ..@ sca               :Formal class 'SingleCellAssay' [package "MAST"] with 11 slots
  ..@ deviance          : num [1:461, 1:2] 10435 2690 7425 3121 167 ...
  .. ..- attr(*, "dimnames")=List of 2
  ..@ loglik            : num [1:461, 1:2] -26116 -6476 -18857 -7701 -489 ...
  .. ..- attr(*, "dimnames")=List of 2
  ..@ df.null           : int [1:461, 1:2] 28482 6754 20937 8257 666 8133 8197 18956 3531 71 ...
  .. ..- attr(*, "dimnames")=List of 2
  ..@ df.resid          : num [1:461, 1:2] 28448 6721 20903 8223 641 ...
  .. ..- attr(*, "dimnames")=List of 2
  ..@ dispersion        : num [1:461, 1:2] 0.366 0.398 0.355 0.378 0.254 ...
  .. ..- attr(*, "dimnames")=List of 2
  ..@ dispersionNoshrink: num [1:461, 1:2] 0.366 0.398 0.355 0.378 0.251 ...
  .. ..- attr(*, "dimnames")=List of 2
  ..@ priorDOF          : num 9.36
  ..@ priorVar          : num 0.459
  ..@ converged         : logi [1:461, 1:2] TRUE TRUE TRUE TRUE TRUE TRUE ...
  .. ..- attr(*, "dimnames")=List of 2
  ..@ hookOut           : NULL
  ..@ exprs_values      : int 2

I have not yet closely looked at numbers but at least I did not get the atomic vector error.

I will leave the question open for @lauren-fish .

@amcdavid
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Based on the backtrace, the error looks like it's in Seurat, probably a regression introduced in 4.0.6.

@lauren-fish
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Hi @akhst7 and @amcdavid, thanks for the tips! I'll keep this bookmarked in case I need to do this in the future.

I downloaded Seurat 4.0.5 from CRAN and restarted R and things are working now.

@lauren-fish lauren-fish mentioned this issue Dec 23, 2021
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@akhst7 akhst7 closed this as completed Dec 24, 2021
@YannLeGuen
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YannLeGuen commented Jan 9, 2022 •
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Should an issue be opened on the Seurat package side? Seems like previous scripts are incompatible with this update when using "MAST" in some Seurat functions.

I see it has already been opened satijalab/seurat#5419

@amcdavid amcdavid mentioned this issue Jan 10, 2022
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@amcdavid @akhst7 @YannLeGuen @lauren-fish