Error loading default models

[MartaBenegas]

Hi! I was just following the vignette to try scAnnotatR but it gave me an error while loading the default models:

> BiocManager::install("scAnnotatR", force = TRUE)
'getOption("repos")' replaces Bioconductor standard repositories, see 'help("repositories", package = "BiocManager")' for
details.
Replacement repositories:
    CRAN: https://cloud.r-project.org
Bioconductor version 3.17 (BiocManager 1.30.22), R 4.3.1 (2023-06-16)
Installing package(s) 'scAnnotatR'
trying URL 'https://bioconductor.org/packages/3.17/bioc/src/contrib/scAnnotatR_1.6.0.tar.gz'
Content type 'application/gzip' length 522173 bytes (509 KB)
==================================================
downloaded 509 KB

* installing *source* package ‘scAnnotatR’ ...
** using staged installation
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading
Warning: replacing previous import ‘ape::where’ by ‘dplyr::where’ when loading ‘scAnnotatR’
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
Warning: replacing previous import ‘ape::where’ by ‘dplyr::where’ when loading ‘scAnnotatR’
** testing if installed package can be loaded from final location
Warning: replacing previous import ‘ape::where’ by ‘dplyr::where’ when loading ‘scAnnotatR’
** testing if installed package keeps a record of temporary installation path
* DONE (scAnnotatR)

The downloaded source packages are in
	‘/tmp/RtmphTT7Ar/downloaded_packages’
Installation paths not writeable, unable to update packages
  path: /usr/lib/R/library
  packages:
    boot, Matrix, spatial
> library(scAnnotatR)
Loading required package: Seurat
Loading required package: SeuratObject
Loading required package: sp

Attaching package: ‘SeuratObject’

The following object is masked from ‘package:base’:

    intersect

Loading required package: SingleCellExperiment
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: ‘MatrixGenerics’

The following objects are masked from ‘package:matrixStats’:

    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods,
    colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, colMads, colMaxs, colMeans2, colMedians,
    colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, colSums2, colTabulates,
    colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars,
    rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians,
    rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, rowSdDiffs, rowSds, rowSums2, rowTabulates,
    rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars

Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: ‘BiocGenerics’

The following object is masked from ‘package:SeuratObject’:

    intersect

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, aperm, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval,
    evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget, order, paste,
    pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which.max, which.min

Loading required package: S4Vectors

Attaching package: ‘S4Vectors’

The following object is masked from ‘package:utils’:

    findMatches

The following objects are masked from ‘package:base’:

    expand.grid, I, unname

Loading required package: IRanges

Attaching package: ‘IRanges’

The following object is masked from ‘package:sp’:

    %over%

Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: ‘Biobase’

The following object is masked from ‘package:MatrixGenerics’:

    rowMedians

The following objects are masked from ‘package:matrixStats’:

    anyMissing, rowMedians


Attaching package: ‘SummarizedExperiment’

The following object is masked from ‘package:Seurat’:

    Assays

The following object is masked from ‘package:SeuratObject’:

    Assays

Warning message:
replacing previous import ‘ape::where’ by ‘dplyr::where’ when loading ‘scAnnotatR’ 
> default_models <- load_models("default")
Error in `collect()`:
! Failed to collect lazy table.
Caused by error in `db_collect()`:
! Arguments in `...` must be used.
✖ Problematic argument:
• ..1 = Inf
ℹ Did you misspell an argument name?
Run `rlang::last_trace()` to see where the error occurred.

Additionally, I would like to ask a few questions.

• Are you planning on including more classifiers in the package?
• If no marker genes are available for a given dataset, would it be a good practice to identify them from the data itself? For example, using scoreMarkers() from scran and using the DE genes for each cell type as marker genes. Or maybe using representative gene sets computed by ActiveSVM. Have you benchmarked this?

Thanks in advance,
Marta.

[jgriss]

Hi @MartaBenegas

Sorry about the delayed response! Thanks a lot for your interest in our package!

The error was caused by BioConductors feature to load large data (ie our models in this case). In the log, I saw the following error: installation paths not writeable, unable to update packages

Could you maybe post the output of ".libPaths()" to see whether alternative locations were set?

About the package development: We are planning to release more classifiers. But our research is very much focused on skin-related cell types.

In order to define your "own" cell types, we found that a mixture of top-differentially regulated genes + canonical markers works quite well. But this very much depends on the cell type in question.