I was following the OSCA book for finding variable genes and while trying to calculate the variable genes with modelGeneVarByPoisson I ran into an error.

I have found only another mention to this function in the support forum but it was not related to this error AFAIK.

Code and error message + traceback and sessionInfo:

> var_poiss <- scran::modelGeneVarByPoisson(gex_filt, 
                                            size.factors = gex_filt$sum,
                                            block = gex_filt$type, subset_row = top_g, 
                                            BPPARAM = multi_core)
|==============================================================| 100%

|==============================================================| 100%

Error in density.default(x, adjust = adjust, from = min(x), to = max(x)) : 
  need at least 2 points to select a bandwidth automatically
In addition: Warning message:
  In seq.default(from = log(min(means)), to = log(max(means)), length = npts) :
  partial argument match of 'length' to 'length.out'
> traceback()
15: stop("need at least 2 points to select a bandwidth automatically")
14: density.default(x, adjust = adjust, from = min(x), to = max(x))
13: density(x, adjust = adjust, from = min(x), to = max(x)) at utils_variance.R#183
12: .inverse_density_weights(m, adjust = 1) at fitTrendVar.R#100
11: fitTrendVar(fm, fv, ...) at utils_variance.R#80
10: .decompose_log_exprs(x.stats$means, x.stats$vars, sim.out$means, 
                         sim.out$vars, x.stats$ncells, ...) at modelGeneVarByPoisson.R#123
9: .model_gene_var_by_poisson(assay(x, i = assay.type), ...) at modelGeneVarByPoisson.R#147
8: .local(x, ...)
7: .nextMethod(x = x, size.factors = size.factors, ...)
6: eval(call, callEnv)
5: eval(call, callEnv)
4: callNextMethod(x = x, size.factors = size.factors, ...) at modelGeneVarByPoisson.R#156
3: .local(x, ...)
2: scran::modelGeneVarByPoisson(gex_filt, size.factors = gex_filt$sum, 
                                block = gex_filt$type, subset_row = top_g, BPPARAM = multi_core) at modelGeneVarByPoisson.R#136
1: scran::modelGeneVarByPoisson(gex_filt, size.factors = gex_filt$sum, 
                                block = gex_filt$type, subset_row = top_g, BPPARAM = multi_core)
> sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS

Matrix 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_GB.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=es_ES.UTF-8        LC_COLLATE=en_GB.UTF-8    
 [5] LC_MONETARY=es_ES.UTF-8    LC_MESSAGES=en_GB.UTF-8   
 [7] LC_PAPER=es_ES.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C       

time zone: Europe/Madrid
tzcode source: system (glibc)

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

other attached packages:
 [1] readxl_1.4.3                rutils_0.0.1.9004          
 [3] PCAtools_2.16.0             ggrepel_0.9.5              
 [5] ensembldb_2.28.0            AnnotationFilter_1.28.0    
 [7] GenomicFeatures_1.56.0      forcats_1.0.0              
 [9] dplyr_1.1.4                 here_1.0.1                 
[11] ggplot2_3.5.1               DelayedMatrixStats_1.26.0  
[13] DelayedArray_0.30.1         SparseArray_1.4.8          
[15] S4Arrays_1.4.1              abind_1.4-5                
[17] Matrix_1.7-0                robustbase_0.99-2          
[19] GO.db_3.19.1                AnnotationDbi_1.66.0       
[21] BiocSingular_1.20.0         patchwork_1.2.0            
[23] scDblFinder_1.18.0          scds_1.20.0                
[25] BiocParallel_1.38.0         scuttle_1.14.0             
[27] biomaRt_2.60.0              AnnotationHub_3.12.0       
[29] BiocFileCache_2.12.0        dbplyr_2.5.0               
[31] DropletUtils_1.24.0         SingleCellExperiment_1.26.0
[33] SummarizedExperiment_1.34.0 Biobase_2.64.0             
[35] GenomicRanges_1.56.0        GenomeInfoDb_1.40.1        
[37] IRanges_2.38.0              S4Vectors_0.42.0           
[39] BiocGenerics_0.50.0         MatrixGenerics_1.16.0      
[41] matrixStats_1.3.0           targets_1.7.0              

loaded via a namespace (and not attached):
  [1] BiocIO_1.14.0            bitops_1.0-7            
  [3] filelock_1.0.3           tibble_3.2.1            
  [5] R.oo_1.26.0              cellranger_1.1.0        
  [7] pROC_1.18.5              XML_3.99-0.16.1         
  [9] lifecycle_1.0.4          httr2_1.0.1             
 [11] edgeR_4.2.0              rprojroot_2.0.4         
 [13] processx_3.8.4           lattice_0.22-6          
 [15] MASS_7.3-60.2            backports_1.5.0         
 [17] magrittr_2.0.3           limma_3.60.2            
 [19] yaml_2.3.8               metapod_1.12.0          
 [21] cowplot_1.1.3            DBI_1.2.3               
 [23] zlibbioc_1.50.0          purrr_1.0.2             
 [25] R.utils_2.12.3           RCurl_1.98-1.14         
 [27] rappdirs_0.3.3           GenomeInfoDbData_1.2.12 
 [29] irlba_2.3.5.1            dqrng_0.4.1             
 [31] codetools_0.2-20         xml2_1.3.6              
 [33] tidyselect_1.2.1         farver_2.1.2            
 [35] UCSC.utils_1.0.0         ScaledMatrix_1.12.0     
 [37] viridis_0.6.5            GenomicAlignments_1.40.0
 [39] jsonlite_1.8.8           BiocNeighbors_1.22.0    
 [41] scater_1.32.0            tools_4.4.0             
 [43] progress_1.2.3           Rcpp_1.0.12             
 [45] glue_1.7.0               gridExtra_2.3           
 [47] xfun_0.44                HDF5Array_1.32.0        
 [49] withr_3.0.0              BiocManager_1.30.23     
 [51] fastmap_1.2.0            rhdf5filters_1.16.0     
 [53] bluster_1.14.0           fansi_1.0.6             
 [55] callr_3.7.6              digest_0.6.35           
 [57] rsvd_1.0.5               secretbase_0.5.0        
 [59] R6_2.5.1                 colorspace_2.1-0        
 [61] scattermore_1.2          RSQLite_2.3.7           
 [63] R.methodsS3_1.8.2        utf8_1.2.4              
 [65] generics_0.1.3           renv_1.0.7.9000         
 [67] data.table_1.15.4        rtracklayer_1.64.0      
 [69] prettyunits_1.2.0        httr_1.4.7              
 [71] pkgconfig_2.0.3          gtable_0.3.5            
 [73] blob_1.2.4               XVector_0.44.0          
 [75] base64url_1.4            ProtGenerics_1.36.0     
 [77] scales_1.3.0             png_0.1-8               
 [79] scran_1.32.0             knitr_1.47              
 [81] rstudioapi_0.16.0        reshape2_1.4.4          
 [83] rjson_0.2.21             curl_5.2.1              
 [85] cachem_1.1.0             rhdf5_2.48.0            
 [87] stringr_1.5.1            BiocVersion_3.19.1      
 [89] parallel_4.4.0           vipor_0.4.7             
 [91] restfulr_0.0.15          pillar_1.9.0            
 [93] grid_4.4.0               vctrs_0.6.5             
 [95] beachmat_2.20.0          cluster_2.1.6           
 [97] beeswarm_0.4.0           cli_3.6.2               
 [99] locfit_1.5-9.9           compiler_4.4.0          
 [ reached getOption("max.print") -- omitted 24 entries ]

Sorry that I don't provide a reproducible example, I'm not sure if it is worth to create it as I'm also checking the variable genes via modelGeneVar.

Without a reproducible example or access to the data, it's a bit hard to say. I assume the examples work for you (example("modelGeneVarByPoisson", "scran"))?