Doublet numbers still not reproduced even though I used BPPARAM and bpstart
Moonju411 opened this issue · comments
Dear developers,
Thank you for nice package.
I know doublet reproducibility already discussed a lot in issue and I also read them.
But when I adjust that code to my data, it's still not reproducible. Always give me a different results.
I checked my data by using the code which was uploaded on the issue #53.
This is the code which I used and the results.
> sce <- as.SingleCellExperiment(my_seurat_object)
> bp <- MulticoreParam(2, RNGseed=123)
> bpstart(bp)
> m1 <- scDblFinder(sce, clusters=sce$cluster, BPPARAM=bp)$scDblFinder.score
Creating ~5000 artificial doublets...
Dimensional reduction
Evaluating kNN...
Training model...
iter=0, 83 cells excluded from training.
iter=1, 83 cells excluded from training.
iter=2, 80 cells excluded from training.
Threshold found:0.738
50 (4.7%) doublets called
> bpstop(bp)
> bpstart(bp)
> m2 <- scDblFinder(sce, clusters=sce$cluster, BPPARAM=bp)$scDblFinder.score
Creating ~5000 artificial doublets...
Dimensional reduction
Evaluating kNN...
Training model...
iter=0, 76 cells excluded from training.
iter=1, 89 cells excluded from training.
iter=2, 79 cells excluded from training.
Threshold found:0.784
44 (4.1%) doublets called
> bpstop(bp)
> identical(m1,m2)
[1] FALSE
Do you have any ideas about this? My BiocParallel package version is already 1.28.3.
I tried a lot but it's not matched again and again... Please help!
This is the sessioninfo of my R.
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
[6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] rsvd_1.0.5 batchelor_1.10.0 remotes_2.4.2 Nebulosa_1.4.0 patchwork_1.1.1
[6] SeuratWrappers_0.3.0 harmony_0.1.0 Rcpp_1.0.8.3 cowplot_1.1.1 dplyr_1.0.9
[11] Seurat_4.1.0 SeuratObject_4.0.4 scDblFinder_1.11.4 SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
[16] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1 IRanges_2.28.0 S4Vectors_0.32.4 MatrixGenerics_1.6.0
[21] matrixStats_0.62.0 scaterlegacy_1.5.0 ggplot2_3.3.6 Biobase_2.54.0 BiocGenerics_0.40.0
[26] BiocParallel_1.28.3
loaded via a namespace (and not attached):
[1] utf8_1.2.2 shinydashboard_0.7.2 ks_1.13.5 R.utils_2.11.0 reticulate_1.24
[6] tidyselect_1.1.2 RSQLite_2.2.12 AnnotationDbi_1.56.2 htmlwidgets_1.5.4 grid_4.1.2
[11] Rtsne_0.16 munsell_0.5.0 ScaledMatrix_1.2.0 codetools_0.2-18 ica_1.0-2
[16] xgboost_1.6.0.1 statmod_1.4.36 scran_1.22.1 future_1.24.0 miniUI_0.1.1.1
[21] withr_2.5.0 spatstat.random_2.2-0 colorspace_2.0-3 filelock_1.0.2 rstudioapi_0.13
[26] ROCR_1.0-11 tensor_1.5 listenv_0.8.0 labeling_0.4.2 tximport_1.22.0
[31] GenomeInfoDbData_1.2.7 polyclip_1.10-0 farver_2.1.0 bit64_4.0.5 rhdf5_2.38.1
[36] parallelly_1.31.0 vctrs_0.4.1 generics_0.1.2 BiocFileCache_2.2.1 R6_2.5.1
[41] ggbeeswarm_0.6.0 locfit_1.5-9.5 bitops_1.0-7 rhdf5filters_1.6.0 spatstat.utils_2.3-0
[46] cachem_1.0.6 DelayedArray_0.20.0 assertthat_0.2.1 BiocIO_1.4.0 promises_1.2.0.1
[51] scales_1.2.0 beeswarm_0.4.0 gtable_0.3.0 beachmat_2.10.0 globals_0.14.0
[56] goftest_1.2-3 rlang_1.0.2 splines_4.1.2 rtracklayer_1.54.0 lazyeval_0.2.2
[61] spatstat.geom_2.4-0 BiocManager_1.30.16 yaml_2.3.5 reshape2_1.4.4 abind_1.4-5
[66] httpuv_1.6.5 tools_4.1.2 ellipsis_0.3.2 spatstat.core_2.4-2 RColorBrewer_1.1-3
[71] ggridges_0.5.3 plyr_1.8.7 sparseMatrixStats_1.6.0 progress_1.2.2 zlibbioc_1.40.0
[76] purrr_0.3.4 RCurl_1.98-1.6 prettyunits_1.1.1 rpart_4.1.16 deldir_1.0-6
[81] pbapply_1.5-0 viridis_0.6.2 zoo_1.8-10 ggrepel_0.9.1 cluster_2.1.3
[86] magrittr_2.0.3 data.table_1.14.2 scattermore_0.8 ResidualMatrix_1.4.0 lmtest_0.9-40
[91] RANN_2.6.1 mvtnorm_1.1-3 fitdistrplus_1.1-8 hms_1.1.1 mime_0.12
[96] xtable_1.8-4 XML_3.99-0.9 mclust_5.4.9 gridExtra_2.3 scater_1.22.0
[101] compiler_4.1.2 biomaRt_2.50.3 tibble_3.1.7 KernSmooth_2.23-20 crayon_1.5.1
[106] R.oo_1.24.0 htmltools_0.5.2 mgcv_1.8-40 later_1.3.0 tidyr_1.2.0
[111] DBI_1.1.2 dbplyr_2.1.1 MASS_7.3-56 rappdirs_0.3.3 Matrix_1.4-1
[116] cli_3.3.0 R.methodsS3_1.8.1 metapod_1.2.0 parallel_4.1.2 igraph_1.3.1
[121] pkgconfig_2.0.3 GenomicAlignments_1.30.0 scuttle_1.4.0 plotly_4.10.0 spatstat.sparse_2.1-1
[126] xml2_1.3.3 vipor_0.4.5 dqrng_0.3.0 XVector_0.34.0 stringr_1.4.0
[131] digest_0.6.29 pracma_2.3.8 sctransform_0.3.3 RcppAnnoy_0.0.19 spatstat.data_2.2-0
[136] Biostrings_2.62.0 leiden_0.3.9 uwot_0.1.11 edgeR_3.36.0 DelayedMatrixStats_1.16.0
[141] restfulr_0.0.13 curl_4.3.2 shiny_1.7.1 Rsamtools_2.10.0 rjson_0.2.21
[146] lifecycle_1.0.1 nlme_3.1-157 jsonlite_1.8.0 Rhdf5lib_1.16.0 BiocNeighbors_1.12.0
[151] viridisLite_0.4.0 limma_3.50.3 fansi_1.0.3 pillar_1.7.0 lattice_0.20-45
[156] ggrastr_1.0.1 KEGGREST_1.34.0 fastmap_1.1.0 httr_1.4.2 survival_3.3-1
[161] glue_1.6.2 png_0.1-7 bluster_1.4.0 bit_4.0.4 stringi_1.7.6
[166] blob_1.2.3 BiocSingular_1.10.0 memoise_2.0.1 irlba_2.3.5 future.apply_1.8.1
Hi,
Judging from your code, your dataset has a single sample (i.e. capture). If not, you'd have to use the `samples` argument. Multithreading is used only with multiple captures, so in the absence of the `samples` argument `BPPARAM` is ignored. Therefore, in your case you should be able to ensure reproducibility using simply `set.seed` before the scDblFinder call.
Hi, will close this issue unless you have something to add