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I'm running DESeq2 on an RNA-seq dataset with 25 samples divided into 2 clusters (9 and 16). The DESeq2 object was created without any issues, but when I run DEseq
, I get the following error from replaceOutliers
:
Any help is appreciated.
design(dds_select_time ) <- ~ cluster + batch
dds_select_time <- DESeq(dds_select_time )
Error message
using pre-existing size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
Error in mcols(colData(object), use.names = TRUE)["replaceable", ] <- DataFrame(type = "intermediate", :
invalid type/length (S4/0) in vector allocation
3.
replaceOutliers(object, minReplicates = minReplicatesForReplace) at core.R#2513
2.
refitWithoutOutliers(object, test = test, betaPrior = betaPrior,
full = full, reduced = reduced, quiet = quiet, minReplicatesForReplace = minReplicatesForReplace,
modelMatrix = modelMatrix, modelMatrixType = modelMatrixType) at core.R#428
1.
DESeq(dds_select_time)
sessionInfo( )
R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 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
Random number generation:
RNG: L'Ecuyer-CMRG
Normal: Inversion
Sample: Rejection
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 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: America/Los_Angeles
tzcode source: system (glibc)
attached base packages:
[1] splines stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] plotly_4.10.4 writexl_1.5.4 ggalluvial_0.12.5 cowplot_1.1.3 corrplot_0.95 dtwclust_6.0.0
[7] dtw_1.23-1 proxy_0.4-27 DESeq2_1.46.0 SummarizedExperiment_1.36.0 Biobase_2.66.0 MatrixGenerics_1.18.1
[13] matrixStats_1.5.0 GenomicRanges_1.58.0 GenomeInfoDb_1.42.3 IRanges_2.40.1 S4Vectors_0.44.0 BiocGenerics_0.52.0
[19] pheatmap_1.0.12 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.4
[25] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 readxl_1.4.3
loaded via a namespace (and not attached):
[1] rlang_1.1.5 magrittr_2.0.3 clue_0.3-66 flexclust_1.5.0 compiler_4.4.2 vctrs_0.6.5 reshape2_1.4.4
[8] pkgconfig_2.0.3 crayon_1.5.3 fastmap_1.2.0 XVector_0.46.0 labeling_0.4.3 utf8_1.2.4 promises_1.3.2
[15] rmarkdown_2.29 tzdb_0.4.0 UCSC.utils_1.2.0 xfun_0.52 modeltools_0.2-24 zlibbioc_1.52.0 jsonlite_1.8.9
[22] later_1.4.1 DelayedArray_0.32.0 BiocParallel_1.40.2 parallel_4.4.2 cluster_2.1.8 R6_2.5.1 stringi_1.8.4
[29] RColorBrewer_1.1-3 cellranger_1.1.0 Rcpp_1.0.14 iterators_1.0.14 knitr_1.50 httpuv_1.6.15 Matrix_1.7-1
[36] timechange_0.3.0 tidyselect_1.2.1 rstudioapi_0.16.0 abind_1.4-8 yaml_2.3.10 codetools_0.2-19 lattice_0.22-5
[43] plyr_1.8.9 shiny_1.10.0 withr_3.0.2 evaluate_1.0.3 RcppParallel_5.1.10 pillar_1.10.1 renv_1.1.4
[50] foreach_1.5.2 shinyjs_2.1.0 generics_0.1.3 rprojroot_2.0.4 hms_1.1.3 munsell_0.5.1 scales_1.3.0
[57] xtable_1.8-4 class_7.3-23 glue_1.8.0 lazyeval_0.2.2 tools_4.4.2 data.table_1.16.4 RSpectra_0.16-2
[64] locfit_1.5-9.12 grid_4.4.2 crosstalk_1.2.1 colorspace_2.1-1 GenomeInfoDbData_1.2.13 cli_3.6.3 viridisLite_0.4.2
[71] S4Arrays_1.6.0 gtable_0.3.6 digest_0.6.37 SparseArray_1.6.2 ggrepel_0.9.6 htmlwidgets_1.6.4 farver_2.1.2
[78] htmltools_0.5.8.1 lifecycle_1.0.4 httr_1.4.7 here_1.0.1 mime_0.12