Dear author, thanks for your development of such great methods to do differential abundance analysis.
I try to use this method for my data, but I couldn't run STGmarkerFinder() function properly.
My code is
STG_markers.sex <- STGmarkerFinder(
X = as.matrix(seurat.object@assays$RNA@data),
da.regions = da_regions,
lambda = 1.5, n.runs = 5, return.model = T,
python.use = python2use, GPU=''
)
But I get the error message
2021-12-17 08:11:14.670028: W tensorflow/core/framework/op_kernel.cc:1692] OP_REQUIRES failed at cwise_ops_common.h:128 : Resource exhausted: OOM when allocating tensor with shape[24364,2000] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator cpu
Error in py_call_impl(callable, dots$args, dots$keywords) :
ResourceExhaustedError: OOM when allocating tensor with shape[24364,2000] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator cpu
[[node gates/clip_by_value (defined at :221) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
Errors may have originated from an input operation.
Input Source operations connected to node gates/clip_by_value:
gates/Const (defined at :203)
Original stack trace for 'gates/clip_by_value':
File "", line 49, in STG_FS
File "", line 116, in init
File "", line 234, in feature_selector
File "", line 221, in hard_sigmoid
File "/home/caolei/.local/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py", line 206, in wrapper
return target(*args, **kwargs)
File "/home/caolei/.local/lib/python3.6/site-pac
sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Gentoo/Linux
Matrix products: default
BLAS/LAPACK: /home/caolei/software/anaconda3/envs/scRNA-seq/lib/libopenblasp-r0.3.18.so
locale:
[1] C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] DAseq_1.0.0 future_1.23.0 patchwork_1.1.1 ggplot2_3.3.5
[5] dplyr_1.0.7 cowplot_1.1.1 SeuratObject_4.0.4 Seurat_4.0.5
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_2.0-2 deldir_1.0-6
[4] ellipsis_0.3.2 class_7.3-19 ggridges_0.5.3
[7] rprojroot_2.0.2 proxy_0.4-26 spatstat.data_2.1-0
[10] farver_2.1.0 leiden_0.3.9 listenv_0.8.0
[13] ggrepel_0.9.1 prodlim_2019.11.13 fansi_0.5.0
[16] lubridate_1.8.0 codetools_0.2-18 splines_4.1.1
[19] polyclip_1.10-0 jsonlite_1.7.2 pROC_1.18.0
[22] caret_6.0-90 ica_1.0-2 cluster_2.1.2
[25] png_0.1-7 uwot_0.1.11 shiny_1.7.1
[28] sctransform_0.3.2 spatstat.sparse_2.0-0 BiocManager_1.30.16
[31] compiler_4.1.1 httr_1.4.2 assertthat_0.2.1
[34] Matrix_1.4-0 fastmap_1.1.0 lazyeval_0.2.2
[37] limma_3.50.0 later_1.3.0 htmltools_0.5.2
[40] tools_4.1.1 igraph_1.2.9 gtable_0.3.0
[43] glue_1.5.1 RANN_2.6.1 reshape2_1.4.4
[46] Rcpp_1.0.7 scattermore_0.7 vctrs_0.3.8
[49] nlme_3.1-153 iterators_1.0.13 lmtest_0.9-39
[52] timeDate_3043.102 gower_0.2.2 stringr_1.4.0
[55] globals_0.14.0 mime_0.12 miniUI_0.1.1.1
[58] lifecycle_1.0.1 irlba_2.3.5 goftest_1.2-3
[61] MASS_7.3-54 zoo_1.8-9 scales_1.1.1
[64] ipred_0.9-12 spatstat.core_2.3-2 promises_1.2.0.1
[67] spatstat.utils_2.2-0 parallel_4.1.1 RColorBrewer_1.1-2
[70] reticulate_1.22-9000 pbapply_1.5-0 gridExtra_2.3
[73] rpart_4.1-15 stringi_1.7.6 foreach_1.5.1
[76] e1071_1.7-9 lava_1.6.10 shape_1.4.6
[79] rlang_0.4.12 pkgconfig_2.0.3 matrixStats_0.61.0
[82] lattice_0.20-45 ROCR_1.0-11 purrr_0.3.4
[85] tensor_1.5 labeling_0.4.2 recipes_0.1.17
[88] htmlwidgets_1.5.4 tidyselect_1.1.1 here_1.0.1
[91] parallelly_1.29.0 RcppAnnoy_0.0.19 plyr_1.8.6
[94] magrittr_2.0.1 R6_2.5.1 generics_0.1.1
[97] DBI_1.1.1 pillar_1.6.4 withr_2.4.3
[100] mgcv_1.8-38 fitdistrplus_1.1-6 survival_3.2-13
[103] abind_1.4-5 nnet_7.3-16 tibble_3.1.6
[106] future.apply_1.8.1 crayon_1.4.2 KernSmooth_2.23-20
[109] utf8_1.2.2 spatstat.geom_2.3-0 plotly_4.10.0
[112] grid_4.1.1 data.table_1.14.2 ModelMetrics_1.2.2.2
[115] digest_0.6.29 xtable_1.8-4 tidyr_1.1.4
[118] httpuv_1.6.3 stats4_4.1.1 munsell_0.5.0
[121] glmnet_4.1-3 viridisLite_0.4.0
Do you know how to figure it out ? Do you need any more detailed information?
Dear author, thanks for your development of such great methods to do differential abundance analysis.
I try to use this method for my data, but I couldn't run STGmarkerFinder() function properly.
My code is
But I get the error message
Do you know how to figure it out ? Do you need any more detailed information?