dyno
dyno copied to clipboard
dynverse
"Error during trajectory inference" => out of memory
Hello,
I am currently getting this error:
model <- infer_trajectory(dataset, methods_selected[1]) Error traceback: 1: Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
Attaching package: ‘purrr’
The following object is masked from ‘package:jsonlite’:
flatten
Loading required package: dynutils Killed
Error: Error during trajectory inference
Any help is appreciated!
Note: I am using methods_selected[1] because when I use first(methods_selected) I get: Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘first’ for signature ‘"character"’
I am not sure if this is related.
Hi!
Could you try to run this using
model <- infer_trajectory(dataset, methods_selected[1], verbose = TRUE)
This will output what the lines that are ran and the errors that are generated inside the method wrapper.
Thanks
PS: first(methods_selected) does not work probably because some bioconductor packages overwrite the first function. in the end, both first(...) and ...[1] do the same.
Hello! I'm running into the same issue as @rlittman16. Mine does not kill until later though. I get this additional part of the error before it gets killed:
Attaching package: ‘BiocGenerics’ The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:Matrix’:
colMeans, colSums, rowMeans, rowSums, which
The following objects are masked from ‘package:dplyr’:
combine, intersect, setdiff, union
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colMeans, colnames, colSums, dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, lengths, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rowMeans, rownames, rowSums, sapply, setdiff, sort, table, tapply, union, unique, unsplit, which, which.max, which.min
Loading required package: ggplot2 Loading required package: VGAM Loading required package: stats4 Loading required package: splines Loading required package: DDRTree Loading required package: irlba Removing 64 outliers Killed
Error: Error during trajectory inference
Hi @zouter,
Thank you for your prompt response!
Here is the full output:
model``` <- infer_trajectory(dataset, methods_selected[1], verbose = TRUE)
Executing 'comp1' on '20190305_132425__data_wrapper__v5WYZz6sdi'
With parameters: list(dimred = "pca", ndim = 2L, component = 1L)
And inputs: expression
Input saved to /tmp/folders/h9/2q6w939d19768zkkhcz4c7dr0000gn/T//RtmpbCN2Qx/file16f3c712cc6c7/ti/input:
data.rds
params.json
Running /usr/local/bin/docker run -e 'TMPDIR=/tmp2' --workdir /ti/workspace -v \
'/tmp/folders/h9/2q6w939d19768zkkhcz4c7dr0000gn/T//RtmpbCN2Qx/file16f3c712cc6c7/ti:/ti' -v \
'/tmp/folders/h9/2q6w939d19768zkkhcz4c7dr0000gn/T//RtmpbCN2Qx/file16f3c32ae6cdd/tmp:/tmp2' dynverse/ti_comp1
-
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
|
Attaching package: ‘purrr’
The following object is masked from ‘package:jsonlite’:
flatten
Loading required package: dynutils
Killed
Output found in /tmp/folders/h9/2q6w939d19768zkkhcz4c7dr0000gn/T//RtmpbCN2Qx/file16f3c712cc6c7/ti/output:
Error traceback:
1: cannot open the connection
Error: Error during trajectory inference
In addition: Warning message:
In gzfile(file, "rb") :
cannot open compressed file '/tmp/folders/h9/2q6w939d19768zkkhcz4c7dr0000gn/T//RtmpbCN2Qx/file16f3c712cc6c7/ti/output/output.rds', probable reason 'No such file or directory`
>
I'm having the same issue....the error message is unfortunately not helpful...I've tried 4 methods and all are failing in the same way
(it does work on the example data for me)
I'm guessing something is wrong with my dataset construction but it looks fine to me
> dataset %>% str()
List of 7
$ id : chr "20190403_122601__data_wrapper__M0AW5yWfNz"
$ cell_ids : chr [1:6397] "AAACCTGAGCTAGTCT_1" "AAACCTGAGTCCAGGA_1" "AAACCTGTCTCGCATC_1" "AAACGGGAGGCCGAAT_1" ...
$ cell_info : NULL
$ counts :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
.. ..@ i : int [1:25627543] 833 1727 3364 3571 3761 4560 4740 4823 5290 5403 ...
.. ..@ p : int [1:24820] 0 11 12 1618 3413 3493 3498 3829 4016 4250 ...
.. ..@ Dim : int [1:2] 6397 24819
.. ..@ Dimnames:List of 2
.. .. ..$ : chr [1:6397] "AAACCTGAGCTAGTCT_1" "AAACCTGAGTCCAGGA_1" "AAACCTGTCTCGCATC_1" "AAACGGGAGGCCGAAT_1" ...
.. .. ..$ : chr [1:24819] "ENSG00000238009" "ENSG00000279928" "ENSG00000279457" "ENSG00000228463" ...
.. ..@ x : num [1:25627543] 1 1 1 1 1 1 1 1 1 1 ...
.. ..@ factors : list()
$ expression :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
.. ..@ i : int [1:25627543] 833 1727 3364 3571 3761 4560 4740 4823 5290 5403 ...
.. ..@ p : int [1:24820] 0 11 12 1618 3413 3493 3498 3829 4016 4250 ...
.. ..@ Dim : int [1:2] 6397 24819
.. ..@ Dimnames:List of 2
.. .. ..$ : chr [1:6397] "AAACCTGAGCTAGTCT_1" "AAACCTGAGTCCAGGA_1" "AAACCTGTCTCGCATC_1" "AAACGGGAGGCCGAAT_1" ...
.. .. ..$ : chr [1:24819] "ENSG00000238009" "ENSG00000279928" "ENSG00000279457" "ENSG00000228463" ...
.. ..@ x : num [1:25627543] 0.467 1.212 0.666 1.101 0.843 ...
.. ..@ factors : list()
$ feature_info:Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 24819 obs. of 1 variable:
..$ feature_id: chr [1:24819] "ENSG00000238009" "ENSG00000279928" "ENSG00000279457" "ENSG00000228463" ...
$ feature_ids : chr [1:24819] "ENSG00000238009" "ENSG00000279928" "ENSG00000279457" "ENSG00000228463" ...
- attr(*, "class")= chr [1:3] "dynwrap::with_expression" "dynwrap::data_wrapper" "list"
> model <- infer_trajectory(dataset, ti_slingshot(), verbose = TRUE)
Executing 'slingshot' on '20190403_122601__data_wrapper__M0AW5yWfNz'
With parameters: list(shrink = 1L, reweight = TRUE, reassign = TRUE, thresh = 0.001, maxit = 10L, stretch = 2L, smoother = "smooth.spline", shrink.method = "cosine"),
inputs: counts, and
priors :
Input saved to /tmp/folders/s4/y5f1tt296dj8088gvczcx11d4lrnr7/T//Rtmp8UpCSo/file117261269780/ti
Running /usr/local/bin/docker run -e 'TMPDIR=/tmp2' --workdir /ti/workspace -v '/tmp/folders/s4/y5f1tt296dj8088gvczcx11d4lrnr7/T//Rtmp8UpCSo/file117261269780/ti:/ti' -v \
'/tmp/folders/s4/y5f1tt296dj8088gvczcx11d4lrnr7/T//Rtmp8UpCSo/file11722ca48654/tmp:/tmp2' 'dynverse/ti_slingshot:v0.9.9' --dataset /ti/input.h5 --output /ti/output.h5
Loading required package: dynutils
Error: Error during trajectory inference
Loading required package: dynutils
sessionInfo()
> sessionInfo()
R version 3.5.0 (2018-04-23)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] dynutils_1.0.2 shiny_1.2.0 scater_1.10.1 DropletUtils_1.2.2 forcats_0.3.0 stringr_1.4.0 dplyr_0.8.0.1
[8] purrr_0.3.2 readr_1.3.1 tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.0.9000 tidyverse_1.2.1 dyno_0.9.9
[15] dynwrap_1.0.0 dynplot_1.0.0 dynmethods_1.0.0 dynguidelines_1.0.0 dynfeature_1.0.0 SingleCellExperiment_1.4.1 SummarizedExperiment_1.12.0
[22] DelayedArray_0.8.0 BiocParallel_1.16.2 matrixStats_0.54.0 Biobase_2.42.0 GenomicRanges_1.34.0 GenomeInfoDb_1.18.1 IRanges_2.16.0
[29] S4Vectors_0.20.1 BiocGenerics_0.28.0
loaded via a namespace (and not attached):
[1] utf8_1.1.4 tidyselect_0.2.5 htmlwidgets_1.3 grid_3.5.0 combinat_0.0-8 ranger_0.11.2 docopt_0.6.1 Rtsne_0.15
[9] devtools_2.0.1 munsell_0.5.0 codetools_0.2-15 withr_2.1.2 colorspace_1.4-0 fastICA_1.2-1 knitr_1.21 rstudioapi_0.10
[17] shinyWidgets_0.4.8 slam_0.1-43 GenomeInfoDbData_1.2.0 polyclip_1.9-1 bit64_0.9-7 farver_1.1.0 pheatmap_1.0.10 rhdf5_2.26.0
[25] rprojroot_1.3-2 generics_0.0.2 xfun_0.4 R6_2.4.0 ggbeeswarm_0.6.0 GA_3.2 pdist_1.2 VGAM_1.1-1
[33] locfit_1.5-9.1 hdf5r_1.0.1 concaveman_1.0.0 bitops_1.0-6 assertthat_0.2.1 promises_1.0.1 scales_1.0.0 ggraph_1.0.2
[41] nnet_7.3-12 beeswarm_0.2.3 gtable_0.2.0 babelwhale_0.0.0.9000 processx_3.3.0 tidygraph_1.1.1 rlang_0.3.3 akima_0.6-2
[49] splines_3.5.0 lazyeval_0.2.1 acepack_1.4.1 broom_0.5.1 checkmate_1.8.5 BiocManager_1.30.4 yaml_2.2.0 reshape2_1.4.3
[57] modelr_0.1.2 backports_1.1.3 httpuv_1.4.5 Hmisc_4.1-1 tools_3.5.0 usethis_1.4.0 RColorBrewer_1.1-2 sessioninfo_1.1.1
[65] Rcpp_1.0.1 plyr_1.8.4 base64enc_0.1-3 zlibbioc_1.28.0 RCurl_1.95-4.11 densityClust_0.3 ps_1.3.0 prettyunits_1.0.2
[73] rpart_4.1-13 deldir_0.1-16 viridis_0.5.1 cowplot_0.9.3 dynparam_1.0.0 haven_2.0.0 ggrepel_0.8.0 cluster_2.0.7-1
[81] fs_1.2.7 magrittr_1.5 data.table_1.11.8 carrier_0.1.0 RANN_2.6 pkgload_1.0.2 shinyjs_1.0 evaluate_0.12
[89] hms_0.4.2 patchwork_0.0.1 mime_0.6 xtable_1.8-3 sparsesvd_0.1-4 readxl_1.1.0 gridExtra_2.3 HSMMSingleCell_1.2.0
[97] testthat_2.0.1 compiler_3.5.0 crayon_1.3.4 rje_1.9 htmltools_0.3.6 later_0.7.5 Formula_1.2-3 lubridate_1.7.4
[105] tweenr_1.0.0 MASS_7.3-50 Matrix_1.2-14 cli_1.1.0 igraph_1.2.4 pkgconfig_2.0.2 sp_1.3-1 foreign_0.8-71
[113] xml2_1.2.0 foreach_1.4.4 vipor_0.4.5 XVector_0.22.0 rvest_0.3.2 callr_3.2.0 digest_0.6.18 dyndimred_1.0.0
[121] DDRTree_0.1.5 rmarkdown_1.11 cellranger_1.1.0 htmlTable_1.12 edgeR_3.24.1 DelayedMatrixStats_1.4.0 monocle_2.9.0 nlme_3.1-137
[129] jsonlite_1.6 Rhdf5lib_1.4.2 fansi_0.4.0 desc_1.2.0 viridisLite_0.3.0 limma_3.38.3 pillar_1.3.1 lattice_0.20-35
[137] shades_1.3.1 httr_1.4.0 pkgbuild_1.0.3 survival_2.42-6 glue_1.3.1 remotes_2.0.2 qlcMatrix_0.9.7 FNN_1.1.2.2
[145] iterators_1.0.10 bit_1.1-14 ggforce_0.1.1 stringi_1.4.3 HDF5Array_1.10.1 latticeExtra_0.6-28 memoise_1.1.0 irlba_2.3.2
@rlittman16 Looks like your issue is related to memory issues? Usually when R gets killed it's because of low memory.
Ah figured it out.....using ALL genes is a bad idea. When I trimmed it down to a few the model could be built. You may want to consider adding some more checks to ensures idiots like myself don't try to build trajectories on 25,000 genes.
Also I first build the dataset with the genes in the rows and the cells as the columns (as this is how SingleCellExperiment outputs the data.
@davemcg I agree this is probably due to memory issues. I have made a few changes in the Slingshot wrapper in order to make it a bit more memory efficient when working with sparse matrices.
@davemcg @rlittman16 @jginder845 Could you try running slingshot again to see if it works? If not, could you try running it on a subset of your data to see if it works?
ti_slingshot() (and most others) work for my version (see below) - as long as I keep the numbers of genes to low thousands. I haven't benchmarked to see what the kill spot is.
Are you saying that the update may allow trajectories to be built with large (tens of thousands) of genes?
other attached packages:
[1] dyno_0.9.9 dynwrap_1.0.0 dynplot_1.0.0 dynmethods_1.0.0 dynguidelines_1.0.0 dynfeature_1.0.0
Yes, version 0.9.9.01 of the ti_slingshot container will be more scalable with respect to the number of genes. This version is not yet merged to the master branch of dyno, but you could already try it out by using
model <- infer_trajectory(dataset, "dynverse/ti_slingshot:v0.9.9.01", verbose = TRUE)
instead of
model <- infer_trajectory(dataset, ti_slingshot(), verbose = TRUE)
. Alternatively, you could wait until I've merged the changes into dyno@master and just reinstall then ;)
While on the subject, slingshot also does not scale very well with respect to the number of cells; this is currently being looked at in kstreet13/slingshot#31.
The changes were merged to master :) It might take quite a while to run, but at least the memory consumption should be a lot better. Could you verify?
Hello,
I am running into similar problems as others where I call infer_trajectories on my dataset using several ti methods do not get a model output for 7/8 models. For 5/7 of the models that failed no error message was generated, and when running with verbose=TRUE it appears that the input is accepted and then the method fails without an error or an output. For 2/7 of the models that failed, the errors were a list of package loading commands and again no informative error message. Is this suggestive that the problem is a memory usage issue? I've included the output from the call below as well as the session info. Note that the only method that produced a model was PAGA. Thank you!
> models <- infer_trajectories(dataset = dataset_dyn, method = dataset_guidelines$methods_selected, verbose = TRUE, give_priors = c("start_id", "end_id", "start_n", "end_n"))
Executing 'slingshot' on '20190618_140027__data_wrapper__H2yyzt8nog'
With parameters: list(shrink = 1L, reweight = TRUE, reassign = TRUE, thresh = 0.001, maxit = 10L, stretch = 2L, smoother = "smooth.spline", shrink.method = "cosine"),
inputs: expression, and
priors : start_id, end_id
Input saved to /tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef51975991fef/ti
Running method using babelwhale
Running /usr/local/bin/docker run -e 'TMPDIR=/tmp2' --workdir /ti/workspace -v \
'/tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef51975991fef/ti:/ti' \
-v \
'/tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef51917ffc03a/tmp:/tmp2' \
'dynverse/ti_slingshot:v0.9.9.01' --dataset /ti/input.h5 --output /ti/output.h5
Executing 'paga_tree' on '20190618_140027__data_wrapper__H2yyzt8nog'
With parameters: list(n_neighbors = 15L, n_comps = 50L, n_dcs = 15L, resolution = 1L, embedding_type = "fa"),
inputs: counts, and
priors : start_id
Input saved to /tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef51917a52c25/ti
Running method using babelwhale
Running /usr/local/bin/docker run -e 'TMPDIR=/tmp2' --workdir /ti/workspace -v \
'/tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef51917a52c25/ti:/ti' \
-v \
'/tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef5195cf53d43/tmp:/tmp2' \
'dynverse/ti_paga_tree:v0.9.9.01' --dataset /ti/input.h5 --output /ti/output.h5
Executing 'paga' on '20190618_140027__data_wrapper__H2yyzt8nog'
With parameters: list(n_neighbors = 15L, n_comps = 50L, n_dcs = 15L, resolution = 1L, embedding_type = "fa", connectivity_cutoff = 0.05),
inputs: counts, and
priors : start_id
Input saved to /tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef51968892962/ti
Running method using babelwhale
Running /usr/local/bin/docker run -e 'TMPDIR=/tmp2' --workdir /ti/workspace -v \
'/tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef51968892962/ti:/ti' \
-v \
'/tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef5194fc168c/tmp:/tmp2' \
'dynverse/ti_paga:v0.9.9.01' --dataset /ti/input.h5 --output /ti/output.h5
Output saved to /tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef51968892962/ti/output.h5
Attempting to read in output with hdf5Executing 'mst' on '20190618_140027__data_wrapper__H2yyzt8nog'
With parameters: list(dimred = "pca", ndim = 2L),
inputs: expression, and
priors :
Input saved to /tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef519422c43e2/ti
Running method using babelwhale
Running /usr/local/bin/docker run -e 'TMPDIR=/tmp2' --workdir /ti/workspace -v \
'/tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef519422c43e2/ti:/ti' \
-v \
'/tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef519681ccc5e/tmp:/tmp2' \
'dynverse/ti_mst:v0.9.9.01' --dataset /ti/input.h5 --output /ti/output.h5
Executing 'pcreode' on '20190618_140027__data_wrapper__H2yyzt8nog'
With parameters: list(n_pca_components = 3L, num_runs = 10L),
inputs: expression, and
priors :
Input saved to /tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef5193aad64b8/ti
Running method using babelwhale
Running /usr/local/bin/docker run -e 'TMPDIR=/tmp2' --workdir /ti/workspace -v \
'/tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef5193aad64b8/ti:/ti' \
-v \
'/tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef5194daf8a20/tmp:/tmp2' \
'dynverse/ti_pcreode:v0.9.9.01' --dataset /ti/input.h5 --output /ti/output.h5
Executing 'monocle_ica' on '20190618_140027__data_wrapper__H2yyzt8nog'
With parameters: list(reduction_method = "ICA", max_components = 2L, norm_method = "log", filter_features = TRUE, filter_features_mean_expression = 0.1),
inputs: counts, and
priors : start_n, end_n
Input saved to /tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef5193f9562b8/ti
Running method using babelwhale
Running /usr/local/bin/docker run -e 'TMPDIR=/tmp2' --workdir /ti/workspace -v \
'/tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef5193f9562b8/ti:/ti' \
-v \
'/tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef51968843ea4/tmp:/tmp2' \
'dynverse/ti_monocle_ica:v0.9.9.01' --dataset /ti/input.h5 --output /ti/output.h5
Loading required package: Matrix
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
|
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:Matrix’:
colMeans, colSums, rowMeans, rowSums, which
The following objects are masked from ‘package:dplyr’:
combine, intersect, setdiff, union
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colMeans,
colnames, colSums, dirname, do.call, duplicated, eval, evalq,
Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply,
lengths, Map, mapply, match, mget, order, paste, pmax, pmax.int,
pmin, pmin.int, Position, rank, rbind, Reduce, rowMeans, rownames,
rowSums, sapply, setdiff, sort, table, tapply, union, unique,
unsplit, which, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: ggplot2
Loading required package: VGAM
Loading required package: stats4
Loading required package: splines
Loading required package: DDRTree
Loading required package: irlba
Executing 'scuba' on '20190618_140027__data_wrapper__H2yyzt8nog'
With parameters: list(rigorous_gap_stats = TRUE, N_dim = 2L, low_gene_threshold = 1L, low_gene_fraction_max = 0.7, min_split = 15L, min_percentage_split = 0.25),
inputs: expression, and
priors :
Input saved to /tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef519422cb697/ti
Running method using babelwhale
Running /usr/local/bin/docker run -e 'TMPDIR=/tmp2' --workdir /ti/workspace -v \
'/tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef519422cb697/ti:/ti' \
-v \
'/tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef5195879572/tmp:/tmp2' \
'dynverse/ti_scuba:v0.9.9.01' --dataset /ti/input.h5 --output /ti/output.h5
Executing 'celltree_vem' on '20190618_140027__data_wrapper__H2yyzt8nog'
With parameters: list(method = "VEM", sd_filter = 0.5, width_scale_factor = 1.5, outlier_tolerance_factor = 0.1, rooting_method = "null", num_topics = 4L, tot_iter = 1000000L, tolerance = 1e-05),
inputs: expression, and
priors : start_id
Input saved to /tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef5198647234/ti
Running method using babelwhale
Running /usr/local/bin/docker run -e 'TMPDIR=/tmp2' --workdir /ti/workspace -v \
'/tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef5198647234/ti:/ti' \
-v \
'/tmp/folders/ws/49ff39x520nbjs4lqhg47bt80000gn/T//Rtmp37P36P/filef5197a85b839/tmp:/tmp2' \
'dynverse/ti_celltree_vem:v0.9.9.01' --dataset /ti/input.h5 --output /ti/output.h5
> sessionInfo()
R version 3.5.3 Patched (2019-03-11 r76644)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.5
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] splines stats4 parallel stats graphics grDevices utils datasets methods
[10] base
other attached packages:
[1] rlist_0.4.6.1 uwot_0.1.3 forcats_0.4.0 stringr_1.4.0
[5] purrr_0.3.2 readr_1.3.1 tibble_2.1.2 tidyverse_1.2.1
[9] dyntoy_0.9.9 dyneval_0.9.9 dyno_0.1.1 dynwrap_1.1.2
[13] dynplot_1.0.1 dynmethods_1.0.1 dynguidelines_1.0.0 dynfeature_1.0.0
[17] BiocManager_1.30.4 pheatmap_1.0.12 RColorBrewer_1.1-2 data.table_1.12.2
[21] tidyr_0.8.3 monocle_2.10.1 DDRTree_0.1.5 irlba_2.3.3
[25] VGAM_1.1-1 Biobase_2.42.0 BiocGenerics_0.28.0 Matrix_1.2-17
[29] gridExtra_2.3 Seurat_3.0.1 dplyr_0.8.1 plyr_1.8.4
[33] hdf5r_1.2.0 cowplot_0.9.4 ggplot2_3.1.1 usethis_1.5.0
[37] devtools_2.0.2 biomaRt_2.38.0
loaded via a namespace (and not attached):
[1] R.methodsS3_1.7.1 GA_3.2 acepack_1.4.1 bit64_0.9-7
[5] knitr_1.23 R.utils_2.8.0 rpart_4.1-15 RCurl_1.95-4.12
[9] generics_0.0.2 metap_1.1 callr_3.2.0 RSQLite_2.1.1
[13] RANN_2.6.1 combinat_0.0-8 carrier_0.1.0 future_1.13.0
[17] bit_1.1-14 xml2_1.2.0 lubridate_1.7.4 httpuv_1.5.1
[21] assertthat_0.2.1 viridis_0.5.1 xfun_0.7 hms_0.4.2
[25] promises_1.0.1 fansi_0.4.0 progress_1.2.2 caTools_1.17.1.2
[29] readxl_1.3.1 igraph_1.2.4.1 DBI_1.0.0 htmlwidgets_1.3
[33] sparsesvd_0.1-4 dyndimred_1.0.1 backports_1.1.4 gbRd_0.4-11
[37] RcppParallel_4.4.3 vctrs_0.1.0 remotes_2.0.4 ROCR_1.0-7
[41] withr_2.1.2 ggforce_0.2.2 checkmate_1.9.3 sctransform_0.2.0
[45] prettyunits_1.0.2 cluster_2.0.9 ape_5.3 lazyeval_0.2.2
[49] crayon_1.3.4 babelwhale_0.0.0.9000 labeling_0.3 pkgconfig_2.0.2
[53] slam_0.1-45 tweenr_1.0.1 nlme_3.1-140 pkgload_1.0.2
[57] nnet_7.3-12 rlang_0.3.4 globals_0.12.4 modelr_0.1.4
[61] rsvd_1.0.1 cellranger_1.1.0 rprojroot_1.3-2 polyclip_1.10-0
[65] matrixStats_0.54.0 lmtest_0.9-37 zoo_1.8-6 base64enc_0.1-3
[69] ggridges_0.5.1 processx_3.3.1 png_0.1-7 viridisLite_0.3.0
[73] bitops_1.0-6 R.oo_1.22.0 KernSmooth_2.23-15 blob_1.1.1
[77] S4Vectors_0.20.1 scales_1.0.0 hexbin_1.27.3 memoise_1.1.0
[81] magrittr_1.5 ica_1.0-2 gplots_3.0.1.1 bibtex_0.4.2
[85] gdata_2.18.0 compiler_3.5.3 HSMMSingleCell_1.2.0 lsei_1.2-0
[89] fitdistrplus_1.0-14 cli_1.1.0 listenv_0.7.0 patchwork_0.0.1
[93] pbapply_1.4-0 ps_1.3.0 htmlTable_1.13.1 Formula_1.2-3
[97] MASS_7.3-51.4 tidyselect_0.2.5 stringi_1.4.3 densityClust_0.3
[101] yaml_2.2.0 latticeExtra_0.6-28 ggrepel_0.8.1 grid_3.5.3
[105] tools_3.5.3 future.apply_1.2.0 rstudioapi_0.10 foreach_1.4.4
[109] foreign_0.8-71 rje_1.9 farver_1.1.0 Rtsne_0.15
[113] ggraph_1.0.2 proxyC_0.1.4 digest_0.6.19 FNN_1.1.3
[117] shiny_1.3.2 dynparam_1.0.0 qlcMatrix_0.9.7 Rcpp_1.0.1
[121] broom_0.5.2 SDMTools_1.1-221.1 later_0.8.0 httr_1.4.0
[125] AnnotationDbi_1.44.0 npsurv_0.4-0 Rdpack_0.11-0 colorspace_1.4-1
[129] rvest_0.3.4 XML_3.98-1.19 fs_1.3.1 pdist_1.2
[133] ranger_0.11.2 reticulate_1.12 IRanges_2.16.0 plotly_4.9.0
[137] sessioninfo_1.1.1 xtable_1.8-4 jsonlite_1.6 tidygraph_1.1.2
[141] zeallot_0.1.0 shades_1.3.1 testthat_2.1.1 R6_2.4.0
[145] Hmisc_4.2-0 pillar_1.4.1 htmltools_0.3.6 mime_0.6
[149] glue_1.3.1 codetools_0.2-16 utf8_1.1.4 pkgbuild_1.0.3
[153] tsne_0.1-3 lattice_0.20-38 gtools_3.8.1 survival_2.44-1.1
[157] limma_3.38.3 docopt_0.6.1 desc_1.2.0 dynutils_1.0.3
[161] fastICA_1.2-1 munsell_0.5.0 iterators_1.0.10 haven_2.1.0
[165] reshape2_1.4.3 gtable_0.3.0
