Entering edit mode
Hello, I've been using slingshot and tradeSeq. Great packages, thanks! After running slingshot, I can successfully call slingPseudotime and slingCurveWeights on my SCE object. However, after applying fitGAM to it, I get errors when calling these functions again. Below are the relevant code excerpts. It looks like the class of colData(secr.ko.sce)$slingshot gets somehow changed from PseudotimeOrdering to DataFrame, after running fitGAM. Am I calling fitGAM incorrectly? Thanks for any advice, Elisabetta
library(tidyverse)
library(Seurat)
library(slingshot)
library(scales)
library(RColorBrewer)
library(rgl)
library(tradeSeq)
library(bioc2020trajectories)
library(pheatmap)
library(msigdbr)
library(fgsea)
library(ggplot2);library(gridExtra)
library(condiments)
library(clusterProfiler)
epi <- readRDS("fromAvital/Epi_sub_clusters.rds")
secr.ko <- subset(epi, sub_cluster_5_17 %in% c('Stem', 'Goblet progenitor', 'Goblet progenitor KO', 'Enteroendocrine',
'Enteroendocrine KO', 'Enteroendocrine-goblet KO') & orig.ident %in% c("analysis07.97372","analysis07.97373"))
secr.ko.sce <- as.SingleCellExperiment(secr.ko, assay='RNA')
secr.ko.sce <- slingshot(secr.ko.sce, reducedDim='UMAP', clusterLabels=colData(secr.ko.sce)$ident, start.clus='Stem', approx_points=150)
rm(epi)
slingLineages(secr.ko.sce)
pto.secr.ko <- slingPseudotime(secr.ko.sce) # NO ERROR HERE
cw.secr.ko <- slingCurveWeights(SlingshotDataSet(secr.ko.sce)) # NO ERROR HERE
genes.use <- readRDS('genes.use.rds')
genes.use.index = which(rownames(secr.ko.sce) %in% genes.use)
length(genes.use)
# 3082
# Select the number of knots
set.seed(3)
icMat.secr.ko <- evaluateK(counts = as.matrix(assays(secr.ko.sce)$counts),
pseudotime = pto.secr.ko,
cellWeights = cw.secr.ko,
conditions = factor(colData(secr.ko.sce)$condition),
k = 3:7)
set.seed(3)
secr.ko.sce <- fitGAM(secr.ko.sce, genes=genes.use.index, nknots=6)
mean(rowData(secr.ko.sce)$tradeSeq$converged[genes.use.index])
# 0.9519636
# Now I get an error calling the two functions
slingPseudotime(secr.ko.sce)
# Error in (function (classes, fdef, mtable) :
# unable to find an inherited method for function ‘slingPseudotime’ for signature ‘"DFrame"’
slingCurveWeights(SlingshotDataSet(secr.ko.sce))
# Error in h(simpleError(msg, call)) :
# error in evaluating the argument 'x' in selecting a method for function 'slingCurveWeights': unable to find an inherited method for function ‘as.SlingshotDataSet’ for signature ‘"DFrame"’
traceback()
# 9: h(simpleError(msg, call))
# 8: .handleSimpleError(function (cond)
# .Internal(C_tryCatchHelper(addr, 1L, cond)), "unable to find an inherited method for function ‘as.SlingshotDataSet’ for signature ‘\"DFrame\"’",
# base::quote((function (classes, fdef, mtable)
# {
# methods <- .findInheritedMethods(classes, fdef, mtable)
# if (length(methods) == 1L)
# return(methods[[1L]])
# else if (length(methods) == 0L) {
# cnames <- paste0("\"", vapply(classes, as.character,
# ""), "\"", collapse = ", ")
# stop(gettextf("unable to find an inherited method for function %s for signature %s",
# sQuote(fdef@generic), sQuote(cnames)), domain = NA)
# }
# else stop("Internal error in finding inherited methods; didn't return a unique method",
# domain = NA)
# })(list(structure("DFrame", package = "S4Vectors")), new("standardGeneric",
# .Data = function (x, ...)
# standardGeneric("as.SlingshotDataSet"), generic = structure("as.SlingshotDataSet", package = "slingshot"),
# package = "slingshot", group = list(), valueClass = character(0),
# signature = "x", default = NULL, skeleton = (function (x,
# ...
# 7: stop(gettextf("unable to find an inherited method for function %s for signature %s",
# sQuote(fdef@generic), sQuote(cnames)), domain = NA)
# 6: (function (classes, fdef, mtable)
# {
# methods <- .findInheritedMethods(classes, fdef, mtable)
# if (length(methods) == 1L)
# return(methods[[1L]])
# else if (length(methods) == 0L) {
# cnames <- paste0("\"", vapply(classes, as.character,
# ""), "\"", collapse = ", ")
# stop(gettextf("unable to find an inherited method for function %s for signature %s",
# sQuote(fdef@generic), sQuote(cnames)), domain = NA)
# }
# else stop("Internal error in finding inherited methods; didn't return a unique method",
# domain = NA)
# })(list(structure("DFrame", package = "S4Vectors")), new("standardGeneric",
# .Data = function (x, ...)
# standardGeneric("as.SlingshotDataSet"), generic = structure("as.SlingshotDataSet", package = "slingshot"),
# package = "slingshot", group = list(), valueClass = character(0),
# signature = "x", default = NULL, skeleton = (function (x,
# ...)
# stop("invalid call in method dispatch to 'as.SlingshotDataSet' (no default method)",
# ...
# 5: as.SlingshotDataSet(colData(data)$slingshot)
# 4: .local(data, ...)
# 3: SlingshotDataSet(secr.ko.sce)
# 2: SlingshotDataSet(secr.ko.sce)
# 1: slingCurveWeights(SlingshotDataSet(secr.ko.sce))
sessionInfo( )
# R version 4.1.2 (2021-11-01)
# Platform: x86_64-w64-mingw32/x64 (64-bit)
# Running under: Windows 10 x64 (build 19044)
#
# Matrix products: default
#
# locale:
# [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252
# [4] LC_NUMERIC=C LC_TIME=English_United States.1252
#
# attached base packages:
# [1] parallel stats4 stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] clusterProfiler_4.0.5 BiocManager_1.30.18 caret_6.0-92
# [4] lattice_0.20-45 condiments_1.0.0 gridExtra_2.3
# [7] fgsea_1.18.0 msigdbr_7.5.1 pheatmap_1.0.12
# [10] UpSetR_1.4.0 bioc2020trajectories_0.0.0.93 tradeSeq_1.6.0
# [13] rgl_0.108.3.2 RColorBrewer_1.1-3 scales_1.2.0
# [16] slingshot_2.0.0 TrajectoryUtils_1.0.0 SingleCellExperiment_1.14.1
# [19] SummarizedExperiment_1.22.0 Biobase_2.52.0 GenomicRanges_1.44.0
# [22] GenomeInfoDb_1.28.4 IRanges_2.26.0 S4Vectors_0.30.2
# [25] BiocGenerics_0.38.0 MatrixGenerics_1.4.3 matrixStats_0.62.0
# [28] princurve_2.1.6 sp_1.4-7 SeuratObject_4.1.0
# [31] Seurat_4.1.1 forcats_0.5.1 stringr_1.4.0
# [34] dplyr_1.0.9 purrr_0.3.4 readr_2.1.2
# [37] tidyr_1.2.0 tibble_3.1.7 ggplot2_3.3.6
# [40] tidyverse_1.3.1
#
# loaded via a namespace (and not attached):
# [1] ica_1.0-2 class_7.3-20 foreach_1.5.2 lmtest_0.9-40
# [5] crayon_1.5.1 spatstat.core_2.4-4 MASS_7.3-57 rhdf5filters_1.4.0
# [9] nlme_3.1-157 backports_1.4.1 qlcMatrix_0.9.7 reprex_2.0.1
# [13] transport_0.12-2 GOSemSim_2.18.1 rlang_1.0.2 XVector_0.32.0
# [17] ROCR_1.0-11 readxl_1.4.0 irlba_2.3.5 limma_3.48.3
# [21] filelock_1.0.2 BiocParallel_1.26.2 bit64_4.0.5 glue_1.6.2
# [25] sctransform_0.3.3 spatstat.sparse_2.1-1 AnnotationDbi_1.54.1 DOSE_3.18.3
# [29] spatstat.geom_2.4-0 VGAM_1.1-6 haven_2.5.0 tidyselect_1.1.2
# [33] fitdistrplus_1.1-8 XML_3.99-0.10 zoo_1.8-10 xtable_1.8-4
# [37] magrittr_2.0.3 cli_3.3.0 zlibbioc_1.38.0 rstudioapi_0.13
# [41] miniUI_0.1.1.1 spatstat.linnet_2.3-2 rpart_4.1.16 fastmatch_1.1-3
# [45] treeio_1.16.2 shiny_1.7.1 xfun_0.31 cluster_2.1.3
# [49] tidygraph_1.2.1 KEGGREST_1.32.0 ggrepel_0.9.1 ape_5.6-2
# [53] listenv_0.8.0 Biostrings_2.60.2 png_0.1-7 future_1.26.1
# [57] ipred_0.9-13 withr_2.5.0 bitops_1.0-7 slam_0.1-50
# [61] ggforce_0.3.3 plyr_1.8.7 cellranger_1.1.0 sparsesvd_0.2
# [65] hardhat_1.1.0 e1071_1.7-11 pROC_1.18.0 pillar_1.7.0
# [69] cachem_1.0.6 Ecume_0.9.1 fs_1.5.2 kernlab_0.9-31
# [73] DelayedMatrixStats_1.14.3 vctrs_0.4.1 ellipsis_0.3.2 generics_0.1.2
# [77] lava_1.6.10 tools_4.1.2 munsell_0.5.0 tweenr_1.0.2
# [81] proxy_0.4-27 DelayedArray_0.18.0 fastmap_1.1.0 HSMMSingleCell_1.12.0
# [85] compiler_4.1.2 abind_1.4-5 httpuv_1.6.5 plotly_4.10.0
# [89] rgeos_0.5-9 GenomeInfoDbData_1.2.6 prodlim_2019.11.13 edgeR_3.34.1
# [93] deldir_1.0-6 snow_0.4-4 utf8_1.2.2 later_1.3.0
# [97] BiocFileCache_2.0.0 recipes_0.2.0 jsonlite_1.8.0 docopt_0.7.1 \
# [101] tidytree_0.3.9 pbapply_1.5-0 sparseMatrixStats_1.4.2 genefilter_1.74.1
# [105] lazyeval_0.2.2 promises_1.2.0.1 spatstat_2.3-4 goftest_1.2-3
# [109] spatstat.utils_2.3-1 reticulate_1.25 cowplot_1.1.1 Rtsne_0.16
# [113] downloader_0.4 uwot_0.1.11 igraph_1.3.1 HDF5Array_1.20.0
# [117] survival_3.3-1 DDRTree_0.1.5 htmltools_0.5.2 memoise_2.0.1
# [121] locfit_1.5-9.5 graphlayouts_0.8.0 viridisLite_0.4.0 digest_0.6.29
# [125] assertthat_0.2.1 mime_0.12 rappdirs_0.3.3 densityClust_0.3.2
# [129] RSQLite_2.2.14 yulab.utils_0.0.4 future.apply_1.9.0 data.table_1.14.2
# [133] blob_1.2.3 fastICA_1.2-3 splines_4.1.2 labeling_0.4.2
# [137] Rhdf5lib_1.14.2 RCurl_1.98-1.6 broom_0.8.0 monocle_2.20.0
# [141] hms_1.1.1 modelr_0.1.8 rhdf5_2.36.0 colorspace_2.0-3
# [145] aplot_0.1.6 nnet_7.3-17 Rcpp_1.0.8.3 RANN_2.6.1
# [149] enrichplot_1.12.3 fansi_1.0.3 tzdb_0.3.0 parallelly_1.32.0
# [153] ModelMetrics_1.2.2.2 R6_2.5.1 grid_4.1.2 ggridges_0.5.3
# [157] lifecycle_1.0.1 curl_4.3.2 leiden_0.4.2 DO.db_2.9
# [161] Matrix_1.4-1 qvalue_2.24.0 RcppAnnoy_0.0.19 iterators_1.0.14
# [165] gower_1.0.0 htmlwidgets_1.5.4 polyclip_1.10-0 shadowtext_0.1.2
# [169] gridGraphics_0.5-1 rvest_1.0.2 mgcv_1.8-40 globals_0.15.0
# [173] patchwork_1.1.1 spatstat.random_2.2-0 progressr_0.10.1 codetools_0.2-18
# [177] lubridate_1.8.0 GO.db_3.13.0 FNN_1.1.3.1 randomForest_4.7-1.1
# [181] dbplyr_2.2.0 gtable_0.3.0 DBI_1.1.2 ggfun_0.0.6
# [185] tensor_1.5 httr_1.4.3 highr_0.9 KernSmooth_2.23-20
# [189] stringi_1.7.6 reshape2_1.4.4 farver_2.1.0 annotate_1.70.0
# [193] viridis_0.6.2 timeDate_3043.102 ggtree_3.0.4 xml2_1.3.3
# [197] combinat_0.0-8 geneplotter_1.70.0 ggplotify_0.1.0 scattermore_0.8
# [201] DESeq2_1.32.0 bit_4.0.4 scatterpie_0.1.7 spatstat.data_2.2-0
# [205] ggraph_2.0.5 pkgconfig_2.0.3 babelgene_22.3 knitr_1.39
I tried with a random dataset and can't reproduce your issue, maybe you could try to share a small subset of your dataset ? Could you show us the output of
secr.ko.sce?Thanks for your feedback. I just tried again with another slice of my original dataset and am getting the same issue. The SCE object is now called col.sce and, in the relevant code excerpt below, I indicate the output of col.sce after fitGAM. I also saved col.sce$slingshot (in col.slingshot) before running fitGAM (and show that its class is PseudotimeOrdering) and then show that the class of col.sce$slingshot after running fitGAM is DataFrame. I can share a subset of my dataset, not sure what would be the best way and what subset would be most useful though. Thanks
Indeed, this behaviour does look strange. I think you should open a github issue here : https://github.com/statOmics/tradeSeq/issues. The creators of tradeSeq are very reactive and try to answer all questions. I hope you will solve your problem here !
Will do, thanks!
I was also unable to reproduce this, but this may be a result of the older package versions. The current version of slingshot is 2.4.0 and of tradeSeq is 1.10.0, whereas you seem to have versions 2.0.0 and 1.6.0, respectively. Can you try updating these packages and see if the issue persists?
Thanks for the suggestion, I've updated my packages and am trying again. However, it's now taking much much longer to fitGAM, so I don't know if it's working yet... BTW, I have 19200 cells, 2 conditions, 2 lineages, 6 knots and I'm providing 3285 genes to the gene argument of fitGAM.
Updating the package version resolved the issue, thanks!