addMonocleTrajectory is throwing an error - Error in .validInput(input = ArchRProj, name = "ArchRProj", valid = c("ArchRProj")) : could not find function ".validInput"

[susrinivasan]

Attach your log file
I am unable to get a log file for this call.

Describe the bug
THis used to work before but now it says could not find function .validInput, Am I missing a package?

**Context **
processPartialTrajectoriesForEntireSet <- function(object, useGps, trajName, principalGpName) {
archRproj = object

head(archRproj$trajName[!is.na(archRproj$trajName)])

traj_cds <- getMonocleTrajectories(
ArchRProj = archRproj,
name = trajName,
groupBy = "ClustersHar",
clusterParams = list(k = 50),
useGroups = useGps,
principalGroup = principalGpName,
embedding = "Harmony_Combined",
graphParams = list("use_partition"=FALSE,"close_loop"=FALSE),
)
print ("Done with getTrajectories")
archRproj <- addMonocleTrajectory(
ArchRProj = object,
name = trajName,
useGroups = useGps,
groupBy = "ClustersHar",
monocleCDS = traj_cds,
force = TRUE
)

return(archRproj)
}

test4Gps = c("ML.adult", "ML.w8")
projBaTP_coverage = processPartialTrajectoriesForEntireSet(projBaTP_coverage,test4Gps, "Test4_Traj", "ML.w8")

******************* error happens ********

projBaTP_coverage = processPartialTrajectoriesForEntireSet(projBaTP_coverage,test4Gps, "Test4_Traj", "ML.w8")
Running Monocole3 Trajectory Infrastructure!
Adding Embedding
Clustering Embedding
Learning Graphs
Getting Principal Node
Ordering Cells
Plotting Results - /home/sumana/Salk_project/BasalTransplant_scMultiome/ArchR_new/projBaTP_integrated/Monocole3/Plot-Results-Test4_Traj.pdf
[1] "Done with getTrajectories"
Error in .validInput(input = ArchRProj, name = "ArchRProj", valid = c("ArchRProj")) :
could not find function ".validInput"
In addition: Warning message:
In igraph::graph.dfs(stree_ori, root = root_cell, neimode = "all", :
Argument neimode' is deprecated; use mode' instead

Session Info

sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Rocky Linux 8.10 (Green Obsidian)

Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblasp-r0.3.15.so

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
[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] parallel stats4 grid stats graphics grDevices utils datasets methods base

other attached packages:
[1] decoupleR_2.2.2 readxl_1.4.1 RColorBrewer_1.1-3 pathview_1.3.5 org.Hs.eg.db_3.15.0
[6] AnnotationDbi_1.58.0 KEGGgraph_1.56.0 STRINGdb_2.8.4 Biostrings_2.64.1 XVector_0.36.0
[11] hexbin_1.28.2 circlize_0.4.15 ComplexHeatmap_2.12.1 presto_1.0.0 SeuratWrappers_0.3.0
[16] garnett_0.2.19 patchwork_1.1.2 enrichR_3.1 sp_1.5-0 SeuratObject_4.1.2
[21] Seurat_4.2.0 ggvenn_0.1.9 forcats_0.5.2 purrr_0.3.4 readr_2.1.2
[26] tidyr_1.2.1 tibble_3.1.8 tidyverse_1.3.2 textshape_1.7.3 dorothea_1.8.0
[31] reshape2_1.4.4 nabor_0.5.0 ReactomePA_1.40.0 enrichplot_1.16.2 clusterProfiler_4.4.4
[36] msigdbr_7.5.1 ggpubr_0.4.0 monocle3_1.2.9 SingleCellExperiment_1.18.0 dplyr_1.0.10
[41] rhdf5_2.40.0 SummarizedExperiment_1.26.1 Biobase_2.56.0 MatrixGenerics_1.8.1 Rcpp_1.0.9
[46] Matrix_1.5-1 GenomicRanges_1.48.0 GenomeInfoDb_1.32.4 IRanges_2.30.1 S4Vectors_0.34.0
[51] BiocGenerics_0.42.0 matrixStats_0.62.0 data.table_1.14.2 stringr_1.4.1 plyr_1.8.7
[56] magrittr_2.0.3 ggplot2_3.3.6 gtable_0.3.1 gtools_3.9.3 gridExtra_2.3
[61] ArchR_1.0.2 TFBSTools_1.34.0

loaded via a namespace (and not attached):
[1] rsvd_1.0.5 ica_1.0-3 Rsamtools_2.12.0 foreach_1.5.2 lmtest_0.9-40
[6] crayon_1.5.1 spatstat.core_2.4-4 MASS_7.3-58.1 rhdf5filters_1.8.0 nlme_3.1-160
[11] backports_1.4.1 reprex_2.0.2 GOSemSim_2.22.0 rlang_1.0.5 ROCR_1.0-11
[16] irlba_2.3.5 nloptr_2.0.3 proto_1.0.0 BiocParallel_1.30.3 rjson_0.2.21
[21] bcellViper_1.32.0 CNEr_1.32.0 bit64_4.0.5 glue_1.6.2 pheatmap_1.0.12
[26] poweRlaw_0.70.6 sctransform_0.3.5 vipor_0.4.5 spatstat.sparse_2.1-1 DOSE_3.22.1
[31] spatstat.geom_2.4-0 haven_2.5.1 tidyselect_1.1.2 fitdistrplus_1.1-8 XML_3.99-0.10
[36] zoo_1.8-11 GenomicAlignments_1.32.1 chron_2.3-61 xtable_1.8-4 cli_3.4.0
[41] zlibbioc_1.42.0 rstudioapi_0.14 miniUI_0.1.1.1 rpart_4.1.19 fastmatch_1.1-3
[46] treeio_1.20.2 shiny_1.7.3 leidenbase_0.1.11 clue_0.3-61 cluster_2.1.4
[51] caTools_1.18.2 tidygraph_1.2.2 KEGGREST_1.36.3 ggrepel_0.9.1 ape_5.6-2
[56] listenv_0.8.0 TFMPvalue_0.0.8 png_0.1-7 future_1.28.0 withr_2.5.0
[61] bitops_1.0-7 ggforce_0.3.4 cellranger_1.1.0 pracma_2.4.2 pillar_1.8.1
[66] gplots_3.1.3 GlobalOptions_0.1.2 cachem_1.0.6 fs_1.5.2 GetoptLong_1.0.5
[71] graphite_1.42.0 hash_2.2.6.3 vctrs_0.4.1 ellipsis_0.3.2 generics_0.1.3
[76] gsubfn_0.7 tools_4.2.0 beeswarm_0.4.0 munsell_0.5.0 tweenr_2.0.2
[81] proxy_0.4-27 fgsea_1.22.0 DelayedArray_0.22.0 fastmap_1.1.0 compiler_4.2.0
[86] abind_1.4-5 httpuv_1.6.6 rtracklayer_1.56.1 plotly_4.10.0 rgeos_0.5-9
[91] GenomeInfoDbData_1.2.8 lattice_0.20-45 deldir_1.0-6 utf8_1.2.2 later_1.3.0
[96] jsonlite_1.8.0 scales_1.2.1 graph_1.74.0 tidytree_0.4.0 pbapply_1.5-0
[101] carData_3.0-5 lazyeval_0.2.2 promises_1.2.0.1 car_3.1-0 doParallel_1.0.17
[106] R.utils_2.12.0 goftest_1.2-3 spatstat.utils_2.3-1 reticulate_1.26 cowplot_1.1.1
[111] Rtsne_0.16 downloader_0.4 BSgenome_1.64.0 uwot_0.1.14 igraph_1.3.4
[116] survival_3.4-0 yaml_2.3.5 plotrix_3.8-2 htmltools_0.5.3 memoise_2.0.1
[121] BiocIO_1.6.0 graphlayouts_0.8.1 viridisLite_0.4.1 digest_0.6.29 assertthat_0.2.1
[126] mime_0.12 rappdirs_0.3.3 RSQLite_2.2.17 sqldf_0.4-11 yulab.utils_0.0.5
[131] future.apply_1.9.1 remotes_2.4.2 blob_1.2.3 R.oo_1.25.0 labeling_0.4.2
[136] splines_4.2.0 Rhdf5lib_1.18.2 Cairo_1.6-0 googledrive_2.0.0 RCurl_1.98-1.8
[141] broom_1.0.1 hms_1.1.2 modelr_0.1.9 colorspace_2.0-3 BiocManager_1.30.18
[146] ggbeeswarm_0.6.0 shape_1.4.6 aplot_0.1.7 ggrastr_1.0.1 RANN_2.6.1
[151] fansi_1.0.3 tzdb_0.3.0 parallelly_1.32.1 R6_2.5.1 ggridges_0.5.3
[156] lifecycle_1.0.2 curl_4.3.2 ggsignif_0.6.3 googlesheets4_1.0.1 minqa_1.2.4
[161] leiden_0.4.3 DO.db_2.9 qvalue_2.28.0 RcppAnnoy_0.0.19 iterators_1.0.14
[166] htmlwidgets_1.5.4 polyclip_1.10-0 shadowtext_0.1.2 gridGraphics_0.5-1 seqLogo_1.62.0
[171] terra_1.6-18 reactome.db_1.81.0 rvest_1.0.3 mgcv_1.8-41 globals_0.16.1
[176] spatstat.random_2.2-0 progressr_0.11.0 codetools_0.2-18 lubridate_1.8.0 GO.db_3.15.0
[181] dbplyr_2.2.1 R.methodsS3_1.8.2 DBI_1.1.3 ggfun_0.0.7 tensor_1.5
[186] httr_1.4.4 KernSmooth_2.23-20 stringi_1.7.8 farver_2.1.1 annotate_1.74.0
[191] viridis_0.6.2 Rgraphviz_2.40.0 ggtree_3.4.2 xml2_1.3.3 boot_1.3-28.1
[196] lme4_1.1-30 restfulr_0.0.15 ggplotify_0.1.0 scattermore_0.8 bit_4.0.4
[201] scatterpie_0.1.8 spatstat.data_2.2-0 ggraph_2.0.6 pkgconfig_2.0.3 babelgene_22.9
[206] gargle_1.2.1 DirichletMultinomial_1.38.0 rstatix_0.7.0

Additional context
Add any other context about the problem here.

[rcorces]

Hi @susrinivasan! Thanks for using ArchR! Lately, it has been very challenging for me to keep up with maintenance of this package and all of my other
responsibilities as a PI. I have not been responding to issue posts and I have not been pushing updates to the software. We are actively searching to hire
a computational biologist to continue to develop and maintain ArchR and related tools. If you know someone who might be a good fit, please let us know!
In the meantime, your issue will likely go without a reply. Most issues with ArchR right not relate to compatibility. Try reverting to R 4.1 and Bioconductor 3.15.
Newer versions of Seurat and Matrix also are causing issues. Sorry for not being able to provide active support for this package at this time.