mathewchamberlain / signacx Goto Github PK

Hi Mathew,

I’m running into some issues with SignacX and was hoping you might be able to help.

I’m trying to run SigncX on integrated Seurat Objects. When I do this I get the following error :
Error in E@graphs[[which(grepl(paste0(default.assay, "_nn"), names(E@graphs)))]] :
attempt to select less than one element in get1index

It seems to me that it doesn’t like the naming of the integrated graphs - though its unclear to me why this would be.

I’ve also attempted running it on exported counts as you can do with SingleR using:
T.Cells.Data <- T.Cells@assays[["Integrated"]]@CountS

However, when I try to run it this way it seems to request the spring directory, and im not using Spring.

Error in CID.LoadEdges(data.dir = spring.dir) :

The pipeline still runs perfectly with the example data. I’m wondering if the issue might relate to the version of CellRanger used?

If you have any ideas this would be most appreciated.

Hello,

Faced a similar issue #9 a while back that you were able to quickly resolve. I noticed this issue today while running the same version of Seurat that I was running when you resolved the previous issue. I tried updating Seurat to 4.0.4 and SignacX to 2.2.4 but still getting the errors when trying to run it.

Please let me know if you need additional information to troubleshoot this!

packageVersion("SignacX") #
[1] ‘2.2.4’
packageVersion("Seurat")
[1] ‘4.0.4’

Sobj
An object of class Seurat
15692 features across 49062 samples within 1 assay
Active assay: RNA (15692 features, 2000 variable features)
2 dimensional reductions calculated: pca, umap

  labels <- Signac(Sobj, num.cores = 32)

.......... Entry in Signac
.......... Running Signac on Seurat object :
nrow = 15692
ncol = 49062
| | 0%, ETA NA

.......... Exit Signac.
Execution time = 13.799 s.
Warning message:
In mclapply(X, function(...) { :
all scheduled cores encountered errors in user code

I am opening this issue as a notification because SignacX is listed here as a package that relies (depends/imports/suggests) on Seurat. As you may know, we recently released Seurat v5 as a beta in March of this year, with new updates for spatial, multimodal, and massively scalable analysis. For more information on updates and improvements, check out our website https://satijalab.org/seurat/.
We are now preparing to release Seurat v5 to CRAN, and plan to submit it on October 23rd. While we have tried our best to keep things backward-compatible, it is possible that updates to Seurat and SeuratObject might break your existing functionality or tests. We wanted to reach out before the new version is on CRAN, so that there's time to report issues/incompatibilities and prepare you for any changes in your code base that might be necessary.

We apologize for any disruption or inconvenience, but hope that the improvements to Seurat v5 will benefit your users going forward.
To test the upcoming release, you can install Seurat from the seurat5 branch using the instructions available on this page: https://satijalab.org/seurat/articles/install.

Thank you!
Seurat v5 team

When using Signac/SignacFast after integrating a large dataset ~325k cells with Harmony, I'm getting this error. Is there a way around it? I'm using 8 cores each with 200gb of memory.

`.......... Entry in SignacFast
.......... Running SignacFast on Seurat object :
nrow = 36601
ncol = 325267
Error in A %*% (A %^% (n - 1)): Cholmod error 'problem too large' at file ../Core/cholmod_sparse.c, line 89
Traceback:

1. SignacFast(merged.dataset, num.cores = 8)
2. CID.GetDistMat(edges)
3. m %^% j
4. A %*% (A %^% (n - 1))
5. A %*% (A %^% (n - 1))`

Hi Mathew,

Thanks for your fantastic work. Do you have any plan of releasing code/scripts used to generate built-in HPCA training data? It would be good having some guide for us who what to apply SignacX to non-human model organisms.

Hi, trying to run the package as indicated in the vignette, I found the following message.
ERROR: from CID.LoadEdges:
edges = /edges.csv does not exist.
Error in CID.LoadEdges(data.dir = spring.dir) :

Hi Matthew,

I was trying to train NNs for a dataset and am getting this error. Do you know what might be happening / anything i could try?
Thanks!

nns.boot = SignacBoot(E = so.train, # so.train is a seurat object
L = c("monocytes", "B_cells"), # these labels are present in the seurat object
labels = [email protected]$labels) # labels has many other non-monocytes/B-cells also present

.......... Entry in SignacBoot
.......... Running SignacBoot on input Seurat object :
nrow = 16161
ncol = 3915
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 52s
Error in intI(j, n = x@Dim[2], dn[[2]], give.dn = FALSE) :
'NA' indices are not (yet?) supported for sparse Matrices

sessionInfo() output below.

R version 4.0.2 (2020-06-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS: /usr/lib64/libblas.so.3.4.2
LAPACK: /hpc/apps/2018/R/v4.0.2.app/lib/R/lib/libRlapack.so

locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
[4] 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
[10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] SignacX_2.2.0 htmlwidgets_1.5.3 highcharter_0.8.2 forcats_0.5.1 stringr_1.4.0
[6] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.0 ggplot2_3.3.3
[11] tidyverse_1.3.0 dplyr_1.0.5 janitor_2.1.0 SeuratObject_4.0.0 Seurat_4.0.1
[16] magrittr_2.0.1 data.table_1.14.0

There is sentence "We demonstrate that Signac accurately classified single cell RNA-sequencing data across diseases, technologies, species and tissues." in the abstract of the paper "Cell type classification and discovery across diseases, technologies and tissues reveals conserved gene signatures and enables standardized single-cell readouts". The Signac should be able to use in other species, especially mouse. Would you please let me know how to apply Signac to mouse single cell data? Thank you so much.

Hello. I'm trying out SignacX.

For Signac() function, I got 5 warning messages. Although, it did generate a "labels" object.
Warning messages:

1: In if (class(E) == "matrix") { :
  the condition has length > 1 and only the first element will be used
2: In min(x) : no non-missing arguments to min; returning Inf
3: In max(x) : no non-missing arguments to max; returning -Inf
4: In min(x) : no non-missing arguments to min; returning Inf
5: In mclapply(X, function(...) { :
  all scheduled cores encountered errors in user code

Below is a picture of warning messages and how the generated labels look like.
(a) the 5 warning messages. You will also observe "0%, ETA NA"

(b) the table of "labels" object

I still tried to proceed with the next step, but now it got an error.

For GenerateLabels() function, the error:
_Error in Cspdiagprod(x, y, boolArith = NA) : non-matching dimensions_

It seems like the issue is in dimensions? But I don't know how to resolve this. May I ask for your help please?

Hi! Thanks a lot for the SignacX package!
I've just encountered a problem, trying to run it with the Seurat v4.3.0, where they had changed column names of a dataframe, returned by the FindMarkers function. So instead of avg_logFC they have avg_log2FC. And because of it the cluster assignment in SignacBoot doesn't work properly.

Hi Matthew,
I am trying to run example code given on https://mathewchamberlain.github.io/SignacX/reference/SignacBoot.html to prepare training dataset. The pbmcs.rds is prepared from the SignacXFast example.

The code throws the following error -

Error in rbind(...) : 
  number of columns of matrices must match (see arg 2)
Calls: <Anonymous> ... rbind -> standardGeneric -> eval -> eval -> eval -> rbind

I tried debugging the function and printed out dd and dd2 dataframes, turns out that dd2 is empty due to which rbind can't be applied.

I am using the latest CRAN package with version 2.2.5.

Hello Mathew! Hope you're doing fine! I do neutrophil research and I'm a great fan of your SignacX, especially that other methods cannot identify of can only poorly identify neutrophils from scRNA-seq data!

Today, I found your benchmark analysis on synovial fluid data (https://cran.r-project.org/web/packages/SignacX/vignettes/signac-Seurat_AMP_RA.html) from Google. I just wonder if you have performed any benchmark study on PBMC data to calculate accuracy comparisons?

My colleagues, who are studying other immune cells, are always using SingleR and I want to stop them, I mean convince them to use SignacX instead. So when I saw this benchmark study that SignacX accuracy is ~0.95 while SingleR is ~0.55, I wonder if you have data on PBMC as well.

Thank you and warmest regards!

Hello,

I'm trying to understand and use Signac. I tried the tutorial with a sample dataset explained here "https://htmlpreview.github.io/?https://github.com/mathewchamberlain/SignacX/master/vignettes/signac-Seurat_pbmcs.html". The initial steps were running fine until the "Signac()" method was called. However, during the runtime, the method is raising the following error
"Error in E@graphs[[1]] : subscript out of bounds". Which seems to be because it was trying to access objects that do not exist in the data. It would be really appreciable if someone could look into this.

Thank you
Mohan

Hi, I am trying to complete this vignette, but I don't know how to obtain the counts and metadata files from the kidney experiment ('SDY997_EXP15176_celseq_matrix_ru10_molecules.tsv.gz' and 'SDY997_EXP15176_celseq_meta.tsv'). I would like to use this example dataset in order to explore the code on my own and fully understand the workflow. Thank you.

Hi,

I tried to test both Signac or SignacFast on the pbmc test data, following line by line the example, but after well over 10 mins, it is still showing.

I

labels = SignacFast(E = pbmc)
.......... Entry in SignacFast
.......... Running SignacFast on Seurat object :
nrow = 14043
ncol = 1222
| | 0%, ETA NA

I tried to debug it and it looks like it gets stuck within the pbmcapply function. I edited your function and replaced pbmcapply with the standard mcapply, and it completed.
I then reinstalled pbmcapply and also tried the dev version, but it does not help.

Thanks

sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux 8.6 (Ootpa)

Matrix products: default
BLAS/LAPACK: FlexiBLAS OPENBLAS; LAPACK version 3.10.1

locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

time zone: America/New_York
tzcode source: system (glibc)

attached base packages:
[1] parallel stats4 grid stats graphics grDevices utils
[8] datasets methods base

other attached packages:
[1] SignacX_2.2.5 dsb_1.0.3
[3] SoupX_1.6.2 nat.utils_0.6.1
[5] clustree_0.5.0 ggraph_2.1.0
[7] HGNChelper_0.8.1 ProjecTILs_3.1.3
[9] DoubletFinder_2.0.3 scDblFinder_1.14.0
[11] djvdj_0.1.0 scater_1.28.0
[13] scuttle_1.10.2 SingleCellExperiment_1.22.0
[15] SummarizedExperiment_1.30.2 Biobase_2.60.0
[17] GenomicRanges_1.52.0 GenomeInfoDb_1.36.1
[19] IRanges_2.34.1 S4Vectors_0.38.1
[21] BiocGenerics_0.46.0 MatrixGenerics_1.12.2
[23] matrixStats_1.0.0 muscat_1.14.0
[25] CIPR_0.1.0 ComplexHeatmap_2.16.0
[27] gprofiler2_0.2.2 ggplot2_3.4.2
[29] cowplot_1.1.1 future_1.32.0
[31] purrr_1.0.1 openxlsx_4.2.5.2
[33] patchwork_1.1.2 miQC_1.8.0
[35] SeuratWrappers_0.3.1 SeuratDisk_0.0.0.9020
[37] glmGamPoi_1.12.2 SeuratObject_4.1.3
[39] Seurat_4.3.0.1 dplyr_1.1.2

loaded via a namespace (and not attached):
[1] R.methodsS3_1.8.2 progress_1.2.2
[3] nnet_7.3-18 goftest_1.2-3
[5] Biostrings_2.68.1 TH.data_1.1-2
[7] vctrs_0.6.2 spatstat.random_3.1-5
[9] digest_0.6.31 png_0.1-8
[11] shape_1.4.6 ggrepel_0.9.3
[13] deldir_1.0-6 parallelly_1.35.0
[15] MASS_7.3-59 reshape2_1.4.4
[17] httpuv_1.6.9 foreach_1.5.2
[19] withr_2.5.0 ellipsis_0.3.2
[21] survival_3.5-5 ggbeeswarm_0.7.2
[23] emmeans_1.8.5 zoo_1.8-12
[25] GlobalOptions_0.1.2 gtools_3.9.4
[27] pbapply_1.7-0 R.oo_1.25.0
[29] prettyunits_1.1.1 promises_1.2.0.1
[31] httr_1.4.5 restfulr_0.0.15
[33] globals_0.16.2 fitdistrplus_1.1-11
[35] miniUI_0.1.1.1 generics_0.1.3
[37] zlibbioc_1.46.0 ScaledMatrix_1.8.1
[39] polyclip_1.10-4 GenomeInfoDbData_1.2.10
[41] xtable_1.8-4 stringr_1.5.0
[43] pracma_2.4.2 doParallel_1.0.17
[45] S4Arrays_1.0.5 hms_1.1.3
[47] irlba_2.3.5.1 colorspace_2.1-0
[49] hdf5r_1.3.8 ROCR_1.0-11
[51] reticulate_1.31 spatstat.data_3.0-1
[53] flexmix_2.3-19 magrittr_2.0.3
[55] lmtest_0.9-40 readr_2.1.4
[57] later_1.3.0 viridis_0.6.2
[59] modeltools_0.2-23 lattice_0.21-8
[61] spatstat.geom_3.2-4 future.apply_1.11.0
[63] scattermore_1.2 XML_3.99-0.14
[65] RcppAnnoy_0.0.21 pillar_1.9.0
[67] nlme_3.1-162 iterators_1.0.14
[69] caTools_1.18.2 compiler_4.3.0
[71] beachmat_2.16.0 stringi_1.7.12
[73] tensor_1.5 minqa_1.2.5
[75] GenomicAlignments_1.36.0 plyr_1.8.8
[77] crayon_1.5.2 abind_1.4-5
[79] BiocIO_1.10.0 blme_1.0-5
[81] locfit_1.5-9.7 sp_2.0-0
[83] graphlayouts_1.0.0 bit_4.0.5
[85] sandwich_3.0-2 scGate_1.4.1
[87] codetools_0.2-19 multcomp_1.4-23
[89] BiocSingular_1.16.0 GetoptLong_1.0.5
[91] plotly_4.10.1 remaCor_0.0.16
[93] mime_0.12 splines_4.3.0
[95] circlize_0.4.15 Rcpp_1.0.10
[97] sparseMatrixStats_1.12.2 utf8_1.2.3
[99] clue_0.3-64 lme4_1.1-33
[101] listenv_0.9.0 DelayedMatrixStats_1.22.5
[103] Rdpack_2.4 estimability_1.4.1
[105] tibble_3.2.1 Matrix_1.5-4
[107] statmod_1.5.0 tzdb_0.3.0
[109] tweenr_2.0.2 pkgconfig_2.0.3
[111] pheatmap_1.0.12 tools_4.3.0
[113] RhpcBLASctl_0.23-42 rbibutils_2.2.13
[115] viridisLite_0.4.1 numDeriv_2016.8-1.1
[117] fastmap_1.1.1 scales_1.2.1
[119] ica_1.0-3 Rsamtools_2.16.0
[121] broom_1.0.4 abdiv_0.2.0
[123] coda_0.19-4 BiocManager_1.30.21.1
[125] RANN_2.6.1 farver_2.1.1
[127] aod_1.3.2 tidygraph_1.2.3
[129] yaml_2.3.7 rtracklayer_1.60.0
[131] cli_3.6.1 UCell_2.4.0
[133] leiden_0.4.3 lifecycle_1.0.3
[135] uwot_0.1.16 glmmTMB_1.1.7
[137] mvtnorm_1.1-3 bluster_1.10.0
[139] backports_1.4.1 BiocParallel_1.34.2
[141] gtable_0.3.3 rjson_0.2.21
[143] ggridges_0.5.4 progressr_0.13.0
[145] limma_3.56.2 jsonlite_1.8.4
[147] edgeR_3.42.4 bitops_1.0-7
[149] bit64_4.0.5 xgboost_1.7.5.1
[151] Rtsne_0.16 spatstat.utils_3.0-3
[153] BiocNeighbors_1.18.0 zip_2.3.0
[155] metapod_1.8.0 dqrng_0.3.0
[157] ggtrace_0.2.0 R.utils_2.12.2
[159] pbkrtest_0.5.2 lazyeval_0.2.2
[161] shiny_1.7.4 htmltools_0.5.5
[163] sctransform_0.3.5 glue_1.6.2
[165] XVector_0.40.0 RCurl_1.98-1.12
[167] mclust_6.0.0 scran_1.28.2
[169] gridExtra_2.3 EnvStats_2.7.0
[171] boot_1.3-28.1 igraph_1.5.1
[173] variancePartition_1.30.2 TMB_1.9.4
[175] R6_2.5.1 tidyr_1.3.0
[177] DESeq2_1.40.2 gplots_3.1.3
[179] STACAS_2.1.3 cluster_2.1.4
[181] neuralnet_1.44.2 nloptr_2.0.3
[183] DelayedArray_0.26.6 tidyselect_1.2.0
[185] vipor_0.4.5 ggforce_0.4.1
[187] rsvd_1.0.5 munsell_0.5.0
[189] KernSmooth_2.23-20 data.table_1.14.8
[191] htmlwidgets_1.6.2 RColorBrewer_1.1-3
[193] rlang_1.1.1 spatstat.sparse_3.0-2
[195] spatstat.explore_3.2-1 lmerTest_3.1-3
[197] remotes_2.4.2 fansi_1.0.4
[199] beeswarm_0.4.0

Hello. I am currently investigating platelets. May I ask whether SignacX also automatically annotate platelets? Thank you!

I found the following download link is not available for me; is there any method to store the data file with the package? after loading the package, I can have access to the training data.

> labels <- Signac(SeuratIntegrate, num.cores = 4)
Error in url("https://github.com/mathewchamberlain/SignacX/blob/master/assets/training_HPCA.rds?raw=TRUE",  : 
  cannot open the connection to 'https://github.com/mathewchamberlain/SignacX/blob/master/assets/training_HPCA.rds?raw=TRUE'
In addition: Warning message:
In url("https://github.com/mathewchamberlain/SignacX/blob/master/assets/training_HPCA.rds?raw=TRUE",  :
  URL 'https://raw.githubusercontent.com/mathewchamberlain/SignacX/master/assets/training_HPCA.rds': status was 'Couldn't connect to server'
> P = GetTrainingData_HPCA()
Warning in url("https://github.com/mathewchamberlain/SignacX/blob/master/assets/training_HPCA.rds?raw=TRUE",  :
  URL 'https://github.com/mathewchamberlain/SignacX/blob/master/assets/training_HPCA.rds?raw=TRUE': Timeout of 60 seconds was reached
Error in url("https://github.com/mathewchamberlain/SignacX/blob/master/assets/training_HPCA.rds?raw=TRUE",  : 
  cannot open the connection to 'https://github.com/mathewchamberlain/SignacX/blob/master/assets/training_HPCA.rds?raw=TRUE'

Hello, I am wondering how I can download the datasets from your website. It seems selecting 'explore' and selecting cells gives an option to download the raw data. However, this option doesn't seem to work and shows a message as follows.

Preparing data...
This may take several minutes.
You will be notified of completion by email.

Feel free to close this window.

Afterwards, there is no followup. Is there a different way to download the raw data? Thanks,

Hi Mathew, good day! I've been using your amazing package for more than a year already and it's my first time to encounter an issue now. I wonder if you are familiar with this and if you have any suggestions. I'm using the SignacFast() function since I have a big data.

When I ran the GenerateLabels(), here's the error I encountered.

Would you be able to enlighten me how we can resolve this please? Thank you.

Hello,

I have a few custom datasets consisting of (47 markers) x (1,000 ~ 10,000 cells) on which I want to run a supervised cell annotation.

Initially, running Signac on my dataset gave me some errors, so I wanted to figure out what the issue is and wondered if it could be stemming from the small number of rows (47 markers) that I used, perhaps causing some mathematical/linear algebraic issues.

So I tried to size down the given "pbmc" dataset from the vignette (https://cran.r-project.org/web/packages/SignacX/vignettes/signac-Seurat_CITE-seq.html) to contain the first 47 markers only. This process is shown in the code below. (FYI: the original "pbmc" dataset from the vignette contains 33538 markers x 7865 cells.)

After running SignacX on this "smaller" pbmc dataset, I noticed that the same types of error are produced. Specifically, these error messages pop up after "SCTransform" and "Signac" functions, and I commented the exact error messages below. For SCTransform, I eventually skipped this step and instead used the "NormalizeData, FindVariableFeatures and ScaleData" sequence, which did not produce any error.

On a note, I tried to adjust the parameters such as "npcs", "nfeatures.print", and "dims" in the functions "RunPCA", "RunUMAP" and "FindNeighbors" functions wondering if these could be the issues, but no avail. However, I'm not too familiar with parameters in these functions, so my parameters may still be wrong.

What could possibly be the issue here and how can I reduce these error messages?

Thank you!

library(Seurat)
require(SignacX)
# Minimally reproducible example
E = Read10X_h5(filename = "fls/pbmc_10k_protein_v3_filtered_feature_bc_matrix.h5")
E.small <- E$`Gene Expression`[c(1:47),]


pbmc <- CreateSeuratObject(counts = E.small, project = "pbmc")

#pbmc <- SCTransform(pbmc) #, variable.features.n = 30

# Error message # 1:
#Calculating cell attributes from input UMI matrix: log_umi
#Error in make_cell_attr(umi, cell_attr, latent_var, batch_var, latent_var_nonreg,  : 
#                          cell attribute "log_umi" contains NA, NaN, or infinite value

pbmc <- NormalizeData(pbmc)
pbmc <- FindVariableFeatures(pbmc)
pbmc <- ScaleData(pbmc)

pbmc <- RunPCA(pbmc, npcs=10, nfeatures.print = 10)
pbmc <- RunUMAP(pbmc, dims = 1:10)
pbmc <- FindNeighbors(pbmc, dims = 1:10)

labels <- Signac(pbmc, verbose=T)

# Error message # 2:

#..........  Entry in Signac 
#..........  Running Signac on Seurat object :
#  nrow = 47
#  ncol = 7865
# |                                  |   0%, ETA NA

# Error in order(rownames(Z)) : argument 1 is not a vector




sessionInfo()

R version 4.1.3 (2022-03-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.2.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/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] stats graphics grDevices utils datasets methods base

other attached packages:
[1] pbmc3k.SeuratData_3.1.4 SeuratData_0.2.2 SignacX_2.2.5 patchwork_1.1.1
[5] ggplot2_3.3.6 SeuratDisk_0.0.0.9020 sp_1.4-7 SeuratObject_4.1.0
[9] Seurat_4.1.1

loaded via a namespace (and not attached):
[1] Rtsne_0.16 colorspace_2.0-3 deldir_1.0-6 ellipsis_0.3.2
[5] ggridges_0.5.3 rprojroot_2.0.3 fs_1.5.2 rstudioapi_0.13
[9] spatstat.data_2.2-0 farver_2.1.0 leiden_0.4.2 listenv_0.8.0
[13] remotes_2.4.2 ggrepel_0.9.1 bit64_4.0.5 RSpectra_0.16-1
[17] fansi_1.0.3 codetools_0.2-18 splines_4.1.3 cachem_1.0.6
[21] pkgload_1.2.4 polyclip_1.10-0 jsonlite_1.8.0 ica_1.0-2
[25] cluster_2.1.3 png_0.1-7 rgeos_0.5-9 uwot_0.1.11
[29] shiny_1.7.1 sctransform_0.3.3 spatstat.sparse_2.1-1 compiler_4.1.3
[33] httr_1.4.3 Matrix_1.4-1 fastmap_1.1.0 lazyeval_0.2.2
[37] cli_3.3.0 later_1.3.0 prettyunits_1.1.1 htmltools_0.5.2
[41] tools_4.1.3 igraph_1.3.1 gtable_0.3.0 glue_1.6.2
[45] RANN_2.6.1 reshape2_1.4.4 dplyr_1.0.9 rappdirs_0.3.3
[49] Rcpp_1.0.8.3 scattermore_0.8 vctrs_0.4.1 nlme_3.1-157
[53] progressr_0.10.0 lmtest_0.9-40 spatstat.random_2.2-0 stringr_1.4.0
[57] brio_1.1.3 ps_1.7.0 globals_0.15.0 testthat_3.1.4
[61] mime_0.12 miniUI_0.1.1.1 lifecycle_1.0.1 irlba_2.3.5
[65] devtools_2.4.3 goftest_1.2-3 future_1.26.1 MASS_7.3-57
[69] zoo_1.8-10 scales_1.2.0 spatstat.core_2.4-4 promises_1.2.0.1
[73] spatstat.utils_2.3-1 parallel_4.1.3 RColorBrewer_1.1-3 curl_4.3.2
[77] memoise_2.0.1 reticulate_1.25 pbapply_1.5-0 gridExtra_2.3
[81] rpart_4.1.16 stringi_1.7.6 desc_1.4.1 pkgbuild_1.3.1
[85] rlang_1.0.2 pkgconfig_2.0.3 matrixStats_0.62.0 lattice_0.20-45
[89] ROCR_1.0-11 purrr_0.3.4 tensor_1.5 htmlwidgets_1.5.4
[93] labeling_0.4.2 processx_3.5.3 cowplot_1.1.1 bit_4.0.4
[97] tidyselect_1.1.2 parallelly_1.31.1 RcppAnnoy_0.0.19 plyr_1.8.7
[101] magrittr_2.0.3 R6_2.5.1 generics_0.1.2 pillar_1.7.0
[105] withr_2.5.0 mgcv_1.8-40 fitdistrplus_1.1-8 survival_3.3-1
[109] abind_1.4-5 tibble_3.1.7 future.apply_1.9.0 crayon_1.5.1
[113] hdf5r_1.3.5 KernSmooth_2.23-20 utf8_1.2.2 spatstat.geom_2.4-0
[117] plotly_4.10.0 usethis_2.1.6 grid_4.1.3 data.table_1.14.2
[121] callr_3.7.0 digest_0.6.29 pbmcapply_1.5.1 xtable_1.8-4
[125] tidyr_1.2.0 httpuv_1.6.5 munsell_0.5.0 viridisLite_0.4.0
[129] sessioninfo_1.2.2
Warning messages:
1: ggrepel: 5 unlabeled data points (too many overlaps). Consider increasing max.overlaps
2: ggrepel: 5 unlabeled data points (too many overlaps). Consider increasing max.overlaps
3: ggrepel: 5 unlabeled data points (too many overlaps). Consider increasing max.overlaps
4: ggrepel: 5 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Hi,

I noticed, that in comparison with SingleR here: https://mathewchamberlain.github.io/SignacX/articles/signac-Seurat_AMP_RA.html the parameter fine.tune = FALSE was used. I'm curious if you run SingleR with the parameter set TRUE and how results were different?

Thanks!

I've been using SignacX without issue for a while now. I recently updated a number of R packages, including Seurat, and now SignacX doesn't work. I've tried downgrading Seurat before creating the Seurat object, but that hasn't resolved the issue, so I'm wondering if another package that was automatically updated is causing the underlying problem. Strangely, loading an older Seurat object into R even with a new version of Seurat seems to work fine...

>   packageVersion("SignacX") #
[1] ‘2.2.1’
> packageVersion("Seurat")
[1] ‘4.0.3’

> Jobj
An object of class Seurat 
14633 features across 69630 samples within 2 assays 
Active assay: RNA (14623 features, 2000 variable features)
 1 other assay present: ADT
 3 dimensional reductions calculated: pca, tsne, umap
> labels2 <- Signac(Jobj, num.cores = 32)
 ..........  Entry in Signac 
 ..........  Running Signac on Seurat object :
             nrow = 14623
             ncol = 69630
  |                                                                                                                           |   0%, ETA NA

 ..........  Exit Signac.
             Execution time = 59.185 s.
Warning message:
In mclapply(X, function(...) { :
  all scheduled cores encountered errors in user code

This produces "labels" but clearly only full of errors:

> summary(labels)
                      Length Class     Mode     
All                       1  try-error character
Immune                    1  try-error character
Lymphocytes               1  try-error character
Myeloid                   1  try-error character
B                         1  try-error character
B.NoPlasma                1  try-error character
TNK                       1  try-error character
T                         1  try-error character
T.CD4                     1  try-error character
T.CD4.memory.regs         1  try-error character
T.CD8                     1  try-error character
MPh                       1  try-error character
Monocytes.Neutrophils     1  try-error character
Monocytes                 1  try-error character
NonImmune                 1  try-error character
Non.Fibroblasts           1  try-error character
Non.Epithelial            1  try-error character
T.CD8.memory              1  try-error character
louvain               69630  -none-    character