Comments (10)
Hi @ShashTrip1 can you check if the 'ident' variable is a factor, character or numeric type. Pre-converting to character may help.
from milor.
Reran some old code I had (last ran 1 month aga without any errors) however today it threw an error. Code below:
I ran: plotDAbeeswarm(da_results, group.by = "ident")
And it gave the following error: converting group.by to factor... Error in
scale_color_gradient2()
: ! Discrete values supplied to continuous scale.
I encountered the same problem. I group.by = "celltype", but it still gave me the same error, even I converted "celltype" to character. Did you solve it?
from milor.
Your code states that the group.by
variable is "ident" not "celltype".
from milor.
I also met this problem when I run " plotDAbeeswarm":
Converting group_by to factor...
Error in scale_color_gradient2()
:
! Discrete values supplied to continuous scale.
ℹ Example values: NA, NA, NA, NA, and NA
Run rlang::last_trace()
to see where the error occurred.
I check the 'ident' variable I used is a factor.
Do you have any suggestion?
from milor.
Yes - you have NA values in your variables, as stated in the error traceback.
from milor.
does someone have a straight answer?
I have the exact same error as @ionzhanghui
I created a new environment (conda) today with all the up to date packages
still have the issue
ps: I used miloR a couple of weeks before, with the exact same code, and it ran fine
I do not have NA values in my idents
from milor.
Please give the output of your sessionInfo() whenever posting issues. There is no guarantee that the conda versions are the most up to date.
from milor.
head(da_results)
class(da_results$Cluster)
pdf(paste0(condition,"_epithelium_plotDAbeeswarm.pdf"),height= 15, width=17)
plotDAbeeswarm(da_results, group.by = "Cluster") + theme(text = element_text(size = 40))
dev.off()
logFC logCPM F PValue FDR Nhood SpatialFDR NhoodGroup
1 -4.528506 13.11798 1.1565313 0.28239395 0.3968780 1 0.3851987 1
2 -6.788070 11.67183 4.2893203 0.03855765 0.2823152 2 0.2670588 1
3 1.391347 11.78975 0.2876107 0.59185130 0.6808005 3 0.6684219 1
4 -7.740415 12.46967 4.5495640 0.03312179 0.2823152 4 0.2670588 1
5 6.110077 11.19585 2.3146440 0.12841396 0.2823152 5 0.2670588 2
6 6.128929 11.20877 2.3420949 0.12617354 0.2823152 6 0.2670588 2
Cluster Cluster_fraction anno_celltype_cluster
1 HES1+ Macs 1.0000000 HES1+ Macs
2 HES1+ Macs 1.0000000 HES1+ Macs
3 HES1+ Macs 1.0000000 HES1+ Macs
4 HES1+ Macs 1.0000000 HES1+ Macs
5 Cytokine-rich Mono 2 0.7297297 Cytokine-rich Mono 2
6 HES1+ Macs 1.0000000 HES1+ Macs
[1] "character"
Converting group_by to factor...
Error in scale_color_gradient2()
:
! Discrete values supplied to continuous scale.
ℹ Example values: NA, NA, NA, NA, and NA
Run rlang::last_trace()
to see where the error occurred.
sessioninfo:
R version 4.3.3 (2024-02-29)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Rocky Linux 8.9 (Green Obsidian)
Matrix products: default
BLAS/LAPACK: /lustre1/project/stg_00075/software/miniconda/envs/miloR/lib/libopenblasp-r0.3.27.so; LAPACK version 3.12.0
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: NA
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] Seurat_5.1.0 SeuratObject_5.0.2
[3] sp_2.1-4 patchwork_1.2.0
[5] dplyr_1.1.4 miloR_1.10.0
[7] edgeR_4.0.16 limma_3.58.1
[9] scater_1.30.1 ggplot2_3.5.1
[11] scuttle_1.12.0 SingleCellExperiment_1.24.0
[13] SummarizedExperiment_1.32.0 Biobase_2.62.0
[15] GenomicRanges_1.54.1 GenomeInfoDb_1.38.8
[17] IRanges_2.36.0 S4Vectors_0.40.2
[19] BiocGenerics_0.48.1 MatrixGenerics_1.14.0
[21] matrixStats_1.3.0
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.22 splines_4.3.3
[3] later_1.3.2 bitops_1.0-8
[5] tibble_3.2.1 polyclip_1.10-7
[7] fastDummies_1.7.3 lifecycle_1.0.4
[9] globals_0.16.3 lattice_0.22-6
[11] MASS_7.3-60.0.1 magrittr_2.0.3
[13] plotly_4.10.4 httpuv_1.6.15
[15] sctransform_0.4.1 spam_2.10-0
[17] spatstat.sparse_3.1-0 reticulate_1.38.0
[19] cowplot_1.1.3 pbapply_1.7-2
[21] RColorBrewer_1.1-3 abind_1.4-5
[23] zlibbioc_1.48.2 Rtsne_0.17
[25] purrr_1.0.2 ggraph_2.2.1
[27] RCurl_1.98-1.16 tweenr_2.0.3
[29] GenomeInfoDbData_1.2.11 ggrepel_0.9.5
[31] irlba_2.3.5.1 listenv_0.9.1
[33] spatstat.utils_3.0-5 goftest_1.2-3
[35] RSpectra_0.16-2 spatstat.random_3.3-1
[37] fitdistrplus_1.2-1 parallelly_1.38.0
[39] DelayedMatrixStats_1.24.0 leiden_0.4.3.1
[41] codetools_0.2-20 DelayedArray_0.28.0
[43] ggforce_0.4.2 tidyselect_1.2.1
[45] farver_2.1.2 ScaledMatrix_1.10.0
[47] viridis_0.6.5 spatstat.explore_3.3-1
[49] jsonlite_1.8.8 BiocNeighbors_1.20.2
[51] tidygraph_1.3.1 progressr_0.14.0
[53] ggridges_0.5.6 survival_3.7-0
[55] tools_4.3.3 ica_1.0-3
[57] Rcpp_1.0.13 glue_1.7.0
[59] gridExtra_2.3 SparseArray_1.2.4
[61] withr_3.0.0 BiocManager_1.30.23
[63] fastmap_1.2.0 fansi_1.0.6
[65] digest_0.6.36 rsvd_1.0.5
[67] R6_2.5.1 mime_0.12
[69] colorspace_2.1-1 scattermore_1.2
[71] gtools_3.9.5 tensor_1.5
[73] spatstat.data_3.1-2 utf8_1.2.4
[75] tidyr_1.3.1 generics_0.1.3
[77] data.table_1.15.4 graphlayouts_1.1.1
[79] httr_1.4.7 htmlwidgets_1.6.4
[81] S4Arrays_1.2.1 uwot_0.2.2
[83] pkgconfig_2.0.3 gtable_0.3.5
[85] lmtest_0.9-40 XVector_0.42.0
[87] htmltools_0.5.8.1 dotCall64_1.1-1
[89] scales_1.3.0 png_0.1-8
[91] spatstat.univar_3.0-0 reshape2_1.4.4
[93] nlme_3.1-165 cachem_1.1.0
[95] zoo_1.8-12 stringr_1.5.1
[97] KernSmooth_2.23-24 parallel_4.3.3
[99] miniUI_0.1.1.1 vipor_0.4.7
[101] pillar_1.9.0 grid_4.3.3
[103] vctrs_0.6.5 RANN_2.6.1
[105] promises_1.3.0 BiocSingular_1.18.0
[107] beachmat_2.18.1 xtable_1.8-4
[109] cluster_2.1.6 beeswarm_0.4.0
[111] cli_3.6.3 locfit_1.5-9.10
[113] compiler_4.3.3 rlang_1.1.4
[115] crayon_1.5.3 future.apply_1.11.2
[117] labeling_0.4.3 plyr_1.8.9
[119] ggbeeswarm_0.7.2 stringi_1.8.4
[121] deldir_2.0-4 viridisLite_0.4.2
[123] BiocParallel_1.36.0 munsell_0.5.1
[125] lazyeval_0.2.2 spatstat.geom_3.3-2
[127] Matrix_1.6-5 RcppHNSW_0.6.0
[129] sparseMatrixStats_1.14.0 future_1.34.0
[131] statmod_1.5.0 shiny_1.9.0
[133] ROCR_1.0-11 igraph_2.0.3
[135] memoise_2.0.1
from milor.
The current version of miloR is 2.0.0: https://www.bioconductor.org/packages/release/bioc/html/miloR.html Please make sure you are using the most up to date version first.
Also note that the error traceback gives example values causing the problem - these are all NA
- so check if you have NA
values in your da_results
object.
from milor.
The current version of miloR is 2.0.0: https://www.bioconductor.org/packages/release/bioc/html/miloR.html Please make sure you are using the most up to date version first.
Also note that the error traceback gives example values causing the problem - these are all
NA
- so check if you haveNA
values in yourda_results
object.
thanks for your reply. I'm reinstalling the last version of miloR (don't know why it installed a 1.10)
Also, i exported my "da_result" and check for NA values - I do not have any
After it re-install the last version of miloR, I will see if it has anything to do with the fact that my "group.by" has "+" characters in that column of da_result, maybe that is causing the whole issue
==> update: still doesn't work
new update: adding "alpha=0.3" solved it for me !
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Related Issues (20)
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- nhood size distribution: some neighbourhoods have over 2000 cells HOT 4
- Progress Bar for GLMM HOT 6
- object 'as.SimpleList' of mode 'function' was not found when running calcNhoodDistance HOT 2
- Multiple comparisons gives identical results by using model.contrasts HOT 12
- Does MiloR take into account that the two compared conditions have different number of cells? HOT 2
- Log10 FC or Log2 FC? HOT 1
- How to use MiloR after subsetting the cell types from total cell types? HOT 4
- Direction of test for logFC calculation HOT 1
- Existence of 2 tuitorials for the "Differential abundance testing with Milo - Mouse gastrulation example" HOT 3
- Import precomputed graph HOT 7
- makeNhoods graph refinement (issue with isolated vertices) HOT 4
- No Significant Neighbourhoods Result is Error HOT 1
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- Gene expression testing of only DA neighborhoods within group?? HOT 3
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