there were an error in testNhoods()

[herong2006]

Describe the bug
There were an error in testNhoods(),would you help me?

Minimum code example
Minimum example to reproduce the error

## your code

devtools::install_local("github/miloR-master.zip")
library(stringr)
library(Seurat)
library(miloR)

library(SingleCellExperiment)
library(scater)
library(dplyr)
library(patchwork)
data("sim_trajectory",package="miloR")

traj_sce<-sim_trajectory[['SCE']]

traj_meta<-sim_trajectory[["meta"]]

colData(traj_sce)<-DataFrame(traj_meta)

logcounts(traj_sce)<-log(counts(traj_sce)+1)
traj_sce<-runPCA(traj_sce,ncomponents=30)
traj_sce<-runUMAP(traj_sce)

plotUMAP(traj_sce)

traj_milo<-Milo(traj_sce)
reducedDim(traj_milo,"UMAP")<-reducedDim(traj_sce,"UMAP")

traj_milo
traj_milo<-buildGraph(traj_milo,k=10,d=30)
traj_milo<-makeNhoods(traj_milo,prop=0.1,k=10,d=30,refined=TRUE)
plotNhoodSizeHist(traj_milo)

traj_milo<-countCells(traj_milo,meta.data=data.frame(colData(traj_milo)),samples="Sample")
head(nhoodCounts(traj_milo))

traj_design<-data.frame(colData(traj_milo))[,c("Sample","Condition")]
traj_design<-distinct(traj_design)
rownames(traj_design)<-traj_design$Sample

traj_design<-traj_design[colnames(nhoodCounts(traj_milo)),,drop=FALSE]

traj_design

traj_milo<-calcNhoodDistance(traj_milo,d=30)
rownames(traj_design)<-traj_design$Sample
da_results<-testNhoods(traj_milo,design=~Condition,design.df=traj_design)
Full error traceback

## the error

traj_design
Sample Condition
B_R1 B_R1 B
A_R1 A_R1 A
A_R2 A_R2 A
B_R2 B_R2 B
B_R3 B_R3 B
A_R3 A_R3 A
traj_milo<-calcNhoodDistance(traj_milo,d=30)
rownames(traj_design)<-traj_design$Sample
da_results<-testNhoods(traj_milo,design=~Condition,design.df=traj_design)
Using TMM normalisation
Performing spatial FDR correction withk-distance weighting
Error in testNhoods(traj_milo, design = ~Condition, design.df = traj_design) :
lazy-load database 'C:/Users/ASUS/AppData/Local/R/win-library/4.3/miloR/R/miloR.rdb' is corrupt
此外: Warning messages:
1: In testNhoods(traj_milo, design = ~Condition, design.df = traj_design) :
重新评估被中断的许诺
2: In testNhoods(traj_milo, design = ~Condition, design.df = traj_design) :
internal error -3 in R_decompress1

Session info
Output of sessionInfo()

[herong2006]

When using the example code from the official website, I encountered an error as shown above. The same error persists when using my own data.
scRNA1 <- as.SingleCellExperiment(scRNA1)

构建miloR对象

scRNA1 <- Milo(scRNA1)
scRNA1
scRNA1 <- buildGraph(scRNA1, k = 10, d = 30)
scRNA1 <- makeNhoods(scRNA1, prop = 0.1, k = 10, d=30, refined = TRUE)
plotNhoodSizeHist(scRNA1)

scRNA1 <- countCells(scRNA1, meta.data = data.frame(colData(scRNA1)), samples="orig.ident")
head(nhoodCounts(scRNA1))

traj_design <- data.frame(colData(scRNA1))[,c("orig.ident", "Group")]
traj_design <- distinct(traj_design)
rownames(traj_design) <- traj_design$orig.ident

Reorder rownames to match columns of nhoodCounts(milo)

traj_design <- traj_design[colnames(nhoodCounts(scRNA1)), , drop=FALSE]

traj_design

scRNA1 <- calcNhoodDistance(scRNA1, d=30)
rownames(traj_design) <- traj_design$orig.ident
da_results <- testNhoods(scRNA1, design = ~ Group, design.df = traj_design)

error as follow:

traj_design
orig.ident Group
Normal1 Normal1 Normal
Normal2 Normal2 Normal
Normal3 Normal3 Normal
Normal4 Normal4 Normal
TBI1 TBI1 TBI
TBI2 TBI2 TBI
TBI3 TBI3 TBI
scRNA1 <- calcNhoodDistance(scRNA1, d=30)
rownames(traj_design) <- traj_design$orig.ident
da_results <- testNhoods(scRNA1, design = ~ Group, design.df = traj_design)
Using TMM normalisation
Performing spatial FDR correction withk-distance weighting
Error in testNhoods(scRNA1, design = ~Group, design.df = traj_design) :
lazy-load database 'C:/Users/ASUS/AppData/Local/R/win-library/4.3/miloR/R/miloR.rdb' is corrupt
此外: Warning message:
In testNhoods(scRNA1, design = ~Group, design.df = traj_design) :
internal error -3 in R_decompress1

I want to know where the error is in my code. I've downloaded and installed the latest version of MiloR from GitHub, but the issue still persists. What modifications do I need to make to my code?

[MikeDMorgan]

If you don't include the output from sessionInfo() I can't help you.

[herong2006]

sessionInfo() as follow:

sessionInfo()
R version 4.3.2 (2023-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22621)

Matrix products: default

locale:
[1] LC_COLLATE=Chinese (Simplified)_China.utf8 LC_CTYPE=Chinese (Simplified)_China.utf8 LC_MONETARY=Chinese (Simplified)_China.utf8
[4] LC_NUMERIC=C LC_TIME=Chinese (Simplified)_China.utf8

time zone: Asia/Shanghai
tzcode source: internal

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

loaded via a namespace (and not attached):
[1] vctrs_0.6.5 cli_3.6.3 rlang_1.1.4 purrr_1.0.2 pkgload_1.4.0 promises_1.3.2 shiny_1.10.0
[8] xtable_1.8-4 glue_1.8.0 htmltools_0.5.8.1 httpuv_1.6.15 pkgbuild_1.4.7 ellipsis_0.3.2 fastmap_1.2.0
[15] lifecycle_1.0.4 memoise_2.0.1 BiocManager_1.30.25 compiler_4.3.2 miniUI_0.1.1.1 fs_1.6.5 sessioninfo_1.2.3
[22] htmlwidgets_1.6.4 Rcpp_1.0.13-1 urlchecker_1.0.1 rstudioapi_0.17.1 later_1.4.1 digest_0.6.37 R6_2.6.1
[29] usethis_3.1.0 magrittr_2.0.3 tools_4.3.2 mime_0.12 devtools_2.4.5 profvis_0.4.0 cachem_1.1.0
[36] remotes_2.5.0

Thank you

[herong2006]

when i library all packages,i got sessionInfo() as follow:
library(stringr)
library(Seurat)
library(miloR)
library(SingleCellExperiment)
library(scater)
library(dplyr)
library(patchwork)
library(qs)

sessionInfo()
R version 4.3.2 (2023-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22621)

Matrix products: default

locale:
[1] LC_COLLATE=Chinese (Simplified)_China.utf8 LC_CTYPE=Chinese (Simplified)_China.utf8 LC_MONETARY=Chinese (Simplified)_China.utf8
[4] LC_NUMERIC=C LC_TIME=Chinese (Simplified)_China.utf8

time zone: Asia/Shanghai
tzcode source: internal

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

other attached packages:
[1] qs_0.27.3 patchwork_1.3.0 dplyr_1.1.4 scater_1.30.1 ggplot2_3.5.1
[6] scuttle_1.12.0 SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0 Biobase_2.62.0 GenomicRanges_1.54.1
[11] GenomeInfoDb_1.38.8 IRanges_2.36.0 S4Vectors_0.40.2 BiocGenerics_0.48.1 MatrixGenerics_1.14.0
[16] matrixStats_1.4.1 miloR_2.1.3 edgeR_4.0.16 limma_3.58.1 Seurat_5.2.1
[21] SeuratObject_5.0.2 sp_2.1-4 stringr_1.5.1 devtools_2.4.5 usethis_3.1.0

loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.22 splines_4.3.2 later_1.4.1 bitops_1.0-9 tibble_3.2.1
[6] polyclip_1.10-7 fastDummies_1.7.5 lifecycle_1.0.4 globals_0.16.3 lattice_0.22-6
[11] MASS_7.3-60.0.1 magrittr_2.0.3 plotly_4.10.4 remotes_2.5.0 httpuv_1.6.15
[16] sctransform_0.4.1 spam_2.11-0 sessioninfo_1.2.3 pkgbuild_1.4.7 spatstat.sparse_3.1-0
[21] reticulate_1.40.0 cowplot_1.1.3 pbapply_1.7-2 RColorBrewer_1.1-3 abind_1.4-8
[26] pkgload_1.4.0 zlibbioc_1.48.2 Rtsne_0.17 purrr_1.0.2 ggraph_2.2.1
[31] RCurl_1.98-1.16 pracma_2.4.4 tweenr_2.0.3 GenomeInfoDbData_1.2.11 ggrepel_0.9.6
[36] irlba_2.3.5.1 listenv_0.9.1 spatstat.utils_3.1-3 goftest_1.2-3 RSpectra_0.16-2
[41] spatstat.random_3.3-2 fitdistrplus_1.2-2 parallelly_1.41.0 DelayedMatrixStats_1.24.0 codetools_0.2-20
[46] DelayedArray_0.28.0 RApiSerialize_0.1.4 ggforce_0.4.2 tidyselect_1.2.1 farver_2.1.2
[51] ScaledMatrix_1.10.0 viridis_0.6.5 spatstat.explore_3.3-3 jsonlite_1.8.9 BiocNeighbors_1.20.2
[56] ellipsis_0.3.2 tidygraph_1.3.1 progressr_0.15.1 ggridges_0.5.6 survival_3.8-3
[61] tools_4.3.2 ica_1.0-3 Rcpp_1.0.13-1 glue_1.8.0 gridExtra_2.3
[66] SparseArray_1.2.4 numDeriv_2016.8-1.1 withr_3.0.2 BiocManager_1.30.25 fastmap_1.2.0
[71] rsvd_1.0.5 digest_0.6.37 R6_2.6.1 mime_0.12 colorspace_2.1-1
[76] scattermore_1.2 gtools_3.9.5 tensor_1.5 spatstat.data_3.1-6 tidyr_1.3.1
[81] generics_0.1.3 data.table_1.16.4 graphlayouts_1.2.2 httr_1.4.7 htmlwidgets_1.6.4
[86] S4Arrays_1.2.1 uwot_0.2.2 pkgconfig_2.0.3 gtable_0.3.6 lmtest_0.9-40
[91] XVector_0.42.0 htmltools_0.5.8.1 profvis_0.4.0 dotCall64_1.2 scales_1.3.0
[96] png_0.1-8 spatstat.univar_3.1-1 rstudioapi_0.17.1 reshape2_1.4.4 nlme_3.1-168
[101] cachem_1.1.0 zoo_1.8-12 KernSmooth_2.23-24 vipor_0.4.7 parallel_4.3.2
[106] miniUI_0.1.1.1 pillar_1.10.1 grid_4.3.2 vctrs_0.6.5 RANN_2.6.2
[111] urlchecker_1.0.1 promises_1.3.2 stringfish_0.16.0 BiocSingular_1.18.0 beachmat_2.18.1
[116] xtable_1.8-4 cluster_2.1.8 beeswarm_0.4.0 cli_3.6.3 locfit_1.5-9.12
[121] compiler_4.3.2 rlang_1.1.4 crayon_1.5.3 future.apply_1.11.3 ggbeeswarm_0.7.2
[126] plyr_1.8.9 fs_1.6.5 stringi_1.8.4 viridisLite_0.4.2 deldir_2.0-4
[131] BiocParallel_1.36.0 munsell_0.5.1 lazyeval_0.2.2 spatstat.geom_3.3-4 Matrix_1.6-5
[136] RcppHNSW_0.6.0 sparseMatrixStats_1.14.0 future_1.34.0 statmod_1.5.0 shiny_1.10.0
[141] ROCR_1.0-11 igraph_2.1.2 memoise_2.0.1 RcppParallel_5.1.10

[MikeDMorgan]

I'd strongly recommend updating to latest R (4.4.3) and Bioconductor (3.20) - the version of Milo you have is not the latest.

[herong2006]

The version of miloR I am using is 2.1.3, downloaded the package from website at https://github.com/MarioniLab/miloR. May I ask where to download the latest version of miloR, and what is the latest version number?

[MikeDMorgan]

As it says on the repo landing page:

## Milo is available from Bioconductor (preferred stable installation)
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("miloR")

You need to update Bioconductor to the latest version: https://www.bioconductor.org/install/

[herong2006]

Thank you

[herong2006]

code as follow:
scRNA1 <- calcNhoodDistance(scRNA1, d=30)
rownames(traj_design) <- traj_design$orig.ident
da_results <- testNhoods(scRNA1, design = ~ Group, design.df = traj_design)

da_results %>%
arrange(- SpatialFDR) %>%
head()

plotUMAP(scRNA1) + plotNhoodGraphDA(scRNA1, da_results, alpha=0.05) +

• plot_layout(guides="collect")

ggplot(da_results, aes(PValue)) + geom_histogram(bins=50)

scRNA1 <- buildNhoodGraph(scRNA1)
da_results <- annotateNhoods(scRNA1, da_results, coldata_col = "celltype")
head(da_results)
ggplot(da_results, aes(celltype_fraction)) + geom_histogram(bins=50)
da_results$celltype <- ifelse(da_results$celltype_fraction < 0.7, "Mixed", da_results$celltype)
dim(da_results)
unique(da_results$celltype)
plotDAbeeswarm(da_results, group.by = "celltype")

results as follow:

da_results %>%

• arrange(- SpatialFDR) %>%
• head()
  logFC logCPM F PValue FDR Nhood SpatialFDR
  7839 0.0018372519 7.203229 4.201819e-07 0.9994828 0.9995396 7839 0.9995396
  8613 -0.0003378292 6.751858 3.328996e-07 0.9995396 0.9995396 8613 0.9995396
  1268 -0.0018185543 6.698875 9.623325e-07 0.9992173 0.9993897 1268 0.9993314
  7902 -0.0021256938 6.314727 2.282021e-06 0.9987947 0.9990532 7902 0.9989896
  8789 -0.0028578066 6.553282 2.365185e-06 0.9987729 0.9990532 8789 0.9989896
  8922 -0.0036386152 6.835571 2.932497e-06 0.9986337 0.9990532 8922 0.9989401

scRNA1 <- buildNhoodGraph(scRNA1)
plotUMAP(scRNA1) + plotNhoodGraphDA(scRNA1, da_results, alpha=0.05) +

• plot_layout(guides="collect")
  Adding nhood effect sizes to neighbourhood graph attributes

ggplot(da_results, aes(PValue)) + geom_histogram(bins=50)
da_results <- annotateNhoods(scRNA1, da_results, coldata_col = "celltype")
Converting celltype to factor...
head(da_results)
logFC logCPM F PValue FDR Nhood SpatialFDR celltype celltype_fraction
1 -2.641325 7.851987 0.7659455 0.38147789 0.5228889 1 0.5022718 Immune 0.9285714
2 4.274507 7.066683 2.7077264 0.09986865 0.2460492 2 0.2244751 ExN 0.9411765
3 -6.931837 7.611479 3.9979885 0.04555926 0.1749356 3 0.1518710 InN 1.0000000
4 3.068745 7.153488 1.5106656 0.21904254 0.3808521 4 0.3566222 InN 1.0000000
5 -3.774695 7.865194 1.3901037 0.23839243 0.3998058 5 0.3758232 Astro_RG 1.0000000
6 -4.052649 7.013778 2.0165519 0.15559749 0.3240748 6 0.2989748 Immune 0.9729730
ggplot(da_results, aes(celltype_fraction)) + geom_histogram(bins=50)
da_results$celltype <- ifelse(da_results$celltype_fraction < 0.7, "Mixed", da_results$celltype)
plotDAbeeswarm(da_results, group.by = "celltype")
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.
dim(da_results)
[1] 11592 9

unique(da_results$celltype)
[1] "Immune" "ExN" "InN" "Astro_RG" "Epend" "Mixed" "OPC" "Oligo" "VASC"
plotDAbeeswarm(da_results, group.by = "celltype")
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.
what's wrong in the code:plotDAbeeswarm(da_results, group.by = "celltype")

[herong2006]

I found this issue (Error in scale_color_gradient2()) in (#321), so I tried to solve it according to issue 321, but the problem wasn't resolved. I have raised the error issue again in issue 321.