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Published 20 hours ago • verified publisher icon jinworks

CellChat2 computeCommunProb error

Open kcavagnero opened this issue 1 year ago • 8 comments

Hi Sqjin,

Thank you for the great package. v1 has been such a great resource for the single cell community. Looking forward to using v2 with the larger L-R database but I am having some issues getting started.

When running computeCommunProb() I get the below error in if (object@options$data type != "rna") {: argument is of length zero

I suspect this is because dim([email protected]) 0,0

Previously with same data using v1 did not have this issue and dim([email protected]) 744,14891

For both versions I created my cellchat objects from seurat using the code below.

library(Seurat)
library(CellChat)
data.input <- GetAssayData(healthy, assay = "RNA", slot = "data") # normalized data matrix
Idents(healthy) <- "celltype"
labels <- Idents(healthy)
meta <- data.frame(group = labels, row.names = names(labels))
cellchat <- createCellChat(object = data.input, meta = meta, group.by = "group")

Any idea how to fix?

sessionInfo() R version 4.1.3 (2022-03-10) Platform: x86_64-conda-linux-gnu (64-bit) Running under: CentOS Linux 7 (Core)

Matrix products: default BLAS/LAPACK: /home/kecavagn/miniconda3/envs/seurat_cellchat2/lib/libopenblasp-r0.3.20.so

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

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

other attached packages: [1] patchwork_1.1.3 CellChat_2.0.0 Biobase_2.54.0
[4] BiocGenerics_0.40.0 ggplot2_3.4.4 igraph_1.5.1
[7] dplyr_1.1.4 sp_1.4-7 SeuratObject_4.1.0 [10] Seurat_4.1.1

loaded via a namespace (and not attached): [1] backports_1.4.1 circlize_0.4.15 uuid_1.1-1
[4] systemfonts_1.0.5 NMF_0.26 plyr_1.8.9
[7] repr_1.1.4 lazyeval_0.2.2 splines_4.1.3
[10] BiocParallel_1.28.3 listenv_0.9.0 scattermore_0.8
[13] ggnetwork_0.5.12 gridBase_0.4-7 digest_0.6.33
[16] foreach_1.5.2 htmltools_0.5.7 ggalluvial_0.12.5
[19] fansi_1.0.5 magrittr_2.0.3 tensor_1.5
[22] cluster_2.1.3 doParallel_1.0.17 ROCR_1.0-11
[25] sna_2.7-1 ComplexHeatmap_2.15.4 globals_0.16.2
[28] matrixStats_1.1.0 svglite_2.1.2 spatstat.sparse_2.1-1 [31] colorspace_2.1-0 ggrepel_0.9.4 crayon_1.5.2
[34] jsonlite_1.8.7 progressr_0.10.0 spatstat.data_2.2-0
[37] survival_3.3-1 zoo_1.8-10 iterators_1.0.14
[40] glue_1.6.2 polyclip_1.10-0 registry_0.5-1
[43] gtable_0.3.4 leiden_0.4.2 GetoptLong_1.0.5
[46] car_3.1-2 shape_1.4.6 future.apply_1.11.0
[49] abind_1.4-5 scales_1.2.1 rngtools_1.5.2
[52] rstatix_0.7.2 spatstat.random_2.2-0 miniUI_0.1.1.1
[55] Rcpp_1.0.11 viridisLite_0.4.2 xtable_1.8-4
[58] clue_0.3-65 reticulate_1.34.0 spatstat.core_2.4-2
[61] stats4_4.1.3 htmlwidgets_1.6.2 httr_1.4.7
[64] FNN_1.1.3.2 RColorBrewer_1.1-3 ellipsis_0.3.2
[67] ica_1.0-2 farver_2.1.1 pkgconfig_2.0.3
[70] sass_0.4.7 uwot_0.1.11 deldir_1.0-6
[73] utf8_1.2.4 labeling_0.4.3 tidyselect_1.2.0
[76] rlang_1.1.2 reshape2_1.4.4 later_1.3.1
[79] munsell_0.5.0 tools_4.1.3 cachem_1.0.8
[82] cli_3.6.1 generics_0.1.3 statnet.common_4.9.0 [85] broom_1.0.5 ggridges_0.5.3 evaluate_0.23
[88] stringr_1.5.1 fastmap_1.1.1 goftest_1.2-3
[91] fitdistrplus_1.1-8 purrr_1.0.2 RANN_2.6.1
[94] pbapply_1.7-2 future_1.33.0 nlme_3.1-157
[97] mime_0.12 compiler_4.1.3 plotly_4.10.3
[100] png_0.1-8 ggsignif_0.6.4 spatstat.utils_2.3-1 [103] tibble_3.2.1 bslib_0.5.1 stringi_1.8.1
[106] RSpectra_0.16-1 rgeos_0.5-9 lattice_0.20-45
[109] IRdisplay_1.1 Matrix_1.6-3 vctrs_0.6.4
[112] pillar_1.9.0 lifecycle_1.0.4 BiocManager_1.30.22
[115] GlobalOptions_0.1.2 spatstat.geom_2.4-0 lmtest_0.9-40
[118] jquerylib_0.1.4 RcppAnnoy_0.0.19 BiocNeighbors_1.12.0 [121] data.table_1.14.8 cowplot_1.1.1 irlba_2.3.5.1
[124] httpuv_1.6.12 R6_2.5.1 network_1.18.1
[127] promises_1.2.1 KernSmooth_2.23-20 gridExtra_2.3
[130] IRanges_2.28.0 parallelly_1.36.0 codetools_0.2-18
[133] MASS_7.3-57 rjson_0.2.21 withr_2.5.2
[136] presto_1.0.0 sctransform_0.3.3 S4Vectors_0.32.4
[139] mgcv_1.8-40 parallel_4.1.3 grid_4.1.3
[142] rpart_4.1.16 tidyverse_2.0.0 IRkernel_1.3
[145] coda_0.19-4 tidyr_1.3.0 carData_3.0-5
[148] Cairo_1.5-15 Rtsne_0.16 ggpubr_0.6.0
[151] pbdZMQ_0.3-7 shiny_1.8.0 base64enc_0.1-3

kcavagnero avatar Nov 21 '23 01:11 kcavagnero

Update: this issue resolves when switching from CellChatDB.use <- CellChatDB # simply use the default CellChatDB to CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling", key = "annotation") # use Secreted Signaling

kcavagnero avatar Nov 21 '23 03:11 kcavagnero

I think u can create CellChat from SeuratObj directly，rather than a normalized data matrix.😊

suye0620 avatar Nov 21 '23 03:11 suye0620

hi @suye0620 Thank you for your excellent work on Cellchat. Thanks for your attention. When running computeCommunProb() I get the below error

The suggested minimum value of scaled distances is in [1,2], and the calculated value here is  Inf 
  |                                                                                                                                                               |   0%Error in if (sum(P1_Pspatial) == 0) { : 
  missing value where TRUE/FALSE needed

and my data in [email protected] and [email protected] as below

> str([email protected])
Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
  ..@ i       : int [1:199179] 20 21 45 77 108 109 122 136 145 171 ...
  ..@ p       : int [1:7126] 0 29 64 109 126 155 190 215 248 277 ...
  ..@ Dim     : int [1:2] 615 7125
  ..@ Dimnames:List of 2
  .. ..$ : chr [1:615] "SLITRK2" "APLN" "AGTR2" "PORCN" ...
  .. ..$ : chr [1:7125] "A02784B2_10_168" "A02784B2_76_240" "A02784B2_229_271" "A02784B2_12_152" ...
  ..@ x       : num [1:199179] 1.386 0.693 1.386 1.386 1.386 ...
  ..@ factors : list()
> str([email protected])
 num [1:615, 1:7125] 0 0 0 0 0.000478 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:615] "SLITRK2" "APLN" "AGTR2" "PORCN" ...
  ..$ : chr [1:7125] "A02784B2_10_168" "A02784B2_76_240" "A02784B2_229_271" "A02784B2_12_152" ...

Thanks for your beautiful work! Looking forward for your reply and help!

zzz1314520 avatar Nov 28 '23 13:11 zzz1314520

I've got the same error when I ran computeCommunProb(),

The suggested minimum value of scaled distances is in [1,2], and the calculated value here is  Inf 
  |                                                                                                                                                               |   0%Error in if (sum(P1_Pspatial) == 0) { : 
  missing value where TRUE/FALSE needed

do you have any solution regarding to this issue? thanks

xinmiaoyan avatar Jan 24 '24 17:01 xinmiaoyan

@xinmiaoyan Can you share your object so that we can replicate your error?

sqjin avatar Jan 25 '24 00:01 sqjin

Hi all!

I was following the tutorial with my ST data and I got the following error: Error in nn.ranked[idx.i, ] : subscript out of bounds

I used an integrated Seurat object of eight samples to build data.input. I annotated the spots and let some of them as NA.

data.input <- Seurat::GetAssayData(together, slot = "data", assay = "Spatial") Idents(together) <- "Manual_annotation" meta = data.frame(labels = Idents(together), samples = [email protected]$sample_id) # manually create a dataframe consisting of the cell labels rownames(meta) <- colnames(data.input) meta$labels <- factor(meta$labels, levels = levels(Idents(together))) meta$samples <- factor(meta$samples, levels = unique([email protected]$sample_id)) meta$labels = droplevels(meta$labels, exclude = NA) spatial.locs <- Seurat::GetTissueCoordinates(together, scale = NULL, cols = c("imagerow", "imagecol")) spot.size = 65 # the theoretical spot size (um) in 10X Visium

spatial.factors <- rbind(spatial.factors1, spatial.factors2, spatial.factors3, spatial.factors4, spatial.factors5, spatial.factors6, spatial.factors7, spatial.factors8) cellchat_sig <- createCellChat(object = data.input, meta = meta, group.by = "labels", datatype = "spatial", coordinates = spatial.locs,spatial.factors = spatial.factors) cellchat_sig@DB <- CellChatDB.secreted cellchat_sig <- subsetData(cellchat_sig) # This step is necessary even if using the whole database future::plan("multisession", workers = 8) options(future.rng.onMisuse="ignore") cellchat_sig@DB <- CellChatDB.secreted cellchat_sig <- subsetData(cellchat_sig) # This step is necessary even if using the whole database future::plan("multisession", workers = 8) options(future.rng.onMisuse="ignore") cellchat_sig@DB <- CellChatDB.secreted cellchat_sig <- subsetData(cellchat_sig) # This step is necessary even if using the whole database future::plan("multisession", workers = 8) options(future.rng.onMisuse="ignore") cellchat_sig <- identifyOverExpressedGenes(cellchat_sig) cellchat_sig <- identifyOverExpressedInteractions(cellchat_sig) cellchat_sig <- computeCommunProb(cellchat_sig, type = "truncatedMean", trim = 0.1, distance.use = FALSE, interaction.range = 250, scale.distance = NULL, contact.dependent = TRUE, contact.range = 100 ) truncatedMean is used for calculating the average gene expression per cell group. Error in nn.ranked[idx.i, ] : subscript out of bounds

str([email protected]) Formal class 'dgCMatrix' [package "Matrix"] with 6 slots ..@ i : int [1:241066] 13 31 41 43 71 96 109 117 129 130 ... ..@ p : int [1:6024] 0 83 129 192 248 303 376 437 449 458 ... ..@ Dim : int [1:2] 702 6023 ..@ Dimnames:List of 2 .. ..$ : chr [1:702] "TNFRSF18" "TNFRSF4" "TNFRSF14" "TNFRSF25" ... .. ..$ : chr [1:6023] "PX0296_AAACACCAATAACTGC-1" "PX0296_AAACAGGGTCTATATT-1" "PX0296_AAACAGTGTTCCTGGG-1" "PX0296_AAACCGGGTAGGTACC-1" ... ..@ x : num [1:241066] 1.414 0.938 0.938 2.172 0.938 ... ..@ factors : list()

Does anyone know why it occurs? I will appreciate your help.

Best

Here is my R session:

sessionInfo() R version 4.3.2 (2023-10-31) Platform: x86_64-conda-linux-gnu (64-bit) Running under: Ubuntu 20.04.6 LTS

Matrix products: default BLAS/LAPACK: /anaconda3/envs/liana_env/lib/libopenblasp-r0.3.26.so; LAPACK version 3.12.0

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

time zone: Europe/Madrid tzcode source: system (glibc)

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

other attached packages: [1] Seurat_4.4.0 SeuratObject_4.1.4 sp_2.1-3 future.apply_1.11.1 future_1.33.1 patchwork_1.2.0 CellChat_2.1.2
[8] Biobase_2.62.0 BiocGenerics_0.48.1 ggplot2_3.4.4 igraph_1.6.0 dplyr_1.1.4

loaded via a namespace (and not attached): [1] RcppAnnoy_0.0.22 splines_4.3.2 later_1.3.2 tibble_3.2.1 polyclip_1.10-6 ggnetwork_0.5.12
[7] lifecycle_1.0.4 rstatix_0.7.2 doParallel_1.0.17 globals_0.16.2 lattice_0.22-5 MASS_7.3-60.0.1
[13] dendextend_1.17.1 backports_1.4.1 magrittr_2.0.3 plotly_4.10.4 sass_0.4.8 jquerylib_0.1.4
[19] httpuv_1.6.14 NMF_0.30.4.900 sctransform_0.4.1 spatstat.sparse_3.0-3 reticulate_1.35.0 cowplot_1.1.3
[25] pbapply_1.7-2 RColorBrewer_1.1-3 abind_1.4-5 Rtsne_0.17 purrr_1.0.2 presto_1.0.0
[31] pkgmaker_0.32.10 circlize_0.4.15 IRanges_2.36.0 S4Vectors_0.40.2 ggrepel_0.9.5 irlba_2.3.5.1
[37] listenv_0.9.1 spatstat.utils_3.0-4 goftest_1.2-3 RSpectra_0.16-1 spatstat.random_3.2-2 fitdistrplus_1.1-11
[43] parallelly_1.36.0 svglite_2.1.3 leiden_0.4.3.1 codetools_0.2-19 tidyselect_1.2.0 shape_1.4.6
[49] viridis_0.6.5 matrixStats_1.2.0 stats4_4.3.2 spatstat.explore_3.2-6 jsonlite_1.8.8 GetoptLong_1.0.5
[55] BiocNeighbors_1.20.0 ellipsis_0.3.2 progressr_0.14.0 ggridges_0.5.6 ggalluvial_0.12.5 survival_3.5-7
[61] iterators_1.0.14 systemfonts_1.0.5 foreach_1.5.2 tools_4.3.2 sna_2.7-2 ica_1.0-3
[67] Rcpp_1.0.12 glue_1.7.0 gridExtra_2.3 xfun_0.41 withr_3.0.0 fastmap_1.1.1
[73] fansi_1.0.6 digest_0.6.34 R6_2.5.1 mime_0.12 colorspace_2.1-0 scattermore_1.2
[79] tensor_1.5 spatstat.data_3.0-4 utf8_1.2.4 tidyr_1.3.1 generics_0.1.3 data.table_1.14.10
[85] FNN_1.1.4 httr_1.4.7 htmlwidgets_1.6.4 uwot_0.1.16 pkgconfig_2.0.3 gtable_0.3.4
[91] registry_0.5-1 ComplexHeatmap_2.18.0 lmtest_0.9-40 htmltools_0.5.7 carData_3.0-5 clue_0.3-65
[97] scales_1.3.0 tidyverse_2.0.0 png_0.1-8 knitr_1.45 rstudioapi_0.15.0 reshape2_1.4.4
[103] rjson_0.2.21 coda_0.19-4.1 statnet.common_4.9.0 nlme_3.1-164 cachem_1.0.8 zoo_1.8-12
[109] GlobalOptions_0.1.2 stringr_1.5.1 KernSmooth_2.23-22 parallel_4.3.2 miniUI_0.1.1.1 pillar_1.9.0
[115] grid_4.3.2 vctrs_0.6.5 RANN_2.6.1 promises_1.2.1 ggpubr_0.6.0 car_3.1-2
[121] xtable_1.8-4 cluster_2.1.6 cli_3.6.2 compiler_4.3.2 rlang_1.1.3 crayon_1.5.2
[127] rngtools_1.5.2 ggsignif_0.6.4 plyr_1.8.9 stringi_1.8.3 viridisLite_0.4.2 network_1.18.2
[133] deldir_2.0-2 gridBase_0.4-7 BiocParallel_1.36.0 assertthat_0.2.1 munsell_0.5.0 lazyeval_0.2.2
[139] spatstat.geom_3.2-8 Matrix_1.6-5 shiny_1.8.0 ROCR_1.0-11 broom_1.0.5 bslib_0.6.1

nsdelablancac avatar Feb 23 '24 10:02 nsdelablancac

@nsdelablancac "I annotated the spots and let some of them as NA." This is not allowed in CellChat analysis. If these are low quality spots, you should delete them before running CellChat. BTW, the cell labels also should not have a label "0".

sqjin avatar Feb 23 '24 16:02 sqjin

Thanks a lot for your early response.

I removed those spots in the Seurat object and I ensured the labels names. However, I still having the same error.

Thank you again for your help.

nsdelablancac avatar Feb 26 '24 10:02 nsdelablancac