"Error: None of the features provided found in this assay" when running nichenet_seuratobj_aggregate

[kangjiajinlong]

Dear Nichetnetr team,

I am using Nichenet for my own Seurat data and I met an error in running the following code:

.libPaths("~/R/ubuntu/4.4.1/")

library(nichenetr)
library(Seurat)
library(SeuratObject)
library(tidyverse)
library(qs)

#Get Seurat object
obj.sub <- qread("Jack_analysis/filtered_gene_expression_without_doublet_29009_gsea_obj_new.qs")

obj.sub
An object of class Seurat
53711 features across 84827 samples within 13 assays
Active assay: SCT (16177 features, 3000 variable features)
3 layers present: counts, data, scale.data
12 other assays present: RNA, Antibody, escape.BP, escape.H, escape.C6, escape.Reactome, escape.WikiPathways, escape.BP_normalized, escape.H_normalized, escape.C6_normalized, escape.Reactome_normalized, escape.WikiPathways_normalized
2 dimensional reductions calculated: pca, umap

#Read in nichnet network
lr_network <- readRDS("Jack_analysis/nichnet/lr_network_human_21122021.rds")
ligand_target_matrix <- readRDS("Jack_analysis/nichnet/ligand_target_matrix_nsga2r_final.rds")
weighted_networks <- readRDS("Jack_analysis/nichnet/weighted_networks_nsga2r_final.rds")

#Perform the NicheNet analysis
#sender-focused approach
Idents(obj.sub) <- "major_celltype"

table(Idents(obj.sub))
CAR-NK Tumor PBMC
35161 23687 25979

table(obj.sub$condition)
Tumor + CAR Tumor + CAR + PBMC Tumor + PBMC
21441 35554 27832

nichenet_output <- nichenet_seuratobj_aggregate(
seurat_obj = obj.sub,
sender = "CAR-NK",
receiver = "Tumor",
condition_colname = "condition",
condition_oi = "Tumor + CAR + PBMC",
condition_reference = "Tumor + CAR",
expression_pct = 0.05,
ligand_target_matrix = ligand_target_matrix,
lr_network = lr_network,
weighted_networks = weighted_networks
)
[1] "The SCT assay will be used for the analysis."
[1] "Read in and process NicheNet's networks"
[1] "Define expressed ligands and receptors in receiver and sender cells"
[1] "Perform DE analysis in receiver cell"
[1] "Perform NicheNet ligand activity analysis"
[1] "Infer active target genes of the prioritized ligands"
[1] "Infer receptors of the prioritized ligands"
[1] "Perform DE analysis in sender cells"
Error: None of the features provided found in this assay

Session info is as follows:
R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0

locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C 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 LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

time zone: Etc/UTC
tzcode source: system (glibc)

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

other attached packages:
[1] qs_0.27.2 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2
[7] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 Seurat_5.1.0
[13] SeuratObject_5.0.2 sp_2.1-4 nichenetr_2.2.0

loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.22 splines_4.4.1 later_1.3.2 bitops_1.0-9 polyclip_1.10-7
[6] hardhat_1.4.0 pROC_1.18.5 rpart_4.1.23 fastDummies_1.7.4 lifecycle_1.0.4
[11] rstatix_0.7.2 doParallel_1.0.17 globals_0.16.3 lattice_0.22-6 MASS_7.3-60.2
[16] backports_1.5.0 magrittr_2.0.3 rmarkdown_2.29 Hmisc_5.2-0 plotly_4.10.4
[21] httpuv_1.6.15 sctransform_0.4.1 spam_2.11-0 spatstat.sparse_3.1-0 reticulate_1.40.0
[26] cowplot_1.1.3 pbapply_1.7-2 RColorBrewer_1.1-3 pkgload_1.4.0 abind_1.4-8
[31] Rtsne_0.17 presto_1.0.0 BiocGenerics_0.52.0 nnet_7.3-19 tweenr_2.0.3
[36] ipred_0.9-15 circlize_0.4.16 lava_1.8.0 IRanges_2.40.0 S4Vectors_0.44.0
[41] ggrepel_0.9.6 irlba_2.3.5.1 listenv_0.9.1 spatstat.utils_3.1-1 goftest_1.2-3
[46] RSpectra_0.16-2 spatstat.random_3.3-2 fitdistrplus_1.2-1 parallelly_1.39.0 leiden_0.4.3.1
[51] codetools_0.2-20 RApiSerialize_0.1.4 ggforce_0.4.2 tidyselect_1.2.1 shape_1.4.6.1
[56] farver_2.1.2 matrixStats_1.4.1 stats4_4.4.1 base64enc_0.1-3 spatstat.explore_3.3-3
[61] jsonlite_1.8.9 caret_6.0-94 GetoptLong_1.0.5 e1071_1.7-16 progressr_0.15.1
[66] Formula_1.2-5 ggridges_0.5.6 survival_3.6-4 iterators_1.0.14 foreach_1.5.2
[71] tools_4.4.1 ggnewscale_0.5.0 ica_1.0-3 Rcpp_1.0.13-1 glue_1.8.0
[76] prodlim_2024.06.25 gridExtra_2.3 xfun_0.49 withr_3.0.2 fastmap_1.2.0
[81] fansi_1.0.6 caTools_1.18.3 digest_0.6.37 timechange_0.3.0 R6_2.5.1
[86] mime_0.12 colorspace_2.1-1 scattermore_1.2 tensor_1.5 spatstat.data_3.1-4
[91] DiagrammeR_1.0.11 utf8_1.2.4 generics_0.1.3 data.table_1.16.2 recipes_1.1.0
[96] class_7.3-22 httr_1.4.7 htmlwidgets_1.6.4 uwot_0.2.2 ModelMetrics_1.2.2.2
[101] pkgconfig_2.0.3 gtable_0.3.6 timeDate_4041.110 ComplexHeatmap_2.22.0 lmtest_0.9-40
[106] shadowtext_0.1.4 htmltools_0.5.8.1 carData_3.0-5 dotCall64_1.2 clue_0.3-66
[111] scales_1.3.0 png_0.1-8 gower_1.0.1 spatstat.univar_3.1-1 knitr_1.49
[116] rstudioapi_0.17.1 tzdb_0.4.0 reshape2_1.4.4 rjson_0.2.23 checkmate_2.3.2
[121] visNetwork_2.1.2 nlme_3.1-164 proxy_0.4-27 zoo_1.8-12 GlobalOptions_0.1.2
[126] KernSmooth_2.23-24 parallel_4.4.1 miniUI_0.1.1.1 foreign_0.8-86 pillar_1.9.0
[131] grid_4.4.1 vctrs_0.6.5 RANN_2.6.2 ggpubr_0.6.0 randomForest_4.7-1.2
[136] promises_1.3.0 car_3.1-3 stringfish_0.16.0 xtable_1.8-4 cluster_2.1.6
[141] htmlTable_2.4.3 evaluate_1.0.1 cli_3.6.3 compiler_4.4.1 rlang_1.1.4
[146] crayon_1.5.3 ggsignif_0.6.4 future.apply_1.11.3 labeling_0.4.3 fdrtool_1.2.18
[151] plyr_1.8.9 stringi_1.8.4 viridisLite_0.4.2 deldir_2.0-4 munsell_0.5.1
[156] lazyeval_0.2.2 spatstat.geom_3.3-4 Matrix_1.7-0 RcppHNSW_0.6.0 hms_1.1.3
[161] patchwork_1.3.0 future_1.34.0 shiny_1.9.1 ROCR_1.0-11 broom_1.0.7
[166] igraph_2.1.1 RcppParallel_5.1.9

The error is the same whether I use RNA or SCT assay. Could you kindly advise what is the cause of this issue and what I should do? Thanks for the help.

Jack

[csangara]

Hi,

It seems like the error is occurring in one of the visualizations, i.e., the creation of the LFC plot. I would suggest that you follow the step-by-step vignette instead to pinpoint where the error is occurring.

Chananchida