bulkPseudotime(expMat = exps)运行错误，永不完结

[Leiyan22]

Junjun大神您好！
对您的开发速度赶到无比惊讶。
感谢您的努力。

我遇到了一个问题，当我试图运行

library(bulkPseudotime)
library(ClusterGVis)

# load test data
data(exps)

# check
head(exps,3) #到这步没问题

可是到下一步时

psetime_res <- bulkPseudotime(expMat = exps)

出现如下错误一直滚动，需要手动停止

Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,  :
  Chernobyl! trL>n 6

Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,  :
  Chernobyl! trL>n 6

Warning in sqrt(sum.squares/one.delta) : NaNs produced
Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,  :
  Chernobyl! trL>n 6

Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,  :
  Chernobyl! trL>n 6

Warning in sqrt(sum.squares/one.delta) : NaNs produced
Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,  :
  Chernobyl! trL>n 6

环境如下

R version 4.4.0 (2024-04-24)
Platform: x86_64-apple-darwin20
Running under: macOS Monterey 12.7.5

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Asia/Tokyo
tzcode source: internal

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

other attached packages:
 [1] ClusterGVis_0.1.1    monocle_2.32.0       DDRTree_0.1.5        irlba_2.3.5.1       
 [5] VGAM_1.1-11          ggplot2_3.5.1        Biobase_2.64.0       BiocGenerics_0.50.0 
 [9] Matrix_1.7-0         bulkPseudotime_0.0.1

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3          rstudioapi_0.16.0           jsonlite_1.8.8             
  [4] shape_1.4.6.1               magrittr_2.0.3              magick_2.8.3               
  [7] TH.data_1.1-2               estimability_1.5.1          nloptr_2.1.1               
 [10] rmarkdown_2.27              GlobalOptions_0.1.2         zlibbioc_1.50.0            
 [13] vctrs_0.6.5                 Cairo_1.6-2                 minqa_1.2.7                
 [16] memoise_2.0.1               fastICA_1.2-4               htmltools_0.5.8.1          
 [19] S4Arrays_1.4.1              broom_1.0.6                 SparseArray_1.4.8          
 [22] parallelly_1.37.1           HSMMSingleCell_1.24.0       htmlwidgets_1.6.4          
 [25] plyr_1.8.9                  sandwich_3.1-0              emmeans_1.10.3             
 [28] zoo_1.8-12                  cachem_1.1.0                igraph_2.0.3               
 [31] lifecycle_1.0.4             iterators_1.0.14            pkgconfig_2.0.3            
 [34] R6_2.5.1                    fastmap_1.2.0               future_1.33.2              
 [37] GenomeInfoDbData_1.2.12     MatrixGenerics_1.16.0       clue_0.3-65                
 [40] digest_0.6.36               colorspace_2.1-0            AnnotationDbi_1.66.0       
 [43] S4Vectors_0.42.0            GenomicRanges_1.56.1        RSQLite_2.3.7              
 [46] org.Mm.eg.db_3.19.1         fansi_1.0.6                 httr_1.4.7                 
 [49] abind_1.4-5                 compiler_4.4.0              bit64_4.0.5                
 [52] withr_3.0.0                 doParallel_1.0.17           backports_1.5.0            
 [55] viridis_0.6.5               DBI_1.2.3                   UpSetR_1.4.0               
 [58] MASS_7.3-61                 DelayedArray_0.30.1         rjson_0.2.21               
 [61] scatterplot3d_0.3-44        flashClust_1.01-2           tools_4.4.0                
 [64] FactoMineR_2.11             glue_1.7.0                  nlme_3.1-165               
 [67] grid_4.4.0                  Rtsne_0.17                  cluster_2.1.6              
 [70] reshape2_1.4.4              generics_0.1.3              gtable_0.3.5               
 [73] tidyr_1.3.1                 utf8_1.2.4                  XVector_0.44.0             
 [76] ggrepel_0.9.5               RANN_2.6.1                  foreach_1.5.2              
 [79] pillar_1.9.0                stringr_1.5.1               limma_3.60.3               
 [82] circlize_0.4.16             dplyr_1.1.4                 lattice_0.22-6             
 [85] survival_3.7-0              bit_4.0.5                   tidyselect_1.2.1           
 [88] monocle3_1.3.7              SingleCellExperiment_1.26.0 ComplexHeatmap_2.20.0      
 [91] Biostrings_2.72.1           knitr_1.48                  gridExtra_2.3              
 [94] IRanges_2.38.0              SummarizedExperiment_1.34.0 xfun_0.45                  
 [97] statmod_1.5.0               matrixStats_1.3.0           pheatmap_1.0.12            
[100] DT_0.33                     leidenbase_0.1.27           stringi_1.8.4              
[103] UCSC.utils_1.0.0            boot_1.3-30                 yaml_2.3.9                 
[106] evaluate_0.24.0             codetools_0.2-20            tibble_3.2.1               
[109] multcompView_0.1-10         cli_3.6.3                   xtable_1.8-4               
[112] munsell_0.5.1               modelr_0.1.11               Rcpp_1.0.12                
[115] GenomeInfoDb_1.40.1         globals_0.16.3              coda_0.19-4.1              
[118] png_0.1-8                   parallel_4.4.0              leaps_3.2                  
[121] blob_1.2.4                  lme4_1.1-35.5               listenv_0.9.1              
[124] viridisLite_0.4.2           mvtnorm_1.2-5               slam_0.1-50                
[127] scales_1.3.0                purrr_1.0.2                 crayon_1.5.3               
[130] combinat_0.0-8              GetoptLong_1.0.5            rlang_1.1.4                
[133] KEGGREST_1.44.1             multcomp_1.4-25  

感谢您的帮助！

[junjunlab]

我这没有问题，你试试在windows上试试？我的是windows系统。

[junjunlab]

重新安装一下，应该是所有样本为0的基因

[Leiyan22]

感谢junjun大神的升级和回复 : )
我运行了如下代码，重新安装了此包
devtools::install_github("junjunlab/bulkPseudotime", force = TRUE)
又运行了如下代码
psetime_res <- bulkPseudotime(expMat = exps)

结果还是一样同样的结果
Show in New Window Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : Chernobyl! trL>n 6 Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : Chernobyl! trL>n 6 Warning in sqrt(sum.squares/one.delta) : NaNs produced

但是没关系，我复制您的源代码然后一步步调试着也能用 😄
谢谢! 🌹

[CQMUyan]

我也有同样的报错，代码可以运行，就是有warning

[junjunlab]

可以把矩阵发我看看。