我一直在尝试解决这个问题，但一整天都是空的。在尝试制作这个对象时
singleR = CreateSinglerSeuratObject(counts = counts,
annot = metadata,
project.name = 'project',
min.genes = 1,
min.cells = 2,
npca = 5,
regress.out ='nUMI',
species = 'Mouse',
citation = '',
reduce.file.size = T,
variable.genes = 'de',
normalize.gene.length = T,
numCores = 5)
我得到这个错误代码：
irlba(A = t(x = data.use), nv = pcs.compute, ...) 中的错误：max(nu, nv) 必须严格小于 min(nrow(A), ncol(A))
我的计数是 [6 x 13732] 的矩阵。
session info():
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.14
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] Seurat_2.3.4 Matrix_1.2-15 cowplot_0.9.4
[4] ggplot2_3.1.0 SingleR_0.2.0 DESeq2_1.21.23
[7] SummarizedExperiment_1.11.6 DelayedArray_0.7.48 BiocParallel_1.15.15
[10] matrixStats_0.54.0 Biobase_2.41.2 GenomicRanges_1.33.14
[13] GenomeInfoDb_1.17.3 IRanges_2.15.18 S4Vectors_0.19.22
[16] BiocGenerics_0.27.1
loaded via a namespace (and not attached):
[1] snow_0.4-3 backports_1.1.3 Hmisc_4.2-0 plyr_1.8.4
[5] igraph_1.2.2 lazyeval_0.2.1 GSEABase_1.43.1 splines_3.5.1
[9] listenv_0.7.0 digest_0.6.18 foreach_1.4.4 htmltools_0.3.6
[13] lars_1.2 gdata_2.18.0 magrittr_1.5 checkmate_1.9.1
[17] memoise_1.1.0 cluster_2.0.7-1 mixtools_1.1.0 ROCR_1.0-7
[21] globals_0.12.4 annotate_1.59.1 R.utils_2.7.0 colorspace_1.4-0
[25] blob_1.1.1 xfun_0.4 dplyr_0.7.8 crayon_1.3.4
[29] RCurl_1.95-4.11 jsonlite_1.6 graph_1.59.2 genefilter_1.63.2
[33] bindr_0.1.1 survival_2.43-3 zoo_1.8-4 iterators_1.0.10
[37] ape_5.2 glue_1.3.0 gtable_0.2.0 zlibbioc_1.27.0
[41] XVector_0.21.4 kernlab_0.9-27 prabclus_2.2-7 DEoptimR_1.0-8
[45] scales_1.0.0 pheatmap_1.0.12 mvtnorm_1.0-8 DBI_1.0.0
[49] bibtex_0.4.2 Rcpp_1.0.0 metap_1.0 dtw_1.20-1
[53] xtable_1.8-3 htmlTable_1.13.1 reticulate_1.10 foreign_0.8-71
[57] bit_1.1-14 proxy_0.4-22 mclust_5.4.2 SDMTools_1.1-221
[61] Formula_1.2-3 GSVA_1.29.3 tsne_0.1-3 htmlwidgets_1.3
[65] httr_1.4.0 gplots_3.0.1.1 RColorBrewer_1.1-2 fpc_2.1-11.1
[69] acepack_1.4.1 modeltools_0.2-22 ica_1.0-2 pkgconfig_2.0.2
[73] XML_3.98-1.16 R.methodsS3_1.7.1 flexmix_2.3-14 nnet_7.3-12
[77] locfit_1.5-9.1 later_0.7.5 tidyselect_0.2.5 rlang_0.3.1
[81] reshape2_1.4.3 AnnotationDbi_1.43.1 pbmcapply_1.3.1 munsell_0.5.0
[85] tools_3.5.1 RSQLite_2.1.1 ggridges_0.5.1 stringr_1.3.1
[89] yaml_2.2.0 npsurv_0.4-0 outliers_0.14 knitr_1.21
[93] bit64_0.9-7 fitdistrplus_1.0-14 robustbase_0.93-3 caTools_1.17.1.1
[97] purrr_0.2.5 RANN_2.6.1 bindrcpp_0.2.2 future_1.11.1.1
[101] pbapply_1.3-4 nlme_3.1-137 mime_0.6 R.oo_1.22.0
[105] shinythemes_1.1.2 hdf5r_1.0.1 compiler_3.5.1 rstudioapi_0.9.0
[109] png_0.1-7 lsei_1.2-0 tibble_2.0.1 geneplotter_1.59.0
[113] stringi_1.2.4 lattice_0.20-38 trimcluster_0.1-2.1 pillar_1.3.1
[117] Rdpack_0.10-1 lmtest_0.9-36 data.table_1.12.0 bitops_1.0-6
[121] irlba_2.3.2 gbRd_0.4-11 httpuv_1.4.5.1 R6_2.3.0
[125] latticeExtra_0.6-28 promises_1.0.1 KernSmooth_2.23-15 gridExtra_2.3
[129] codetools_0.2-16 MASS_7.3-51.1 gtools_3.8.1 assertthat_0.2.0
[133] withr_2.1.2 GenomeInfoDbData_1.2.0 diptest_0.75-7 doSNOW_1.0.16
[137] grid_3.5.1 rpart_4.1-13 tidyr_0.8.2 class_7.3-15
[141] segmented_0.5-3.0 Rtsne_0.15 shiny_1.2.0 base64enc_0.1-3
提前感谢您的帮助！