最近我开始使用 monocle3 进行轨迹分析。
在我结合了我的单细胞 RNA seq 的 CDS 之后。数据集（由 cell_ranger 预处理），我试图对齐它们，但我没有找到
错误：下标包含无效名称
**数据集是人类癌症单细胞 RNA seq 的免疫细胞。数据。
任何帮助将不胜感激。
> cds1 <- load_cellranger_data("D:/Single cell/T1/2020.01.15/")
cds2 <-
> load_cellranger_data("D:/Single cell/T2/2020.07.15/") cds3 <-
> load_cellranger_data("D:/Single cell/T3/2021.03.01/")
>
> big_cds <- combine_cds(list(cds1, cds2, cds3)) big_cds <-
> preprocess_cds(big_cds, num_dim = 20)
big_cds <- align_cds(big_cds,
> alignment_group = "batch")
Aligning cells from different batches using
> Batchelor. Please remember to cite: Haghverdi L, Lun ATL, Morgan MD,
> Marioni JC (2018). 'Batch effects in single-cell RNA-sequencing data
> are corrected by matching mutual nearest neighbors.' Nat. Biotechnol.,
> 36(5), 421-427. doi: 10.1038/nbt.4091
Error: subscript contains
> invalid names
>
> traceback()
>
> 11: stop(wmsg(...), call. = FALSE)
>
> 10: .subscript_error("subscript contains invalid ", what)
>
> 9: NSBS(i, x, exact = exact, strict.upper.bound = !allow.append,
> allow.NAs = allow.NAs)
>
> 8: NSBS(i, x, exact = exact, strict.upper.bound = !allow.append,
> allow.NAs = allow.NAs)
>
> 7: normalizeSingleBracketSubscript(i, xstub)
>
> 6: extractCOLS(x, j)
>
> 5: extractCOLS(x, j)
>
> 4: colData(cds)[, alignment_group]
>
> 3: colData(cds)[, alignment_group]
>
> 2: batchelor::reducedMNN(as.matrix(preproc_res), batch =
> colData(cds)[, alignment_group], k = alignment_k)
>
> 1: align_cds(big_cds, alignment_group = "batch")
>
>
> sessionInfo: R version 4.1.1 (2021-08-10) Platform:
> x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 10 x64 (build
> 19043)
>
> Matrix products: default
>
> locale: [1] LC_COLLATE=Korean_Korea.949 LC_CTYPE=Korean_Korea.949
> LC_MONETARY=Korean_Korea.949 LC_NUMERIC=C LC_TIME=Korean_Korea.949
>
> attached base packages: [1] stats4 parallel stats graphics grDevices
> utils datasets methods base
>
> other attached packages: [1] batchelor_1.8.1 Nebulosa_1.2.0
> monocle3_1.0.0 SingleCellExperiment_1.14.1 SummarizedExperiment_1.22.0
> [6] GenomicRanges_1.44.0 GenomeInfoDb_1.28.4 IRanges_2.26.0
> S4Vectors_0.30.0 MatrixGenerics_1.4.3 [11] matrixStats_0.61.0
> Biobase_2.52.0 BiocGenerics_0.38.0 colorRamps_2.3 colorspace_2.0-2
> [16] RColorBrewer_1.1-2 ggplot2_3.3.5 patchwork_1.1.1 dplyr_1.0.7
> SeuratObject_4.0.2 [21] Seurat_4.0.4
>
> loaded via a namespace (and not attached): [1] plyr_1.8.6 igraph_1.2.6
> lazyeval_0.2.2 splines_4.1.1 BiocParallel_1.26.2 [6] listenv_0.8.0
> scattermore_0.7 digest_0.6.28 htmltools_0.5.2 viridis_0.6.1 [11]
> fansi_0.5.0 magrittr_2.0.1 ScaledMatrix_1.0.0 tensor_1.5 cluster_2.1.2
> [16] ks_1.13.2 ROCR_1.0-11 globals_0.14.0 spatstat.sparse_2.0-0
> ggrepel_0.9.1 [21] crayon_1.4.1 RCurl_1.98-1.5 jsonlite_1.7.2
> spatstat.data_2.1-0 survival_3.2-13 [26] zoo_1.8-9 glue_1.4.2
> polyclip_1.10-0 gtable_0.3.0 zlibbioc_1.38.0 [31] XVector_0.32.0
> leiden_0.3.9 DelayedArray_0.18.0 BiocSingular_1.8.1 future.apply_1.8.1
> [36] abind_1.4-5 scales_1.1.1 mvtnorm_1.1-2 miniUI_0.1.1.1 Rcpp_1.0.7
> [41] viridisLite_0.4.0 xtable_1.8-4 reticulate_1.22
> spatstat.core_2.3-0 rsvd_1.0.5 [46] mclust_5.4.7 ResidualMatrix_1.2.0
> htmlwidgets_1.5.4 httr_1.4.2 ellipsis_0.3.2 [51] ica_1.0-2
> pkgconfig_2.0.3 farver_2.1.0 scuttle_1.2.1 uwot_0.1.10 [56]
> deldir_0.2-10 utf8_1.2.2 tidyselect_1.1.1 labeling_0.4.2 rlang_0.4.11
> [61] reshape2_1.4.4 later_1.3.0 munsell_0.5.0 tools_4.1.1 cli_3.0.1
> [66] generics_0.1.0 ggridges_0.5.3 stringr_1.4.0 fastmap_1.1.0
> goftest_1.2-2 [71] fitdistrplus_1.1-5 purrr_0.3.4 RANN_2.6.1
> pbapply_1.5-0 future_1.22.1 [76] nlme_3.1-153 sparseMatrixStats_1.4.2
> mime_0.11 pracma_2.3.3 compiler_4.1.1 [81] rstudioapi_0.13
> plotly_4.9.4.1 png_0.1-7 spatstat.utils_2.2-0 tibble_3.1.4 [86]
> stringi_1.7.4 RSpectra_0.16-0 lattice_0.20-45 Matrix_1.3-4 vctrs_0.3.8
> [91] pillar_1.6.3 lifecycle_1.0.1 BiocManager_1.30.16
> spatstat.geom_2.2-2 lmtest_0.9-38 [96] RcppAnnoy_0.0.19
> BiocNeighbors_1.10.0 data.table_1.14.0 cowplot_1.1.1 bitops_1.0-7
> [101] irlba_2.3.3 httpuv_1.6.3 R6_2.5.1 promises_1.2.0.1
> KernSmooth_2.23-20 [106] gridExtra_2.3 parallelly_1.28.1
> codetools_0.2-18 MASS_7.3-54 assertthat_0.2.1 [111] withr_2.4.2
> sctransform_0.3.2 GenomeInfoDbData_1.2.6 mgcv_1.8-37 grid_4.1.1 [116]
> rpart_4.1-15 beachmat_2.8.1 tidyr_1.1.3 DelayedMatrixStats_1.14.3
> Rtsne_0.15 [121] shiny_1.7.0