Error in array(NA_real_, dim = c(nrow(b), ncol(cc), length(lev)), dimnames = c(rownames(b), : length of 'dimnames' [1686] must match that of 'dims' [3]
charlesgwellem opened this issue · 2 comments
Hi there,
I wish to deconvolve my bulk RNA seq data with single cell RNA seq data from the same tissue. I run in to the following error when running the code line below and I do not understand why. Can any one please help?
csfit <- bseqsc_csdiff(ilc_bulk[genes, ] ~ treatment | fibroblast + macrophage,
verbose = 2, nperms = 100, .rng = 12345)
Then comes the error:
Groups: a_nacl1=4L | a_nacl2=4L | a_nacl3=4L | a_nacl4=4L | a_nacl5=4L | NA0L
Cell type(s): 'fibroblast', 'macrophage' (2 total)
Fitting mode: auto
Data (filtered): 1684 features x 20 samples
Model has factor effect with more than 2 levels: fitting lm interaction model
Fitting model with nonnegative effects
Model with more than 2 groups: switching to version 2
Fitting linear interaction model ... OK
Computing FDR using 100 permutations ... 101/100
Alternative 'two.sided' ... OK
Alternative 'greater' ... OK
Alternative 'less' ... OK
OK
Timing:
user system elapsed
2.136 0.153 2.267
Error in array(NA_real_, dim = c(nrow(b), ncol(cc), length(lev)), dimnames = c(rownames(b), :
length of 'dimnames' [1687] must match that of 'dims' [3]
> sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=de_DE.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=de_DE.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] edgeR_3.26.6 limma_3.40.6 forcats_0.4.0 stringr_1.4.0
[5] purrr_0.3.2 readr_1.3.1 tidyr_0.8.3 tibble_2.1.3
[9] tidyverse_1.2.1 SoupX_0.3.1 ggplot2_3.2.0 dplyr_0.8.3
[13] DropletUtils_1.4.3 SingleCellExperiment_1.6.0 SummarizedExperiment_1.14.0 DelayedArray_0.10.0
[17] BiocParallel_1.18.0 matrixStats_0.54.0 GenomicRanges_1.36.0 GenomeInfoDb_1.20.0
[21] RColorBrewer_1.1-2 xbioc_0.1.17 AnnotationDbi_1.46.0 IRanges_2.18.1
[25] S4Vectors_0.22.0 BisqueRNA_1.0 Seurat_3.0.2 preprocessCore_1.46.0
[29] e1071_1.7-2 bseqsc_1.0 csSAM_1.4 Rcpp_1.0.2
[33] openxlsx_4.1.0.1 Biobase_2.44.0 BiocGenerics_0.30.0
loaded via a namespace (and not attached):
[1] reticulate_1.13 R.utils_2.9.0 tidyselect_0.2.5 RSQLite_2.1.2 htmlwidgets_1.3
[6] grid_3.6.1 Rtsne_0.15 devtools_2.1.0 munsell_0.5.0 codetools_0.2-16
[11] ica_1.0-2 future_1.14.0 withr_2.1.2 colorspace_1.4-1 rstudioapi_0.10
[16] ROCR_1.0-7 gbRd_0.4-11 listenv_0.7.0 NMF_0.22 Rdpack_0.11-0
[21] labeling_0.3 GenomeInfoDbData_1.2.1 bit64_0.9-7 rhdf5_2.28.0 rprojroot_1.3-2
[26] vctrs_0.2.0 generics_0.0.2 R6_2.4.0 doParallel_1.0.14 rsvd_1.0.2
[31] locfit_1.5-9.1 bitops_1.0-6 assertthat_0.2.1 SDMTools_1.1-221.1 scales_1.0.0
[36] gtable_0.3.0 npsurv_0.4-0 globals_0.12.4 processx_3.4.1 rlang_0.4.0
[41] zeallot_0.1.0 splines_3.6.1 lazyeval_0.2.2 broom_0.5.2 modelr_0.1.4
[46] BiocManager_1.30.4 yaml_2.2.0 reshape2_1.4.3 backports_1.1.4 tools_3.6.1
[51] usethis_1.5.1 gridBase_0.4-7 gplots_3.0.1.1 sessioninfo_1.1.1 ggridges_0.5.1
[56] plyr_1.8.4 zlibbioc_1.30.0 RCurl_1.95-4.12 ps_1.3.0 prettyunits_1.0.2
[61] pbapply_1.4-1 viridis_0.5.1 cowplot_1.0.0 zoo_1.8-6 haven_2.1.1
[66] ggrepel_0.8.1 cluster_2.1.0 fs_1.3.1 magrittr_1.5 data.table_1.12.2
[71] lmtest_0.9-37 RANN_2.6.1 fitdistrplus_1.0-14 pkgload_1.0.2 hms_0.5.0
[76] lsei_1.2-0 xtable_1.8-4 readxl_1.3.1 gridExtra_2.3 testthat_2.2.1
[81] compiler_3.6.1 KernSmooth_2.23-15 crayon_1.3.4 R.oo_1.22.0 htmltools_0.3.6
[86] Formula_1.2-3 lubridate_1.7.4 DBI_1.0.0 MASS_7.3-51.4 Matrix_1.2-17
[91] cli_1.1.0 R.methodsS3_1.7.1 gdata_2.18.0 metap_1.1 igraph_1.2.4.1
[96] pkgconfig_2.0.2 registry_0.5-1 plotly_4.9.0 xml2_1.2.1 foreach_1.4.7
[101] dqrng_0.2.1 rngtools_1.4 pkgmaker_0.28 XVector_0.24.0 rvest_0.3.4
[106] bibtex_0.4.2 callr_3.3.1 digest_0.6.20 sctransform_0.2.0 tsne_0.1-3
[111] cellranger_1.1.0 dendextend_1.12.0 curl_4.0 gtools_3.8.1 nlme_3.1-141
[116] jsonlite_1.6 Rhdf5lib_1.6.0 desc_1.2.0 viridisLite_0.3.0 pillar_1.4.2
[121] lattice_0.20-38 httr_1.4.0 pkgbuild_1.0.3 survival_2.44-1.1 glue_1.3.1
[126] remotes_2.1.0 zip_2.0.3 png_0.1-7 iterators_1.0.12 bit_1.1-14
[131] class_7.3-15 stringi_1.4.3 HDF5Array_1.12.1 blob_1.2.0 caTools_1.17.1.2
[136] memoise_1.1.0 irlba_2.3.3 future.apply_1.3.0 ape_5.3
**Hi there,
I wish to analysis celltype specific differentially expressed genes with cell proportion though csSamfit.R, but I got the same problem with charlesgwellem.**
csfit_test <- bseqsc_csdiff(eset[1:5,] ~Timepoint+Infection | + B.Cells + CD4.T.Cells + CD8.T.Cells + Dendritic.Cells + Monocytes, verbose = 2, nperms = 50, .rng = 12345)
Groups: Mex4482=15L | Anhui01=15L | NL219=15L | VN1203=14L | NA0L
Cell type(s): 'B.Cells', 'CD4.T.Cells', ..., 'Monocytes' (5 total)
Fitting mode: auto
Data (filtered): 5 features x 59 samples
Model has extra covariates: fitting lm interaction model
Fitting model with nonnegative effects
Model with more than 2 groups: switching to version 2
Fitting linear interaction model ... OK
Computing FDR using 50 permutations ... 51/50
Alternative 'two.sided' ... OK
Alternative 'greater' ... OK
Alternative 'less' ... OK
OK
Timing:
user system elapsed
0.70 0.00 0.75
Error in array(NA_real_, dim = c(nrow(b), ncol(cc), length(lev)), dimnames = c(rownames(b), :
length of 'dimnames' [11] must match that of 'dims' [3]
length of 'dimnames' [11] six more than length of gene feature which I input.
Looking forward to someone reply
Hi developers,
I am getting the same issues while working with the example dataset given in the website, wondering if there is any solution to this
csfit <- bseqsc_csdiff(eset[genes, ] ~ gender + ageN + hba1c_class2 | alpha + beta + ductal + acinar, verbose = 2, nperms = 5000, .rng = 12345)
Groups: Normal=47L | Hyper=23L | NA12L
Cell type(s): 'alpha', 'beta', ..., 'acinar' (4 total)
Warning: Dropping samples with NA group/value: 'GSM1216754', 'GSM1216756', ..., 'GSM1216831' [12]
Fitting mode: auto
Groups (filtered): Normal=47L | Hyper=23L | NA0L
Data (filtered): 255 features x 70 samples
Model has extra covariates: fitting lm interaction model
Fitting model with nonnegative effects
Fitting linear interaction model ... Error in cbind(covariates, D[, -1L, drop = FALSE]) :
number of rows of matrices must match (see arg 2)