sce to anndata error
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hchintalapudi commented
Hi,
I'm trying to convert a SingleCellExperiment object to anndata and I get an error.
My code:
library("sceasy")
library("reticulate")
use_condaenv('/gstore/home/chintalh/miniconda3/envs/env', required = T)
tmp<- sceasy::convertFormat(merged, from="sce", to="anndata", outFile='output_merge.h5ad')
Errror:
Error in value[[3L]](cond) :
'assay(<SingleCellExperiment>, i="character", ...)' invalid subscript 'i'
length(Nindex) == length(dim) is not TRUE
My sce object:
> merged
class: SingleCellExperiment
dim: 58302 32982
metadata(3): merge.info pca.info .internal
assays(3): reconstructed counts logcounts
rownames(58302): ENSG00000210049 ENSG00000211459 ... ENSG00000200220 ENSG00000262477
rowData names(8): rotation ID ... symbol desc
colnames(32982): LIB5437925_SAM24396519_AAACCTGCATGTTCCC-1
LIB5437925_SAM24396519_AAACCTGGTAAATGAC-1 ... LIB5437933_SAM24396523_TTTGTCATCTTCAACT-1
LIB5437933_SAM24396523_TTTGTCATCTTGAGAC-1
colData names(37): batch Sample ... sizeFactor cluster
reducedDimNames(3): corrected TSNE UMAP
altExpNames(0):
R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /usr/local/lib/R/lib/libRblas.so
LAPACK: /gstore/home/penikals/.conda/envs/testr/lib/libmkl_rt.so
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
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] ShadowArray_0.99.25 DelayedArray_0.14.1 IRanges_2.22.2 S4Vectors_0.26.1 BiocGenerics_0.34.0 matrixStats_0.56.0
[7] sceasy_0.0.6 reticulate_1.16
loaded via a namespace (and not attached):
[1] bitops_1.0-6 fs_1.5.0 usethis_1.6.1 bit64_4.0.5
[5] devtools_2.3.1 httr_1.4.2 filelock_1.0.2 rprojroot_1.3-2
[9] GenomeInfoDb_1.24.2 tools_4.0.0 backports_1.1.10 R6_2.4.1
[13] irlba_2.3.3 HDF5Array_1.16.1 gp.version_0.99.2 vipor_0.4.5
[17] DBI_1.1.0 gp.cache_0.98.8 colorspace_1.4-1 gp.auth_0.98.6
[21] withr_2.2.0 tidyselect_1.1.0 gridExtra_2.3 prettyunits_1.1.1
[25] processx_3.4.3 bit_4.0.4 curl_4.3 compiler_4.0.0
[29] cli_2.1.0 Biobase_2.48.0 BiocNeighbors_1.6.0 desc_1.2.0
[33] scales_1.1.1 callr_3.4.3 rappdirs_0.3.1 digest_0.6.26
[37] rmarkdown_2.3 XVector_0.28.0 scater_1.16.3 pkgconfig_2.0.3
[41] htmltools_0.5.0 sessioninfo_1.1.1 dbplyr_1.4.4 rlang_0.4.8
[45] RSQLite_2.2.0 rstudioapi_0.11 DelayedMatrixStats_1.10.1 generics_0.0.2
[49] jsonlite_1.7.0 BiocParallel_1.22.0 dplyr_1.0.2 RCurl_1.98-1.2
[53] magrittr_1.5 BiocSingular_1.4.0 GenomeInfoDbData_1.2.3 Matrix_1.3-4
[57] Rhdf5lib_1.10.1 Rcpp_1.0.5 ggbeeswarm_0.6.0 munsell_0.5.0
[61] fansi_0.4.1 viridis_0.5.1 lifecycle_0.2.0 yaml_2.2.1
[65] SummarizedExperiment_1.18.2 zlibbioc_1.34.0 rhdf5_2.32.2 BiocFileCache_1.12.1
[69] pkgbuild_1.1.0 blob_1.2.1 grid_4.0.0 crayon_1.3.4
[73] lattice_0.20-41 knitr_1.29 ps_1.3.4 pillar_1.4.6
[77] GenomicRanges_1.40.0 base64url_1.4 pkgload_1.1.0 glue_1.4.2
[81] evaluate_0.14 getPass_0.2-2 remotes_2.2.0 vctrs_0.3.4
[85] testthat_2.3.2 gtable_0.3.0 purrr_0.3.4 assertthat_0.2.1
[89] ggplot2_3.3.2 xfun_0.16 rsvd_1.0.3 viridisLite_0.3.0
[93] ArtifactDB_1.0.35 SingleCellExperiment_1.10.1 tibble_3.0.4 beeswarm_0.2.3
[97] memoise_1.1.0 ellipsis_0.3.1
Any tips appreciated, thanks!