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Description
Hi, I am trying to use the RP model to generate the gene activity score to 10x scATAC data. I installed MAESTRO R version as well as python version and setup reticulate. Yet, it is still throwing errors as follows. Do you have a suggestion?
# python related
library(reticulate)
use_python("/path/to/python", required = TRUE)
obj.gene <- ATACCalculateGenescore(obj,
organism = "GRCh38",
decaydistance = 10000,
model = "Enhanced")
output
Error in as.integer(as_r_value(x$indices)): cannot coerce type 'environment' to vector of type 'integer'
Traceback:
1. ATACCalculateGenescore(obj, organism = "GRCh38", decaydistance = 10000,
. model = "Enhanced")
2. calculate_RP_score(cell_peaks = inputMat, peaks_list = peaks_list,
. gene_bed_df = refgenes.genescore, genes_list = NULL, decay = decaydistance,
. model = model)
3. py_to_r(result)
4. py_to_r.python.builtin.tuple(result)
5. lapply(converted, function(object) {
. if (inherits(object, "python.builtin.object"))
. py_to_r(object)
. else object
. })
6. FUN(X[[i]], ...)
7. py_to_r(object)
8. py_to_r.scipy.sparse.csc.csc_matrix(object)
9. new("dgCMatrix", i = as.integer(as_r_value(x$indices)), p = as.integer(as_r_value(x$indptr)),
. x = as.vector(as_r_value(x$data)), Dim = as.integer(dim(x)))
10. initialize(value, ...)
11. initialize(value, ...)
12. callNextMethod()
13. .nextMethod(.Object = .Object, ... = ...)
session info
R version 4.0.3 (2020-10-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: ***/conda/envs/r403/lib/libblas.so.3.8.0
LAPACK: ***/conda/envs/r403/lib/libmkl_rt.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] 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
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] reticulate_1.18 Seurat_3.2.3 MAESTRO_1.3.1
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 RSQLite_2.2.2
[3] AnnotationDbi_1.52.0 htmlwidgets_1.5.3
[5] grid_4.0.3 Rtsne_0.15
[7] devtools_2.3.2 munsell_0.5.0
[9] codetools_0.2-18 ica_1.0-2
[11] pbdZMQ_0.3-4 future_1.21.0
[13] miniUI_0.1.1.1 withr_2.3.0
[15] colorspace_2.0-0 Biobase_2.50.0
[17] knitr_1.30 uuid_0.1-4
[19] rstudioapi_0.13 stats4_4.0.3
[21] ROCR_1.0-11 tensor_1.5
[23] listenv_0.8.0 MatrixGenerics_1.2.0
[25] repr_1.1.0 GenomeInfoDbData_1.2.4
[27] polyclip_1.10-0 bit64_4.0.5
[29] rprojroot_2.0.2 parallelly_1.23.0
[31] vctrs_0.3.6 generics_0.1.0
[33] xfun_0.20 BiocFileCache_1.14.0
[35] R6_2.5.0 GenomeInfoDb_1.26.2
[37] rsvd_1.0.3 hdf5r_1.3.3
[39] bitops_1.0-6 spatstat.utils_1.20-2
[41] DelayedArray_0.16.0 assertthat_0.2.1
[43] promises_1.1.1 scales_1.1.1
[45] nnet_7.3-14 gtable_0.3.0
[47] globals_0.14.0 processx_3.4.5
[49] goftest_1.2-2 rlang_0.4.10
[51] splines_4.0.3 lazyeval_0.2.2
[53] checkmate_2.0.0 yaml_2.2.1
[55] reshape2_1.4.4 abind_1.4-5
[57] backports_1.2.1 httpuv_1.5.4
[59] Hmisc_4.4-2 tools_4.0.3
[61] usethis_2.0.0 ggplot2_3.3.3
[63] ellipsis_0.3.1 gplots_3.1.1
[65] RColorBrewer_1.1-2 BiocGenerics_0.36.0
[67] sessioninfo_1.1.1 ggridges_0.5.3
[69] Rcpp_1.0.5 plyr_1.8.6
[71] base64enc_0.1-3 progress_1.2.2
[73] zlibbioc_1.36.0 purrr_0.3.4
[75] RCurl_1.98-1.2 ps_1.5.0
[77] prettyunits_1.1.1 rpart_4.1-15
[79] openssl_1.4.3 deldir_0.2-3
[81] pbapply_1.4-3 cowplot_1.1.1
[83] S4Vectors_0.28.1 zoo_1.8-8
[85] SummarizedExperiment_1.20.0 ggrepel_0.9.0
[87] cluster_2.1.0 fs_1.5.0
[89] magrittr_2.0.1 data.table_1.13.6
[91] scattermore_0.7 forestplot_1.10.1
[93] lmtest_0.9-38 RANN_2.6.1
[95] fitdistrplus_1.1-3 matrixStats_0.57.0
[97] pkgload_1.1.0 hms_0.5.3
[99] patchwork_1.1.1 mime_0.9
[101] evaluate_0.14 xtable_1.8-4
[103] XML_3.99-0.5 jpeg_0.1-8.1
[105] IRanges_2.24.1 gridExtra_2.3
[107] Gmisc_1.11.0 testthat_3.0.1
[109] compiler_4.0.3 biomaRt_2.46.0
[111] tibble_3.0.4 KernSmooth_2.23-18
[113] crayon_1.3.4 htmltools_0.5.1
[115] mgcv_1.8-33 later_1.1.0.1
[117] Formula_1.2-4 tidyr_1.1.2
[119] lubridate_1.7.9.2 DBI_1.1.0
[121] dbplyr_2.0.0 MASS_7.3-53
[123] rappdirs_0.3.1 Matrix_1.3-2
[125] cli_2.2.0 parallel_4.0.3
[127] igraph_1.2.6 GenomicRanges_1.42.0
[129] pkgconfig_2.0.3 foreign_0.8-81
[131] IRdisplay_0.7.0 plotly_4.9.3
[133] xml2_1.3.2 XVector_0.30.0
[135] stringr_1.4.0 callr_3.5.1
[137] digest_0.6.27 sctransform_0.3.2
[139] RcppAnnoy_0.0.18 spatstat.data_1.7-0
[141] rmarkdown_2.6 leiden_0.3.6
[143] htmlTable_2.1.0 uwot_0.1.10
[145] curl_4.3 shiny_1.5.0
[147] gtools_3.8.2 lifecycle_0.2.0
[149] nlme_3.1-151 jsonlite_1.7.2
[151] desc_1.2.0 viridisLite_0.3.0
[153] askpass_1.1 fansi_0.4.1
[155] pillar_1.4.7 lattice_0.20-41
[157] fastmap_1.0.1 httr_1.4.2
[159] pkgbuild_1.2.0 survival_3.2-7
[161] glue_1.4.2 remotes_2.2.0
[163] spatstat_1.64-1 png_0.1-7
[165] bit_4.0.4 presto_1.0.0
[167] stringi_1.5.3 blob_1.2.1
[169] latticeExtra_0.6-29 caTools_1.18.1
[171] memoise_1.1.0 IRkernel_1.1.1
[173] dplyr_1.0.2 irlba_2.3.3
[175] future.apply_1.7.0
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