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View Code? Open in Web Editor NEWR package for transfer learning of single-cell RNA-seq denoising
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Error: ModuleNotFoundError: No module named 'sctransfer'
I'm encountering the error Error: ModuleNotFoundError: No module named 'sctransfer' whenever I try to run saverx. I tried the solution in #15, but it also didn't work for me. I can see the sctransfer module in Users/tina/Library/Python/3.7/lib/python/sitepackages, but from py_config() I can see that the python being used is /Users/tina/Library/r-miniconda/envs/r-reticulate/bin/python. I tried to set the python path used using these commands
path_to_python <- "Users/tina/Library/Python/3.7"
use_python(path_to_python)
but it hasn't worked for me. Would greatly appreciate any advice on how to get around this, thanks!
R crash: *** caught illegal operation ***
Hello,
I am using SAVERX with my data and caught this error.
Anybody aware with this sign??
Thank you!
process the data
In UMI data, how to process the data to make it fit SAVERX when using the pretrained model? For some raw UMI data, it seems hard to use it and the error is that : no gene name mathces..
Thank you!
Recommended workflow for multiple samples
Hi again,
I wonder what your recommendations are for using SAVERX with multiple samples. In my work I integrate > 20 samples using Seurat V3 to identify overarching cell clusters. If I want to use SAVERX on these samples, would you recommend to
a) integrate the data and run SAVERX on the integrated data (probably not, since UMI count data is lost on the way)
b) run SAVERX on one big matrix with UMI counts from all cells of all samples
c) run SAVERX on the UMI counts of each sample separately and integrate the resulting data using Seurat?
Or is there another way altogether?
Thanks for your help and thanks again for developing this!
Best Max
Error in py_get_attr_impl(x, name, silent) : AttributeError: 'AxisArrays' object has no attribute 'X_dca'
I got a similar error with an issue posted previously.
Error in py_get_attr_impl(x, name, silent) : AttributeError: 'AxisArrays' object has no attribute 'X_dca'
Have you get solution for this?
Error in py_module_import(module, convert = convert) : ImportError: cannot import name 'getargspec_no_self' from 'scipy._lib._util'
Just installed SAVERX and ran into this error message in the example.
# install_github("jingshuw/SAVERX")
library(SAVERX)
library(reticulate)
reticulate::use_condaenv("/opt/anaconda3/envs/echoR")
reticulate::use_python("/opt/anaconda3/envs/echoR/bin/python3.7")
reticulate::py_install("sctransfer", pip = T)
file <- saverx("~/Downloads/shekhar_downsampled.csv")
[1] "Input file is: ~/Downloads/shekhar_downsampled.csv"
[1] "Use a pretrained model: No"
[1] "Processed file saved as: 1613680966.07345/tmpdata.rds"
[1] "Data preprocessed ..."
Error in py_module_import(module, convert = convert) :
ImportError: cannot import name 'getargspec_no_self' from 'scipy._lib._util' (/opt/anaconda3/envs/echoR/lib/python3.7/site-packages/scipy/_lib/_util.py)
>
Thanks!,
Brian
Conda env
⋊> ~/D/model_celltype_conservation on main ⨯ conda list (echoR) 20:43:33
# packages in environment at /opt/anaconda3/envs/echoR:
#
# Name Version Build Channel
_r-mutex 1.0.0 anacondar_1
absl-py 0.11.0 pypi_0 pypi
anndata 0.7.5 py37hf985489_0 conda-forge
arrow-cpp 0.15.1 py37hb41def8_6
astunparse 1.6.3 pypi_0 pypi
attrs 20.3.0 pyhd3eb1b0_0
bcftools 1.10.2 h27336cd_0 bioconda
bioconductor-annotationdbi 1.48.0 r36_0 bioconda
bioconductor-annotationfilter 1.10.0 r36_0 bioconda
bioconductor-biobase 2.46.0 r36h01d97ff_0 bioconda
bioconductor-biocfilecache 1.10.0 r36_0 bioconda
bioconductor-biocgenerics 0.32.0 r36_0 bioconda
bioconductor-biocparallel 1.20.0 r36h6de7cb9_0 bioconda
bioconductor-biomart 2.42.0 r36_0 bioconda
bioconductor-biostrings 2.54.0 r36h01d97ff_0 bioconda
bioconductor-biovizbase 1.34.0 r36h01d97ff_0 bioconda
bioconductor-bsgenome 1.54.0 r36_0 bioconda
bioconductor-delayedarray 0.12.0 r36h01d97ff_0 bioconda
bioconductor-ensdb.hsapiens.v75 2.99.0 r36_6 bioconda
bioconductor-ensembldb 2.10.0 r36_0 bioconda
bioconductor-genomeinfodb 1.22.0 r36_0 bioconda
bioconductor-genomeinfodbdata 1.2.2 r36_0 bioconda
bioconductor-genomicalignments 1.22.0 r36h01d97ff_0 bioconda
bioconductor-genomicfeatures 1.38.0 r36_0 bioconda
bioconductor-genomicranges 1.38.0 r36h01d97ff_0 bioconda
bioconductor-ggbio 1.34.0 r36_0 bioconda
bioconductor-graph 1.64.0 r36h01d97ff_0 bioconda
bioconductor-iranges 2.20.0 r36h01d97ff_0 bioconda
bioconductor-organismdbi 1.28.0 r36_0 bioconda
bioconductor-protgenerics 1.18.0 r36_0 bioconda
bioconductor-rbgl 1.62.1 r36h6de7cb9_0 bioconda
bioconductor-rgraphviz 2.30.0 r36h6de7cb9_0 bioconda
bioconductor-rhtslib 1.18.0 r36h90b7f41_1 bioconda
bioconductor-rsamtools 2.2.0 r36h6de7cb9_0 bioconda
bioconductor-rtracklayer 1.46.0 r36h01d97ff_0 bioconda
bioconductor-s4vectors 0.24.0 r36h01d97ff_0 bioconda
bioconductor-snpstats 1.36.0 r36h01d97ff_0 bioconda
bioconductor-summarizedexperiment 1.16.0 r36_0 bioconda
bioconductor-suprahex 1.24.0 r36_0 bioconda
bioconductor-variantannotation 1.32.0 r36h01d97ff_0 bioconda
bioconductor-xvector 0.26.0 r36h01d97ff_0 bioconda
bioconductor-zlibbioc 1.32.0 r36h01d97ff_0 bioconda
bitarray 1.6.1 py37h9ed2024_0
blas 2.11 openblas conda-forge
bokeh 2.2.3 py37_0
boost-cpp 1.67.0 h1de35cc_4
brotli 1.0.9 hb1e8313_2
brotlipy 0.7.0 py37h9ed2024_1003
bwidget 1.9.11 1
bzip2 1.0.8 h1de35cc_0
c-ares 1.16.1 haf1e3a3_0
ca-certificates 2020.12.5 h033912b_0 conda-forge
cachetools 4.2.1 pypi_0 pypi
cairo 1.16.0 h0ab9d94_1001 conda-forge
certifi 2020.12.5 py37hf985489_1 conda-forge
cffi 1.14.3 py37h2125817_2
chardet 3.0.4 py37hecd8cb5_1003
clang 10.0.0 default_hf57f61e_0
clang_osx-64 4.0.1 h1ce6c1d_16 anaconda
clangxx 10.0.0 default_hf57f61e_0
clangxx_osx-64 4.0.1 h22b1bf0_16
click 7.1.2 py_0
cloudpickle 1.6.0 py_0
cryptography 3.2.1 py37hbcfaee0_1
curl 7.67.0 ha441bb4_0
cycler 0.10.0 pypi_0 pypi
cytoolz 0.11.0 py37haf1e3a3_0
dask 2.30.0 py_0
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decorator 4.4.2 py_0
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distributed 2.30.1 py37hecd8cb5_0
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freetype 2.10.4 ha233b18_0
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get-version 2.1 pypi_0 pypi
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gflags 2.2.2 h0a44026_0
gfortran_osx-64 4.8.5 h22b1bf0_8
glib 2.66.1 h9bbe63b_0
glog 0.4.0 h0a44026_0
google-auth 1.27.0 pypi_0 pypi
google-auth-oauthlib 0.4.2 pypi_0 pypi
google-pasta 0.2.0 pypi_0 pypi
graphite2 1.3.14 h38d11af_0
grpc-cpp 1.26.0 h044775b_0
grpcio 1.32.0 pypi_0 pypi
gsl 2.5 ha2d443c_1 conda-forge
h5py 2.10.0 nompi_py37h106b333_102 conda-forge
harfbuzz 2.4.0 h831d699_1
hdf5 1.10.5 nompi_h0cbb7df_1103 conda-forge
heapdict 1.0.1 py_0
htslib 1.10.2 h862b14c_0 bioconda
icu 58.2 h0a44026_3
idna 2.10 py_0
importlib-metadata 2.0.0 py_1
importlib_metadata 2.0.0 1
iniconfig 1.1.1 py_0
jinja2 2.11.2 py_0
joblib 0.17.0 py_0
jpeg 9d hbcb3906_0 conda-forge
keras 2.4.3 pypi_0 pypi
keras-preprocessing 1.1.2 pypi_0 pypi
kiwisolver 1.3.1 pypi_0 pypi
krb5 1.16.4 hddcf347_0
lcms2 2.11 h92f6f08_0
legacy-api-wrap 1.2 pypi_0 pypi
libblas 3.8.0 11_openblas conda-forge
libboost 1.67.0 hebc422b_4
libcblas 3.8.0 11_openblas conda-forge
libcurl 7.67.0 h051b688_0
libcxx 10.0.0 1
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liblapacke 3.8.0 11_openblas conda-forge
libllvm10 10.0.1 h76017ad_5
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libpng 1.6.37 ha441bb4_0
libprotobuf 3.11.2 hd9629dc_0
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libxml2 2.9.10 h7cdb67c_3
llvm-openmp 10.0.0 h28b9765_0
llvmlite 0.34.0 py37h739e7dc_4
locket 0.2.0 py37_1
lz4-c 1.8.1.2 h1de35cc_0
macs2 2.2.7.1 py37hea0d0e9_1 bioconda
make 4.2.1 h3efe00b_1
markdown 3.3.3 pypi_0 pypi
markupsafe 1.1.1 py37h1de35cc_0
matplotlib 3.3.4 pypi_0 pypi
more-itertools 8.6.0 pyhd3eb1b0_0
msgpack-python 1.0.0 py37h04f5b5a_1
natsort 7.1.0 pyhd8ed1ab_0 conda-forge
ncurses 6.2 h0a44026_1
networkx 2.5 py_0
numba 0.51.2 py37h959d312_1
numexpr 2.7.2 pypi_0 pypi
numpy 1.19.2 py37h63973fd_0
numpy-base 1.19.2 py37h68fea81_0
oauthlib 3.1.0 pypi_0 pypi
olefile 0.46 py37_0
openssl 1.1.1j hbcf498f_0 conda-forge
opt-einsum 3.3.0 pypi_0 pypi
packaging 20.4 py_0
pandas 1.1.3 py37hb1e8313_0
pandas-plink 2.2.3 py37he6e4e01_0 conda-forge
pango 1.40.14 h0da9b22_1005 conda-forge
partd 1.1.0 py_0
patsy 0.5.1 pypi_0 pypi
pcre 8.44 hb1e8313_0
perl 5.26.2 h4e221da_0
pillow 8.0.1 py37h5270095_0
pip 20.2.4 py37hecd8cb5_0
pixman 0.38.0 h1de35cc_0
plink 1.90b6.18 h0b31af3_0 bioconda
pluggy 0.13.1 py37_0
protobuf 3.14.0 pypi_0 pypi
psutil 5.7.2 py37haf1e3a3_0
py 1.9.0 py_0
pyarrow 0.15.1 py37h6c726b0_0
pyasn1 0.4.8 pypi_0 pypi
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pyparsing 2.4.7 py_0
pysocks 1.7.1 py37hecd8cb5_0
pytest 6.1.1 py37_0
python 3.7.9 h26836e1_0
python-dateutil 2.8.1 py_0
python_abi 3.7 1_cp37m conda-forge
pytz 2020.1 py_0
pyyaml 5.3.1 py37haf1e3a3_1
r 3.6.0 r36_0
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r-class 7.3_15 r36h46e59ec_0
r-cli 1.1.0 r36h6115d3f_0
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r-cluster 2.0.8 r36hfffe0aa_0
r-coda 0.19_2 r36h6115d3f_0
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r-coloc 3.1 r36h6115d3f_2 bioconda
r-colorspace 1.4_1 r36h46e59ec_0
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r-desctools 0.99.29 r36hb1f44f5_0 conda-forge
r-devtools 2.0.2 r36h6115d3f_0
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requests 2.24.0 py_0
requests-oauthlib 1.3.0 pypi_0 pypi
rpy2 2.9.4 py37r36h1d22016_0
rsa 4.7.1 pypi_0 pypi
scanpy 1.7.0 pypi_0 pypi
scikit-learn 0.23.2 py37h959d312_0
scipy 1.2.1 py37hbd7caa9_1 conda-forge
sctransfer 0.0.9 pypi_0 pypi
seaborn 0.11.1 pypi_0 pypi
setuptools 50.3.1 py37hecd8cb5_1
sinfo 0.3.1 pypi_0 pypi
six 1.15.0 py37hecd8cb5_0
snappy 1.1.8 hb1e8313_0
sortedcontainers 2.2.2 py_0
sqlite 3.33.0 hffcf06c_0
statsmodels 0.12.2 pypi_0 pypi
stdlib-list 0.8.0 pypi_0 pypi
tabix 0.2.6 ha92aebf_0 bioconda
tables 3.6.1 pypi_0 pypi
tblib 1.7.0 py_0
tensorboard 2.4.1 pypi_0 pypi
tensorboard-plugin-wit 1.8.0 pypi_0 pypi
tensorflow 2.4.1 pypi_0 pypi
tensorflow-estimator 2.4.0 pypi_0 pypi
termcolor 1.1.0 pypi_0 pypi
threadpoolctl 2.1.0 pyh5ca1d4c_0
thrift 0.11.0 py37h0a44026_0
thrift-cpp 0.11.0 hd79cdb6_3
tk 8.6.10 hb0a8c7a_0
tktable 2.10 h1de35cc_0
toml 0.10.1 py_0
toolz 0.11.1 py_0
tornado 6.0.4 py37h1de35cc_1
tqdm 4.51.0 pyhd3eb1b0_0
typing_extensions 3.7.4.3 py_0
umap-learn 0.5.1 pypi_0 pypi
uriparser 0.9.3 h0a44026_1
urllib3 1.25.11 py_0
werkzeug 1.0.1 pypi_0 pypi
wget 1.20.1 h051b688_0
wheel 0.35.1 pyhd3eb1b0_0
wrapt 1.12.1 py37h1de35cc_1
xarray 0.16.1 py_0
xz 5.2.5 h1de35cc_0
yaml 0.2.5 haf1e3a3_0
zict 2.0.0 py_0
zipp 3.4.0 pyhd3eb1b0_0
zlib 1.2.11 h1de35cc_3
zstandard 0.14.1 py37h23ab428_0
zstd 1.3.7 h5bba6e5_0
R Session Info
> sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] reticulate_1.18 SAVERX_1.0.2 SeuratObject_4.0.0
[4] Seurat_4.0.0 SummarizedExperiment_1.20.0 Biobase_2.50.0
[7] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2 IRanges_2.24.1
[10] S4Vectors_0.28.1 BiocGenerics_0.36.0 MatrixGenerics_1.2.1
[13] matrixStats_0.58.0 dplyr_1.0.4
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3 rtracklayer_1.50.0
[3] scattermore_0.7 ggthemes_4.2.4
[5] R.methodsS3_1.8.1 tidyr_1.1.2
[7] knitr_1.31 ggplot2_3.3.3
[9] bit64_4.0.5 R.utils_2.10.1
[11] irlba_2.3.3 DelayedArray_0.16.1
[13] data.table_1.13.6 rpart_4.1-15
[15] RCurl_1.98-1.2 generics_0.1.0
[17] callr_3.5.1 cowplot_1.1.1
[19] usethis_2.0.1 RSQLite_2.2.3
[21] RANN_2.6.1 proxy_0.4-24
[23] future_1.21.0 bit_4.0.4
[25] spatstat.data_2.0-0 webshot_0.5.2
[27] xml2_1.3.2 httpuv_1.5.5
[29] assertthat_0.2.1 viridis_0.5.1
[31] xfun_0.21 One2One_0.1.0
[33] hms_1.0.0 evaluate_0.14
[35] promises_1.2.0.1 TSP_1.1-10
[37] DEoptimR_1.0-8 progress_1.2.2
[39] caTools_1.18.1 dendextend_1.14.0
[41] dbplyr_2.1.0 readxl_1.3.1
[43] igraph_1.2.6 DBI_1.1.1
[45] htmlwidgets_1.5.3 purrr_0.3.4
[47] ellipsis_0.3.1 RSpectra_0.16-0
[49] biomaRt_2.46.3 deldir_0.2-9
[51] vctrs_0.3.6 SingleCellExperiment_1.12.0
[53] remotes_2.2.0 here_1.0.1
[55] TTR_0.24.2 ROCR_1.0-11
[57] abind_1.4-5 cachem_1.0.3
[59] RcppEigen_0.3.3.9.1 withr_2.4.1
[61] BSgenome_1.58.0 robustbase_0.93-7
[63] vcd_1.4-8 sctransform_0.3.2
[65] GenomicAlignments_1.26.0 xts_0.12.1
[67] prettyunits_1.1.1 goftest_1.2-2
[69] cluster_2.1.0 repmis_0.5
[71] ape_5.4-1 lazyeval_0.2.2
[73] laeken_0.5.1 crayon_1.4.1
[75] pkgconfig_2.0.3 nlme_3.1-152
[77] pkgload_1.1.0 seriation_1.2-9
[79] pals_1.6 nnet_7.3-15
[81] devtools_2.3.2 rlang_0.4.10
[83] globals_0.14.0 lifecycle_1.0.0
[85] miniUI_0.1.1.1 SNPlocs.Hsapiens.dbSNP144.GRCh37_0.99.20
[87] registry_0.5-1 SNPlocs.Hsapiens.dbSNP144.GRCh38_0.99.20
[89] BiocFileCache_1.14.0 dichromat_2.0-0
[91] cellranger_1.1.0 rprojroot_2.0.2
[93] polyclip_1.10-0 RcppHNSW_0.3.0
[95] lmtest_0.9-38 Matrix_1.3-2
[97] carData_3.0-4 Rhdf5lib_1.12.1
[99] boot_1.3-27 zoo_1.8-8
[101] ggridges_0.5.3 processx_3.4.5
[103] png_0.1-7 viridisLite_0.3.0
[105] bitops_1.0-6 R.oo_1.24.0
[107] rhdf5filters_1.2.0 KernSmooth_2.23-18
[109] Biostrings_2.58.0 anndata_0.7.5.1
[111] blob_1.2.1 stringr_1.4.0
[113] parallelly_1.23.0 R.cache_0.14.0
[115] MAGMA.Celltyping_1.0.0 scales_1.1.1
[117] memoise_2.0.0 magrittr_2.0.1
[119] plyr_1.8.6 hexbin_1.28.2
[121] ica_1.0-2 gplots_3.1.1
[123] zlibbioc_1.36.0 compiler_4.0.3
[125] RColorBrewer_1.1-2 pcaMethods_1.82.0
[127] fitdistrplus_1.1-3 Rsamtools_2.6.0
[129] homologene_1.4.68.19.3.27 cli_2.3.0
[131] XVector_0.30.0 listenv_0.8.0
[133] patchwork_1.1.0.9000 pbapply_1.4-3
[135] ps_1.5.0 ggplot.multistats_1.0.0
[137] MASS_7.3-53.1 mgcv_1.8-33
[139] tidyselect_1.1.0 stringi_1.5.3
[141] forcats_0.5.1 yaml_2.2.1
[143] askpass_1.1 ggrepel_0.9.1
[145] grid_4.0.3 tools_4.0.3
[147] future.apply_1.7.0 rio_0.5.16
[149] rstudioapi_0.13 RNOmni_1.0.0
[151] foreach_1.5.1 foreign_0.8-81
[153] gridExtra_2.3 smoother_1.1
[155] scatterplot3d_0.3-41 Rtsne_0.15
[157] HGNChelper_0.8.1 digest_0.6.27
[159] shiny_1.6.0 Rcpp_1.0.6
[161] car_3.0-10 later_1.1.0.1
[163] RcppAnnoy_0.0.18 httr_1.4.2
[165] ggdendro_0.1.22 AnnotationDbi_1.52.0
[167] colorspace_2.0-0 XML_3.99-0.5
[169] fs_1.5.0 tensor_1.5
[171] ranger_0.12.1 splines_4.0.3
[173] uwot_0.1.10 spatstat.utils_2.0-0
[175] sp_1.4-5 mapproj_1.2.7
[177] plotly_4.9.3 sessioninfo_1.1.1
[179] xtable_1.8-4 jsonlite_1.7.2
[181] heatmaply_1.2.1 spatstat_1.64-1
[183] sceasy_0.0.6 testthat_3.0.2
[185] destiny_3.4.0 R6_2.5.0
[187] EWCE_0.99.2 pillar_1.4.7
[189] htmltools_0.5.1.1 mime_0.10
[191] BiocParallel_1.24.1 glue_1.4.2
[193] fastmap_1.1.0 VIM_6.1.0
[195] class_7.3-18 codetools_0.2-18
[197] maps_3.3.0 pkgbuild_1.2.0
[199] lattice_0.20-41 tibble_3.0.6
[201] curl_4.3 leiden_0.3.7
[203] gtools_3.8.2 zip_2.1.1
[205] openxlsx_4.2.3 openssl_1.4.3
[207] survival_3.2-7 limma_3.46.0
[209] rmarkdown_2.6 desc_1.2.0
[211] munsell_0.5.0 e1071_1.7-4
[213] rhdf5_2.34.0 GenomeInfoDbData_1.2.4
[215] iterators_1.0.13 HDF5Array_1.18.1
[217] haven_2.3.1 reshape2_1.4.4
[219] gtable_0.3.0
Can not run example
The file is not on dropbox 'mouse_retina.hdf5'.
Error in importing sctransfer
Hi,
When running the example file <- saverx("./testdata/shekhar_downsampled.csv"), I encountered the error saying
Error in py_module_import(module, convert = convert) :
ModuleNotFoundError: No module named 'sctransfer'
I know there is a related post here, #15. I tried its solution, but it didn't work for me.
My Python version is Python 3.8.5, and I double-checked that sctransfer has been installed:
Name: sctransfer
Version: 0.0.9
Summary: Python part for scRNA-seq transfer learning denoising tool SAVER-X
Home-page: https://github.com/jingshuw/sctransfer
Author: Jingshu Wang
Author-email: [email protected]
License: GPL-3
Location: /Users/zhli12/miniconda3/lib/python3.8/site-packages
Requires: pandas, anndata, scikit-learn, numpy, h5py, six, keras, scanpy, tensorflow
Based on its installation location, I also tried the command
path_to_python <- "Users/zhli12/miniconda3/lib/python3.8"
use_python(path_to_python)
However, the same error persists.
Does anyone know how to fix this problem? Thank you!!
Error in 'scanpy.api'
I meet the 'scanpy.api' error when I run SAVERX on my own samples
[1] "Input is a data matrix"
[1] "Use a pretrained model: Yes"
[1] "Data species is: Human"
[1] "Pretrained weights file is: ./human_Tcells.hdf5"
[1] "Model species is: Human"
[1] "8850 genes mapped out of 9069"
[1] "Gene names mapped, resulting file saved as: 1680935433.67681/tmpdata.rds"
[1] "Reshaped file saved as: 1680935433.67681/tmpdata.mtx"
[1] "Nonmissing indicator saved as: 1680935433.67681/tmpdata_nonmissing.txt"
[1] "Data preprocessed ..."
Error: ModuleNotFoundError: No module named 'scanpy.api'
Seems the 06/13/2022 update dosen't work
What could I do with this error
Input library-size-normalized expression
Hi,
Thank you for developing SAVERX. I am trying to impute multiple-sample single-cell UMI data using SAVERX. In order to make the gene expression comparable across samples, I have already normalized the UMI by library size. Now I want to impute the cells of each individual sample separately using these library-size-normalized expression, but SAVERX paper states that the input is UMI count. May I know whether we could input library-size-normalized expression to SAVERX, and how that would affect the imputation performance compared to using original UMI count?
Thank you!
Winnie
Denoised matrix contains NaN
Hey,
I ran SAVERX on my data and am quite happy with the results. However I noticed, that the denoised matrix contains NaN for some genes (but not all cells for these gene). It's a bit unclear to me what this means and can get clumsy to work with downstream. Can you explain?
Best Max
input/output as matrix instead of path to file
Hi,
Is it possible to set the input and output as matrices within R environment instead of path to file, just like SAVER? I'd like to incorporate SAVERX as part of a pipeline in the future and path to file is quite inconvenient.
Thanks!
Request for a worked python environment for SAVERX
I have tried lots of python packages' version, but it always show a misssing for some python modules or functions. So I hope author could share the environment used by building it into docker image, releasing a requirements.txt or environments.yml. Besides, I found the SAVER-X server is uavailale to create a new account with a backend database error.
difference between saver and saver-x without pretraining
hi,
Just wondering whether there is difference between saver and saver-x without pretraining? Thx!
WebServer - cannot create a new account.
I'm currently encountering this error while creating a new account in an attempt to use the webserver. Please advise how to use the tool.
Error in py_module_import
Hi, I am running SAVERX with Rstudio, R3.6.2 under Ubuntu 16.04 and a Python 3.5 virtual environment with the sctransfer package v0.0.9. No error messages loading SAVERX or installing the python dependencies but I get this while running SAVERX:
> library("SAVERX")
> scFile <- saverx("./rawcounts.rds")
[1] "Input file is: ./rawcounts.rds"
[1] "Use a pretrained model: No"
[1] "Processed file saved as: 1582598887.77262/tmpdata.rds"
[1] "Data preprocessed ..."
Error in py_module_import(module, convert = convert) :
SyntaxError: invalid syntax (_settings.py, line 351)
Python packages available:
Package Version
-------------------- ----------
absl-py 0.9.0
anndata 0.6.20
astor 0.8.1
cachetools 4.0.0
certifi 2019.11.28
chardet 3.0.4
cTPnet 0.2.6
cycler 0.10.0
decorator 4.4.1
gast 0.2.2
google-auth 1.11.2
google-auth-oauthlib 0.4.1
google-pasta 0.1.8
grpcio 1.27.2
h5py 2.10.0
idna 2.9
joblib 0.14.1
Keras 2.3.1
Keras-Applications 1.0.8
Keras-Preprocessing 1.1.0
kiwisolver 1.1.0
llvmlite 0.31.0
Markdown 3.2.1
matplotlib 3.0.3
natsort 7.0.1
networkx 2.4
numba 0.47.0
numexpr 2.7.1
numpy 1.18.1
oauthlib 3.1.0
opt-einsum 3.1.0
pandas 0.24.2
patsy 0.5.1
pip 20.0.2
pkg-resources 0.0.0
protobuf 3.11.3
pyasn1 0.4.8
pyasn1-modules 0.2.8
pyparsing 2.4.6
python-dateutil 2.8.1
pytz 2019.3
PyYAML 5.3
requests 2.23.0
requests-oauthlib 1.3.0
rsa 4.0
scanpy 1.4.3
scikit-learn 0.22.1
scipy 1.4.1
sctransfer 0.0.9
seaborn 0.9.1
setuptools 45.2.0
six 1.14.0
statsmodels 0.11.1
tables 3.6.1
tensorboard 2.0.2
tensorflow 2.0.0
tensorflow-estimator 2.0.1
termcolor 1.1.0
torch 1.4.0
tqdm 4.43.0
umap-learn 0.3.10
urllib3 1.25.8
Werkzeug 1.0.0
wheel 0.34.2
wrapt 1.12.0
I also tried with tensorflow 2.1 which produces the same error message with some GPU related warnings on top. Tracing those other warnings led me to this thread: tensorflow/tensorflow#35968
However, whereas downgrading to 2.0.0 solved the issue for them it does not remove the error reported here.
Any ideas? Thanks in advance!
Jerry
CreateSeuratObject ISSUE
Hi,
thanks for have developed thi interesting tool. I have done the denoising of my data (30k cells),now i want create my seurat object but i encountered thi issue:
I think that my output data are denoised correctly without any error (but i'm not sure):
Have you any suggestion?
Best,
Domenico
Error in py_module_import (sctansfer)
Hello, I am running SAVERX with R 3.6.2. and python 3.6.10 in a conda environment. I got this error even if sctransfer (0.0.9) is present in the environment:
[1] "Input file is: ~/concatSingleCell_doubleQC.csv"
[1] "Use a pretrained model: Yes"
[1] "Data species is: Human"
[1] "Pretrained weights file is: ~/Downloads/human_Devbrain_nomanno.hdf5"
[1] "Model species is: Human"
[1] "53043 genes mapped out of 63677"
[1] "Gene names mapped, resulting file saved as: 1586351812.23148/tmpdata.rds"
[1] "Reshaped file saved as: 1586351812.23148/tmpdata.mtx"
[1] "Nonmissing indicator saved as: 1586351812.23148/tmpdata_nonmissing.txt"
[1] "Data preprocessed ..."
Error in py_module_import(module, convert = convert) :
ModuleNotFoundError: No module named 'sctransfer'
The list of packages in the environment is this one:
packages in environment at /Users/chaaya/miniconda/envs/saverx:
Name Version Build Channel
absl-py 0.9.0 pypi_0 pypi
anndata 0.7.1 pypi_0 pypi
astor 0.8.1 pypi_0 pypi
ca-certificates 2020.1.1 0 anaconda
cachetools 4.1.0 pypi_0 pypi
certifi 2020.4.5.1 py36_0 anaconda
chardet 3.0.4 pypi_0 pypi
cycler 0.10.0 pypi_0 pypi
decorator 4.4.2 pypi_0 pypi
gast 0.2.2 pypi_0 pypi
get-version 2.1 pypi_0 pypi
google-auth 1.13.1 pypi_0 pypi
google-auth-oauthlib 0.4.1 pypi_0 pypi
google-pasta 0.2.0 pypi_0 pypi
grpcio 1.28.1 pypi_0 pypi
h5py 2.10.0 pypi_0 pypi
idna 2.9 pypi_0 pypi
importlib-metadata 1.6.0 pypi_0 pypi
joblib 0.14.1 pypi_0 pypi
keras 2.3.1 pypi_0 pypi
keras-applications 1.0.8 pypi_0 pypi
keras-preprocessing 1.1.0 pypi_0 pypi
kiwisolver 1.2.0 pypi_0 pypi
legacy-api-wrap 1.2 pypi_0 pypi
libcxx 4.0.1 hcfea43d_1
libcxxabi 4.0.1 hcfea43d_1
libedit 3.1.20181209 hb402a30_0
libffi 3.2.1 h475c297_4
llvmlite 0.31.0 pypi_0 pypi
markdown 3.2.1 pypi_0 pypi
matplotlib 3.2.1 pypi_0 pypi
natsort 7.0.1 pypi_0 pypi
ncurses 6.2 h0a44026_0
networkx 2.4 pypi_0 pypi
numba 0.48.0 pypi_0 pypi
numexpr 2.7.1 pypi_0 pypi
numpy 1.18.2 pypi_0 pypi
oauthlib 3.1.0 pypi_0 pypi
openssl 1.1.1 h1de35cc_0 anaconda
opt-einsum 3.2.0 pypi_0 pypi
packaging 20.3 pypi_0 pypi
pandas 1.0.3 pypi_0 pypi
patsy 0.5.1 pypi_0 pypi
pip 20.0.2 py36_1 anaconda
protobuf 3.11.3 pypi_0 pypi
pyasn1 0.4.8 pypi_0 pypi
pyasn1-modules 0.2.8 pypi_0 pypi
pyparsing 2.4.7 pypi_0 pypi
python 3.6.10 hc70fcce_1
python-dateutil 2.8.1 pypi_0 pypi
pytz 2019.3 pypi_0 pypi
pyyaml 5.3.1 pypi_0 pypi
readline 8.0 h1de35cc_0
requests 2.23.0 pypi_0 pypi
requests-oauthlib 1.3.0 pypi_0 pypi
rsa 4.0 pypi_0 pypi
scanpy 1.4.6 pypi_0 pypi
scikit-learn 0.22.2.post1 pypi_0 pypi
scipy 1.4.1 pypi_0 pypi
sctransfer 0.0.9 pypi_0 pypi
seaborn 0.10.0 pypi_0 pypi
setuptools 46.1.3 py36_0
setuptools-scm 3.5.0 pypi_0 pypi
six 1.14.0 pypi_0 pypi
sqlite 3.31.1 ha441bb4_0
statsmodels 0.11.1 pypi_0 pypi
tables 3.6.1 pypi_0 pypi
tbb 2019.0 pypi_0 pypi
tensorboard 2.1.1 pypi_0 pypi
tensorflow 2.1.0 pypi_0 pypi
tensorflow-estimator 2.1.0 pypi_0 pypi
termcolor 1.1.0 pypi_0 pypi
tk 8.6.8 ha441bb4_0
tqdm 4.45.0 pypi_0 pypi
umap-learn 0.4.0 pypi_0 pypi
urllib3 1.25.8 pypi_0 pypi
werkzeug 1.0.1 pypi_0 pypi
wheel 0.34.2 py36_0
wrapt 1.12.1 pypi_0 pypi
xz 5.2.4 h1de35cc_4
zipp 3.1.0 pypi_0 pypi
zlib 1.2.11 h1de35cc_3
Am I doing something not correct and is it fixable? Thank you in advance!
Giordano
KeyError: rmsprop
I'm getting this error trying to run SAVER-X. Can it be fixed?
**Error in py_call_impl(callable, dots$args, dots$keywords) :
KeyError: 'rmsprop'**
9.
stop(structure(list(message = "KeyError: 'rmsprop'", call = py_call_impl(callable,
dots$args, dots$keywords), cppstack = structure(list(file = "",
line = -1L, stack = c("/home/tjcooper/R/x86_64-pc-linux-gnu-library/4.0/reticulate/libs/reticulate.so(Rcpp::exception::exception(char const*, bool)+0x74) [0x7fb3e9008294]",
"/home/tjcooper/R/x86_64-pc-linux-gnu-library/4.0/reticulate/libs/reticulate.so(Rcpp::stop(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)+0x29) [0x7fb3e8ff8a66]", ...
8.
train_joint at train_joint.py#95
7.
autoencode at api_pretrain.py#123
6.
api$autoencode(n_inoutnodes_human = n_human, n_inoutnodes_mouse = n_mouse,
shared_size = shared_size, mtx_file = mtx_file, pred_mtx_file = test_mtx_file,
species = model.species, nonmissing_indicator = nonmissing_indicator,
initial_file = pretrain_file, out_dir = out_dir, batch_size = batch_size, ...
5.
autoencode(x[, train.idx], python.module, main, x.test, pretrain_file,
nonmissing_indicator, n_human, n_mouse, shared_size, model.species,
out_dir, batch_size, write_output_to_tsv, verbose_sum = F,
verbose_fit = 0L, ...)
4.
autoFilterCV(x, sctransfer, main, pretrain_file = pretrained.weights.file,
nonmissing_indicator = nonmissing_indicator, model.species = model.species,
out_dir = out.dir, batch_size = batch_size, write_output_to_tsv = write_output_to_tsv,
...)
3.
system.time(result <- autoFilterCV(x, sctransfer, main, pretrain_file = pretrained.weights.file,
nonmissing_indicator = nonmissing_indicator, model.species = model.species,
out_dir = out.dir, batch_size = batch_size, write_output_to_tsv = write_output_to_tsv,
...))
2.
computePrediction(task.id, input.file.name, data.matrix, data.species,
use.pretrain, pretrained.weights.file, model.species, model.nodes.ID,
verbose = verbose, is.large.data = is.large.data, batch_size = batch_size,
clearup.python.session = clearup.python.session, ...)
1.
saverx("/home/tjcooper/Documents/mouse_retina/shekhar_downsampled.csv",
data.species = "Mouse", use.pretrain = T, pretrained.weights.file = "/home/tjcooper/Documents/SAVERX_pretrained_weights/mouse_Retina.hdf5",
model.species = "Mouse")
sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] en_IL.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] SAVERX_1.0.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 rstudioapi_0.11 magrittr_1.5 rappdirs_0.3.1 tidyselect_1.1.0 munsell_0.5.0 cowplot_1.0.0 colorspace_1.4-1 lattice_0.20-41 R6_2.4.1 rlang_0.4.7 dplyr_1.0.0
[13] tools_4.0.2 grid_4.0.2 gtable_0.3.0 ellipsis_0.3.1 tibble_3.0.3 lifecycle_0.2.0 crayon_1.3.4 Matrix_1.2-18 purrr_0.3.4 RColorBrewer_1.1-2 ggplot2_3.3.2 vctrs_0.3.2
[25] glue_1.4.1 compiler_4.0.2 pillar_1.4.6 generics_0.0.2 scales_1.1.1 jsonlite_1.7.0 reticulate_1.16 pkgconfig_2.0.3
Weights loaded from /home/tjcooper/Documents/SAVERX_pretrained_weights/mouse_Retina.hdf5!
//usr/local/lib/python3.8/dist-packages/scanpy/api/__init__.py:3: FutureWarning:
In a future version of Scanpy, `scanpy.api` will be removed.
Simply use `import scanpy as sc` and `import scanpy.external as sce` instead.
warnings.warn(
build users' own pretrained model
Hi,
Just wondering whether it's possible for users to build their own pretrained model, if the scRNA-seq comes from a cell type not included in the current pretrained model provided by saver-x (for example, HEK293t and Jurkat cell lines)? The corresponding bulk RNA-seq data are available.
Thanks!
S4 error
I excute the command:
denoised_rds_file <- saverx(seurat_rds_file,is.large.data = T)
seurat_rds_file is a rds object file, but there was a error:
[1] "Use a pretrained model: No"
Error in as.vector(data) :
no method for coercing this S4 class to a vector
ModuleNotFoundError: No module named 'scanpy.api'
Hi,
I got this error when using SAVERX.
RRuntimeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 r('''
2 file <- saverx("./test.csv")
3 denoised.data <- readRDS(file)
4 ''')
File ~/.local/lib/python3.9/site-packages/rpy2/robjects/__init__.py:459, in R.__call__(self, string)
457 def __call__(self, string):
458 p = rinterface.parse(string)
--> 459 res = self.eval(p)
460 return conversion.get_conversion().rpy2py(res)
File ~/.local/lib/python3.9/site-packages/rpy2/robjects/functions.py:202, in SignatureTranslatedFunction.__call__(self, *args, **kwargs)
200 v = kwargs.pop(k)
201 kwargs[r_k] = v
--> 202 return (super(SignatureTranslatedFunction, self)
203 .__call__(*args, **kwargs))
File ~/.local/lib/python3.9/site-packages/rpy2/robjects/functions.py:125, in Function.__call__(self, *args, **kwargs)
123 else:
124 new_kwargs[k] = cv.py2rpy(v)
--> 125 res = super(Function, self).__call__(*new_args, **new_kwargs)
126 res = cv.rpy2py(res)
127 return res
File ~/.local/lib/python3.9/site-packages/rpy2/rinterface_lib/conversion.py:45, in _cdata_res_to_rinterface.<locals>._(*args, **kwargs)
44 def _(*args, **kwargs):
---> 45 cdata = function(*args, **kwargs)
46 # TODO: test cdata is of the expected CType
47 return _cdata_to_rinterface(cdata)
File ~/.local/lib/python3.9/site-packages/rpy2/rinterface.py:813, in SexpClosure.__call__(self, *args, **kwargs)
806 res = rmemory.protect(
807 openrlib.rlib.R_tryEval(
808 call_r,
809 call_context.__sexp__._cdata,
810 error_occured)
811 )
812 if error_occured[0]:
--> 813 raise embedded.RRuntimeError(_rinterface._geterrmessage())
814 return res
RRuntimeError: Error in py_module_import(module, convert = convert) :
ModuleNotFoundError: No module named 'scanpy.api'
Probably "scanpy.api" is deprecated.
Could you fix this?
Error in data processed
I meet the following error when I run SAVERX example:
file <- saverx("./shekhar_downsampled.csv", data.species = "Mouse", use.pretrain = T, pretrained.weights.file = "./mouse_Retina.hdf5", model.species = "Mouse")
[1] "Use a pretrained model: Yes"
[1] "Data species is: Mouse"
[1] "Pretrained weights file is: ./mouse_Retina.hdf5"
[1] "Model species is: Mouse"
[1] "13004 genes mapped out of 13166"
[1] "Gene names mapped, resulting file saved as: 1608194219.12617/tmpdata.rds"
[1] "Reshaped file saved as: 1608194219.12617/tmpdata.mtx"
[1] "Nonmissing indicator saved as: 1608194219.12617/tmpdata_nonmissing.txt"
[1] "Data preprocessed ..."
.../.local/lib/python3.8/site-packages/scanpy/api/init.py:3: FutureWarning:
In a future version of Scanpy, scanpy.api will be removed.
Simply use import scanpy as sc and import scanpy.external as sce instead.
warnings.warn(
Error in py_module_import(module, convert = convert) :
ImportError: cannot import name 'stacked_violin' from 'scanpy.plotting._anndata' (.../.local/lib/python3.8/site-packages/scanpy/plotting/_anndata.py)
Detailed traceback:
File ".../.local/lib/python3.8/site-packages/sctransfer/init.py", line 5, in
from . import api, api_pretrain
File ".../.local/lib/python3.8/site-packages/sctransfer/api.py", line 14, in
from .io import read_dataset, normalize, write_text_matrix
File ".../.local/lib/python3.8/site-packages/sctransfer/io.py", line 6, in
import scanpy.api as sc
File ".../.local/lib/python3.8/site-packages/scanpy/api/init.py", line 27, in
from . import pl
File ".../.local/lib/python3.8/site-packages/scanpy/api/pl.py", line 1, in
from ..plotting._anndata import scatter, violin, ranking, clustermap, stacked
I guess the problem was caused by scanpy: SAVERX use old version of scanpy which included stacked_violin function, but latest version of scanpy(v1.6.0) does not include that function.
How can we avoid library size normalization
Hello,
I need to use unnormalized count data for the analysis following saverx.
I am wondering if there is a way to skip the normalization of the resulting denoised dataset.
For example, when using the saver function we could set size.factor=1 to avoid normalization.
Is this also possible for saverx ?
Thank you very much!
Problem running SAVERX:: Module not found
Hi everyone!
I'm trying to run SAVERX, on a docker. I've installed all the necessary libraries as far as I know, but I still hitting this wall:
filepretrained <- saverx( "./hemato_data_raw.txt", data.species = "Human",
is.large.data = T, use.pretrain = T, pretrained.weights.file = "SAVERX_pretrained_weights/human_Immune.hdf5",
model.species = "Human")
[1] "Input file is: ./hemato_data_raw.txt"
[1] "Use a pretrained model: Yes"
[1] "Data species is: Human"
[1] "Pretrained weights file is: SAVERX_pretrained_weights/human_Immune.hdf5"
[1] "Model species is: Human"
[1] "15548 genes mapped out of 15766"
[1] "Gene names mapped, resulting file saved as: 1635183112.38965/tmpdata.rds"
[1] "Reshaped file saved as: 1635183112.38965/tmpdata.mtx"
[1] "Nonmissing indicator saved as: 1635183112.38965/tmpdata_nonmissing.txt"
[1] "Data preprocessed ..."
Error in py_module_import(module, convert = convert) :
ModuleNotFoundError: No module named 'keras.objectives'
Detailed traceback:
File "/usr/local/lib/R/site-library/reticulate/python/rpytools/loader.py", line 39, in _import_hook
module = _import(
File "/usr/local/lib/python3.8/dist-packages/sctransfer/__init__.py", line 5, in <module>
from . import api, api_pretrain
File "/usr/local/lib/R/site-library/reticulate/python/rpytools/loader.py", line 39, in _import_hook
module = _import(
File "/usr/local/lib/python3.8/dist-packages/sctransfer/api.py", line 16, in <module>
from .network import NBConstantDispAutoencoder
File "/usr/local/lib/R/site-library/reticulate/python/rpytools/loader.py", line 39, in _import_hook
module = _import(
File "/usr/local/lib/python3.8/dist-packages/sctransfer/network.py", line 10, in <module>
from keras.objectives import mean_squared_error
File "/usr/local/lib/R/site-library/reticulate/python/rpytools/loader.py", line 39, in _import_hook
module = _import(
The problem seem to at the moment of passing the 'keras.objectives' from python to R... But I cannot find this keras.objectives on the keras documentation, so I do not know if it's a deprecated function or anything...
tf.version == '2.6.0'
What should I do?
Thanks!
py_get_attr_impl(x, name, silent) : AttributeError: 'AxisArrays' object has no attribute 'X_dca'
Hi,
thanks for developing SAVERX - great idea. I tried to run it on MacOS Mohave 10.14.3 with R 3.5.2 using your test dataset and the suggested command from the manual but it fails with an error. Here is the output:
saverx("./testdata/shekhar_downsampled.csv")
[1] "Input file is: ./testdata/shekhar_downsampled.csv"
[1] "Use a pretrained model: No"
Using TensorFlow backend.[1] "Python module sctransfer imported ..."
[1] "Processed file saved as: ./testdata/shekhar_downsampled_temp.rds"
[1] "Data preprocessed ..."
[1] "Cross-validation round: 1"Error in py_get_attr_impl(x, name, silent) :
AttributeError: 'AxisArrays' object has no attribute 'X_dca'
Timing stopped at: 208.4 15.33 28.45
help appreciated.
Best
Max
EDIT: the test sample runs through fine when setting is.large.data = T. I don't think it's a memory issue though, since I have plenty of RAM to spare while running.
Specify number of cores for cross-validation
Hi,
First of all, thanks a lot for your package and the documentation. I'm trying to run the main function saverx on a shared Linux server (18.04.2 LTS) but I'm having troubles limiting the number of cores that are used during the [1] "Cross-validation round: 1" step. I have tried to use the ncore argument and to set a tensorflow session but it doesn't seem to work. The function still uses all cores of the server.
library(tensorflow)
library(keras)
library(SAVERX)
config <- tf$ConfigProto(intra_op_parallelism_threads = 6L,
inter_op_parallelism_threads = 6L)
session = tf$Session(config = config)
k_set_session(session)
res <- saverx(input.file.name = "data.rds", data.species = "Human",
ncores = 6, clearup.python.session = TRUE)Do you know how I could limit the CPU usage ?
Best,
Anthony
pre-trained models across technologies
Does it make sense to use pre-trained models for other technologies? For example, human immune model is based on 10x data. Can it be used for non-10x datasets? Do you know how much of an effect the dropout rate and coverage (3' or 5' versus full transcript) have?
On a related note, your publication states SAVER-X can only denoise UMI counts. Do you have any recommendations for non-UMI data? Can it perhaps be adjusted in some way?
AttributeError: module 'tensorflow' has no attribute 'get_default_graph'
Error in py_module_import(module, convert = convert) :
AttributeError: module 'tensorflow' has no attribute 'get_default_graph'
sctransfter was successfully installed, together with tensorflow 2.0.0
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