hhcho/densvis

Density-preserving data visualization tools den-SNE and densMAP

densvis's Issues

How can I get the cluster information of cells after densvis processing?

I'm wondering whether the cluster information of cells be changed after densvis processing,in other words,how can I get that ? The mapping of colors is required when drawing.

Integration with UMAP repository

Thanks for all your work -- this looks like a great addition/advancement over the standard UMAP approach. I was wondering if you would consider integrating densmap into the Python UMAP repository? I am currently adding new features in the 0.5dev branch, and I feel like DensMAP support would be a very valuable option to have for many users. If you do not have the time I certainly understand, and I can endeavour to add the feature myself, but I would certainly appreciate PRs from the original authors if possible.

If you do wish to make PRs please make sure to update READMEs and documentation to include citations/references to your paper to ensure proper credit.

Can densMAP (in development version of Seurat) yield reproducible clustering?

I"m running densMAP in the development version of Seurat, and it seems to work (hallelujah!) but the clustering is not reproducible. If I run the same pipeline twice over, the clustering is similar yet different. Is there a way to make it reproducible?

setting the seed doesn't help. Is this intrinsic to the densMAP algorithm? Or is there a way to make it reproducible?

thanks,

Nicolaas

ps command I run is:
RunUMAP(MySeuratObject, dims = 1:UMAP.dimensions, densmap=TRUE)

prediction on out of sample

Hi,

I'm trying to fit the model on a dataset with densmap.fit() and then to evaluate the embedding on a new dataset with different number of rows with densmap.transform() but I get an error of "index out of bound" for the row index. Is it a bug or the algorithm, by definition, is not allowed to do out-of-sample prediction?
Thanks

Error in py_call_impl(callable, dots$args, dots$keywords) : ValueError: Buffer has wrong number of dimensions (expected 1, got 2)

Unless my input (data) has equal row and column numbers, I receive this error. Am I doing something wrong?

dim(data)
[1] 8910 41

traceback()
4: stop(structure(list(message = "ValueError: Buffer has wrong number of dimensions (expected 1, got 2)",
call = py_call_impl(callable, dots$args, dots$keywords),
cppstack = structure(list(file = "", line = -1L, stack = c("/d0/home/rasmusr/R/x86_64-pc-linux-gnu-library/4.0/reticulate/libs/reticulate.so(Rcpp::exception::exception(char const*, bool)+0x78) [0x7f66e414eea8]",
"/d0/home/rasmusr/R/x86_64-pc-linux-gnu-library/4.0/reticulate/libs/reticulate.so(Rcpp::stop(std::__cxx11::basic_string<char, std::char_traits, std::allocator > const&)+0x27) [0x7f66e414ef17]",
"/d0/home/rasmusr/R/x86_64-pc-linux-gnu-library/4.0/reticulate/libs/reticulate.so(py_call_impl(PyObjectRef, Rcpp::Vector<19, Rcpp::PreserveStorage>, Rcpp::Vector<19, Rcpp::PreserveStorage>)+0x52d) [0x7f66e416109d]",
"/d0/home/rasmusr/R/x86_64-pc-linux-gnu-library/4.0/reticulate/libs/reticulate.so(_reticulate_py_call_impl+0x95) [0x7f66e4149f15]",
"/usr/lib/R/lib/libR.so(+0xf9290) [0x7f6715177290]", "/usr/lib/R/lib/libR.so(+0x13940c) [0x7f67151b740c]",
"/usr/lib/R/lib/libR.so(Rf_eval+0x180) [0x7f67151c1670]",
"/usr/lib/R/lib/libR.so(+0x14548f) [0x7f67151c348f]", "/usr/lib/R/lib/libR.so(Rf_applyClosure+0x1c7) [0x7f67151c4257]",
"/usr/lib/R/lib/libR.so(+0x13a909) [0x7f67151b8909]", "/usr/lib/R/lib/libR.so(Rf_eval+0x180) [0x7f67151c1670]",
"/usr/lib/R/lib/libR.so(+0x14548f) [0x7f67151c348f]", "/usr/lib/R/lib/libR.so(Rf_applyClosure+0x1c7) [0x7f67151c4257]",
"/usr/lib/R/lib/libR.so(+0x13a909) [0x7f67151b8909]", "/usr/lib/R/lib/libR.so(Rf_eval+0x180) [0x7f67151c1670]",
"/usr/lib/R/lib/libR.so(+0x14548f) [0x7f67151c348f]", "/usr/lib/R/lib/libR.so(Rf_applyClosure+0x1c7) [0x7f67151c4257]",
"/usr/lib/R/lib/libR.so(Rf_eval+0x353) [0x7f67151c1843]",
"/usr/lib/R/lib/libR.so(+0x148613) [0x7f67151c6613]", "/usr/lib/R/lib/libR.so(Rf_eval+0x572) [0x7f67151c1a62]",
"/usr/lib/R/lib/libR.so(Rf_ReplIteration+0x23a) [0x7f67151f539a]",
"/usr/lib/R/lib/libR.so(+0x177761) [0x7f67151f5761]", "/usr/lib/R/lib/libR.so(run_Rmainloop+0x48) [0x7f67151f5818]",
"/usr/lib/rstudio-server/bin/rsession(+0x887166) [0x557db1872166]",
"/usr/lib/rstudio-server/bin/rsession(+0x8632ad) [0x557db184e2ad]",
"/usr/lib/rstudio-server/bin/rsession(+0x7c666) [0x557db1067666]",
"/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xe7) [0x7f6713456b97]",
"/usr/lib/rstudio-server/bin/rsession(+0x9fe4a) [0x557db108ae4a]"
)), class = "Rcpp_stack_trace")), class = c("Rcpp::exception",
"C++Error", "error", "condition")))
3: py_call_impl(callable, dots$args, dots$keywords)
2: densMAP$fit_transform(data) at densmap.R#54
1: densMAP(data, verbose = T)

sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.4 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=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
[6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C 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] magrittr_1.5 optparse_1.6.6 reticulate_1.16-9000

loaded via a namespace (and not attached):
[1] fastmatch_1.1-0 plyr_1.8.6 igraph_1.2.5 splines_4.0.2 BiocParallel_1.22.0
[6] GenomeInfoDb_1.24.2 ggplot2_3.3.2 scater_1.16.2 urltools_1.7.3 digest_0.6.25
[11] htmltools_0.5.0 GOSemSim_2.14.0 viridis_0.5.1 GO.db_3.11.4 memoise_1.1.0
[16] limma_3.44.3 annotate_1.66.0 graphlayouts_0.7.0 matrixStats_0.56.0 sccore_0.1
[21] enrichplot_1.8.1 prettyunits_1.1.1 colorspace_1.4-1 blob_1.2.1 rappdirs_0.3.1
[26] ggrepel_0.8.2 pagoda2_0.1.1 xfun_0.15 dplyr_1.0.0 crayon_1.3.4
[31] RCurl_1.98-1.2 jsonlite_1.7.0 scatterpie_0.1.4 genefilter_1.70.0 brew_1.0-6
[36] survival_3.2-3 glue_1.4.1 polyclip_1.10-0 gtable_0.3.0 zlibbioc_1.34.0
[41] XVector_0.28.0 DelayedArray_0.14.0 BiocSingular_1.4.0 Rook_1.1-1 SingleCellExperiment_1.10.1
[46] BiocGenerics_0.34.0 scales_1.1.1 DOSE_3.14.0 DBI_1.1.0 edgeR_3.30.3
[51] Rcpp_1.0.5 viridisLite_0.3.0 xtable_1.8-4 progress_1.2.2 gridGraphics_0.5-0
[56] dqrng_0.2.1 bit_1.1-15.2 europepmc_0.4 rsvd_1.0.3 stats4_4.0.2
[61] httr_1.4.1 getopt_1.20.3 fgsea_1.14.0 RColorBrewer_1.1-2 ellipsis_0.3.1
[66] pkgconfig_2.0.3 XML_3.99-0.4 farver_2.0.3 locfit_1.5-9.4 ggplotify_0.0.5
[71] tidyselect_1.1.0 rlang_0.4.7 reshape2_1.4.4 later_1.1.0.1 AnnotationDbi_1.50.1
[76] munsell_0.5.0 tools_4.0.2 downloader_0.4 generics_0.0.2 RSQLite_2.2.0
[81] ggridges_0.5.2 stringr_1.4.0 fastmap_1.0.1 yaml_2.2.1 knitr_1.29
[86] bit64_0.9-7 tidygraph_1.2.0 purrr_0.3.4 dendextend_1.13.4 ggraph_2.0.3
[91] mime_0.9 scran_1.16.0 DO.db_2.9 xml2_1.3.2 compiler_4.0.2
[96] rstudioapi_0.11 beeswarm_0.2.3 tibble_3.0.3 statmod_1.4.34 tweenr_1.0.1
[101] geneplotter_1.66.0 stringi_1.4.6 lattice_0.20-41 Matrix_1.2-18 vctrs_0.3.1
[106] pillar_1.4.6 lifecycle_0.2.0 BiocManager_1.30.10 triebeard_0.3.0 BiocNeighbors_1.6.0
[111] data.table_1.12.8 cowplot_1.0.0 bitops_1.0-6 irlba_2.3.3 httpuv_1.5.4
[116] conos_1.3.0 GenomicRanges_1.40.0 qvalue_2.20.0 R6_2.4.1 promises_1.1.1
[121] gridExtra_2.3 vipor_0.4.5 IRanges_2.22.2 MASS_7.3-51.6 SummarizedExperiment_1.18.1
[126] rjson_0.2.20 DESeq2_1.28.1 cacoa_0.1 S4Vectors_0.26.1 GenomeInfoDbData_1.2.3
[131] parallel_4.0.2 hms_0.5.3 clusterProfiler_3.16.0 grid_4.0.2 tidyr_1.1.0
[136] DelayedMatrixStats_1.10.1 rvcheck_0.1.8 ggforce_0.3.2 base64enc_0.1-3 Biobase_2.48.0
[141] shiny_1.5.0 tinytex_0.24 ggbeeswarm_0.6.0

Support for Numba 0..55.2

Dear densmap tool,

Any plan for further supporting the newest version of Numba? There are known issues for Numba when running on ARM64/aarch64 CPU structure. Both umap and trimap are now supporting the newest Numba.

Thanks,

Jianshu

Can I know the simulation settings?

Hi,

How did you generate the synthetic data? I only find a "trial.txt" file here but not the process of generating it. Could you let me know the process of it?

Install issue on apple silicon

I have an issue installing densvis (and the parent package scater) on M1 apple silicon.

It appears another user has an identical issue, so Id don't see the need to produce a working example.

https://stackoverflow.com/questions/35999874/mac-os-x-r-error-ld-warning-directory-not-found-for-option

Probably related to fortran, macOS, homerbrew etc.

How to find the quantitative evaluation of density preservation?

I read the paper and wondering how to compute the correlation between the log local radii in the original dataset. Could you let me know which file descripts it?

ZeroDivisionError: division by zero

I met a ZeroDivisionError. It is very confusing.

Traceback (most recent call last):

File "/Users/uqyyao4/Documents/Project/03_GNN_for_visualization/code/densvis-master/densmap/trial_densmap.py", line 17, in
emb, ro, re = densmap.densMAP(verbose=True, n_components=2,

File "/Users/uqyyao4/Documents/Project/03_GNN_for_visualization/code/densvis-master/densmap/densmap/densmap_.py", line 2086, in fit_transform
self.fit(X, y)

File "/Users/uqyyao4/Documents/Project/03_GNN_for_visualization/code/densvis-master/densmap/densmap/densmap_.py", line 1967, in fit
self.embedding_ = simplicial_set_embedding(

File "/Users/uqyyao4/Documents/Project/03_GNN_for_visualization/code/densvis-master/densmap/densmap/densmap_.py", line 1295, in simplicial_set_embedding
embedding = optimize_layout(

ZeroDivisionError: division by zero

Distribution as R package

Would you have any objections if I package a (possibly slightly modified) version of this repository in an R package to submit to CRAN? Of course with appropriate credit and citation.

where is requirements.txt

Dear,
I tried to install the package with pip command.
But I couldn't find requirements.txt. Where is the file to install the package?
Thanks.