{"action":"opened","issue":{"url":"https://api.github.com/repos/satijalab/seurat/issues/904","repository_url":"https://api.github.com/repos/satijalab/seurat","labels_url":"https://api.github.com/repos/satijalab/seurat/issues/904/labels{/name}","comments_url":"https://api.github.com/repos/satijalab/seurat/issues/904/comments","events_url":"https://api.github.com/repos/satijalab/seurat/issues/904/events","html_url":"https://github.com/satijalab/seurat/issues/904","id":375681948,"node_id":"MDU6SXNzdWUzNzU2ODE5NDg=","number":904,"title":"RunTSNE with FIt-SNE on mouse cell atlas tutorial","user":{"login":"michelled3","id":5728195,"node_id":"MDQ6VXNlcjU3MjgxOTU=","avatar_url":"https://avatars3.githubusercontent.com/u/5728195?v=4","gravatar_id":"","url":"https://api.github.com/users/michelled3","html_url":"https://github.com/michelled3","followers_url":"https://api.github.com/users/michelled3/followers","following_url":"https://api.github.com/users/michelled3/following{/other_user}","gists_url":"https://api.github.com/users/michelled3/gists{/gist_id}","starred_url":"https://api.github.com/users/michelled3/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/michelled3/subscriptions","organizations_url":"https://api.github.com/users/michelled3/orgs","repos_url":"https://api.github.com/users/michelled3/repos","events_url":"https://api.github.com/users/michelled3/events{/privacy}","received_events_url":"https://api.github.com/users/michelled3/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-10-30T20:35:57Z","updated_at":"2018-10-30T20:35:57Z","closed_at":null,"author_association":"NONE","body":"I am trying to run the tutorial for 250K Mouse cell atlas example, and run into trouble on the Visualization step. I have installed FIt-SNE and FFTW. They seem to be working properly, but when I changed the fast_tsne_path in the tutorial to my source directory, I get the following error (attached). \r\n\r\nI can't seem to figure this one out, but it looks like it's calling Rtsne when I am looking for FIt-SNE?\r\nI have also attached my session info below. Worst case scenario I will go back to the default, but this looks like a promising and faster approach. Any help will be appreciated. Thank you!\r\n\r\n####################################\r\n#Script: \r\nmca <- RunTSNE(object = mca, reduction.use = \"pca\", dims.use = 1:75, tsne.method = \"FIt-SNE\", \r\n    nthreads = 4, reduction.name = \"FItSNE\", reduction.key = \"FItSNE_\", fast_tsne_path = \"/cellar/users/mdow/Program/FIt-SNE/bin/fast_tsne\", \r\n    max_iter = 2000)\r\n#Error:\r\nError in fftRtsne(X = as.matrix(x = data.use), dims = dim.embed, rand_seed = seed.use, : tsne call failed\r\nTraceback:\r\n\r\n1. RunTSNE(object = mca, reduction.use = \"pca\", dims.use = 1:75, \r\n .     tsne.method = \"FIt-SNE\", nthreads = 4, reduction.name = \"FItSNE\", \r\n .     reduction.key = \"FItSNE_\", fast_tsne_path = \"/cellar/users/mdow/Program/FIt-SNE/bin/fast_tsne\", \r\n .     max_iter = 2000)\r\n2. fftRtsne(X = as.matrix(x = data.use), dims = dim.embed, rand_seed = seed.use, \r\n .     ...)\r\n3. stop(\"tsne call failed\")\r\n###############\r\n###############\r\n###\r\nR version 3.5.0 (2018-04-23)\r\nPlatform: x86_64-conda_cos6-linux-gnu (64-bit)\r\nRunning under: Ubuntu 16.04.3 LTS\r\n\r\nMatrix products: default\r\nBLAS/LAPACK: /cellar/users/mdow/anaconda2/lib/R/lib/libRblas.so\r\n\r\nlocale:\r\n[1] en_US.UTF-8\r\n\r\nattached base packages:\r\n[1] stats     graphics  grDevices utils     datasets  methods   base     \r\n\r\nother attached packages:\r\n[1] bindrcpp_0.2.2  dplyr_0.7.7     devtools_1.13.6 Seurat_2.3.4   \r\n[5] Matrix_1.2-14   cowplot_0.9.3   ggplot2_3.0.0  \r\n\r\nloaded via a namespace (and not attached):\r\n  [1] Rtsne_0.13          colorspace_1.3-2    class_7.3-14       \r\n  [4] modeltools_0.2-22   ggridges_0.5.1      mclust_5.4.1       \r\n  [7] IRdisplay_0.6       htmlTable_1.12      base64enc_0.1-3    \r\n [10] rstudioapi_0.8      proxy_0.4-22        npsurv_0.4-0       \r\n [13] flexmix_2.3-14      bit64_0.9-7         mvtnorm_1.0-8      \r\n [16] codetools_0.2-15    splines_3.5.0       R.methodsS3_1.7.1  \r\n [19] lsei_1.2-0          robustbase_0.93-3   knitr_1.20         \r\n [22] IRkernel_0.8.11     Formula_1.2-3       jsonlite_1.5       \r\n [25] ica_1.0-2           cluster_2.0.7-1     kernlab_0.9-27     \r\n [28] png_0.1-7           R.oo_1.22.0         compiler_3.5.0     \r\n [31] httr_1.3.1          backports_1.1.2     assertthat_0.2.0   \r\n [34] lazyeval_0.2.1      lars_1.2            acepack_1.4.1      \r\n [37] htmltools_0.3.6     tools_3.5.0         igraph_1.2.2       \r\n [40] gtable_0.2.0        glue_1.3.0          reshape2_1.4.3     \r\n [43] RANN_2.6            Rcpp_0.12.19        trimcluster_0.1-2.1\r\n [46] gdata_2.18.0        ape_5.2             nlme_3.1-137       \r\n [49] iterators_1.0.10    fpc_2.1-11.1        gbRd_0.4-11        \r\n [52] lmtest_0.9-36       stringr_1.3.1       irlba_2.3.2        \r\n [55] gtools_3.8.1        DEoptimR_1.0-8      MASS_7.3-51        \r\n [58] zoo_1.8-4           scales_1.0.0        doSNOW_1.0.16      \r\n [61] parallel_3.5.0      RColorBrewer_1.1-2  memoise_1.1.0      \r\n [64] reticulate_1.10     pbapply_1.3-4       gridExtra_2.3      \r\n [67] segmented_0.5-3.0   rpart_4.1-13        latticeExtra_0.6-28\r\n [70] stringi_1.2.4       foreach_1.4.4       checkmate_1.8.5    \r\n [73] caTools_1.17.1.1    bibtex_0.4.2        repr_0.15.0        \r\n [76] Rdpack_0.10-1       SDMTools_1.1-221    rlang_0.2.2        \r\n [79] pkgconfig_2.0.2     dtw_1.20-1          prabclus_2.2-6     \r\n [82] bitops_1.0-6        evaluate_0.12       lattice_0.20-35    \r\n [85] ROCR_1.0-7          purrr_0.2.5         bindr_0.1.1        \r\n [88] labeling_0.3        htmlwidgets_1.3     bit_1.1-14         \r\n [91] tidyselect_0.2.5    plyr_1.8.4          magrittr_1.5       \r\n [94] R6_2.3.0            snow_0.4-3          gplots_3.0.1       \r\n [97] Hmisc_4.1-1         pbdZMQ_0.3-3        pillar_1.3.0       \r\n[100] foreign_0.8-71      withr_2.1.2         mixtools_1.1.0     \r\n[103] fitdistrplus_1.0-11 survival_2.42-6     nnet_7.3-12        \r\n[106] tsne_0.1-3          tibble_1.4.2        crayon_1.3.4       \r\n[109] hdf5r_1.0.1         uuid_0.1-2          KernSmooth_2.23-15 \r\n[112] grid_3.5.0          data.table_1.11.8   metap_1.0          \r\n[115] digest_0.6.18       diptest_0.75-7      tidyr_0.8.1        \r\n[118] R.utils_2.7.0       stats4_3.5.0        munsell_0.5.0  \r\n"}}
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