fitTPT stuck

[mghanizada]

Dear Joseph

Thanks for creating such an excellent package for CSR analysis - fits perfectly for our use!

I'm stuck with the fitTPT analysis and i hoped you had any suggestions.

First i tried running the whole dataset containing 70K cells, but with no progession in the code. I'm have currenlty downsampled the dataset to ~3.5K cells but experince the same issue - no progession in the code after hours.

Any help is appreciated!

LAIV <-models$LAIV
LAIV_M_A2 <- fitTPT(
anndata_file = "all_cells_assay-RNA_LAIV.h5ad",
CellrankObj = LAIV, group.cells.by = "isotype",
source_state = 'M', target_state = 'A2'
)
fit TPT with source state: 'M' and target state: 'A2'.
determine transitions expected under a 'null' background.
0%| | 0/100 [00:00<?, ?it/s]

sessionInfo()
R version 4.5.0 (2025-04-11)
Platform: x86_64-pc-linux-gnu
Running under: Rocky Linux 8.10 (Green Obsidian)

Matrix products: default
BLAS/LAPACK: /services/tools/intel/basekit/2023.0.0/mkl/2023.0.0/lib/intel64/libmkl_gf_lp64.so.2; LAPACK version 3.10.1

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 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

time zone: Europe/Copenhagen
tzcode source: system (glibc)

attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base

other attached packages:
[1] Seurat_5.3.0 SeuratObject_5.2.0 sp_2.2-0 cowplot_1.2.0 ggplot2_4.0.0 sciCSR_0.3.3 reticulate_1.43.0 S4Vectors_0.46.0 BiocGenerics_0.54.0
[10] generics_0.1.4 Matrix_1.7-4

loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.17.1 jsonlite_2.0.0 magrittr_2.0.4 spatstat.utils_3.2-0 farver_2.1.2
[7] vctrs_0.6.5 ROCR_1.0-11 Rsamtools_2.24.1 spatstat.explore_3.5-3 S4Arrays_1.8.1 htmltools_0.5.8.1
[13] SparseArray_1.8.1 sctransform_0.4.2 parallelly_1.45.1 KernSmooth_2.23-26 htmlwidgets_1.6.4 ica_1.0-3
[19] plyr_1.8.9 plotly_4.11.0 zoo_1.8-14 GenomicAlignments_1.44.0 igraph_2.2.0 mime_0.13
[25] lifecycle_1.0.4 pkgconfig_2.0.3 R6_2.6.1 fastmap_1.2.0 GenomeInfoDbData_1.2.14 MatrixGenerics_1.20.0
[31] fitdistrplus_1.2-4 future_1.67.0 shiny_1.11.1 digest_0.6.37 patchwork_1.3.2 tensor_1.5.1
[37] RSpectra_0.16-2 irlba_2.3.5.1 GenomicRanges_1.60.0 philentropy_0.9.0 progressr_0.16.0 nnls_1.6
[43] spatstat.sparse_3.1-0 httr_1.4.7 polyclip_1.10-7 abind_1.4-8 compiler_4.5.0 withr_3.0.2
[49] bit64_4.6.0-1 markovchain_0.10.0 S7_0.2.0 BiocParallel_1.42.1 fastDummies_1.7.5 MASS_7.3-65
[55] rappdirs_0.3.3 DelayedArray_0.34.1 tools_4.5.0 lmtest_0.9-40 httpuv_1.6.16 future.apply_1.20.0
[61] goftest_1.2-3 glue_1.8.0 nlme_3.1-168 promises_1.3.3 grid_4.5.0 Rtsne_0.17
[67] cluster_2.1.8.1 reshape2_1.4.4 hdf5r_1.3.12 gtable_0.3.6 spatstat.data_3.1-8 tidyr_1.3.1
[73] data.table_1.17.8 XVector_0.48.0 spatstat.geom_3.6-0 RcppAnnoy_0.0.22 ggrepel_0.9.6 RANN_2.6.2
[79] pillar_1.11.1 stringr_1.5.2 spam_2.11-1 RcppHNSW_0.6.0 later_1.4.4 splines_4.5.0
[85] dplyr_1.1.4 lattice_0.22-7 survival_3.8-3 bit_4.6.0 deldir_2.0-4 tidyselect_1.2.1
[91] Biostrings_2.76.0 miniUI_0.1.2 pbapply_1.7-4 gridExtra_2.3 IRanges_2.42.0 SummarizedExperiment_1.38.1
[97] scattermore_1.2 expm_1.0-0 Biobase_2.68.0 matrixStats_1.5.0 stringi_1.8.7 UCSC.utils_1.4.0
[103] lazyeval_0.2.2 codetools_0.2-20 tibble_3.3.0 cli_3.6.5 uwot_0.2.3 RcppParallel_5.1.11-1
[109] xtable_1.8-4 harmony_1.2.4 dichromat_2.0-0.1 Rcpp_1.1.0 GenomeInfoDb_1.44.1 globals_0.18.0
[115] spatstat.random_3.4-2 png_0.1-8 spatstat.univar_3.1-4 parallel_4.5.0 dotCall64_1.2 sceasy_0.0.7
[121] bitops_1.0-9 listenv_0.9.1 viridisLite_0.4.2 scales_1.4.0 ggridges_0.5.7 crayon_1.5.3
[127] purrr_1.1.0 rlang_1.1.6

[josef0731]

Hello, thank you for posting an issue & your interest in using sciCSR! I suspect there are issues/errors at the python side of the code that aren't fed back to the R environment hence the lagging. Could you also check the package versions in the python environment? You can either do it on the command line or if you prefer to stay in R, loading the 'reticulate' package and running `py_list_packages( envname = 'xxx' )` should work (replacing 'xxx' with the name of the conda environment you set up for sciCSR to run). If you could reply with the python packages here I can begin to check what's wrong. I am aware there are updates to CellRank (now v2) since sciCSR was released but I was yet to update the package to make sure things don't break (sorry it's a one-man job at the moment!), but I'm more than happy to check for you. Best wishes, Joseph Ng

…

On Tue, 21 Oct 2025 at 08:15, mghanizada ***@***.***> wrote: *mghanizada* created an issue (Fraternalilab/sciCSR#13) <#13> Dear Joseph Thanks for creating such an excellent package for CSR analysis - fits perfectly for our use! I'm stuck with the fitTPT analysis and i hoped you had any suggestions. First i tried running the whole dataset containing 70K cells, but with no progession in the code. I'm have currenlty downsampled the dataset to ~3.5K cells but experince the same issue - no progession in the code after hours. Any help is appreciated! LAIV <-models$LAIV LAIV_M_A2 <- fitTPT( anndata_file = "all_cells_assay-RNA_LAIV.h5ad", CellrankObj = LAIV, group.cells.by = "isotype", source_state = 'M', target_state = 'A2' ) fit TPT with source state: 'M' and target state: 'A2'. determine transitions expected under a 'null' background. 0%| | 0/100 [00:00<?, ?it/s] sessionInfo() R version 4.5.0 (2025-04-11) Platform: x86_64-pc-linux-gnu Running under: Rocky Linux 8.10 (Green Obsidian) Matrix products: default BLAS/LAPACK: /services/tools/intel/basekit/2023.0.0/mkl/2023.0.0/lib/intel64/libmkl_gf_lp64.so.2; LAPACK version 3.10.1 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 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C time zone: Europe/Copenhagen tzcode source: system (glibc) attached base packages: [1] stats4 stats graphics grDevices utils datasets methods base other attached packages: [1] Seurat_5.3.0 SeuratObject_5.2.0 sp_2.2-0 cowplot_1.2.0 ggplot2_4.0.0 sciCSR_0.3.3 reticulate_1.43.0 S4Vectors_0.46.0 BiocGenerics_0.54.0 [10] generics_0.1.4 Matrix_1.7-4 loaded via a namespace (and not attached): [1] RColorBrewer_1.1-3 rstudioapi_0.17.1 jsonlite_2.0.0 magrittr_2.0.4 spatstat.utils_3.2-0 farver_2.1.2 [7] vctrs_0.6.5 ROCR_1.0-11 Rsamtools_2.24.1 spatstat.explore_3.5-3 S4Arrays_1.8.1 htmltools_0.5.8.1 [13] SparseArray_1.8.1 sctransform_0.4.2 parallelly_1.45.1 KernSmooth_2.23-26 htmlwidgets_1.6.4 ica_1.0-3 [19] plyr_1.8.9 plotly_4.11.0 zoo_1.8-14 GenomicAlignments_1.44.0 igraph_2.2.0 mime_0.13 [25] lifecycle_1.0.4 pkgconfig_2.0.3 R6_2.6.1 fastmap_1.2.0 GenomeInfoDbData_1.2.14 MatrixGenerics_1.20.0 [31] fitdistrplus_1.2-4 future_1.67.0 shiny_1.11.1 digest_0.6.37 patchwork_1.3.2 tensor_1.5.1 [37] RSpectra_0.16-2 irlba_2.3.5.1 GenomicRanges_1.60.0 philentropy_0.9.0 progressr_0.16.0 nnls_1.6 [43] spatstat.sparse_3.1-0 httr_1.4.7 polyclip_1.10-7 abind_1.4-8 compiler_4.5.0 withr_3.0.2 [49] bit64_4.6.0-1 markovchain_0.10.0 S7_0.2.0 BiocParallel_1.42.1 fastDummies_1.7.5 MASS_7.3-65 [55] rappdirs_0.3.3 DelayedArray_0.34.1 tools_4.5.0 lmtest_0.9-40 httpuv_1.6.16 future.apply_1.20.0 [61] goftest_1.2-3 glue_1.8.0 nlme_3.1-168 promises_1.3.3 grid_4.5.0 Rtsne_0.17 [67] cluster_2.1.8.1 reshape2_1.4.4 hdf5r_1.3.12 gtable_0.3.6 spatstat.data_3.1-8 tidyr_1.3.1 [73] data.table_1.17.8 XVector_0.48.0 spatstat.geom_3.6-0 RcppAnnoy_0.0.22 ggrepel_0.9.6 RANN_2.6.2 [79] pillar_1.11.1 stringr_1.5.2 spam_2.11-1 RcppHNSW_0.6.0 later_1.4.4 splines_4.5.0 [85] dplyr_1.1.4 lattice_0.22-7 survival_3.8-3 bit_4.6.0 deldir_2.0-4 tidyselect_1.2.1 [91] Biostrings_2.76.0 miniUI_0.1.2 pbapply_1.7-4 gridExtra_2.3 IRanges_2.42.0 SummarizedExperiment_1.38.1 [97] scattermore_1.2 expm_1.0-0 Biobase_2.68.0 matrixStats_1.5.0 stringi_1.8.7 UCSC.utils_1.4.0 [103] lazyeval_0.2.2 codetools_0.2-20 tibble_3.3.0 cli_3.6.5 uwot_0.2.3 RcppParallel_5.1.11-1 [109] xtable_1.8-4 harmony_1.2.4 dichromat_2.0-0.1 Rcpp_1.1.0 GenomeInfoDb_1.44.1 globals_0.18.0 [115] spatstat.random_3.4-2 png_0.1-8 spatstat.univar_3.1-4 parallel_4.5.0 dotCall64_1.2 sceasy_0.0.7 [121] bitops_1.0-9 listenv_0.9.1 viridisLite_0.4.2 scales_1.4.0 ggridges_0.5.7 crayon_1.5.3 [127] purrr_1.1.0 rlang_1.1.6 — Reply to this email directly, view it on GitHub <#13>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AFPBID3I67VQRY27VMGV32D3YXMQDAVCNFSM6AAAAACJYCVCVKVHI2DSMVQWIX3LMV43ASLTON2WKOZTGUZTKMRTGAYDOOA> . You are receiving this because you are subscribed to this thread.Message ID: ***@***.***>

[mghanizada]

Thanks for the quick reply - no worries, any help is appreciated.

I have already downgraded cellrank to 1.9.

I've included the requested list of Python packages below.

Thanks,
Mustafa

pkgs <- py_list_packages(envname = "scicsr")
head(pkgs,n=320)
package version requirement channel
1 _libgcc_mutex 0.1 _libgcc_mutex=0.1 conda-forge
2 _openmp_mutex 4.5 _openmp_mutex=4.5 conda-forge
3 absl-py 2.3.1 absl-py=2.3.1 conda-forge
4 alsa-lib 1.2.14 alsa-lib=1.2.14 conda-forge
5 anndata 0.9.2 anndata=0.9.2 conda-forge
6 aom 3.9.1 aom=3.9.1 conda-forge
7 arpack 3.9.1 arpack=3.9.1 conda-forge
8 attr 2.5.2 attr=2.5.2 conda-forge
9 blosc 1.21.6 blosc=1.21.6 conda-forge
10 brotli 1.1.0 brotli=1.1.0 conda-forge
11 brotli-bin 1.1.0 brotli-bin=1.1.0 conda-forge
12 brotli-python 1.0.9 brotli-python=1.0.9 conda-forge
13 bzip2 1.0.8 bzip2=1.0.8 conda-forge
14 c-ares 1.34.5 c-ares=1.34.5 conda-forge
15 c-blosc2 2.15.2 c-blosc2=2.15.2 conda-forge
16 ca-certificates 2025.10.5 ca-certificates=2025.10.5 conda-forge
17 cached-property 1.5.2 cached-property=1.5.2 conda-forge
18 cached_property 1.5.2 cached_property=1.5.2 conda-forge
19 cairo 1.18.4 cairo=1.18.4 conda-forge
20 cellrank 1.5.1 cellrank=1.5.1 pypi
21 certifi 2025.8.3 certifi=2025.8.3 conda-forge
22 cffi 1.17.1 cffi=1.17.1 conda-forge
23 charset-normalizer 3.4.3 charset-normalizer=3.4.3 conda-forge
24 chex 0.1.83 chex=0.1.83 conda-forge
25 click 8.1.8 click=8.1.8 conda-forge
26 colorama 0.4.6 colorama=0.4.6 conda-forge
27 contextlib2 21.6.0 contextlib2=21.6.0 conda-forge
28 contourpy 1.3.0 contourpy=1.3.0 conda-forge
29 cpython 3.9.23 cpython=3.9.23 conda-forge
30 cycler 0.12.1 cycler=0.12.1 conda-forge
31 cyrus-sasl 2.1.28 cyrus-sasl=2.1.28 conda-forge
32 dav1d 1.2.1 dav1d=1.2.1 conda-forge
33 dbus 1.13.6 dbus=1.13.6 conda-forge
34 deeptime 0.4.4 deeptime=0.4.4 conda-forge
35 dill 0.4.0 dill=0.4.0 conda-forge
36 dm-tree 0.1.8 dm-tree=0.1.8 conda-forge
37 docrep 0.3.2 docrep=0.3.2 conda-forge
38 et_xmlfile 2.0.0 et_xmlfile=2.0.0 conda-forge
39 etils 1.5.1 etils=1.5.1 conda-forge
40 expat 2.7.1 expat=2.7.1 conda-forge
41 filelock 3.19.1 filelock=3.19.1 conda-forge
42 flax 0.8.5 flax=0.8.5 conda-forge
43 font-ttf-dejavu-sans-mono 2.37 font-ttf-dejavu-sans-mono=2.37 conda-forge
44 font-ttf-inconsolata 3.000 font-ttf-inconsolata=3.000 conda-forge
45 font-ttf-source-code-pro 2.038 font-ttf-source-code-pro=2.038 conda-forge
46 font-ttf-ubuntu 0.83 font-ttf-ubuntu=0.83 conda-forge
47 fontconfig 2.15.0 fontconfig=2.15.0 conda-forge
48 fonts-conda-ecosystem 1 fonts-conda-ecosystem=1 conda-forge
49 fonts-conda-forge 1 fonts-conda-forge=1 conda-forge
50 fonttools 4.59.1 fonttools=4.59.1 conda-forge
51 freetype 2.14.1 freetype=2.14.1 conda-forge
52 fsspec 2025.7.0 fsspec=2025.7.0 conda-forge
53 get-annotations 0.1.2 get-annotations=0.1.2 conda-forge
54 gettext 0.25.1 gettext=0.25.1 conda-forge
55 gettext-tools 0.25.1 gettext-tools=0.25.1 conda-forge
56 glib 2.84.0 glib=2.84.0 conda-forge
57 glib-tools 2.84.0 glib-tools=2.84.0 conda-forge
58 glpk 5.0 glpk=5.0 conda-forge
59 gmp 6.3.0 gmp=6.3.0 conda-forge
60 gmpy2 2.2.1 gmpy2=2.2.1 conda-forge
61 graphite2 1.3.14 graphite2=1.3.14 conda-forge
62 gst-plugins-base 1.24.7 gst-plugins-base=1.24.7 conda-forge
63 gstreamer 1.24.7 gstreamer=1.24.7 conda-forge
64 h2 4.2.0 h2=4.2.0 conda-forge
65 h5py 3.11.0 h5py=3.11.0 pypi
66 harfbuzz 11.0.0 harfbuzz=11.0.0 conda-forge
67 hdf5 1.14.3 hdf5=1.14.3 conda-forge
68 hpack 4.1.0 hpack=4.1.0 conda-forge
69 hyperframe 6.1.0 hyperframe=6.1.0 conda-forge
70 icu 75.1 icu=75.1 conda-forge
71 idna 3.10 idna=3.10 conda-forge
72 igraph 0.10.16 igraph=0.10.16 conda-forge
73 importlib-metadata 8.7.0 importlib-metadata=8.7.0 conda-forge
74 importlib-resources 6.5.2 importlib-resources=6.5.2 conda-forge
75 importlib_metadata 8.7.0 importlib_metadata=8.7.0 conda-forge
76 importlib_resources 6.5.2 importlib_resources=6.5.2 conda-forge
77 jax 0.4.28 jax=0.4.28 conda-forge
78 jaxlib 0.4.28 jaxlib=0.4.28 conda-forge
79 jinja2 3.0.3 jinja2=3.0.3 pypi
80 joblib 1.5.1 joblib=1.5.1 conda-forge
81 keyutils 1.6.3 keyutils=1.6.3 conda-forge
82 kiwisolver 1.4.7 kiwisolver=1.4.7 conda-forge
83 krb5 1.21.3 krb5=1.21.3 conda-forge
84 lame 3.100 lame=3.100 conda-forge
85 lcms2 2.17 lcms2=2.17 conda-forge
86 ld_impl_linux-64 2.44 ld_impl_linux-64=2.44 conda-forge
87 leidenalg 0.10.2 leidenalg=0.10.2 conda-forge
88 lerc 4.0.0 lerc=4.0.0 conda-forge
89 libabseil 20240116.2 libabseil=20240116.2 conda-forge
90 libaec 1.1.4 libaec=1.1.4 conda-forge
91 libasprintf 0.25.1 libasprintf=0.25.1 conda-forge
92 libasprintf-devel 0.25.1 libasprintf-devel=0.25.1 conda-forge
93 libavif16 1.3.0 libavif16=1.3.0 conda-forge
94 libblas 3.9.0 libblas=3.9.0 conda-forge
95 libbrotlicommon 1.1.0 libbrotlicommon=1.1.0 conda-forge
96 libbrotlidec 1.1.0 libbrotlidec=1.1.0 conda-forge
97 libbrotlienc 1.1.0 libbrotlienc=1.1.0 conda-forge
98 libcap 2.76 libcap=2.76 conda-forge
99 libcblas 3.9.0 libcblas=3.9.0 conda-forge
100 libclang-cpp20.1 20.1.8 libclang-cpp20.1=20.1.8 conda-forge
101 libclang13 21.1.0 libclang13=21.1.0 conda-forge
102 libcups 2.3.3 libcups=2.3.3 conda-forge
103 libcurl 8.14.1 libcurl=8.14.1 conda-forge
104 libdeflate 1.24 libdeflate=1.24 conda-forge
105 libdrm 2.4.125 libdrm=2.4.125 conda-forge
106 libedit 3.1.20250104 libedit=3.1.20250104 conda-forge
107 libegl 1.7.0 libegl=1.7.0 conda-forge
108 libev 4.33 libev=4.33 conda-forge
109 libevent 2.1.12 libevent=2.1.12 conda-forge
110 libexpat 2.7.1 libexpat=2.7.1 conda-forge
111 libffi 3.5.2 libffi=3.5.2 conda-forge
112 libflac 1.4.3 libflac=1.4.3 conda-forge
113 libfreetype 2.14.1 libfreetype=2.14.1 conda-forge
114 libfreetype6 2.14.1 libfreetype6=2.14.1 conda-forge
115 libgcc 15.2.0 libgcc=15.2.0 conda-forge
116 libgcc-ng 15.2.0 libgcc-ng=15.2.0 conda-forge
117 libgcrypt-lib 1.11.1 libgcrypt-lib=1.11.1 conda-forge
118 libgettextpo 0.25.1 libgettextpo=0.25.1 conda-forge
119 libgettextpo-devel 0.25.1 libgettextpo-devel=0.25.1 conda-forge
120 libgfortran 15.2.0 libgfortran=15.2.0 conda-forge
121 libgfortran-ng 15.2.0 libgfortran-ng=15.2.0 conda-forge
122 libgfortran5 15.2.0 libgfortran5=15.2.0 conda-forge
123 libgl 1.7.0 libgl=1.7.0 conda-forge
124 libglib 2.84.0 libglib=2.84.0 conda-forge
125 libglvnd 1.7.0 libglvnd=1.7.0 conda-forge
126 libglx 1.7.0 libglx=1.7.0 conda-forge
127 libgomp 15.2.0 libgomp=15.2.0 conda-forge
128 libgpg-error 1.55 libgpg-error=1.55 conda-forge
129 libgrpc 1.62.2 libgrpc=1.62.2 conda-forge
130 libhwloc 2.12.1 libhwloc=2.12.1 conda-forge
131 libiconv 1.18 libiconv=1.18 conda-forge
132 libjpeg-turbo 3.1.0 libjpeg-turbo=3.1.0 conda-forge
133 liblapack 3.9.0 liblapack=3.9.0 conda-forge
134 libleidenalg 0.11.1 libleidenalg=0.11.1 conda-forge
135 libllvm14 14.0.6 libllvm14=14.0.6 conda-forge
136 libllvm20 20.1.8 libllvm20=20.1.8 conda-forge
137 libllvm21 21.1.0 libllvm21=21.1.0 conda-forge
138 liblzma 5.8.1 liblzma=5.8.1 conda-forge
139 liblzma-devel 5.8.1 liblzma-devel=5.8.1 conda-forge
140 libnghttp2 1.67.0 libnghttp2=1.67.0 conda-forge
141 libnsl 2.0.1 libnsl=2.0.1 conda-forge
142 libntlm 1.8 libntlm=1.8 conda-forge
143 libogg 1.3.5 libogg=1.3.5 conda-forge
144 libopenblas 0.3.25 libopenblas=0.3.25 conda-forge
145 libopengl 1.7.0 libopengl=1.7.0 conda-forge
146 libopus 1.5.2 libopus=1.5.2 conda-forge
147 libpciaccess 0.18 libpciaccess=0.18 conda-forge
148 libpng 1.6.50 libpng=1.6.50 conda-forge
149 libpq 17.6 libpq=17.6 conda-forge
150 libprotobuf 4.25.3 libprotobuf=4.25.3 conda-forge
151 libre2-11 2023.09.01 libre2-11=2023.09.01 conda-forge
152 libsndfile 1.2.2 libsndfile=1.2.2 conda-forge
153 libsqlite 3.50.4 libsqlite=3.50.4 conda-forge
154 libssh2 1.11.1 libssh2=1.11.1 conda-forge
155 libstdcxx 15.2.0 libstdcxx=15.2.0 conda-forge
156 libstdcxx-ng 15.2.0 libstdcxx-ng=15.2.0 conda-forge
157 libsystemd0 257.9 libsystemd0=257.9 conda-forge
158 libtiff 4.7.1 libtiff=4.7.1 conda-forge
159 libtorch 2.4.1 libtorch=2.4.1 conda-forge
160 libuuid 2.41.2 libuuid=2.41.2 conda-forge
161 libuv 1.51.0 libuv=1.51.0 conda-forge
162 libvorbis 1.3.7 libvorbis=1.3.7 conda-forge
163 libwebp-base 1.6.0 libwebp-base=1.6.0 conda-forge
164 libxcb 1.17.0 libxcb=1.17.0 conda-forge
165 libxcrypt 4.4.36 libxcrypt=4.4.36 conda-forge
166 libxkbcommon 1.11.0 libxkbcommon=1.11.0 conda-forge
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[mghanizada]

Dear Joseph

I fixed the problem, and fitTPT is running now. Had to downgrade the Python packages to the correct versions.
However, I do see flux from G2, G4, and A2 to A1. I'm aware that the TPT is agnotic. But would you recommend running each possible conversion from M alone, or just M -> A2 or E? It is my understanding, reading on the model, that running A2 only would decrease the flux G specific conversion.

I hope you understand my question.

Thanks for the help.

Best,
Mustafa

[josef0731]

Dear Mustafa, Apologies for the late response but glad to hear that you managed to resolve your original issue. If you could share with me which packages you needed to downgrade, I will add this to the specification in setting up the environment (so future users won't have to undergo the same issue as you did)! On the interpretation question, in our analysis we run one single run from M to A2. In my experience although as you said TPT is agnostic, the flux tends to 'over-emphasise' both the source and the target - i.e. you are always going to see flux towards A2 even if in the data there were very few observations of A2. However please feel free to try, it's a good idea to see whether you observe the same fluxes consistently with different end points when running fitTPT - I'd say this would provide more confidence in your inference! Hope this helps! If you don't mind sharing the packages you downgraded that would be fantastic! Joseph Ng

…

On Fri, 24 Oct 2025 at 16:05, mghanizada ***@***.***> wrote: *mghanizada* left a comment (Fraternalilab/sciCSR#13) <#13 (comment)> Dear Joseph I fixed the problem, and fitTPT is running now. Had to downgrade the Python packages to the correct versions. However, I do see flux from G2, G4, and A2 to A1. I'm aware that the TPT is agnotic. But would you recommend running each possible conversion from M alone, or just M -> A2 or E? It is my understanding, reading on the model, that running A2 only would decrease the flux G specific conversion. I hope you understand my question. Thanks for the help. Best, Mustafa — Reply to this email directly, view it on GitHub <#13 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AFPBID37PIP7LFYQPBDAFTL3ZI53LAVCNFSM6AAAAACJYCVCVKVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZTINBTGY2DAMBQHE> . You are receiving this because you commented.Message ID: ***@***.***>

[mghanizada]

Dear Joseph Apologies – I said it worked a bit too fast. It only worked on my pilot run - more specific randon_n=10 and shrinking the problem with make_focus(max_25000) inside python- but running the full dataset of 70K cells with default settings gives me this error inside fitTPT: ValueError: could not convert integer scalar. I will send the complete package list once I have fixed the problem – do you have any suggestions for a fix? Thanks for the help with the interpretation! Best, Mustafa From: Joseph Ng ***@***.***> Reply-To: Fraternalilab/sciCSR ***@***.***> Date: Friday, 24 October 2025 at 18.29 To: Fraternalilab/sciCSR ***@***.***> Cc: Mustafa Ghanizada ***@***.***>, Author ***@***.***> Subject: Re: [Fraternalilab/sciCSR] fitTPT stuck (Issue #13) [Image removed by sender.]josef0731 left a comment (Fraternalilab/sciCSR#13)<#13 (comment)> Dear Mustafa, Apologies for the late response but glad to hear that you managed to resolve your original issue. If you could share with me which packages you needed to downgrade, I will add this to the specification in setting up the environment (so future users won't have to undergo the same issue as you did)! On the interpretation question, in our analysis we run one single run from M to A2. In my experience although as you said TPT is agnostic, the flux tends to 'over-emphasise' both the source and the target - i.e. you are always going to see flux towards A2 even if in the data there were very few observations of A2. However please feel free to try, it's a good idea to see whether you observe the same fluxes consistently with different end points when running fitTPT - I'd say this would provide more confidence in your inference! Hope this helps! If you don't mind sharing the packages you downgraded that would be fantastic! Joseph Ng

On Fri, 24 Oct 2025 at 16:05, mghanizada ***@***.***> wrote: *mghanizada* left a comment (Fraternalilab/sciCSR#13) <#13 (comment)> Dear Joseph I fixed the problem, and fitTPT is running now. Had to downgrade the Python packages to the correct versions. However, I do see flux from G2, G4, and A2 to A1. I'm aware that the TPT is agnotic. But would you recommend running each possible conversion from M alone, or just M -> A2 or E? It is my understanding, reading on the model, that running A2 only would decrease the flux G specific conversion. I hope you understand my question. Thanks for the help. Best, Mustafa — Reply to this email directly, view it on GitHub <#13 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AFPBID37PIP7LFYQPBDAFTL3ZI53LAVCNFSM6AAAAACJYCVCVKVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZTINBTGY2DAMBQHE> . You are receiving this because you commented.Message ID: ***@***.***>

— Reply to this email directly, view it on GitHub<#13 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/A37JIK6CVKYJTFLCY3D4BLD3ZJHXTAVCNFSM6AAAAACJYCVCVKVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZTINBUGAYDEOJVGQ>. You are receiving this because you authored the thread.Message ID: ***@***.***>

[josef0731]

Sorry again for the delayed response. I think maybe taking this offline to troubleshoot is the easiest. As you might know the TPT functions were implemented in python, we can actually try to run the code directly within a Python environment which should give more helpful error messages and allow us to see where the problems exactly are. Please drop me an email (joseph.ng [at] ucl.ac.uk) and I can give you more information on what to run and check?

My apologies again for keeping you waiting!
Best wishes,
Joseph