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View Code? Open in Web Editor NEWLIANA x Tensor-cell2cell Protocols
Home Page: https://ccc-protocols.readthedocs.io
License: MIT License
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Change Seurat to SCE in R Preprocessing
Should we change the preprocessing from Seurat to SCE, specfically because liana converts it either way. (Also @hmbaghdassarian converts so that it's more similar to adata).
about the resource of liana+cell2cell and further L-R pairs determination of among cell cell communication
Dear ccc_protocols developer,
Thanks for the combination of the two powerful ccc detection methods.
I have two question:
-
When I read the cell2cell paper, they collect the L-R pairs from LewisLabUCSD/Ligand-Receptor-Pairs(https://raw.githubusercontent.com/LewisLabUCSD/Ligand-Receptor-Pairs/master/Human/Human-2020-Jin-LR-pairs.csv). And the legend of tensor_factors_plot contains three type of L-R pairs (Secreted Signaling, ECM-Receptor and Cell-Cell Contact).
So, could you also integrate the L-R pairs information in liana+? -
After the context-dependent communication pattern detection, we also want to get the information of one L-R pair and their corresponding cell-cell pair.
So, how can we get those information?
And any suggestion for visualization of one cell communication form mutiple conditions?
Integrated Webpage deployment
The webpage is now deployed at: https://ccc-protocols.readthedocs.io/en/latest/
To enable automatic deployment I need admin rights (i.e. we need to move the repo to an organisational account, no rush for this we can do it later).
PS. The logo is a piece of art as you will see - we can keep it or remove it.
Add diagrams to Readme & docs index
We should make the Readme & docs index page more friendly to people that land on it - i.e.:
- Add images
- Add links to the tutorials, R & Python, reference to the biorxiv, etc
c2c.analysis.run_tensor_cell2cell_pipeline() Error
Dear all,
thank you for the nice package. However, unfortunatley i have an issue with the tensor2 = c2c.analysis.run_tensor_cell2cell_pipeline(..) function I tried now many times to run it but it always get this error:
rank = int(_compute_elbow(loss))
else:
rank = manual_elbow
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
After finishing the Elbow analysis.
I am new here, so sorry if sometihing is missing in this question.
Would be great if you could help me.
Python Version: 3.11.7
Quick start code = manuscript code?
Are we doing the quick start exactly the same as the code in the manuscript? If so, we should copy exactly the same code from the manuscript. Notice that I made some modification to the code in the manuscript and it's not exactly the same as in the notebooks we have here.
Interoperability in an independent file
Does it not make more sense to just have a separate supplementary file with interoperability?
E.g. like how S1_batch_corection is named.
Seems to me a bit out of context to have it in the main 02 tutorials. The Seurat part could also go there (or we can keep it as you did @hmbaghdassarian).
Doublet vs Total counts filtering
We currently filter cells by total counts (very minor since the data is already largely preprocessed), but if we want to stick to best practices best to change it to doublet removal by sample :)
This is also related to the Seurat -> SCE issue because doublet removal in Seurat is not great, while in SCE it's largely comparable to scanpy.
Error in GSEA step 10: c2c$external$generate_lr_geneset
I have been able to get up to step 9 in R Tutorial 06. However, when I run the step 10:
#Generate the LR-gene set that will be used for running GSEA
lr_set <- c2c$external$generate_lr_geneset(lr_list = lr_list,
complex_sep='_', # Separation symbol of the genes in the protein complex
lr_sep='^', # Separation symbol between a ligand and a receptor complex
organism='human',
pathwaydb='KEGG',
readable_name=TRUE
)
I get the following error:
Error: AttributeError: module 'cell2cell.external' has no attribute 'generate_lr_geneset'
I am not a python user but I have been able to install and import cell2cell and python's liana using the reticulate R package just fine. Can anyone help me to ameliorate this issue?
> sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.6.3
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] colorspace_2.1-0 scales_1.2.1 patchwork_1.1.2
[4] SCpubr_1.1.2.9000 gt_0.9.0 CellChat_1.6.1
[7] igraph_1.4.2 ComplexHeatmap_2.14.0 decoupleR_2.2.2
[10] cowplot_1.1.1 ggrepel_0.9.3 reticulate_1.28
[13] SingleCellExperiment_1.20.0 SummarizedExperiment_1.28.0 Biobase_2.58.0
[16] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9 IRanges_2.32.0
[19] S4Vectors_0.36.2 BiocGenerics_0.44.0 MatrixGenerics_1.10.0
[22] matrixStats_0.63.0 liana_0.1.12 magrittr_2.0.3
[25] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[28] dplyr_1.1.1 purrr_1.0.1 readr_2.1.4
[31] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.2
[34] tidyverse_2.0.0 SeuratObject_4.1.3 Seurat_4.3.0
loaded via a namespace (and not attached):
[1] terra_1.7-23 graphlayouts_0.8.4 pbapply_1.7-0
[4] lattice_0.21-8 vctrs_0.6.1 usethis_2.1.6
[7] blob_1.2.4 survival_3.5-5 spatstat.data_3.0-1
[10] later_1.3.0 nloptr_2.0.3 DBI_1.1.3
[13] rappdirs_0.3.3 uwot_0.1.14 dqrng_0.3.0
[16] zlibbioc_1.44.0 htmlwidgets_1.6.2 mvtnorm_1.1-3
[19] GlobalOptions_0.1.2 future_1.32.0 formattable_0.2.1
[22] leiden_0.4.3 parallel_4.2.2 scater_1.26.1
[25] irlba_2.3.5.1 tidygraph_1.2.3 Rcpp_1.0.10
[28] KernSmooth_2.23-20 promises_1.2.0.1 DelayedArray_0.24.0
[31] limma_3.54.2 pkgload_1.3.2 magick_2.7.4
[34] RSpectra_0.16-1 fs_1.6.1 fastmatch_1.1-3
[37] basilisk_1.11.2 digest_0.6.31 png_0.1-8
[40] bluster_1.8.0 sctransform_0.3.5 scatterpie_0.1.8
[43] DOSE_3.22.1 here_1.0.1 ggraph_2.1.0
[46] pkgconfig_2.0.3 GO.db_3.15.0 dittoSeq_1.8.1
[49] gridBase_0.4-7 spatstat.random_3.1-4 DelayedMatrixStats_1.20.0
[52] ggbeeswarm_0.7.1 estimability_1.4.1 iterators_1.0.14
[55] minqa_1.2.5 statnet.common_4.8.0 clusterProfiler_4.4.4
[58] network_1.18.1 circlize_0.4.15 beeswarm_0.4.0
[61] GetoptLong_1.0.5 xfun_0.38 zoo_1.8-11
[64] tidyselect_1.2.0 reshape2_1.4.4 ica_1.0-3
[67] viridisLite_0.4.1 pkgbuild_1.4.0 rlang_1.1.0
[70] glue_1.6.2 RColorBrewer_1.1-3 registry_0.5-1
[73] lambda.r_1.2.4 emmeans_1.8.5 monocle3_1.3.1
[76] ggsignif_0.6.4 labeling_0.4.2 httpuv_1.6.9
[79] BiocNeighbors_1.16.0 DO.db_2.9 jsonlite_1.8.4
[82] XVector_0.38.0 bit_4.0.5 mime_0.12
[85] systemfonts_1.0.4 gridExtra_2.3 stringi_1.7.12
[88] processx_3.8.0 spatstat.sparse_3.0-1 scattermore_0.8
[91] spatstat.explore_3.1-0 yulab.utils_0.0.6 bitops_1.0-7
[94] cli_3.6.1 RSQLite_2.3.1 pheatmap_1.0.12
[97] data.table_1.14.8 timechange_0.2.0 rstudioapi_0.14
[100] nlme_3.1-162 qvalue_2.28.0 scran_1.26.2
[103] locfit_1.5-9.7 listenv_0.9.0 miniUI_0.1.1.1
[106] gridGraphics_0.5-1 urlchecker_1.0.1 ggnetwork_0.5.12
[109] sessioninfo_1.2.2 readxl_1.4.2 lifecycle_1.0.3
[112] munsell_0.5.0 cellranger_1.1.0 ggalluvial_0.12.5
[115] qusage_2.30.0 codetools_0.2-19 coda_0.19-4
[118] fftw_1.0-7 vipor_0.4.5 lmtest_0.9-40
[121] xtable_1.8-4 ROCR_1.0-11 formatR_1.14
[124] BiocManager_1.30.20 abind_1.4-5 farver_2.1.1
[127] FNN_1.1.3.2 parallelly_1.35.0 RANN_2.6.1
[130] aplot_0.1.10 ggtree_3.4.4 RcppAnnoy_0.0.20
[133] goftest_1.2-3 logger_0.2.2 futile.options_1.0.1
[136] profvis_0.3.7 cluster_2.1.4 future.apply_1.10.0
[139] Matrix_1.5-4 tidytree_0.4.2 ellipsis_0.3.2
[142] prettyunits_1.1.1 ggridges_0.5.4 VennDiagram_1.7.3
[145] fgsea_1.22.0 remotes_2.4.2 basilisk.utils_1.11.2
[148] spatstat.utils_3.0-2 htmltools_0.5.5 yaml_2.3.7
[151] NMF_0.26 utf8_1.2.3 plotly_4.10.1
[154] ggpubr_0.6.0 withr_2.5.0 scuttle_1.8.4
[157] fitdistrplus_1.1-8 BiocParallel_1.32.5 bit64_4.0.5
[160] rngtools_1.5.2 foreach_1.5.2 Biostrings_2.64.1
[163] progressr_0.13.0 GOSemSim_2.22.0 rsvd_1.0.5
[166] ScaledMatrix_1.6.0 devtools_2.4.5 memoise_2.0.1
[169] evaluate_0.20 tzdb_0.3.0 callr_3.7.3
[172] ps_1.7.4 curl_5.0.0 fansi_1.0.4
[175] tensor_1.5 edgeR_3.40.2 checkmate_2.1.0
[178] cachem_1.0.7 deldir_1.0-6 dir.expiry_1.6.0
[181] metapod_1.6.0 rjson_0.2.21 openxlsx_4.2.5.2
[184] rstatix_0.7.2 clue_0.3-64 rprojroot_2.0.3
[187] tools_4.2.2 RCurl_1.98-1.12 car_3.1-2
[190] ape_5.7-1 ggplotify_0.1.0 xml2_1.3.3
[193] httr_1.4.5 rmarkdown_2.21 boot_1.3-28.1
[196] globals_0.16.2 R6_2.5.1 progress_1.2.2
[199] KEGGREST_1.36.3 treeio_1.20.2 shape_1.4.6
[202] statmod_1.5.0 beachmat_2.14.0 sna_2.7-1
[205] BiocSingular_1.14.0 splines_4.2.2 carData_3.0-5
[208] ggfun_0.0.9 generics_0.1.3 pillar_1.9.0
[211] tweenr_2.0.2 sp_1.6-0 GenomeInfoDbData_1.2.9
[214] plyr_1.8.8 gtable_0.3.3 futile.logger_1.4.3
[217] rvest_1.0.3 zip_2.2.2 knitr_1.42
[220] shadowtext_0.1.2 fastmap_1.1.1 Cairo_1.6-0
[223] doParallel_1.0.17 ComplexUpset_1.3.3 AnnotationDbi_1.58.0
[226] broom_1.0.4 filelock_1.0.2 backports_1.4.1
[229] vroom_1.6.1 lme4_1.1-32 enrichplot_1.16.2
[232] irGSEA_1.1.3 hms_1.1.3 ggforce_0.4.1
[235] Rtsne_0.16 shiny_1.7.4 OmnipathR_3.7.2
[238] polyclip_1.10-4 lazyeval_0.2.2 crayon_1.5.2
[241] MASS_7.3-58.3 downloader_0.4 sparseMatrixStats_1.10.0
[244] viridis_0.6.2 svglite_2.1.1 compiler_4.2.2
[247] spatstat.geom_3.1-0
Gini coeffs Python vs R
In R I calculate gene coefficients separately, in Python Erick does it on the outer product
IMO, let's keep it as it is for now. Ultimately, the interpretation yields the same conclusions.
Frequent Questions Page
Add page about FAQ
Add tool GHs to webpage + cite the paper once we send to Biorxiv
We should add cosmetics to the webpage later on :)
Pathway issue
Hey, sorry for bothering you with more issues.
I have an error with the pathway analysis could you please help me.
I am running seaborn 0.11.2 on Python 3.10.11
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