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quadbio / Pando

Multiome GRN inference.
https://quadbio.github.io/Pando/
MIT License
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Infer_grn Function Returning Error #26

Closed Sandman-1 closed 1 year ago

Sandman-1 commented 1 year ago

Hello! Thank you guys for the amazing GRN analysis package! I am eager to use it, but one of the key functions keeps on returning an error, regardless of which motif matrix or function parameters I use. It's as follows (TKO_int_hep is the Seurat object):

TKO_int_hep <- infer_grn(TKO_int_hep, peak_to_gene_method = "GREAT", upstream = 1e+05, downstream = 1e+05, method = "bagging_ridge")

My session info is attached. Any help would be greatly appreciated!

R version 4.2.1 (2022-06-23 ucrt) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 10 x64 (build 22000)

Matrix products: default

locale: [1] LC_COLLATE=English_United States.utf8 LC_CTYPE=English_United States.utf8 LC_MONETARY=English_United States.utf8 [4] LC_NUMERIC=C LC_TIME=English_United States.utf8

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

other attached packages: [1] slingshot_2.6.0 TrajectoryUtils_1.6.0 princurve_2.1.6
[4] shiny_1.7.4 destiny_3.12.0 Pando_1.0.2
[7] CellChat_1.6.1 igraph_1.3.5 ggpubr_0.5.0
[10] Scillus_0.5.0 org.Mm.eg.db_3.16.0 gageData_2.36.0
[13] fgsea_1.24.0 TFBSTools_1.36.0 JASPAR2022_0.99.7
[16] BiocFileCache_2.6.0 dbplyr_2.2.1 EnsDb.Mmusculus.v102_0.0.1
[19] BSgenome.Mmusculus.UCSC.mm10_1.4.3 BSgenome_1.66.1 rtracklayer_1.58.0
[22] Biostrings_2.66.0 XVector_0.38.0 AnnotationForge_1.40.0
[25] ensembldb_2.22.0 AnnotationFilter_1.22.0 GenomicFeatures_1.50.3
[28] AnnotationDbi_1.60.0 DAseq_1.0.0 Nebulosa_1.8.0
[31] patchwork_1.1.2 HGNChelper_0.8.1 SoupX_1.6.2
[34] scDblFinder_1.12.0 plotly_4.10.1 cicero_1.3.8
[37] Gviz_1.42.0 monocle3_1.3.1 SingleCellExperiment_1.20.0
[40] SeuratWrappers_0.3.0 SeuratObject_4.1.3 Seurat_4.3.0.9001
[43] Signac_1.9.0 Matrix_1.5-3 ABSSeq_1.52.0
[46] DESeq2_1.38.2 SummarizedExperiment_1.28.0 Biobase_2.58.0
[49] MatrixGenerics_1.10.0 matrixStats_0.63.0 GenomicRanges_1.50.2
[52] GenomeInfoDb_1.34.4 IRanges_2.32.0 S4Vectors_0.36.1
[55] BiocGenerics_0.44.0 dbscan_1.1-11 ISnorm_0.0.1
[58] SAVER_1.1.3 toscutil_2.5.0 future_1.30.0
[61] venn_1.11 forcats_0.5.2 stringr_1.5.0
[64] dplyr_1.0.10 purrr_1.0.0 readr_2.1.3
[67] tidyr_1.2.1 tibble_3.1.8 ggplot2_3.4.0
[70] tidyverse_1.3.2 aplot_0.1.9 openxlsx_4.2.5.1
[73] diptest_0.76-0

loaded via a namespace (and not attached): [1] KEGGREST_1.38.0 circlize_0.4.16 locfit_1.5-9.6 remotes_2.4.2
[5] VIM_6.2.2 ggthemes_4.2.4 lattice_0.20-45 spatstat.utils_3.0-1
[9] vctrs_0.5.1 utf8_1.2.2 blob_1.2.3 R.oo_1.25.0
[13] withr_2.5.0 foreign_0.8-84 ggnetwork_0.5.10 registry_0.5-1
[17] TTR_0.24.3 readxl_1.4.1 lifecycle_1.0.3 cellranger_1.1.0
[21] munsell_0.5.0 ragg_1.2.4 ScaledMatrix_1.6.0 ggalluvial_0.12.3
[25] codetools_0.2-18 caret_6.0-93 lmtest_0.9-40 limma_3.54.0
[29] annotate_1.76.0 parallelly_1.33.0 fs_1.5.2 fastmatch_1.1-3
[33] metapod_1.6.0 Rtsne_0.16 biovizBase_1.46.0 stringi_1.7.8
[37] RcppRoll_0.3.0 sctransform_0.3.5 polyclip_1.10-4 yulab.utils_0.0.6
[41] goftest_1.2-3 cluster_2.1.4 ggraph_2.1.0 TFMPvalue_0.0.9
[45] pkgconfig_2.0.3 prettyunits_1.1.1 data.table_1.14.6 sparseMatrixStats_1.10.0
[49] googledrive_2.0.0 ggridges_0.5.4 lubridate_1.9.0 timechange_0.1.1
[53] httr_1.4.4 progress_1.2.2 GetoptLong_1.0.5 terra_1.6-47
[57] beachmat_2.14.0 graphlayouts_0.8.4 haven_2.5.1 amap_0.8-19
[61] ggfun_0.0.9 htmltools_0.5.4 miniUI_0.1.1.1 viridisLite_0.4.1
[65] yaml_2.3.6 NMF_0.25 prodlim_2019.11.13 jquerylib_0.1.4
[69] pillar_1.8.1 hexbin_1.28.2 later_1.3.0 fitdistrplus_1.1-8
[73] glue_1.6.2 DBI_1.1.3 BiocParallel_1.32.5 plyr_1.8.8
[77] foreach_1.5.2 ProtGenerics_1.30.0 robustbase_0.95-0 gtable_0.3.1
[81] pcaMethods_1.90.0 rsvd_1.0.5 caTools_1.18.2 GlobalOptions_0.1.2
[85] latticeExtra_0.6-30 fastmap_1.1.0 crosstalk_1.2.0 vcd_1.4-10
[89] broom_1.0.2 checkmate_2.1.0 promises_1.2.0.1 FNN_1.1.3.1
[93] textshaping_0.3.6 hms_1.1.2 ggforce_0.4.1 png_0.1-8
[97] clue_0.3-63 spatstat.explore_3.0-5 lazyeval_0.2.2 Formula_1.2-4
[101] crayon_1.5.2 gridBase_0.4-7 reprex_2.0.2 svglite_2.1.0
[105] boot_1.3-28.1 tidyselect_1.2.0 xfun_0.36 ks_1.14.0
[109] BiocSingular_1.14.0 VariantAnnotation_1.44.0 splines_4.2.1 knn.covertree_1.0
[113] smoother_1.1 survival_3.4-0 rappdirs_0.3.3 xgboost_1.6.0.1
[117] bit64_4.0.5 rngtools_1.5.2 ggpointdensity_0.1.0 pals_1.7
[121] modelr_0.1.10 CNEr_1.34.0 jpeg_0.1-10 ggsignif_0.6.4
[125] R.methodsS3_1.8.2 VGAM_1.1-7 htmlTable_2.4.1 xtable_1.8-4
[129] admisc_0.30 googlesheets4_1.0.1 cachem_1.0.6 DelayedArray_0.24.0
[133] ipred_0.9-13 abind_1.4-5 mime_0.12 systemfonts_1.0.4
[137] rjson_0.2.21 ggrepel_0.9.2 rstatix_0.7.1 spatstat.sparse_3.0-0
[141] tools_4.2.1 cli_3.4.1 magrittr_2.0.3 proxy_0.4-27
[145] dichromat_2.0-0.1 future.apply_1.10.0 ggplotify_0.1.0 DelayedMatrixStats_1.20.0
[149] ggbeeswarm_0.7.1 assertthat_0.2.1 sna_2.7 ica_1.0-3
[153] pbapply_1.6-0 scuttle_1.8.3 R.utils_2.12.2 tweenr_2.0.2
[157] zlibbioc_1.44.0 zip_2.2.2 formattable_0.2.1 restfulr_0.0.15
[161] biomaRt_2.54.0 tzdb_0.3.0 geneplotter_1.76.0 fansi_1.0.3
[165] tidygraph_1.2.2 xts_0.12.2 tensor_1.5 ROCR_1.0-11
[169] KernSmooth_2.23-20 backports_1.4.1 scatterplot3d_0.3-42 interp_1.1-3
[173] farver_2.1.1 bit_4.0.5 Rsamtools_2.14.0 RANN_2.6.1
[177] ranger_0.14.1 BiocIO_1.8.0 scattermore_0.8 hardhat_1.2.0
[181] sass_0.4.4 RcppAnnoy_0.0.20 maps_3.4.1 glmnet_4.1-6
[185] pROC_1.18.0 viridis_0.6.2 rstudioapi_0.14 minqa_1.2.5
[189] iterators_1.0.14 spatstat.geom_3.0-3 RcppHNSW_0.4.1 nlme_3.1-161
[193] DirichletMultinomial_1.40.0 shape_1.4.6 gtools_3.9.4 beeswarm_0.4.0
[197] network_1.18.0 bslib_0.4.2 listenv_0.9.0 reshape2_1.4.4
[201] gargle_1.2.1 generics_0.1.3 colorspace_2.0-3 base64enc_0.1-3
[205] XML_3.99-0.13 e1071_1.7-12 ModelMetrics_1.2.2.2 spatstat.data_3.0-0
[209] sp_1.5-1 RColorBrewer_1.1-3 ggplot.multistats_1.0.0 dqrng_0.3.0
[213] GenomeInfoDbData_1.2.9 timeDate_4022.108 progressr_0.12.0 mapproj_1.2.9
[217] evaluate_0.19 memoise_2.0.1 coda_0.19-4 ComplexHeatmap_2.15.1
[221] knitr_1.41 laeken_0.5.2 doParallel_1.0.17 vipor_0.4.5
[225] httpuv_1.6.7 class_7.3-20 irlba_2.3.5.1 Rcpp_1.0.9
[229] BiocManager_1.30.19 seqLogo_1.64.0 grr_0.9.5 jsonlite_1.8.4
[233] Hmisc_4.7-2 RSpectra_0.16-1 digest_0.6.31 poweRlaw_0.70.6
[237] cowplot_1.1.1 bitops_1.0-7 RSQLite_2.2.20 rmarkdown_2.19
[241] globals_0.16.2 compiler_4.2.1 nnet_7.3-18 reticulate_1.26
[245] statmod_1.4.37 RcppEigen_0.3.3.9.3 scran_1.26.1 zoo_1.8-11
[249] carData_3.0-5 pracma_2.4.2 gridGraphics_0.5-1 rlang_1.0.6.9000
[253] nloptr_2.0.3 uwot_0.1.14 lava_1.7.1 rvest_1.0.3
[257] recipes_1.0.3 mvtnorm_1.1-3 htmlwidgets_1.6.1 labeling_0.4.2
[261] leiden_0.4.3 curl_4.3.3 scater_1.26.1 parallel_4.2.1
[265] DEoptimR_1.0-11 BiocNeighbors_1.16.0 edgeR_3.40.1 filelock_1.0.2
[269] scales_1.2.1 lme4_1.1-31 deldir_1.0-6 gridExtra_2.3
[273] bluster_1.8.0 motifmatchr_1.20.0 RCurl_1.98-1.9 car_3.1-1
[277] GO.db_3.16.0 MASS_7.3-58.1 ellipsis_0.3.2 spatstat.random_3.0-1
[281] xml2_1.3.3 gower_1.0.1 rpart_4.1.19 R6_2.5.1
[285] mclust_6.0.0 statnet.common_4.7.0 GenomicAlignments_1.34.0

sylestiel commented 1 year ago

@joschif

Hi,

I'm encountering the same error. It reads as follows:

Error in motif2tf[, tfs_use, drop = FALSE] : incorrect number of dimensions

I looked at the seurat_object and it shows the following:

seurat_object An object of class SeuratPlus 443442 features across 7235 samples within 4 assays Active assay: peaks (221840 features, 221840 variable features) 3 other assays present: RNA, ATAC, SCT 2 dimensional reductions calculated: pca, lsi

Can you help suggest ways to troubleshoot this problem.

Many thanks!

joschif commented 1 year ago

Can you check how NetworkTFs(seurat_object) looks like? What did you pass as motifs and TFs to the previous functions?

sylestiel commented 1 year ago

@joschif

Posted part of the screenshot

Screen Shot 2023-01-11 at 12 22 15 PM

library(BSgenome.Mmusculus.UCSC.mm10) data(motifs)

muo_data <- find_motifs( muo_data, pfm = motifs, genome = BSgenome.Mmusculus.UCSC.mm10 )

Sandman-1 commented 1 year ago

Good afternoon! Thank you guys for the responses!

Here is the copy of what NetworkTFs returns.

-

M07783_2.00 M07784_2.00 M02651_2.00 M08707_2.00 M00116_2.00 M01657_2.00 M01661_2.00 M01663_2.00 M01668_2.00 0 0 0 0 0 0 0 0 0 M08708_2.00 M02652_2.00 M07439_2.00 M08754_2.00 M08714_2.00 M08716_2.00 M08769_2.00 M08719_2.00 M09455_2.00 0 0 0 0 0 0 0 0 0 M02799_2.00 M02800_2.00 M04179_2.00 M04180_2.00 M04181_2.00 M04182_2.00 M08758_2.00 M08057_2.00 M01722_2.00 0 0 0 0 0 0 0 0 0 M02790_2.00 M02791_2.00 M04131_2.00 M04132_2.00 M04133_2.00 M04134_2.00 M02794_2.00 M04156_2.00 M04157_2.00 0 0 0 0 0 0 0 0 0 M08736_2.00 M04158_2.00 M04159_2.00 M01743_2.00 M04207_2.00 M04208_2.00 M04209_2.00 M04210_2.00 M04211_2.00 0 0 0 0 0 0 0 0 0 M08734_2.00 M08759_2.00 M08763_2.00 M09471_2.00 M09472_2.00 M01726_2.00 M04090_2.00 M04091_2.00 M08052_2.00 0 0 0 0 0 0 0 0 0 M08077_2.00 M08789_2.00 M04299_2.00 M04300_2.00 M04301_2.00 M04302_2.00 M08822_2.00 M09492_2.00 M01143_2.00 0 0 0 0 0 0 0 0 0 M01812_2.00 M02842_2.00 M02843_2.00 M02844_2.00 M02845_2.00 M02846_2.00 M02847_2.00 M02863_2.00 M02864_2.00 0 0 0 0 0 0 0 0 0 M03639_2.00 M03640_2.00 M03641_2.00 M04029_2.00 M04030_2.00 M04031_2.00 M04293_2.00 M04294_2.00 M04295_2.00 0 0 0 0 0 0 0 0 0 M04296_2.00 M04297_2.00 M04298_2.00 M04309_2.00 M04310_2.00 M04311_2.00 M04312_2.00 M04313_2.00 M04314_2.00 0 0 0 0 0 0 0 0 0 M04315_2.00 M04316_2.00 M04317_2.00 M04318_2.00 M04319_2.00 M04320_2.00 M06020_2.00 M04040_2.00 M08811_2.00 0 0 0 0 0 0 0 0 0 M02872_2.00 M04382_2.00 M04383_2.00 M09499_2.00 M01849_2.00 M08080_2.00 M08850_2.00 M08320_2.00 M08233_2.00 0 0 0 0 0 0 0 0 0 M08277_2.00 M08279_2.00 M08280_2.00 M08235_2.00 M08282_2.00 M08862_2.00 M08866_2.00 M08867_2.00 M08293_2.00 0 0 0 0 0 0 0 0 0 M08298_2.00 M00130_2.00 M07926_2.00 M08301_2.00 M00775_2.00 M08986_2.00 M07590_2.00 M05848_2.00 M07592_2.00 0 0 0 0 0 0 0 0 0 M07594_2.00 M07596_2.00 M09508_2.00 M08897_2.00 M09509_2.00 M08307_2.00 M07606_2.00 M07607_2.00 M08248_2.00 0 0 0 0 0 0 0 0 0 M08321_2.00 M08325_2.00 M00239_2.00 M00240_2.00 M04523_2.00 M04524_2.00 M08909_2.00 M07613_2.00 M07619_2.00 0 0 0 0 0 0 0 0 0 M07620_2.00 M02900_2.00 M08330_2.00 M08331_2.00 M08252_2.00 M08337_2.00 M07627_2.00 M07628_2.00 M07629_2.00 0 0 0 0 0 0 0 0 0 M07634_2.00 M07635_2.00 M08254_2.00 M07638_2.00 M07643_2.00 M04568_2.00 M04569_2.00 M08924_2.00 M07649_2.00 0 0 0 0 0 0 0 0 0 M07650_2.00 M08354_2.00 M07659_2.00 M08356_2.00 M08362_2.00 M07669_2.00 M00243_2.00 M00244_2.00 M00245_2.00 0 0 0 0 0 0 0 0 0 M00246_2.00 M00247_2.00 M00248_2.00 M00249_2.00 M08937_2.00 M07672_2.00 M07676_2.00 M04609_2.00 M04610_2.00 0 0 0 0 0 0 0 0 0 M08365_2.00 M07687_2.00 M08262_2.00 M08367_2.00 M02898_2.00 M02910_2.00 M03682_2.00 M04517_2.00 M04518_2.00 0 0 0 0 0 0 0 0 0 M04558_2.00 M04559_2.00 M04645_2.00 M04646_2.00 M06117_2.00 M06118_2.00 M08920_2.00 M09016_2.00 M07697_2.00 0 0 0 0 0 0 0 0 0 M07698_2.00 M05856_2.00 M07704_2.00 M07705_2.00 M08375_2.00 M07710_2.00 M07711_2.00 M07713_2.00 M07716_2.00 0 0 0 0 0 0 0 0 0 M07720_2.00 M07724_2.00 M07725_2.00 M07726_2.00 M07734_2.00 M08384_2.00 M08386_2.00 M07738_2.00 M07743_2.00 0 0 0 0 0 0 0 0 0 M07746_2.00 M07749_2.00 M08096_2.00 M07761_2.00 M07762_2.00 M07764_2.00 M07765_2.00 M07767_2.00 M07771_2.00 0 0 0 0 0 0 0 0 0 M07773_2.00 M07775_2.00 M07776_2.00 M08399_2.00 M00650_2.00 M01207_2.00 M01903_2.00 M04661_2.00 M04662_2.00 0 0 0 0 0 0 0 0 0 M04663_2.00 M04664_2.00 M01928_2.00 M01929_2.00 M04683_2.00 M04684_2.00 M01931_2.00 M08108_2.00 M09032_2.00 0 0 0 0 0 0 0 0 0 M03721_2.00 M09045_2.00 M09527_2.00 M08111_2.00 M08112_2.00 M08113_2.00 M02680_2.00 M02681_2.00 M09085_2.00 0 0 0 1 1 1 0 0 0 M00161_2.00 M00790_2.00 M01980_2.00 M03016_2.00 M03017_2.00 M03018_2.00 M04809_2.00 M04810_2.00 M09084_2.00 0 0 0 0 0 0 0 0 0 M09100_2.00 M08115_2.00 M08116_2.00 M08118_2.00 M01010_2.00 M01011_2.00 M01012_2.00 M03022_2.00 M03023_2.00 0 0 0 0 0 0 0 0 0 M03024_2.00 M09091_2.00 M04823_2.00 M04824_2.00 M09093_2.00 M06474_2.00 M04847_2.00 M04848_2.00 M03044_2.00 0 0 0 0 0 0 0 0 0 M03045_2.00 M04855_2.00 M04856_2.00 M08121_2.00 M02017_2.00 M03761_2.00 M03762_2.00 M03763_2.00 M06208_2.00 0 0 0 0 0 0 0 0 0 M04885_2.00 M04886_2.00 M04893_2.00 M04894_2.00 M00260_2.00 M00261_2.00 M00262_2.00 M00263_2.00 M00264_2.00 0 0 0 0 0 0 0 0 0 M00265_2.00 M03076_2.00 M04895_2.00 M04896_2.00 M04908_2.00 M04909_2.00 M04910_2.00 M04911_2.00 M09128_2.00 0 0 0 0 0 0 0 0 0 M03079_2.00 M04917_2.00 M04918_2.00 M04919_2.00 M01503_2.00 M01504_2.00 M03081_2.00 M04922_2.00 M04923_2.00 0 0 0 0 0 0 0 0 0 M04924_2.00 M04925_2.00 M04926_2.00 M04927_2.00 M09129_2.00 M04939_2.00 M04940_2.00 M00270_2.00 M00271_2.00 0 0 0 0 0 0 0 0 0 M00272_2.00 M00273_2.00 M00274_2.00 M00275_2.00 M04949_2.00 M04950_2.00 M09132_2.00 M00276_2.00 M00277_2.00 0 0 0 0 0 0 0 0 0 M02687_2.00 M06477_2.00 M06479_2.00 M06481_2.00 M06482_2.00 M06483_2.00 M06484_2.00 M06485_2.00 M06486_2.00 0 0 0 0 0 0 0 0 0 M06488_2.00 M06491_2.00 M06492_2.00 M06493_2.00 M06494_2.00 M06495_2.00 M06496_2.00 M06500_2.00 M06501_2.00 0 0 0 0 0 0 0 0 0 M06506_2.00 M06507_2.00 M06511_2.00 M06515_2.00 M06516_2.00 M06517_2.00 M06520_2.00 M06522_2.00 M06523_2.00 0 0 0 0 0 0 0 0 0 M06524_2.00 M06525_2.00 M06527_2.00 M06528_2.00 M06531_2.00 M06532_2.00 M06536_2.00 M06537_2.00 M06539_2.00 0 0 0 0 0 0 0 0 0 M06544_2.00 M06545_2.00 M06547_2.00 M06549_2.00 M06554_2.00 M08457_2.00 M09137_2.00 M03112_2.00 M05002_2.00 0 0 0 0 0 0 0 0 0 M05003_2.00 M05004_2.00 M05005_2.00 M05006_2.00 M05007_2.00 M05012_2.00 M05013_2.00 M05014_2.00 M09138_2.00 0 0 0 0 0 0 0 0 0 M06489_2.00 M06550_2.00 M05015_2.00 M05016_2.00 M05017_2.00 M09139_2.00 M06518_2.00 M06519_2.00 M03118_2.00 0 0 0 0 0 0 0 0 0 M05026_2.00 M05027_2.00 M05028_2.00 M05029_2.00 M05063_2.00 M05064_2.00 M05065_2.00 M05066_2.00 M05067_2.00 0 0 0 0 0 0 0 0 0 M05068_2.00 M09146_2.00 M02089_2.00 M05159_2.00 M05160_2.00 M03166_2.00 M05176_2.00 M05177_2.00 M05178_2.00 0 0 0 0 0 0 0 0 0 M05179_2.00 M06514_2.00 M05210_2.00 M05211_2.00 M05212_2.00 M05213_2.00 M05214_2.00 M02093_2.00 M01246_2.00 0 0 0 0 0 0 0 0 0 M05255_2.00 M05256_2.00 M05257_2.00 M09154_2.00 M09559_2.00 M03204_2.00 M05283_2.00 M05284_2.00 M03212_2.00 0 0 0 0 0 0 0 0 0 M02688_2.00 M06478_2.00 M06513_2.00 M06538_2.00 M06546_2.00 M06552_2.00 M06553_2.00 M07485_2.00 M08137_2.00 0 0 0 0 0 0 0 0 0 M08138_2.00 M03222_2.00 M05315_2.00 M05316_2.00 M08208_2.00 M08209_2.00 M03226_2.00 M05327_2.00 M05328_2.00 0 0 0 0 0 0 0 0 0 M09160_2.00 M00325_2.00 M00326_2.00 M00327_2.00 M05357_2.00 M05358_2.00 M03233_2.00 M03234_2.00 M05373_2.00 0 0 0 0 0 0 0 0 0 M05374_2.00 M05375_2.00 M05376_2.00 M03236_2.00 M05383_2.00 M05384_2.00 M03242_2.00 M03243_2.00 M05392_2.00 0 0 0 0 0 0 0 0 0 M05393_2.00 M06503_2.00 M06505_2.00 M06512_2.00 M06548_2.00 M03294_2.00 M05428_2.00 M05429_2.00 M09257_2.00 0 0 0 0 0 0 0 0 0 M02698_2.00 M02323_2.00 M09264_2.00 M03923_2.00 M09266_2.00 M09603_2.00 M02394_2.00 M03364_2.00 M03365_2.00 0 0 0 0 0 0 0 0 0 M05585_2.00 M05586_2.00 M05589_2.00 M05590_2.00 M05591_2.00 M05592_2.00 M05593_2.00 M05594_2.00 M09271_2.00 0 0 0 0 0 0 0 0 0 M08153_2.00 M08157_2.00 M03368_2.00 M09275_2.00 M03389_2.00 M03390_2.00 M09288_2.00 M09609_2.00 M09610_2.00 0 0 0 0 0 0 0 0 0 M03412_2.00 M03413_2.00 M03414_2.00 M05673_2.00 M05674_2.00 M05675_2.00 M05676_2.00 M09296_2.00 M00369_2.00 0 0 0 0 0 0 0 0 0 M00370_2.00 M00371_2.00 M00372_2.00 M00373_2.00 M09298_2.00 M01304_2.00 M03447_2.00 M05709_2.00 M05710_2.00 0 0 0 0 0 0 0 0 0 M02454_2.00 M09371_2.00 M09631_2.00 M09632_2.00 M09375_2.00 M02460_2.00 M05737_2.00 M05738_2.00 M08427_2.00 0 0 0 0 0 0 0 0 0 M02487_2.00 M07498_2.00 M00194_2.00 M05892_2.00 M05894_2.00 M09390_2.00 M05769_2.00 M05770_2.00 M09391_2.00 0 0 0 0 0 0 0 0 0 M05896_2.00 M09396_2.00 M08165_2.00 M08166_2.00 M08168_2.00 M08169_2.00 M08170_2.00 M09412_2.00 M09413_2.00 0 0 0 0 0 0 0 0 0 M03554_2.00 M03555_2.00 M03994_2.00 M05795_2.00 M05796_2.00 M05797_2.00 M05798_2.00 M00832_2.00 M03570_2.00 0 0 0 0 0 0 0 0 0 M03571_2.00 M03572_2.00 M03573_2.00 M03574_2.00 M05821_2.00 M05822_2.00 M03581_2.00 M07782_2.00 MA0030.1 0 0 0 0 0 0 0 0 0 MA0031.1 MA0051.1 MA0057.1 MA0059.1 MA0066.1 MA0069.1 MA0070.1 MA0071.1 MA0072.1 0 0 0 0 0 0 0 0 0 MA0073.1 MA0074.1 MA0077.1 MA0084.1 MA0091.1 MA0101.1 MA0107.1 MA0115.1 MA0119.1 0 0 0 0 0 0 0 0 0 MA0130.1 MA0139.1 MA0149.1 MA0138.2 MA0152.1 MA0155.1 MA0159.1 MA0160.1 MA0163.1 0 0 0 0 0 0 0 0 0 MA0259.1 MA0468.1 MA0476.1 MA0478.1 MA0479.1 MA0488.1 MA0489.1 MA0492.1 MA0497.1 0 0 0 0 0 0 0 0 0 MA0501.1 MA0504.1 MA0506.1 MA0507.1 MA0513.1 MA0517.1 MA0523.1 MA0527.1 MA0076.2 0 0 0 0 0 0 0 0 0 MA0258.2 MA0050.2 MA0137.3 MA0144.2 MA0140.2 MA0095.2 MA0593.1 MA0595.1 MA0596.1 0 0 0 0 0 0 0 0 0 MA0597.1 MA0599.1 MA0610.1 MA0613.1 MA0618.1 MA0625.1 MA0628.1 MA0630.1 MA0634.1 0 0 0 0 0 0 0 0 0 MA0635.1 MA0636.1 MA0637.1 MA0638.1 MA0639.1 MA0641.1 MA0136.2 MA0027.2 MA0642.1 0 0 0 0 0 0 0 0 0 MA0644.1 MA0645.1 MA0475.2 MA0042.2 MA0033.2 MA0157.2 MA0646.1 MA0647.1 MA0648.1 0 0 0 0 0 0 0 0 0 MA0649.1 MA0131.2 MA0046.2 MA0153.2 MA0651.1 MA0486.2 MA0652.1 MA0653.1 MA0654.1 0 0 0 0 0 0 0 0 0 MA0655.1 MA0656.1 MA0657.1 MA0658.1 MA0660.1 MA0661.1 MA0662.1 MA0663.1 MA0664.1 0 0 0 0 0 0 0 0 0 MA0665.1 MA0666.1 MA0667.1 MA0668.1 MA0669.1 MA0670.1 MA0671.1 MA0048.2 MA0672.1 0 0 0 0 0 0 0 0 0 MA0673.1 MA0674.1 MA0675.1 MA0678.1 MA0068.2 MA0680.1 MA0683.1 MA0685.1 MA0686.1 0 0 0 0 0 0 0 0 0 MA0687.1 MA0083.3 MA0009.2 MA0688.1 MA0689.1 MA0690.1 MA0691.1 MA0145.3 MA0692.1 0 0 0 0 0 0 0 0 0 MA0694.1 MA0695.1 MA0696.1 MA0697.1 MA0698.1 MA0699.1 MA0706.1 MA0707.1 MA0708.1 0 0 0 0 0 0 0 0 0 MA0710.1 MA0711.1 MA0132.2 MA0713.1 MA0714.1 MA0715.1 MA0716.1 MA0717.1 MA0718.1 0 0 0 0 0 0 0 0 0 MA0719.1 MA0721.1 MA0722.1 MA0723.1 MA0724.1 MA0725.1 MA0726.1 MA0112.3 MA0141.3 0 0 0 0 0 0 0 0 0 MA0017.2 MA0113.3 MA0727.1 MA0729.1 MA0730.1 MA0731.1 MA0472.2 MA0732.1 MA0733.1 0 0 0 0 0 0 0 0 0 MA0735.1 MA0736.1 MA0737.1 MA0738.1 MA0740.1 MA0741.1 MA0747.1 MA0749.1 MA0751.1 0 0 0 0 0 0 0 0 0 MA0088.2 MA0752.1 MA0754.1 MA0755.1 MA0756.1 MA0757.1 MA0758.1 MA0759.1 MA0760.1 0 0 0 0 0 0 0 0 0 MA0474.2 MA0098.3 MA0762.1 MA0763.1 MA0156.2 MA0767.1 MA0768.1 MA0770.1 MA0771.1 0 0 0 0 0 0 0 0 0 MA0772.1 MA0773.1 MA0498.2 MA0774.1 MA0775.1 MA0776.1 MA0777.1 MA0105.4 MA0778.1 0 0 0 0 0 0 0 0 0 MA0779.1 MA0780.1 MA0781.1 MA0783.1 MA0784.1 MA0785.1 MA0786.1 MA0787.1 MA0788.1 0 0 0 0 0 0 0 0 0 MA0789.1 MA0790.1 MA0791.1 MA0792.1 MA0793.1 MA0794.1 MA0600.2 MA0795.1 MA0796.1 0 0 0 0 0 0 0 0 0 MA0797.1 MA0799.1 MA0510.2 MA0511.2 MA0800.1 MA0801.1 MA0802.1 MA0803.1 MA0804.1 0 0 0 0 0 0 0 0 0 MA0805.1 MA0806.1 MA0807.1 MA0808.1 MA0810.1 MA0811.1 MA0812.1 MA0813.1 MA0524.2 0 0 0 0 0 0 0 0 0 MA0815.1 MA0464.2 MA0817.1 MA0818.1 MA0819.1 MA0820.1 MA0821.1 MA0822.1 MA0823.1 0 0 0 0 0 0 0 0 0 MA0058.3 MA0825.1 MA0826.1 MA0827.1 MA0828.1 MA0834.1 MA0466.2 MA0837.1 MA0838.1 0 0 0 0 0 0 0 0 0 MA0839.1 MA0841.1 MA0843.1 MA0844.1 MA0845.1 MA0032.2 MA0846.1 MA0848.1 MA0849.1 0 0 0 0 0 0 0 0 0 MA0850.1 MA0855.1 MA0856.1 MA0525.2 MA0106.3 MA0861.1 MA0862.1 MA0863.1 MA0024.3 0 0 0 0 0 0 0 0 0 MA0865.1 MA0866.1 MA0872.1 MA0028.2 MA0873.1 MA0875.1 MA0876.1 MA0882.1 MA0884.1 0 0 0 0 0 0 0 0 0 MA0886.1 MA0887.1 MA0888.1 MA0889.1 MA0890.1 MA0891.1 MA0892.1 MA0894.1 MA0895.1 0 0 0 0 0 0 0 0 0 MA0899.1 MA0903.1 MA0905.1 MA0906.1 MA0907.1 MA0908.1 MA0914.1 MA0852.2 MA0036.3 0 0 0 0 0 0 0 0 0 MA1106.1 MA0147.3 MA0100.3 MA0104.4 MA1109.1 MA0161.2 MA0060.3 MA1110.1 MA1111.1 0 0 0 0 0 0 0 0 0 MA0014.3 MA1114.1 MA1115.1 MA1116.1 MA1117.1 MA1118.1 MA1119.1 MA0442.2 MA1120.1 0 0 0 0 0 0 0 0 0 MA1121.1 MA1122.1 MA0750.2 MA0103.3 MA1124.1 MA1125.1 MA1154.1 MA1155.1 MA0037.3 0 0 0 0 0 0 0 0 0 MA0099.3 MA1126.1 MA1127.1 MA1128.1 MA1129.1 MA1130.1 MA1131.1 MA1132.1 MA1133.1 0 0 0 0 0 0 0 0 0 MA1134.1 MA1135.1 MA1136.1 MA1137.1 MA1138.1 MA1139.1 MA1141.1 MA1142.1 MA1143.1 0 0 0 0 0 0 0 0 0 MA1144.1 MA1145.1 MA1146.1 MA1147.1 MA1148.1 MA1149.1 MA1150.1 MA1151.1 MA1152.1 0 0 0 0 0 0 0 0 0 MA0831.2 MA0693.2 MA1418.1 MA1419.1 MA1420.1 MA1421.1 MA1463.1 MA1464.1 MA1466.1 0 0 0 0 0 0 0 0 0 MA1467.1 MA1468.1 MA1101.2 MA1470.1 MA1471.1 MA1472.1 MA1473.1 MA1474.1 MA1475.1 0 0 0 0 0 0 0 0 0 MA1476.1 MA1478.1 MA1479.1 MA1480.1 MA1481.1 MA1483.1 MA1484.1 MA1485.1 MA1487.1 0 0 0 0 0 0 0 0 0 MA1489.1 MA1491.1 MA1493.1 MA1494.1 MA1495.1 MA1496.1 MA1497.1 MA1498.1 MA1499.1 0 0 0 0 0 0 0 0 0 MA1500.1 MA1501.1 MA1502.1 MA1503.1 MA1504.1 MA1505.1 MA1506.1 MA1507.1 MA1508.1 0 0 0 0 0 0 0 0 0 MA1509.1 MA1511.1 MA1512.1 MA1513.1 MA1514.1 MA1515.1 MA1516.1 MA1517.1 MA1518.1 0 0 0 0 0 0 0 0 0 MA1519.1 MA1520.1 MA1521.1 MA1522.1 MA1523.1 MA1524.1 MA1525.1 MA1527.1 MA1528.1 0 0 0 0 0 0 0 0 0 MA1529.1 MA1530.1 MA1531.1 MA1532.1 MA1533.1 MA1534.1 MA1535.1 MA1536.1 MA1537.1 0 0 0 0 0 0 0 0 0 MA1538.1 MA1539.1 MA1540.1 MA1541.1 MA1542.1 MA1544.1 MA1545.1 MA1546.1 MA1547.1 0 0 0 0 0 0 0 0 0 MA1548.1 0

I have tried passing three motif matrices into the find_motifs function beforehand. One of them is just the 841 motif Jaspar 2022 matrix, another is the 1590 motif matrix provided in Pando using data("motifs"), and the last one is the same as the second, but I add the "motif2tf = motif2tf" parameter after editing the provided motif2tf data frame in Pando to only contain one instance of each motif, downsizing the data frame size from 2200+ to 1590. All three ways have resulted in similar outputs of NetworkTFs(), and neither has worked when running infer_grn.

On Wed, Jan 11, 2023 at 12:22 PM sylestiel @.***> wrote:

Posted part of the screenshot

[image: Screen Shot 2023-01-11 at 12 22 15 PM] https://user-images.githubusercontent.com/64283689/211887035-e8cfcd51-d2c5-4f50-a6d8-8f6f6a4b23a3.png

— Reply to this email directly, view it on GitHub https://github.com/quadbiolab/Pando/issues/26#issuecomment-1379305951, or unsubscribe https://github.com/notifications/unsubscribe-auth/AMPIKGKCZXZSPRR5CS6CU7LWR327RANCNFSM6AAAAAATUQ6QBI . You are receiving this because you authored the thread.Message ID: @.***>

joschif commented 1 year ago

For both of you it seems there were no TFs selected, so I would guess that the TFs provided by Pando were not found in your object. Since you are using the mouse genome I assume the data is from mouse? It could be that the human TFs don't match the mouse nomenclature

Sandman-1 commented 1 year ago

Okay, thank you for the tip! I have loaded in the mouse cisBP motif matrix from the chromVARmotifs package instead and am finding motifs with it now. Let’s see what happens.

On Wed, Jan 11, 2023 at 1:53 PM Skanda Hebbale @.***> wrote:

Good afternoon! Thank you guys for the responses!

Here is the copy of what NetworkTFs returns.

-

M07783_2.00 M07784_2.00 M02651_2.00 M08707_2.00 M00116_2.00 M01657_2.00 M01661_2.00 M01663_2.00 M01668_2.00 0 0 0 0 0 0 0 0 0 M08708_2.00 M02652_2.00 M07439_2.00 M08754_2.00 M08714_2.00 M08716_2.00 M08769_2.00 M08719_2.00 M09455_2.00 0 0 0 0 0 0 0 0 0 M02799_2.00 M02800_2.00 M04179_2.00 M04180_2.00 M04181_2.00 M04182_2.00 M08758_2.00 M08057_2.00 M01722_2.00 0 0 0 0 0 0 0 0 0 M02790_2.00 M02791_2.00 M04131_2.00 M04132_2.00 M04133_2.00 M04134_2.00 M02794_2.00 M04156_2.00 M04157_2.00 0 0 0 0 0 0 0 0 0 M08736_2.00 M04158_2.00 M04159_2.00 M01743_2.00 M04207_2.00 M04208_2.00 M04209_2.00 M04210_2.00 M04211_2.00 0 0 0 0 0 0 0 0 0 M08734_2.00 M08759_2.00 M08763_2.00 M09471_2.00 M09472_2.00 M01726_2.00 M04090_2.00 M04091_2.00 M08052_2.00 0 0 0 0 0 0 0 0 0 M08077_2.00 M08789_2.00 M04299_2.00 M04300_2.00 M04301_2.00 M04302_2.00 M08822_2.00 M09492_2.00 M01143_2.00 0 0 0 0 0 0 0 0 0 M01812_2.00 M02842_2.00 M02843_2.00 M02844_2.00 M02845_2.00 M02846_2.00 M02847_2.00 M02863_2.00 M02864_2.00 0 0 0 0 0 0 0 0 0 M03639_2.00 M03640_2.00 M03641_2.00 M04029_2.00 M04030_2.00 M04031_2.00 M04293_2.00 M04294_2.00 M04295_2.00 0 0 0 0 0 0 0 0 0 M04296_2.00 M04297_2.00 M04298_2.00 M04309_2.00 M04310_2.00 M04311_2.00 M04312_2.00 M04313_2.00 M04314_2.00 0 0 0 0 0 0 0 0 0 M04315_2.00 M04316_2.00 M04317_2.00 M04318_2.00 M04319_2.00 M04320_2.00 M06020_2.00 M04040_2.00 M08811_2.00 0 0 0 0 0 0 0 0 0 M02872_2.00 M04382_2.00 M04383_2.00 M09499_2.00 M01849_2.00 M08080_2.00 M08850_2.00 M08320_2.00 M08233_2.00 0 0 0 0 0 0 0 0 0 M08277_2.00 M08279_2.00 M08280_2.00 M08235_2.00 M08282_2.00 M08862_2.00 M08866_2.00 M08867_2.00 M08293_2.00 0 0 0 0 0 0 0 0 0 M08298_2.00 M00130_2.00 M07926_2.00 M08301_2.00 M00775_2.00 M08986_2.00 M07590_2.00 M05848_2.00 M07592_2.00 0 0 0 0 0 0 0 0 0 M07594_2.00 M07596_2.00 M09508_2.00 M08897_2.00 M09509_2.00 M08307_2.00 M07606_2.00 M07607_2.00 M08248_2.00 0 0 0 0 0 0 0 0 0 M08321_2.00 M08325_2.00 M00239_2.00 M00240_2.00 M04523_2.00 M04524_2.00 M08909_2.00 M07613_2.00 M07619_2.00 0 0 0 0 0 0 0 0 0 M07620_2.00 M02900_2.00 M08330_2.00 M08331_2.00 M08252_2.00 M08337_2.00 M07627_2.00 M07628_2.00 M07629_2.00 0 0 0 0 0 0 0 0 0 M07634_2.00 M07635_2.00 M08254_2.00 M07638_2.00 M07643_2.00 M04568_2.00 M04569_2.00 M08924_2.00 M07649_2.00 0 0 0 0 0 0 0 0 0 M07650_2.00 M08354_2.00 M07659_2.00 M08356_2.00 M08362_2.00 M07669_2.00 M00243_2.00 M00244_2.00 M00245_2.00 0 0 0 0 0 0 0 0 0 M00246_2.00 M00247_2.00 M00248_2.00 M00249_2.00 M08937_2.00 M07672_2.00 M07676_2.00 M04609_2.00 M04610_2.00 0 0 0 0 0 0 0 0 0 M08365_2.00 M07687_2.00 M08262_2.00 M08367_2.00 M02898_2.00 M02910_2.00 M03682_2.00 M04517_2.00 M04518_2.00 0 0 0 0 0 0 0 0 0 M04558_2.00 M04559_2.00 M04645_2.00 M04646_2.00 M06117_2.00 M06118_2.00 M08920_2.00 M09016_2.00 M07697_2.00 0 0 0 0 0 0 0 0 0 M07698_2.00 M05856_2.00 M07704_2.00 M07705_2.00 M08375_2.00 M07710_2.00 M07711_2.00 M07713_2.00 M07716_2.00 0 0 0 0 0 0 0 0 0 M07720_2.00 M07724_2.00 M07725_2.00 M07726_2.00 M07734_2.00 M08384_2.00 M08386_2.00 M07738_2.00 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MA0066.1 MA0069.1 MA0070.1 MA0071.1 MA0072.1 0 0 0 0 0 0 0 0 0 MA0073.1 MA0074.1 MA0077.1 MA0084.1 MA0091.1 MA0101.1 MA0107.1 MA0115.1 MA0119.1 0 0 0 0 0 0 0 0 0 MA0130.1 MA0139.1 MA0149.1 MA0138.2 MA0152.1 MA0155.1 MA0159.1 MA0160.1 MA0163.1 0 0 0 0 0 0 0 0 0 MA0259.1 MA0468.1 MA0476.1 MA0478.1 MA0479.1 MA0488.1 MA0489.1 MA0492.1 MA0497.1 0 0 0 0 0 0 0 0 0 MA0501.1 MA0504.1 MA0506.1 MA0507.1 MA0513.1 MA0517.1 MA0523.1 MA0527.1 MA0076.2 0 0 0 0 0 0 0 0 0 MA0258.2 MA0050.2 MA0137.3 MA0144.2 MA0140.2 MA0095.2 MA0593.1 MA0595.1 MA0596.1 0 0 0 0 0 0 0 0 0 MA0597.1 MA0599.1 MA0610.1 MA0613.1 MA0618.1 MA0625.1 MA0628.1 MA0630.1 MA0634.1 0 0 0 0 0 0 0 0 0 MA0635.1 MA0636.1 MA0637.1 MA0638.1 MA0639.1 MA0641.1 MA0136.2 MA0027.2 MA0642.1 0 0 0 0 0 0 0 0 0 MA0644.1 MA0645.1 MA0475.2 MA0042.2 MA0033.2 MA0157.2 MA0646.1 MA0647.1 MA0648.1 0 0 0 0 0 0 0 0 0 MA0649.1 MA0131.2 MA0046.2 MA0153.2 MA0651.1 MA0486.2 MA0652.1 MA0653.1 MA0654.1 0 0 0 0 0 0 0 0 0 MA0655.1 MA0656.1 MA0657.1 MA0658.1 MA0660.1 MA0661.1 MA0662.1 MA0663.1 MA0664.1 0 0 0 0 0 0 0 0 0 MA0665.1 MA0666.1 MA0667.1 MA0668.1 MA0669.1 MA0670.1 MA0671.1 MA0048.2 MA0672.1 0 0 0 0 0 0 0 0 0 MA0673.1 MA0674.1 MA0675.1 MA0678.1 MA0068.2 MA0680.1 MA0683.1 MA0685.1 MA0686.1 0 0 0 0 0 0 0 0 0 MA0687.1 MA0083.3 MA0009.2 MA0688.1 MA0689.1 MA0690.1 MA0691.1 MA0145.3 MA0692.1 0 0 0 0 0 0 0 0 0 MA0694.1 MA0695.1 MA0696.1 MA0697.1 MA0698.1 MA0699.1 MA0706.1 MA0707.1 MA0708.1 0 0 0 0 0 0 0 0 0 MA0710.1 MA0711.1 MA0132.2 MA0713.1 MA0714.1 MA0715.1 MA0716.1 MA0717.1 MA0718.1 0 0 0 0 0 0 0 0 0 MA0719.1 MA0721.1 MA0722.1 MA0723.1 MA0724.1 MA0725.1 MA0726.1 MA0112.3 MA0141.3 0 0 0 0 0 0 0 0 0 MA0017.2 MA0113.3 MA0727.1 MA0729.1 MA0730.1 MA0731.1 MA0472.2 MA0732.1 MA0733.1 0 0 0 0 0 0 0 0 0 MA0735.1 MA0736.1 MA0737.1 MA0738.1 MA0740.1 MA0741.1 MA0747.1 MA0749.1 MA0751.1 0 0 0 0 0 0 0 0 0 MA0088.2 MA0752.1 MA0754.1 MA0755.1 MA0756.1 MA0757.1 MA0758.1 MA0759.1 MA0760.1 0 0 0 0 0 0 0 0 0 MA0474.2 MA0098.3 MA0762.1 MA0763.1 MA0156.2 MA0767.1 MA0768.1 MA0770.1 MA0771.1 0 0 0 0 0 0 0 0 0 MA0772.1 MA0773.1 MA0498.2 MA0774.1 MA0775.1 MA0776.1 MA0777.1 MA0105.4 MA0778.1 0 0 0 0 0 0 0 0 0 MA0779.1 MA0780.1 MA0781.1 MA0783.1 MA0784.1 MA0785.1 MA0786.1 MA0787.1 MA0788.1 0 0 0 0 0 0 0 0 0 MA0789.1 MA0790.1 MA0791.1 MA0792.1 MA0793.1 MA0794.1 MA0600.2 MA0795.1 MA0796.1 0 0 0 0 0 0 0 0 0 MA0797.1 MA0799.1 MA0510.2 MA0511.2 MA0800.1 MA0801.1 MA0802.1 MA0803.1 MA0804.1 0 0 0 0 0 0 0 0 0 MA0805.1 MA0806.1 MA0807.1 MA0808.1 MA0810.1 MA0811.1 MA0812.1 MA0813.1 MA0524.2 0 0 0 0 0 0 0 0 0 MA0815.1 MA0464.2 MA0817.1 MA0818.1 MA0819.1 MA0820.1 MA0821.1 MA0822.1 MA0823.1 0 0 0 0 0 0 0 0 0 MA0058.3 MA0825.1 MA0826.1 MA0827.1 MA0828.1 MA0834.1 MA0466.2 MA0837.1 MA0838.1 0 0 0 0 0 0 0 0 0 MA0839.1 MA0841.1 MA0843.1 MA0844.1 MA0845.1 MA0032.2 MA0846.1 MA0848.1 MA0849.1 0 0 0 0 0 0 0 0 0 MA0850.1 MA0855.1 MA0856.1 MA0525.2 MA0106.3 MA0861.1 MA0862.1 MA0863.1 MA0024.3 0 0 0 0 0 0 0 0 0 MA0865.1 MA0866.1 MA0872.1 MA0028.2 MA0873.1 MA0875.1 MA0876.1 MA0882.1 MA0884.1 0 0 0 0 0 0 0 0 0 MA0886.1 MA0887.1 MA0888.1 MA0889.1 MA0890.1 MA0891.1 MA0892.1 MA0894.1 MA0895.1 0 0 0 0 0 0 0 0 0 MA0899.1 MA0903.1 MA0905.1 MA0906.1 MA0907.1 MA0908.1 MA0914.1 MA0852.2 MA0036.3 0 0 0 0 0 0 0 0 0 MA1106.1 MA0147.3 MA0100.3 MA0104.4 MA1109.1 MA0161.2 MA0060.3 MA1110.1 MA1111.1 0 0 0 0 0 0 0 0 0 MA0014.3 MA1114.1 MA1115.1 MA1116.1 MA1117.1 MA1118.1 MA1119.1 MA0442.2 MA1120.1 0 0 0 0 0 0 0 0 0 MA1121.1 MA1122.1 MA0750.2 MA0103.3 MA1124.1 MA1125.1 MA1154.1 MA1155.1 MA0037.3 0 0 0 0 0 0 0 0 0 MA0099.3 MA1126.1 MA1127.1 MA1128.1 MA1129.1 MA1130.1 MA1131.1 MA1132.1 MA1133.1 0 0 0 0 0 0 0 0 0 MA1134.1 MA1135.1 MA1136.1 MA1137.1 MA1138.1 MA1139.1 MA1141.1 MA1142.1 MA1143.1 0 0 0 0 0 0 0 0 0 MA1144.1 MA1145.1 MA1146.1 MA1147.1 MA1148.1 MA1149.1 MA1150.1 MA1151.1 MA1152.1 0 0 0 0 0 0 0 0 0 MA0831.2 MA0693.2 MA1418.1 MA1419.1 MA1420.1 MA1421.1 MA1463.1 MA1464.1 MA1466.1 0 0 0 0 0 0 0 0 0 MA1467.1 MA1468.1 MA1101.2 MA1470.1 MA1471.1 MA1472.1 MA1473.1 MA1474.1 MA1475.1 0 0 0 0 0 0 0 0 0 MA1476.1 MA1478.1 MA1479.1 MA1480.1 MA1481.1 MA1483.1 MA1484.1 MA1485.1 MA1487.1 0 0 0 0 0 0 0 0 0 MA1489.1 MA1491.1 MA1493.1 MA1494.1 MA1495.1 MA1496.1 MA1497.1 MA1498.1 MA1499.1 0 0 0 0 0 0 0 0 0 MA1500.1 MA1501.1 MA1502.1 MA1503.1 MA1504.1 MA1505.1 MA1506.1 MA1507.1 MA1508.1 0 0 0 0 0 0 0 0 0 MA1509.1 MA1511.1 MA1512.1 MA1513.1 MA1514.1 MA1515.1 MA1516.1 MA1517.1 MA1518.1 0 0 0 0 0 0 0 0 0 MA1519.1 MA1520.1 MA1521.1 MA1522.1 MA1523.1 MA1524.1 MA1525.1 MA1527.1 MA1528.1 0 0 0 0 0 0 0 0 0 MA1529.1 MA1530.1 MA1531.1 MA1532.1 MA1533.1 MA1534.1 MA1535.1 MA1536.1 MA1537.1 0 0 0 0 0 0 0 0 0 MA1538.1 MA1539.1 MA1540.1 MA1541.1 MA1542.1 MA1544.1 MA1545.1 MA1546.1 MA1547.1 0 0 0 0 0 0 0 0 0 MA1548.1 0

I have tried passing three motif matrices into the find_motifs function beforehand. One of them is just the 841 motif Jaspar 2022 matrix, another is the 1590 motif matrix provided in Pando using data("motifs"), and the last one is the same as the second, but I add the "motif2tf = motif2tf" parameter after editing the provided motif2tf data frame in Pando to only contain one instance of each motif, downsizing the data frame size from 2200+ to 1590. All three ways have resulted in similar outputs of NetworkTFs(), and neither has worked when running infer_grn.

On Wed, Jan 11, 2023 at 12:22 PM sylestiel @.***> wrote:

Posted part of the screenshot

[image: Screen Shot 2023-01-11 at 12 22 15 PM] https://user-images.githubusercontent.com/64283689/211887035-e8cfcd51-d2c5-4f50-a6d8-8f6f6a4b23a3.png

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sylestiel commented 1 year ago

@Sandman-1

Please clue me in if you succeed.

Sandman-1 commented 1 year ago

I think I have succeeded!

The following code is based on using the version 2 motif matrix from chromVARmotifs for mice, mouse_pwms_v2.

  1. library(Seurat)
  2. library(Signac)
  3. library(chromVARmotifs)
  4. library(Pando)
  5. data("mouse_pwms_v2")
  7. x <- character()
  8. for(i in @.***)){
  9. x[i] <- @.**@.
  10. }
  12. motif2tf <- data.frame(motif = @.), tf = x, origin = "CIS-BP", geneid = gsub("[[:alnum:][:punct:]]", "", **@.***)), family = NA, name = NA, symbol = NA, motif_tf = NA) %>%
  13. subset(gene_id != "XP" & gene_id != "NP")
  14. mouse_pwms_v3 <- subset(mouse_pwms_v2, @.***) %in% motif2tf$motif)
  15. TKO_int_hep <- readRDS("~/TKO Multiome/Final Seurat Objects/TKO Hepatocytes Filtered and Integrated.rds")
  16. TKO_int_hep <- FindVariableFeatures(TKO_int_hep, assay = "RNA", nfeatures = @.***$RNA)) %>%
  17. ScaleData(assay = "RNA")
  18. TKO_int_hep <- initiate_grn(TKO_int_hep, peak_assay = "Peaks", rna_assay = "RNA")
  19. TKO_int_hep <- find_motifs(TKO_int_hep, pfm = mouse_pwms_v3, motif_tfs = motif2tf, genome = BSgenome.Mmusculus.UCSC.mm10)
  20. NetworkTFs(TKO_int_hep)
  21. TKO_int_hep <- infer_grn(TKO_int_hep, peak_to_gene_method = "GREAT", upstream = 1e+05, downstream = 1e+05, method = "bagging_ridge")

Please let me know if it does or doesn't work for you. :)

On Wed, Jan 11, 2023 at 2:31 PM sylestiel @.***> wrote:

@Sandman-1 https://github.com/Sandman-1

Please clue me in if you succeed.

— Reply to this email directly, view it on GitHub https://github.com/quadbiolab/Pando/issues/26#issuecomment-1379448768, or unsubscribe https://github.com/notifications/unsubscribe-auth/AMPIKGMWTEIS7N6UCC3CVHDWR4KA3ANCNFSM6AAAAAATUQ6QBI . You are receiving this because you were mentioned.Message ID: @.***>

sylestiel commented 1 year ago

@Sandman-1

Excuse my naivete. Not sure what to enter for the asterisk in the code. Can you give a dummy example?

Sandman-1 commented 1 year ago

Oh, no worries! I am also new to the whole GRN analysis stuff, so I am learning just as much as you. :)

The asterisk in the gsub function, if that's what you are referring to, is actually part of the expression. Basically, that's the syntax needed to extract the ensembl gene id for each transcription factor from the PWM motif matrix given by chromVARmotifs.

On Thu, Jan 12, 2023 at 10:01 AM sylestiel @.***> wrote:

@Sandman-1 https://github.com/Sandman-1

Excuse my naivete. Not sure what to enter for the asterisk in the code. Can you give a dummy example?

— Reply to this email directly, view it on GitHub https://github.com/quadbiolab/Pando/issues/26#issuecomment-1380613342, or unsubscribe https://github.com/notifications/unsubscribe-auth/AMPIKGKDJY6Z5EU456IJ7SDWSATELANCNFSM6AAAAAATUQ6QBI . You are receiving this because you were mentioned.Message ID: @.***>

sylestiel commented 1 year ago

Still not getting it.

Asterisks in Lines 8, 9, 12, 14 and 16.

Also what is XP and NP

Sandman-1 commented 1 year ago

Ohh, I am sorry! I was replying from my email, but it appears incorrectly formatted on Github. Does this show better?

Also, the XP and NP motifs are just ones that didn't have an Ensembl id, so I omitted them because I don't think they are well characterized. But you can keep them! It shouldn't really matter.

library(Seurat) library(Signac) library(chromVARmotifs) library(Pando) data("mouse_pwms_v2")

x <- character() for(i in 1:length(mouse_pwms_v2@listData)){ x[i] <- mouse_pwms_v2@listData[[i]]@name }

motif2tf <- data.frame(motif = names(mouse_pwms_v2@listData), tf = x, origin = "CIS-BP", geneid = gsub("[[:alnum:][:punct:]]*", "", names(mouse_pwms_v2@listData)), family = NA, name = NA, symbol = NA, motif_tf = NA) %>% subset(gene_id != "XP" & gene_id != "NP") mouse_pwms_v3 <- subset(mouse_pwms_v2, names(mouse_pwms_v2@listData) %in% motif2tf$motif) TKO_int_hep <- readRDS("~/TKO Multiome/Final Seurat Objects/TKO Hepatocytes Filtered and Integrated.rds") TKO_int_hep <- FindVariableFeatures(TKO_int_hep, assay = "RNA", nfeatures = nrow(TKO_int_hep@assays$RNA)) %>% ScaleData(assay = "RNA") TKO_int_hep <- initiate_grn(TKO_int_hep, peak_assay = "Peaks", rna_assay = "RNA") TKO_int_hep <- find_motifs(TKO_int_hep, pfm = mouse_pwms_v3, motif_tfs = motif2tf, genome = BSgenome.Mmusculus.UCSC.mm10) NetworkTFs(TKO_int_hep) TKO_int_hep <- infer_grn(TKO_int_hep, peak_to_gene_method = "GREAT", upstream = 1e+05, downstream = 1e+05, method = "xgb")

sylestiel commented 1 year ago

@Sandman-1 Thank you so very much. The code worked. Curious as to why you had to run ScaleData again. Did you try peak_to_gene_method = "Signac"?

Sandman-1 commented 1 year ago

My pleasure! I thank @joschif for the tip about genotype!

I honestly don't think running the scaledata function is needed, but I just did it because I will be pulling network data from the scale.data slot downstream. Also, I didn't try the Signac method because apparently GREAT does what the Signac method does in addition to other stuff, which sounds very cool.

sylestiel commented 1 year ago

Did you run get_network_graph() ran into the following error Error in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components, : n_neighbors must be smaller than the dataset size

Sandman-1 commented 1 year ago

Oooh, I haven't gotten there yet! However, I would set umap_method = "none"

sylestiel commented 1 year ago

Great. Thank you!!!

Sandman-1 commented 1 year ago

Hello @sylestiel! By any chance, is your GRN returning reasonable modules and target genes for each TF? I am not getting results that are anywhere near expected.

sylestiel commented 1 year ago

@Sandman-1

Hi,

Sorry for the delayed response. I was not happy with the outcome of Pando. Ended up switching to FigR instead.

joschif commented 1 year ago

Closing this since the issue seems to stem from the mouse genome which is discussed in a separate issue

GouQiao commented 11 months ago

HI, do you know where to get mouse_pwms_v2 data?