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smorabit / hdWGCNA

High dimensional weighted gene co-expression network analysis
https://smorabit.github.io/hdWGCNA/
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Error with PlotModuleTrajectory function #211

Closed kasumaz closed 3 months ago

kasumaz commented 3 months ago

Hi there, Thanks for the nice library and the way its organised.

I am trying to run PlotModuleTrajectory on a subset of cells where pseudotime has been run already..

p <- PlotModuleTrajectory(seurat_obj, pseudotime_col = 'COPs_pseudotime')

I get an output which I pasted below. It keeps saying I have duplicate column names and ive double checked this and done other checks to make sure.

In a previous step I defined 'COPs_pseudotime' as:

separate pseudotime trajectories by the different mature cells

seurat_obj$COPs_pseudotime <- ifelse(seurat_obj$integration_col7 %in% c("aNSC4", "OLTAPs", 'cycOPCs', 'OPCs', 'COPs', 'NFOL1','NFOL2','MOLs'), seurat_obj$pseudotime, NA)

I am not sure if the NA values are problematic. I even put in a dummy number to test it out but I cant seem to move away from the error. Any hints would be great.

Thanks a lot.

p <- PlotModuleTrajectory(seurat_obj, pseudotime_col = 'COPs_pseudotime') orig.ident Dataset kaz_cond5b kaz_cond5a integration_col7 kaz_cond5c obj_kp11_scex_AAACCCAGTATGAGAT-1_1 K11 in-house 11 OPCs OPCs obj_kp11_scex_AAACGCTAGTCAATCC-1_1 K11 in-house 11 Astrocytes Astrocytes nCount_RNA nFeature_RNA percent.mt seurat_clusters nCount_Protein obj_kp11_scex_AAACCCAGTATGAGAT-1_1 9487 3942 100 11 NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 6778 2481 100 25 NA nFeature_Protein nCount_SCT MULTI_Seq Sex S.Score G2M.Score Phase Lane obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA 9984 NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA 9871 NA NA Cell_Type velocity_pseudotime Predicted_Region_Bcells Ventral_Score_Bcells obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA Dorsal_Score_Bcells mito.auc mito.init.auc synapse.auc neuro.mig.auc obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA NA NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA NA NA NA dorsoventral.auc cortex.reg.auc Predicted_Region_Acells Ventral_Score_Acells obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA NA Dorsal_Score_Acells Dorsal_Lineage_AUC Ventral_Lineage_AUC Lineage obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA NA nCount_integrated nFeature_integrated RNA_snn_res.1 cellID sampleID obj_kp11_scex_AAACCCAGTATGAGAT-1_1 0 0 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 0 0 TREATMENT_NAME exonic_umis_total exonic_genes_detected obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA log10_exonic_umis_total genotype PctMitoUMI sampleLabel obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA oligodendrocyteNoDPACS.seuratCluster OPCandC19NoDPACS.seuratCluster obj_kp11_scex_AAACCCAGTATGAGAT-1_1 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 genotypeGroup biologicalInterest plotClusters selected.oligo.seuratCluster obj_kp11_scex_AAACCCAGTATGAGAT-1_1 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 treatment TPL2 TauP301S percent_mito_umi passFilter hMAPT_normCount obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA NA hMAPT_Log2NormCount passCells.seuratCluster passCells.clusterInterp obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA timepoint batch all20integrated.seuratClusterInterp age group percent_mito obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA Seuratv3.seuratCluster clusterInterp clusterInterpRevised obj_kp11_scex_AAACCCAGTATGAGAT-1_1 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 choroid.seuratCluster PVM.seuratCluster integratedOligo.cluster obj_kp11_scex_AAACCCAGTATGAGAT-1_1 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 endothelial.subClust.seuratCluster opc cop nfol mfol mol1 mol2 mol56 obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA NA NA NA NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA NA NA NA NA NA mol56_c1 cyc_opc tmol56 interferon mhc1 ieg finalInterps sub.cluster obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA NA NA NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA NA NA NA NA batchinfo mito.percent Library Region CellTypeAnnot tissue gate obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA postnatal_day set condition Estimated.Number.of.Cells Mean.Reads.per.Cell obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA NA Median.Genes.per.Cell Number.of.Reads Valid.Barcodes Sequencing.Saturation obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA Q30.Bases.in.Barcode Q30.Bases.in.RNA.Read Q30.Bases.in.Sample.Index obj_kp11_scex_AAACCCAGTATGAGAT-1_1 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 Q30.Bases.in.UMI Reads.Mapped.to.Genome Reads.Mapped.Confidently.to.Genome obj_kp11_scex_AAACCCAGTATGAGAT-1_1 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 Reads.Mapped.Confidently.to.Intergenic.Regions obj_kp11_scex_AAACCCAGTATGAGAT-1_1 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 Reads.Mapped.Confidently.to.Intronic.Regions obj_kp11_scex_AAACCCAGTATGAGAT-1_1 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 Reads.Mapped.Confidently.to.Exonic.Regions obj_kp11_scex_AAACCCAGTATGAGAT-1_1 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 Reads.Mapped.Confidently.to.Transcriptome Reads.Mapped.Antisense.to.Gene obj_kp11_scex_AAACCCAGTATGAGAT-1_1 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 Fraction.Reads.in.Cells Total.Genes.Detected Median.UMI.Counts.per.Cell obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA scrublet_score scrublet_cluster_score bh_pval is_doublet n_genes n_counts obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA NA NA NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA NA NA NA NA annot_leiden_0_59 annot_leiden QC_pass gate_gfp prediction_Class obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA probability_Class prediction_TaxonomyRank1 probability_TaxonomyRank1 cyclone obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA cyclone_G1 cyclone_S cyclone_G2M annot_leiden_neuronal_glial obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA NA UMAP_dim1_neuronal_glial UMAP_dim2_neuronal_glial annot_leiden_OPCs obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA UMAP_dim1_OPCs UMAP_dim2_OPCs annot_leiden_embryonic_RG obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA UMAP_dim1_embryonic_RG UMAP_dim2_embryonic_RG annot_leiden_NBs UMAP_dim1_NBs obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA NA UMAP_dim2_NBs CC.Difference cell_type_tsne Condition Simplified_clusters obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA Full_clusters lineage_alra Full_detailed_clusters new_clusters obj_kp11_scex_AAACCCAGTATGAGAT-1_1 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 clusters_to_use complete_simplified_clusters complete_full_clusters obj_kp11_scex_AAACCCAGTATGAGAT-1_1 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 percent.ribo percent.y dim30_res1 miQC.probability miQC.keep lineage obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA NA full_clusters simplified_clusters predicted.simplifiedcelltype.score obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA predicted.simplifiedcelltype predicted.fullcelltype.score obj_kp11_scex_AAACCCAGTATGAGAT-1_1 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 predicted.fullcelltype nCount_prediction.score.simplifiedcelltype obj_kp11_scex_AAACCCAGTATGAGAT-1_1 0 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 0 nFeature_prediction.score.simplifiedcelltype obj_kp11_scex_AAACCCAGTATGAGAT-1_1 0 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 0 nCount_prediction.score.fullcelltype nFeature_prediction.score.fullcelltype obj_kp11_scex_AAACCCAGTATGAGAT-1_1 0 0 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 0 0 n_numbers Kages_timepoint3 Kcelltype cells_to_show COPs_AUC COPs_AUC_scores obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA ModifiedKcelltype NewIDs1 MajorID OPCs_AUC OPCs_AUC_scores NFOL1.2_AUC obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NFOL1.2_AUC_scores MFOL1_2_AUC MFOL1_2_AUC_scores MOL1to6_AUC obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA MOL1to6_AUC_scores NFOL1_AUC NFOL1_AUC_scores NFOL2_AUC NFOL2_AUC_scores obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA NA MFOL1_AUC MFOL1_AUC_scores MFOL2_AUC MFOL2_AUC_scores MOL1to4_AUC obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA MOL1to4_AUC_scores MOL5_6_AUC MOL5_6_AUC_scores MOL2_3_AUC MOL2_3_AUC_scores obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA NA MOL5_AUC MOL5_AUC_scores MOL6_AUC MOL6_AUC_scores MOL1_AUC MOL1_AUC_scores obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA NA MOL4_AUC MOL4_AUC_scores MOL2_AUC MOL2_AUC_scores MOL3_AUC MOL3_AUC_scores obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA NA OL_Lineage_AUCdefined COP_AUC COP_AUC_scores qNSC1_AUC qNSC1_AUC_scores obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA aNSC1_AUC aNSC1_AUC_scores qNSC2_AUC qNSC2_AUC_scores qNSC3_AUC obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA qNSC3_AUC_scores aNSC4_AUC aNSC4_AUC_scores aNSC3_AUC aNSC3_AUC_scores obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA NA NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA NA NA aNSC2_AUC aNSC2_AUC_scores Earlier_stages1_AUCdefined Merged_AUCdefined obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA complete_simplified_clusters_no_ages RowNames obj_kp11_scex_AAACCCAGTATGAGAT-1_1 scex11k_scex_AAACCCAGTATGAGAT-1_1 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 scex11k_scex_AAACGCTAGTCAATCC-1_1 RowNames.3 MergedIDs2 MergedIDs3 MergedIDs4 MergedIDs5 MergedIDs6 Cluster1 obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA dorsal_markers1 astrocyte_defined MergedIDs7 MajorIDs3 MergedIDs8 MergedIDs9 obj_kp11_scex_AAACCCAGTATGAGAT-1_1 NA obj_kp11_scex_AAACGCTAGTCAATCC-1_1 NA MergedIDs10 MergedIDs11 MergedIDs12 MergedIDs13 MergedIDs14 MergedIDs15 obj_kp11_scex_AAACCCAGTATGAGAT-1_1 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 MergedIDs16 MergedIDs17 MajorIDs5 MergedIDs18 MergedIDs20 MergedIDs21 obj_kp11_scex_AAACCCAGTATGAGAT-1_1 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 MergedIDs22 integration_col kaz_cond integration_col2 kaz_cond4 kaz_cond5 obj_kp11_scex_AAACCCAGTATGAGAT-1_1 unknown P11 P11 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 unknown P11 P11 Brain_Region main_clusters kaz_cond2 kaz_cond3 simp_col integrated_res1 obj_kp11_scex_AAACCCAGTATGAGAT-1_1 dSVZ+CC . Normal OPCs 30 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 dSVZ+CC . Normal Remaining 1 ident integration_col3 kaz_cond6 percent.rb integration_col4 obj_kp11_scex_AAACCCAGTATGAGAT-1_1 OPCs 4.448192 OPCs obj_kp11_scex_AAACGCTAGTCAATCC-1_1 Astrocytes 1.652405 Astrocytes integration_col5 broader_stages1 integration_col7_df integration_col7_plot obj_kp11_scex_AAACCCAGTATGAGAT-1_1 OPCs OPCs OPCs obj_kp11_scex_AAACGCTAGTCAATCC-1_1 Astrocytes Astrocytes Astrocytes kaz_cond8 clusters metacell_grouping integrated.05_1_res.0.5 kaz_cond5d obj_kp11_scex_AAACCCAGTATGAGAT-1_1 13 OPCs#OPCs 5 OPCs obj_kp11_scex_AAACGCTAGTCAATCC-1_1 11 Astrocytes#Astrocytes 20 Astrocytes ntegration_col7_plot ntegration_col8_plot integration_col8_plot OLTAPs_AUC obj_kp11_scex_AAACCCAGTATGAGAT-1_1 OPCs OPCs OPCs other obj_kp11_scex_AAACGCTAGTCAATCC-1_1 Astrocytes Astrocytes Astrocytes other OLTAPs_AUC_scores integrated.075_1_res.0.75 integrated.1_1_res.1 obj_kp11_scex_AAACCCAGTATGAGAT-1_1 0.07490825 5 11 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 0.02697701 25 25 nFeature_SCT pseudotime brown yellow pink black purple obj_kp11_scex_AAACCCAGTATGAGAT-1_1 3927 33.108879 -3.809962 -6.132482 -1.832806 1.524285 -0.5784735 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 2501 8.146171 -13.791988 -7.637890 -4.775164 -4.476177 -5.5223190 turquoise salmon magenta red grey tan blue obj_kp11_scex_AAACCCAGTATGAGAT-1_1 2.316369 -0.1156856 -1.965892 -3.760517 -2.636385 -0.7092816 7.942873 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 -13.590762 -5.2901340 5.562703 6.352035 13.970372 0.6591107 -3.044767 greenyellow green brown.1 yellow.1 pink.1 black.1 purple.1 obj_kp11_scex_AAACCCAGTATGAGAT-1_1 7.8481404 18.713262 -3.809962 -6.132482 -1.832806 1.524285 -0.5784735 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 0.1632684 -1.400689 -13.791988 -7.637890 -4.775164 -4.476177 -5.5223190 turquoise.1 salmon.1 magenta.1 red.1 grey.1 tan.1 blue.1 obj_kp11_scex_AAACCCAGTATGAGAT-1_1 2.316369 -0.1156856 -1.965892 -3.760517 -2.636385 -0.7092816 7.942873 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 -13.590762 -5.2901340 5.562703 6.352035 13.970372 0.6591107 -3.044767 greenyellow.1 green.1 COPs_pseudotime UMAP1 UMAP2 obj_kp11_scex_AAACCCAGTATGAGAT-1_1 7.8481404 18.713262 33.10888 3.744274 13.230577 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 0.1632684 -1.400689 99.99000 3.653684 -9.383246 COPs_pseudotime_bins_20 brown yellow pink black purple obj_kp11_scex_AAACCCAGTATGAGAT-1_1 [32.9,49.2) -3.809962 -6.132482 -1.832806 1.524285 -0.5784735 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 [51.0, Inf] -13.791988 -7.637890 -4.775164 -4.476177 -5.5223190 turquoise salmon magenta red grey tan blue obj_kp11_scex_AAACCCAGTATGAGAT-1_1 2.316369 -0.1156856 -1.965892 -3.760517 -2.636385 -0.7092816 7.942873 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 -13.590762 -5.2901340 5.562703 6.352035 13.970372 0.6591107 -3.044767 greenyellow green obj_kp11_scex_AAACCCAGTATGAGAT-1_1 7.8481404 18.713262 obj_kp11_scex_AAACGCTAGTCAATCC-1_1 0.1632684 -1.400689 [ reached 'max' / getOption("max.print") -- omitted 4 rows ] Error in group_by(): ! Can't transform a data frame with duplicate names.

smorabit commented 3 months ago

Hi,

Thank you for taking the time to write this issue. However I am a little confused, I do not see your error message here? I am also not sure what the output is that you are showing here? I am also wondering if you got the same error running this on the dataset that we provided with the pseudotime tutorial? I don't think that the NA values are a problem, you can see in the tutorial that there are some NA values which we introduce to denote cells that are not assigned to certain trajectories.

Also I kindly suggest you to consult the markdown style guide and use the preview feature for GitHub issues, it is a lot easier for me to help solve these issues if they are easier to read!

kasumaz commented 3 months ago

Hi there, Sorry, but for some reason the error message was cut off. Thanks for your reply otherwise. I managed to work around the problem by simply building my own script to show the different modules in pseudotime. I can always share it here in due course if anyone else encounters the same issue. Thanks.