Rsugihara01
commented
1 year ago Hi, @TerezaClarence Thanks for contacting us again! I did cheak the code, but not really sure what is happening, because the code should have raised some other errors if dynamic time warping have failed in the first place.
Can you try adding '.copy()' at the end of the code that split the anndata? anndata sometime act weird when they are the copy of anndata. '.copy()' deepcopy and create new anndata and that might clear the problems.
opc_oligACC = adata1[adata1.obs['brain'].isin(['ACC'])].copy()
opc_oligDLPFC = adata1[adata1.obs['brain'].isin(['DLPFC'])].copy()If that did not work, would you plot some genes that exist in ACC_DLPFC.genes_for_tree_align like 'ADAM28'?
Also can you try the codes below and send the result to us ? It would be a great help to check what is going on.
multi_genes = ACC_DLPFC.alignmentdict["alignment001"]["multi_genes"]
print(multi_genes["path"])
print(f"length of path : {len(multi_genes["path"][0])}")
print(f"shape of data1 : {opc_oligACC.adata1.raw.to_adata().X.shape} ")
print(f"shape of data2 : {opc_oligDLPFC.adata1.raw.to_adata().X.shape} ")Best regard Rsugihara01
TerezaClarence
ykat0
rkelly712
Dear CAPITAL developers,
I am reaching again regarding the issue of plotting any sort of gene expression trends in custom dataset. When I run the tutorial and example data, everything works (except for plotting the trajectory tree as described in previous issue).
However, when I follow the exactly same approach on my data, I am unable to plot gene expression trends for any selected genes for which the similarity score was calculated.
AnnData object with n_obs × n_vars = 60767 × 17608 obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA', 'gex_barcode', 'atac_barcode', 'is_cell', 'excluded_reason', 'gex_raw_reads', 'gex_mapped_reads', 'gex_conf_intergenic_reads', 'gex_conf_exonic_reads', 'gex_conf_intronic_reads', 'gex_conf_exonic_unique_reads', 'gex_conf_exonic_antisense_reads', 'gex_conf_exonic_dup_reads', 'gex_exonic_umis', 'gex_conf_intronic_unique_reads', 'gex_conf_intronic_antisense_reads', 'gex_conf_intronic_dup_reads', 'gex_intronic_umis', 'gex_conf_txomic_unique_reads', 'gex_umis_count', 'gex_genes_count', 'atac_raw_reads', 'atac_unmapped_reads', 'atac_lowmapq', 'atac_dup_reads', 'atac_chimeric_reads', 'atac_mitochondrial_reads', 'atac_fragments', 'atac_TSS_fragments', 'atac_peak_region_fragments', 'atac_peak_region_cutsites', 'percent.mt', 'nCount_ATAC', 'nFeature_ATAC', 'sex', 'age', 'mitoRatio', 'percent.ribo', 'riboRatio', 'percent.hb', 'log10GenesPerUMI', 'nucleosome_signal', 'nucleosome_percentile', 'TSS.enrichment', 'TSS.percentile', 'pct_reads_in_peaks', 'blacklist_fraction', 'brain', 'brain.bank', 'nCount_SCT', 'nFeature_SCT', 'SCT_snn_res.0.2', 'seurat_clusters', 'ATAC_snn_res.0.2', 'SCT.weight', 'ATAC.weight', 'wsnn_res.0.2', 'RNA_snn_res.0.2', 'pANN_0.25_0.09_1648', 'DF.classifications_0.25_0.09_1648', 'pANN_0.25_0.09_1940', 'DF.classifications_0.25_0.09_1940', 'pANN_0.25_0.09_2285', 'DF.classifications_0.25_0.09_2285', 'pANN_0.25_0.09_1241', 'DF.classifications_0.25_0.09_1241', 'SCT_snn_res.0.4', 'ATAC_snn_res.0.4', 'wsnn_res.0.4', 'm1c_labels_subclass', 'age.group', 'seurat_clusters_origBB', 'm1c_labels_subclass_origBB', 'anno_clus', 'anno_clus_origBB', 'anno_clus_origBB2', 'SCT.dream2BB.weight', 'ATAC.dream2BB.weight', 'seurat_clusters_dream.origBB', 'seurat_clusters_dreamorigBB', 'm1c_labels_subclass.dreamBB', 'anno_clus_dreamBB', 'anno_clus_dreamorigBB', 'OPC_progenitor1', 'OPC_precursor1', 'Oligo_precursor1', 'preOligo1', 'immOligo1', 'matOligo_non1', 'matOligo_mye1', 'TypeI_opc1', 'TypeII_opc1', 'TypeI_Olig1', 'TypeII_Olig1', 'anno_clus_dreamorigBB_bioarx', 'anno_clus_dreamorigBB_v2' var: 'vst.mean', 'vst.variance', 'vst.variance.expected', 'vst.variance.standardized', 'vst.variable'
random.seed(11) cp.tl.preprocessing(opc_oligACC, n_Top_genes=2000, N_pcs = 30) cp.tl.preprocessing(opc_oligDLPFC, n_Top_genes=2000, N_pcs = 30) opc_oligACC
AnnData object with n_obs × n_vars = 15527 × 2000 obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA', 'gex_barcode', 'atac_barcode', 'is_cell', 'excluded_reason', 'gex_raw_reads', 'gex_mapped_reads', 'gex_conf_intergenic_reads', 'gex_conf_exonic_reads', 'gex_conf_intronic_reads', 'gex_conf_exonic_unique_reads', 'gex_conf_exonic_antisense_reads', 'gex_conf_exonic_dup_reads', 'gex_exonic_umis', 'gex_conf_intronic_unique_reads', 'gex_conf_intronic_antisense_reads', 'gex_conf_intronic_dup_reads', 'gex_intronic_umis', 'gex_conf_txomic_unique_reads', 'gex_umis_count', 'gex_genes_count', 'atac_raw_reads', 'atac_unmapped_reads', 'atac_lowmapq', 'atac_dup_reads', 'atac_chimeric_reads', 'atac_mitochondrial_reads', 'atac_fragments', 'atac_TSS_fragments', 'atac_peak_region_fragments', 'atac_peak_region_cutsites', 'percent.mt', 'nCount_ATAC', 'nFeature_ATAC', 'sex', 'age', 'mitoRatio', 'percent.ribo', 'riboRatio', 'percent.hb', 'log10GenesPerUMI', 'nucleosome_signal', 'nucleosome_percentile', 'TSS.enrichment', 'TSS.percentile', 'pct_reads_in_peaks', 'blacklist_fraction', 'brain', 'brain.bank', 'nCount_SCT', 'nFeature_SCT', 'SCT_snn_res.0.2', 'seurat_clusters', 'ATAC_snn_res.0.2', 'SCT.weight', 'ATAC.weight', 'wsnn_res.0.2', 'RNA_snn_res.0.2', 'pANN_0.25_0.09_1648', 'DF.classifications_0.25_0.09_1648', 'pANN_0.25_0.09_1940', 'DF.classifications_0.25_0.09_1940', 'pANN_0.25_0.09_2285', 'DF.classifications_0.25_0.09_2285', 'pANN_0.25_0.09_1241', 'DF.classifications_0.25_0.09_1241', 'SCT_snn_res.0.4', 'ATAC_snn_res.0.4', 'wsnn_res.0.4', 'm1c_labels_subclass', 'age.group', 'seurat_clusters_origBB', 'm1c_labels_subclass_origBB', 'anno_clus', 'anno_clus_origBB', 'anno_clus_origBB2', 'SCT.dream2BB.weight', 'ATAC.dream2BB.weight', 'seurat_clusters_dream.origBB', 'seurat_clusters_dreamorigBB', 'm1c_labels_subclass.dreamBB', 'anno_clus_dreamBB', 'anno_clus_dreamorigBB', 'OPC_progenitor1', 'OPC_precursor1', 'Oligo_precursor1', 'preOligo1', 'immOligo1', 'matOligo_non1', 'matOligo_mye1', 'TypeI_opc1', 'TypeII_opc1', 'TypeI_Olig1', 'TypeII_Olig1', 'anno_clus_dreamorigBB_bioarx', 'anno_clus_dreamorigBB_v2', 'n_genes', 'leiden' var: 'vst.mean', 'vst.variance', 'vst.variance.expected', 'vst.variance.standardized', 'vst.variable', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm' uns: 'log1p', 'hvg', 'pca', 'neighbors', 'diffmap_evals', 'umap', 'leiden', 'paga', 'leiden_sizes' obsm: 'X_pca', 'X_diffmap', 'X_umap' varm: 'PCs' obsp: 'distances', 'connectivities'
cp.tl.trajectory_tree(opc_oligACC, root_node="5", groupby="leiden", tree=None) cp.tl.trajectory_tree(opc_oligDLPFC, root_node="6", groupby="leiden", tree=None)
------- pairwise alignment of trajectories
ACC_DLPFC = cp.tl.tree_alignment(opc_oligACC, opc_oligDLPFC, num_genes1=2000, num_genes2=2000)Calculating tree alignment 837 genes are used to calculate cost of tree alignment.ACC_DLPFCCapitalData(adata1=AnnData object with n_obs × n_vars = 15527 × 2000 obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA', 'gex_barcode', 'atac_barcode', 'is_cell', 'excluded_reason', 'gex_raw_reads', 'gex_mapped_reads', 'gex_conf_intergenic_reads', 'gex_conf_exonic_reads', 'gex_conf_intronic_reads', 'gex_conf_exonic_unique_reads', 'gex_conf_exonic_antisense_reads', 'gex_conf_exonic_dup_reads', 'gex_exonic_umis', 'gex_conf_intronic_unique_reads', 'gex_conf_intronic_antisense_reads', 'gex_conf_intronic_dup_reads', 'gex_intronic_umis', 'gex_conf_txomic_unique_reads', 'gex_umis_count', 'gex_genes_count', 'atac_raw_reads', 'atac_unmapped_reads', 'atac_lowmapq', 'atac_dup_reads', 'atac_chimeric_reads', 'atac_mitochondrial_reads', 'atac_fragments', 'atac_TSS_fragments', 'atac_peak_region_fragments', 'atac_peak_region_cutsites', 'percent.mt', 'nCount_ATAC', 'nFeature_ATAC', 'sex', 'age', 'mitoRatio', 'percent.ribo', 'riboRatio', 'percent.hb', 'log10GenesPerUMI', 'nucleosome_signal', 'nucleosome_percentile', 'TSS.enrichment', 'TSS.percentile', 'pct_reads_in_peaks', 'blacklist_fraction', 'brain', 'brain.bank', 'nCount_SCT', 'nFeature_SCT', 'SCT_snn_res.0.2', 'seurat_clusters', 'ATAC_snn_res.0.2', 'SCT.weight', 'ATAC.weight', 'wsnn_res.0.2', 'RNA_snn_res.0.2', 'pANN_0.25_0.09_1648', 'DF.classifications_0.25_0.09_1648', 'pANN_0.25_0.09_1940', 'DF.classifications_0.25_0.09_1940', 'pANN_0.25_0.09_2285', 'DF.classifications_0.25_0.09_2285', 'pANN_0.25_0.09_1241', 'DF.classifications_0.25_0.09_1241', 'SCT_snn_res.0.4', 'ATAC_snn_res.0.4', 'wsnn_res.0.4', 'm1c_labels_subclass', 'age.group', 'seurat_clusters_origBB', 'm1c_labels_subclass_origBB', 'anno_clus', 'anno_clus_origBB', 'anno_clus_origBB2', 'SCT.dream2BB.weight', 'ATAC.dream2BB.weight', 'seurat_clusters_dream.origBB', 'seurat_clusters_dreamorigBB', 'm1c_labels_subclass.dreamBB', 'anno_clus_dreamBB', 'anno_clus_dreamorigBB', 'OPC_progenitor1', 'OPC_precursor1', 'Oligo_precursor1', 'preOligo1', 'immOligo1', 'matOligo_non1', 'matOligo_mye1', 'TypeI_opc1', 'TypeII_opc1', 'TypeI_Olig1', 'TypeII_Olig1', 'anno_clus_dreamorigBB_bioarx', 'anno_clus_dreamorigBB_v2', 'n_genes', 'leiden' var: 'vst.mean', 'vst.variance', 'vst.variance.expected', 'vst.variance.standardized', 'vst.variable', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm' uns: 'log1p', 'hvg', 'pca', 'neighbors', 'diffmap_evals', 'umap', 'leiden', 'paga', 'leiden_sizes', 'leiden_colors', 'anno_clus_dreamorigBB_v2_colors', 'cluster_centroid', 'capital' obsm: 'X_pca', 'X_diffmap', 'X_umap' varm: 'PCs' obsp: 'distances', 'connectivities', adata2=AnnData object with n_obs × n_vars = 14596 × 2000 obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA', 'gex_barcode', 'atac_barcode', 'is_cell', 'excluded_reason', 'gex_raw_reads', 'gex_mapped_reads', 'gex_conf_intergenic_reads', 'gex_conf_exonic_reads', 'gex_conf_intronic_reads', 'gex_conf_exonic_unique_reads', 'gex_conf_exonic_antisense_reads', 'gex_conf_exonic_dup_reads', 'gex_exonic_umis', 'gex_conf_intronic_unique_reads', 'gex_conf_intronic_antisense_reads', 'gex_conf_intronic_dup_reads', 'gex_intronic_umis', 'gex_conf_txomic_unique_reads', 'gex_umis_count', 'gex_genes_count', 'atac_raw_reads', 'atac_unmapped_reads', 'atac_lowmapq', 'atac_dup_reads', 'atac_chimeric_reads', 'atac_mitochondrial_reads', 'atac_fragments', 'atac_TSS_fragments', 'atac_peak_region_fragments', 'atac_peak_region_cutsites', 'percent.mt', 'nCount_ATAC', 'nFeature_ATAC', 'sex', 'age', 'mitoRatio', 'percent.ribo', 'riboRatio', 'percent.hb', 'log10GenesPerUMI', 'nucleosome_signal', 'nucleosome_percentile', 'TSS.enrichment', 'TSS.percentile', 'pct_reads_in_peaks', 'blacklist_fraction', 'brain', 'brain.bank', 'nCount_SCT', 'nFeature_SCT', 'SCT_snn_res.0.2', 'seurat_clusters', 'ATAC_snn_res.0.2', 'SCT.weight', 'ATAC.weight', 'wsnn_res.0.2', 'RNA_snn_res.0.2', 'pANN_0.25_0.09_1648', 'DF.classifications_0.25_0.09_1648', 'pANN_0.25_0.09_1940', 'DF.classifications_0.25_0.09_1940', 'pANN_0.25_0.09_2285', 'DF.classifications_0.25_0.09_2285', 'pANN_0.25_0.09_1241', 'DF.classifications_0.25_0.09_1241', 'SCT_snn_res.0.4', 'ATAC_snn_res.0.4', 'wsnn_res.0.4', 'm1c_labels_subclass', 'age.group', 'seurat_clusters_origBB', 'm1c_labels_subclass_origBB', 'anno_clus', 'anno_clus_origBB', 'anno_clus_origBB2', 'SCT.dream2BB.weight', 'ATAC.dream2BB.weight', 'seurat_clusters_dream.origBB', 'seurat_clusters_dreamorigBB', 'm1c_labels_subclass.dreamBB', 'anno_clus_dreamBB', 'anno_clus_dreamorigBB', 'OPC_progenitor1', 'OPC_precursor1', 'Oligo_precursor1', 'preOligo1', 'immOligo1', 'matOligo_non1', 'matOligo_mye1', 'TypeI_opc1', 'TypeII_opc1', 'TypeI_Olig1', 'TypeII_Olig1', 'anno_clus_dreamorigBB_bioarx', 'anno_clus_dreamorigBB_v2', 'n_genes', 'leiden' var: 'vst.mean', 'vst.variance', 'vst.variance.expected', 'vst.variance.standardized', 'vst.variable', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm' uns: 'log1p', 'hvg', 'pca', 'neighbors', 'diffmap_evals', 'umap', 'leiden', 'paga', 'leiden_sizes', 'leiden_colors', 'anno_clus_dreamorigBB_v2_colors', 'cluster_centroid', 'capital' obsm: 'X_pca', 'X_diffmap', 'X_umap' varm: 'PCs' obsp: 'distances', 'connectivities', alignedtree=<networkx.classes.digraph.DiGraph object at 0x2af1aebda1c0>, alignmentcost=array([17.519946217]), genes_for_tree_align=array(['ADAM28', 'SYNPO2', 'NIBAN1', 'ITGB2', 'SPTA1', 'CEP295NL', 'MPP4', 'NEMF', 'PCDH11X', 'ARHGAP29', 'KRT222', 'ZEB1', 'KHDRBS2', 'EFCAB8', 'PDYN', 'PPIG', 'C1QL3', 'SLC1A2', 'PKHD1L1', 'HBZ', 'CDH22', 'CD69', 'KAZN', 'OLFM3', 'PRKCB', 'NELL1', 'STAT4', 'NINJ2', 'SYNGR3', 'NPIPB9', 'FSTL5', 'SERPINE1', 'HS6ST3', 'HRH3', 'PRKCH', 'F2RL3', 'UNC5D', 'NLGN4Y', 'DCLK3', 'GABRD', 'MNS1', 'GPR179', 'NDST3', 'EPS8L2', 'SPC24', 'ADGRL4', 'MYH13', 'CNTNAP2', 'VCAN', 'GRM5', 'RASGEF1B', 'OR9Q1', 'GLRA3', 'FSTL4', 'AQP4', 'IL1RL2', 'SPIB', 'MESP2', 'KCTD16', 'SCN3B', 'KCNQ3', 'STAP1', 'GREM2', 'DAND5', 'LDB2', 'GRIK1', 'PDGFRL', 'KCNC2', 'PRPF4B', 'DPP10', 'ADA', 'NEFL', 'SDK1', 'RYR3', 'SV2B', 'ZNF560', 'DYSF', 'RBM25', 'PLCD3', 'MUC19', 'HLA-DRA', 'NRCAM', 'LYN', 'ARHGEF18', 'AFMID', 'OPHN1', 'NRGN', 'ATP1A4', 'CALR3', 'LINGO2', 'MAL', 'CCAR1', 'ARHGAP15', 'MLIP', 'CYGB', 'RDH12', 'RNMT', 'ANXA3', 'CHRNB3', 'ICE1', 'NXPH1', 'HNF1B', 'IL21', 'IWS1', 'CCHCR1', 'RALYL', 'PCDH15', 'FAM222B', 'TMEM221', 'SAMD3', 'RIMS2', 'DNM2', 'FOXP2', 'TRDN', 'HIPK4', 'EVA1C', 'NEUROD2', 'INCA1', 'COL11A1', 'GOLGA7B', 'ABLIM1', 'GABRG2', 'DYRK3', 'ADAM8', 'ROBO2', 'PTCHD4', 'TESMIN', 'SNTG2', 'BCAS1', 'SSTR2', 'RASGRF1', 'SLC32A1', 'ANGPTL5', 'TFR2', 'ISLR2', 'SLC22A8', 'LRMDA', 'PCSK2', 'KLHL6', 'SMOC1', 'KMT2B', 'ST6GALNAC5', 'ATG9B', 'CFAP299', 'FABP6', 'IQSEC3', 'DOC2A', 'PIK3R5', 'CYP4F3', 'SYTL3', 'TM4SF18', 'GPC6', 'RBBP6', 'TMEM275', 'LUC7L', 'ZFAND2A', 'MACC1', 'EFCAB9', 'SLC12A3', 'MAP1B', 'DNAJB1', 'SLC26A4', 'LRRN4CL', 'TDO2', 'SLC4A1', 'ACSBG1', 'DNASE1L3', 'FLI1', 'CLCN1', 'RGS4', 'KIAA1210', 'EPB41L1', 'TAFA2', 'NUTM1', 'TNFRSF14', 'VWA5B1', 'PYHIN1', 'SYT7', 'CHGA', 'RBM20', 'CA6', 'FKBP5', 'GRIP2', 'P2RY8', 'MDFIC', 'CST3', 'PNMA8B', 'M1AP', 'NMT1', 'SH3RF3', 'AGBL4', 'SNCB', 'DGKG', 'TRIM60', 'PHF20', 'PHYHIP', 'NPY', 'CHD5', 'SYT13', 'ZNF385B', 'RBFOX1', 'CYTIP', 'TMEM163', 'SOSTDC1', 'SLC28A1', 'NFAM1', 'SGPP2', 'OR2M3', 'KIAA0040', 'PVALB', 'MAML2', 'TMEM132B', 'CRYBG2', 'GABRE', 'UBE2QL1', 'ITIH1', 'GABPB2', 'CFAP221', 'HTR5A', 'CCDC27', 'TC2N', 'CDH18', 'RAB17', 'KCNMB3', 'DYNC1H1', 'MMP16', 'SPEF1', 'TSGA10', 'GSG1L2', 'ASPG', 'CNTRL', 'GNG7', 'IL17REL', 'HLA-DRB1', 'SRL', 'PDZD2', 'CCK', 'FYB1', 'BST2', 'FGL1', 'MYL5', 'GAD2', 'OR2F1', 'TMEM52B', 'SLC27A1', 'PRR16', 'XKR4', 'RHOXF1', 'IL10RA', 'OR1I1', 'ZNF418', 'ADH1B', 'DNAH3', 'ZNF804B', 'SV2C', 'MEF2C', 'SRGN', 'SEC14L3', 'NOP14', 'FGF14', 'TNNT1', 'TLCD2', 'RHEX', 'IL1RAPL1', 'CDH9', 'HBB', 'CDH12', 'TEX51', 'PARP8', 'CHRNB4', 'HSP90AB1', 'NKAP', 'SORCS2', 'TMEM108', 'TPRX1', 'MCTP2', 'ICAM1', 'EIF5B', 'LEF1', 'TAS1R1', 'KLRF1', 'CELF2', 'IKZF3', 'ASF1B', 'PLEKHG1', 'PTPN3', 'PTPN22', 'LILRB4', 'REST', 'NDUFA4L2', 'LAMA2', 'OR5AS1', 'RGS6', 'ZNF385D', 'NPTX2', 'FSCN2', 'TCOF1', 'RHEBL1', 'NCF2', 'FBXL19', 'WFDC8', 'MMP11', 'CATSPERB', 'LMX1B', 'FTSJ3', 'CHST11', 'PRKG1', 'NRG3', 'LAMC3', 'KIF21A', 'DOCK8', 'SLC22A9', 'PTPRZ1', 'CNTNAP5', 'CPNE6', 'NKAIN3', 'SHROOM3', 'DSCAM', 'PTHLH', 'PRPF38B', 'KCND2', 'DLGAP1', 'KNOP1', 'CD28', 'AQP1', 'KCNIP4', 'CYSLTR2', 'PTPRT', 'PCDH11Y', 'C1orf146', 'TBX18', 'CR1', 'CACNA2D3', 'KCNH4', 'ZFP36', 'OPRD1', 'SAMD5', 'HPSE2', 'GPR17', 'NES', 'RBMS3', 'ZNF155', 'ICAM5', 'CCDC141', 'KCNV1', 'VIT', 'GALNTL6', 'HBA1', 'FRY', 'ZBTB7A', 'PTPRR', 'IL12RB1', 'HSPA1A', 'DYNLRB2', 'CARMIL1', 'PRKCQ', 'PCDH9', 'IQCM', 'GRM7', 'RRBP1', 'MYO18A', 'CT55', 'DLGAP2', 'CLU', 'DPPA2', 'SERPINH1', 'SFMBT2', 'CD247', 'RHBDL2', 'CHRM3', 'ARHGAP26', 'SYT1', 'OPRL1', 'GPR149', 'PDE3B', 'NPM2', 'IFNLR1', 'TCEAL2', 'SLC5A11', 'PDGFRA', 'PCLO', 'ADGRL2', 'CRH', 'CCBE1', 'FCRL1', 'ANKRD18B', 'CEP162', 'GOLGB1', 'SNAP25', 'PNN', 'AK7', 'P2RX1', 'DNAI2', 'SLC47A1', 'FRMPD4', 'CRISP2', 'SLC17A7', 'EPHA6', 'NRXN1', 'OVCH2', 'CARD11', 'GFAP', 'ZBP1', 'DHRSX', 'IGFN1', 'ADGRV1', 'OR3A2', 'KCNH1', 'UPF2', 'SORBS1', 'RP1', 'ITPR2', 'SHISA9', 'KIF23', 'ARHGAP27', 'DGKB', 'SOHLH1', 'SLK', 'NOX3', 'GPR158', 'TNFRSF11B', 'FCHO1', 'RELN', 'CNDP1', 'YWHAH', 'IL1RAPL2', 'FGF12', 'CATSPERD', 'CHODL', 'MEGF11', 'CYP4F12', 'HS3ST2', 'CCDC168', 'PTPRC', 'MINK1', 'HBA2', 'SLC38A11', 'CD83', 'MAML3', 'MEI1', 'TULP2', 'RBPMS2', 'PRF1', 'ARHGAP8', 'PLCXD3', 'VIM', 'SKAP1', 'CTCF', 'FMN1', 'CACNA1A', 'TAFA1', 'C2orf83', 'CD6', 'MYPN', 'PAX5', 'THSD7B', 'ZC3H13', 'KCNIP1', 'CTXN2', 'C11orf87', 'ATRNL1', 'CYB5D1', 'ITIH5', 'SLC4A4', 'HSPH1', 'PCDH8', 'HSPA6', 'CFB', 'IQGAP2', 'GPC5', 'CCDC144A', 'CCL4', 'ST8SIA6', 'CDK15', 'MLXIPL', 'NYAP2', 'SLC35F2', 'CENPP', 'GRAP2', 'BICDL2', 'OCA2', 'CDK5R2', 'PRSS12', 'SIAH3', 'EFNB3', 'C1QTNF2', 'SULT4A1', 'MAL2', 'SLCO1B7', 'RYR2', 'WFDC3', 'C1orf116', 'SNTG1', 'GFRA2', 'EFNA5', 'VRTN', 'CRTAM', 'NKG7', 'BANK1', 'LRFN5', 'ADGRD2', 'GJA1', 'CBFA2T3', 'CHI3L1', 'GRM4', 'PTH', 'CAMK2A', 'GRM1', 'NDNF', 'NPSR1', 'EMILIN1', 'CACNG3', 'CLEC1A', 'CFAP65', 'GNLY', 'B3GNT6', 'SMKR1', 'SERPINA3', 'BAZ1A', 'CHRNA1', 'C1orf115', 'PLCB1', 'PCF11', 'AOAH', 'BRINP3', 'PDZRN4', 'C8orf34', 'UPF3B', 'GGT5', 'TXK', 'CHRNA2', 'TNFAIP8', 'PRXL2B', 'MAP3K6', 'SRRM1', 'EID3', 'OTOF', 'SLC4A5', 'SLC2A13', 'DDX46', 'SHISA8', 'ARGLU1', 'DNAH10', 'SEZ6L', 'PPP1R13L', 'SAMD15', 'MYL9', 'EIF5AL1', 'SLC6A7', 'APOM', 'STXBP2', 'MPZL3', 'GIPC1', 'TPST1', 'RIMS3', 'GRIN2A', 'APOLD1', 'KCNE4', 'SYT4', 'EMB', 'CALN1', 'MTUS2', 'SYT10', 'SPECC1', 'CNR1', 'LTBP1', 'EPB42', 'TP53I11', 'KMT2A', 'HRH2', 'CNTN4', 'NSG1', 'RASA3', 'ACTG2', 'SULT1A4', 'SLC8A1', 'AKAP12', 'SLFN12L', 'TMEM132D', 'TERB1', 'SGCG', 'OFD1', 'EPHB6', 'RIPOR2', 'A4GALT', 'ARL11', 'RBFOX3', 'TINAG', 'SEMA3E', 'TNNT2', 'KCNK9', 'GET4', 'SORCS1', 'ARAP3', 'COL4A1', 'ZC3H12B', 'IL12RB2', 'RIF1', 'RASD2', 'KLF10', 'GPR26', 'TOB2', 'CDH1', 'NSUN6', 'PLA2G4C', 'DCLK1', 'CHSY3', 'SLIT2', 'IKZF1', 'NPTX1', 'FCMR', 'PLA2G4F', 'SLC5A1', 'BAZ1B', 'ANKRD30B', 'SCML4', 'HDDC3', 'SNCG', 'OGFOD3', 'CCDC33', 'BDP1', 'MDN1', 'PROCA1', 'JHY', 'AXDND1', 'NKTR', 'GRIP1', 'RASSF6', 'DAPK2', 'RGS1', 'IL7R', 'PRR14L', 'CACNA1B', 'TRIP11', 'MPHOSPH8', 'TANGO6', 'TOX', 'EZH1', 'LENG8', 'OPCML', 'SYNPR', 'CDK12', 'CCN2', 'DTHD1', 'SCG2', 'TMEM74B', 'PXT1', 'BOD1L1', 'SLC6A4', 'RUNX1', 'ADH4', 'CFAP99', 'ROR1', 'PRICKLE1', 'KCNQ5', 'IL1R2', 'CFAP58', 'PDE6A', 'SVEP1', 'LUC7L3', 'CALY', 'GABRG3', 'TNR', 'ZMAT4', 'CCDC83', 'CACNB2', 'PTPRG', 'GON4L', 'BSPH1', 'MYO9B', 'ACSBG2', 'CALHM2', 'BAIAP2L1', 'TBR1', 'AFF3', 'CHRNB2', 'STXBP5L', 'POTEG', 'ZNF385C', 'CERS4', 'TMEM266', 'NWD1', 'HCN1', 'SST', 'VWA5B2', 'HLA-DPA1', 'CD74', 'PITPNC1', 'CPAMD8', 'SEC14L4', 'C17orf98', 'MEIOB', 'ANKRD36C', 'APBB1IP', 'SAMSN1', 'HLA-DQB1', 'KYNU', 'ZNF804A', 'NYAP1', 'CAMKV', 'DAB1', 'GALNTL5', 'MMD2', 'COL26A1', 'FCN1', 'CDS1', 'SPEN', 'PARD3B', 'EPB41', 'ARHGAP24', 'CXCL14', 'ADAMTSL3', 'VSNL1', 'L1CAM', 'C1orf216', 'FNDC9', 'MS4A1', 'JSRP1', 'FAM20A', 'ALK', 'UST', 'C1QB', 'RNF213', 'CA10', 'CLIP2', 'TENM3', 'PDGFD', 'CEP290', 'CASP8AP2', 'QRICH2', 'TTF2', 'FPR1', 'GREB1L', 'ARGFX', 'XYLT1', 'RNF144B', 'CALB2', 'RGL3', 'GABRB1', 'C1QTNF4', 'TCERG1L', 'ACIN1', 'LUZP2', 'CRX', 'DCT', 'CNN1', 'KLHL4', 'ANKRD18A', 'ESF1', 'NRG1', 'FCRL5', 'SPARCL1', 'THEMIS', 'SGCZ', 'RHAG', 'MYO3A', 'KCNH7', 'MPND', 'RBP5', 'CCSER1', 'OTOP1', 'KIF1A', 'CPLX4', 'EPHB1', 'PTPRN', 'SYT5', 'TENT5B', 'MYO7B', 'PRRC2C', 'SLIT3', 'EBF1', 'STMN2', 'KCNMB2', 'SEC14L5', 'GJA5', 'CEMIP2', 'SOX6', 'TLR8', 'RASAL1', 'TENM2', 'GALNT17', 'USP8', 'FOS', 'LRRTM4', 'CNTN5', 'RXFP1', 'MECOM', 'GABRA4', 'CNGB1', 'ZER1', 'TMIGD2', 'SEMA6D', 'AKR1D1', 'SCARA5', 'TMEM38A', 'UACA', 'STPG2', 'SLC6A17', 'UTRN', 'SLC35F3', 'GABRA1', 'CDH8', 'DISP2', 'SRRM2', 'MGAT4C', 'CBLN2', 'TNXB', 'MAP1A', 'SDS', 'BMPER', 'ZAN', 'AMPD3', 'PPM1N', 'GRIN1', 'TNS3', 'GTF2IRD1', 'GABBR2', 'GRIN3A', 'KIAA1217', 'DEPDC1', 'CXorf58', 'GCC2', 'TAC1', 'HBM', 'EDA', 'COL24A1', 'ITGA4', 'HSP90AA1', 'HTR2A', 'DPH6', 'GPR143', 'BRSK1', 'SLC26A3', 'FRMD4A', 'CCDC110', 'TPR', 'SAMD11'], dtype=object), alignmentdict={'alignment000': {'data1': ['#', '5', '#'], 'data2': ['6', '7', '13']}, 'alignment001': {'data1': ['#', '5', '18', '8', '10'], 'data2': ['6', '7', '#', '#', '4']}, 'alignment002': {'data1': ['#', '5', '18', '8', '14', '#'], 'data2': ['6', '7', '#', '#', '12', '22']}, 'alignment003': {'data1': ['#', '5', '15', '#'], 'data2': ['6', '7', '5', '21']}, 'alignment004': {'data1': ['#', '5', '15', '16', '11'], 'data2': ['6', '7', '5', '#', '19']}, 'alignment005': {'data1': ['#', '5', '15', '16', '9', '6', '#', '0'], 'data2': ['6', '7', '5', '#', '20', '14', '3', '15']}, 'alignment006': {'data1': ['#', '5', '15', '16', '9', '6', '#', '4', '#'], 'data2': ['6', '7', '5', '#', '20', '14', '3', '1', '16']}, 'alignment007': {'data1': ['#', '5', '15', '16', '9', '6', '#', '4', '1', '#'], 'data2': ['6', '7', '5', '#', '20', '14', '3', '1', '0', '18']}, 'alignment008': {'data1': ['#', '5', '15', '16', '9', '6', '#', '4', '1', '3'], 'data2': ['6', '7', '5', '#', '20', '14', '3', '1', '0', '8']}, 'alignment009': {'data1': ['#', '5', '15', '16', '9', '6', '#', '4', '1', '2', '17', '19'], 'data2': ['6', '7', '5', '#', '20', '14', '3', '1', '0', '2', '17', '#']}, 'alignment010': {'data1': ['#', '5', '15', '16', '9', '6', '7'], 'data2': ['6', '7', '5', '#', '20', '14', '11']}, 'alignment011': {'data1': ['#', '5', '15', '13'], 'data2': ['6', '7', '5', '9']}, 'alignment012': {'data1': ['#', '5', '15', '12'], 'data2': ['6', '7', '5', '10']}}, alignmentlist=[('alignment000', ['#', '5', '#'], ['6', '7', '13']), ('alignment001', ['#', '5', '18', '8', '10'], ['6', '7', '#', '#', '4']), ('alignment002', ['#', '5', '18', '8', '14', '#'], ['6', '7', '#', '#', '12', '22']), ('alignment003', ['#', '5', '15', '#'], ['6', '7', '5', '21']), ('alignment004', ['#', '5', '15', '16', '11'], ['6', '7', '5', '#', '19']), ('alignment005', ['#', '5', '15', '16', '9', '6', '#', '0'], ['6', '7', '5', '#', '20', '14', '3', '15']), ('alignment006', ['#', '5', '15', '16', '9', '6', '#', '4', '#'], ['6', '7', '5', '#', '20', '14', '3', '1', '16']), ('alignment007', ['#', '5', '15', '16', '9', '6', '#', '4', '1', '#'], ['6', '7', '5', '#', '20', '14', '3', '1', '0', '18']), ('alignment008', ['#', '5', '15', '16', '9', '6', '#', '4', '1', '3'], ['6', '7', '5', '#', '20', '14', '3', '1', '0', '8']), ('alignment009', ['#', '5', '15', '16', '9', '6', '#', '4', '1', '2', '17', '19'], ['6', '7', '5', '#', '20', '14', '3', '1', '0', '2', '17', '#']), ('alignment010', ['#', '5', '15', '16', '9', '6', '7'], ['6', '7', '5', '#', '20', '14', '11']), ('alignment011', ['#', '5', '15', '13'], ['6', '7', '5', '9']), ('alignment012', ['#', '5', '15', '12'], ['6', '7', '5', '10'])], similarity_score={})ACC_DLPFC.alignmentlist[('alignment000', ['#', '5', '#'], ['6', '7', '13']), ('alignment001', ['#', '5', '18', '8', '10'], ['6', '7', '#', '#', '4']), ('alignment002', ['#', '5', '18', '8', '14', '#'], ['6', '7', '#', '#', '12', '22']), ('alignment003', ['#', '5', '15', '#'], ['6', '7', '5', '21']), ('alignment004', ['#', '5', '15', '16', '11'], ['6', '7', '5', '#', '19']), ('alignment005', ['#', '5', '15', '16', '9', '6', '#', '0'], ['6', '7', '5', '#', '20', '14', '3', '15']), ('alignment006', ['#', '5', '15', '16', '9', '6', '#', '4', '#'], ['6', '7', '5', '#', '20', '14', '3', '1', '16']), ('alignment007', ['#', '5', '15', '16', '9', '6', '#', '4', '1', '#'], ['6', '7', '5', '#', '20', '14', '3', '1', '0', '18']), ('alignment008', ['#', '5', '15', '16', '9', '6', '#', '4', '1', '3'], ['6', '7', '5', '#', '20', '14', '3', '1', '0', '8']), ('alignment009', ['#', '5', '15', '16', '9', '6', '#', '4', '1', '2', '17', '19'], ['6', '7', '5', '#', '20', '14', '3', '1', '0', '2', '17', '#']), ('alignment010', ['#', '5', '15', '16', '9', '6', '7'], ['6', '7', '5', '#', '20', '14', '11']), ('alignment011', ['#', '5', '15', '13'], ['6', '7', '5', '9']), ('alignment012', ['#', '5', '15', '12'], ['6', '7', '5', '10'])]alignment = select alignments of your interest; e.i. aligning clusters of interest
_--------------------------------------------------------------------------- IndexError Traceback (most recent call last) Input In [128], in <cell line: 1>() 1 for alignment, gene in main_markers: ----> 2 cp.pl.gene_expression_trend( 3 ACC_DLPFC, gene=gene, alignment=alignment, fontsize=16, ticksize=16, 4 multi_genes=True, switch_psedotime=True, 5 data1_name="ACC", data2_name="DLPFC", polyfit_dimension=3 6 )
File /sc/arion/projects/CommonMind/tereza/conda/envs/capital/lib/python3.9/site-packages/capital/pl/pl.py:335, in gene_expression_trend(aligned_data, gene, alignment, outliers, polyfit_dimension, switch_psedotime, multi_genes, data1_name, data2_name, data1_color, data2_color, data1_line_color, data2_line_color, ncols, widthspace, heightspace, fontsize, legend_fontsize, ticksize, dpi, show, save) 331 else: 332 pseudotime = data1[ordered_cells1, :].obs["{}_dptpseudotime".format( 333 alignment)][[i for i, in path]].values --> 335 data1_expressionlevel = expression1[[i for i, in path]] 336 data2_expressionlevel = expression2[[j for , j in path]] 338 array = np.array( 339 [pseudotime, data1_expression_level, data2_expression_level])
File /sc/arion/projects/CommonMind/tereza/conda/envs/capital/lib/python3.9/site-packages/scipy/sparse/_index.py:47, in IndexMixin.getitem(self, key) 46 def getitem(self, key): ---> 47 row, col = self._validate_indices(key) 49 # Dispatch to specialized methods. 50 if isinstance(row, INT_TYPES):
File /sc/arion/projects/CommonMind/tereza/conda/envs/capital/lib/python3.9/site-packages/scipy/sparse/_index.py:159, in IndexMixin._validate_indices(self, key) 157 row += M 158 elif not isinstance(row, slice): --> 159 row = self._asindices(row, M) 161 if isintlike(col): 162 col = int(col)
File /sc/arion/projects/CommonMind/tereza/conda/envs/capital/lib/python3.9/site-packages/scipy/sparse/_index.py:191, in IndexMixin._asindices(self, idx, length) 189 max_indx = x.max() 190 if max_indx >= length: --> 191 raise IndexError('index (%d) out of range' % max_indx) 193 min_indx = x.min() 194 if min_indx < 0:
IndexError: index (2663) out of range_
--------- calculate genes with high expression profile similarity
cp.tl.genes_similarity_score(ACC_DLPFC, alignment="alignment001", min_disp=0.5)ACC_DLPFC.similarity_score["alignment001"]array(['ACTL10', 'MUC6', 'ASGR2', ..., 'GPC5', 'NLGN4Y', 'SGCZ'], dtype=object)session_info.show()
anndata 0.8.0 capital 1.0.14 matplotlib 3.5.2 networkx 2.8.2 numpy 1.23.5 pandas 1.3.5 scanpy 1.9.1 session_info 1.0.0
Click to view modules imported as dependencies
IPython 8.4.0 jupyter_client 7.2.2 jupyter_core 4.10.0
Python 3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 15:55:03) [GCC 10.4.0] Linux-3.10.0-1160.el7.x86_64-x86_64-with-glibc2.33
Session information updated at 2023-02-17 10:12
Can you please navigate me what is wrong?
Thank you!
Best regards, Tereza