Multiomic analyses uncover immunological signatures in acute and chronic coronary syndromes

The uploaded data contains the following files for the coronary syndrome (CS) dataset: 1) Sample Meta Data Information a. Merged_Sample_Data.csv: contains meta-information about the samples (age, gender, clinical data, sc-data library) o ‘sample_id’: identifier of sample (concatenation of subject-id and timepoint) o ‘Subject’: identifier of a subject o ‘measurement’: specifies the timepoint of measurement (TP0 – TP4) o ‘library’: specifies the single-cell library in which the scRNA-seq data of the sample was prepared o ‘sequence’: specifies the sequence of the hashtag for the demultiplexing o ‘hashtag’: HTO hashtag used for the demultiplexing of the scRNA-seq data o ‘sc_rna_seq_data’: specifies whether scRNA-seq data for this sample is available o ‘age’: age of the subject o ‘sex’: gender of the subject o ‘classification’: clinical classification of the subject, either: § ‘acs_subacute’ = ‘acs_with delayed recanalization after vessel occlusion’ § ‘acs_w_infection’ = ‘acs acquiring hospital infection’ § ‘acs_w_o_infection’ = ‘sterile acs’ § ‘ccs’ =’coronary vessel disease’ § ‘koronarsklerose’ = ‘coronary sclerosis’ § ‘vollstaendiger_ausschluss’ = ‘healthy coronaries’ o ‘group’: clinical classification of the subject (parent categories: ‘ccs’, ‘no_ccs’, ‘acs’ of ‘classification’) o ‘delta_ef_value_group: classification of the subject based on the ef value o ‘delta_ef_value’: delta ef (ejection fraction) value of the subject o ‘delta_ef_value_class’: classification of the subject in ‘good’ , ‘intermediate’ or ‘bad’ outcome based on the ef value o ‘CK’: measured CK value of the subject o ‘CK_MB’: measured CK_MB value of the subject o ‘Troponin’: measured Troponin value of the subject o ‘CRP’: measured CRP value of the subject 2) Single-Cell Data: a. For each library (XX ; libraries 01-14) the count output of cellranger including barcodes and features: - L00XX_matrix.mtx - L00XX_features.csv - L00XX_barcodes.tsv b. Prepared_sc_Data.h5ad: preprocessed scRNA-seq data after QC used as input for the MOFA model, contains normalized, log transformed and scaled values for highly variable genes and raw counts on all genes (= input for MOFA model). Annotation of cells (.obs) includes: o ‘sample_id’: identifier of sample (concatenation of subject-id and timepoint) o ‘Subject’: identifier of a subject o ‘B2_Scanorama_Singlet_rb_mt_cluster’: cell-type cluster resulting from the clustering based on the Scanorama embedding (as shown in UMAP in manuscript) o ‘cluster_cell_type_Scanorama’: cell type clusters including annotations (as shown in UMAP in manuscript) o ‘cell_type_Scanorama’: higher level annotation of cell-types o ‘library’: sequencing library the cell was included in (L1-L14) o ‘in_sample’: dummy column (used in the pseudobulk aggregation) o ‘classification’: clinical classification of the sample (see: Sample Meta Data information) 3) Other Omic Data a. Prepared_Neutrophil_Data.csv: contains the neutrophil counts that were used as input for the MOFA model and further downstream analysis o ‘sample_id’: identifier of sample (concatenation of subject-id and timepoint) o Gene columns (‘ENSG…’): specifies the gene that was measured b. Prepared_Cytokine_Data.csv: contains the cytokine measurements that were used as input for the MOFA model and further downstream analysis o ‘sample_id’: identifier of sample (concatenation of subject-id and timepoint) o ‘Cytokine columns’: each column specifies the name of the cytokine that was measured c. Prepared_Proteomic_Data.csv: contains the proteomic measurements that were used as input for the MOFA model and further downstream analysis o ‘sample_id’: identifier of sample (concatenation of subject-id and timepoint) o ‘Proteomic columns’ each column specifies the name of the protein that was measured 4) Cell-Type Annotations a. Cell_Type_Annotation.csv: includes Scanorama based manual annotation and Azimuth based automatic annotation of cells o ‘sample_id’: identifier of sample (concatenation of subject-id and timepoint) o ‘cell_library’: barcode and library of the cell o ‘B2_Scanorama_Singlet_rb_mt_cluster’:: cell-type cluster resulting from the clustering based on the Scanorama embedding (as shown in UMAP in manuscript) o ‘cluster_cell_type_Scanorama’: cell type clusters including annotations (as shown in UMAP in manuscript) o ‘cell_type_Scanorama’: higher level annotation of cell-types o ‘library’: sequencing library the cell was included in (L1-L14) o ‘predicted.celltype.l2’ : azimuth based cell-type prediction o ‘predicted.celltype.l2.score’: score of the the azimuth cell-type prediction b. ‘Cell_Type_Annotation_Levels.csv’: includes a mapping of different levels of cell-types 5) FACS data: a. Prepared_FACS_data.csv: includes cell type percentages of the FACS assay o ‘sample_id’: identifier of sample (concatenation of subject-id and timepoint) o ‘TP’: specifies the timepoint of measurement (TP0 – TP4) o ‘cell_type_facs’: FACS cell-type o ‘percentage’: percentage of cells for the cell-type of the sample given as character string o ‘percentage_numeric’: percentage of cells for the cell-type of the sample given as numeric value 6) Grace-Score: a. Grace_Score.csv: contains the computed GRACE scores for each sample (used for evaluation of the prediction of the MOFA factors) o ‘sample_id’: identifier of sample (concatenation of subject-id and timepoint) o Grace_Score: calculated grace-score for the sample (for more details refer to the manuscript) 7) Pathway Selection: a. REACTOME_Immune_System_Pathways.csv: contains all the immune System pathways that were extracted from REACTOME b. KEGG_pathways_categorized.csv: contains all the KEGG pathways with their categories 8) Plot Configuration Files: contain specifications for the violin plots of the manuscript loaded within the scripts to define which genes will be ploted a. Plot_Config_Violin.csv: for violin plots included in main figures b. Plot_Config_Violin_Supp.csv: for violin plots included in supplementary figures