MgssGroup/snRNASeq_public
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cite : https://zenodo.org/badge/latestdoi/634913347 ├── 1_Preprocessing Methods sections: Sequencing, alignment, and generation of count matrices │ ├── cellranger_script_new_females.sh Cellranger submission script for female cohort │ ├── cellranger_script_new_males.sh Cellranger submission script for male cohort │ ├── combining_metrics.sh Script for combining Cellranger output metrics │ ├── female_cmdlines_May2021.txt All commands for running Cellranger on female cohort, combined │ ├── male_cmdlines_May2021.txt All commands for running Cellranger on male cohort, combined │ ├── Metrics_stats.R Script for running basic stats between groups on metrics │ ├── More_metric_stats.R Additional script for running basic stats between groups on metrics ├── 2_Finalized_merging_normalization Methods sections: Dimensionality reduction and data integration, Clustering │ ├── bash_files Contains the bash scripts used to submit R scripts. │ │ ├── Step1.sh │ │ ├── Step2.sh │ │ ├── Step3.sh │ │ ├── Step4.sh │ │ └── Step5.sh │ ├── Finalized_scripts │ │ ├── 1_loading_matrices.R Load filtered gene barcode matrices for each sample (library), filter cells │ │ ├── 2_merge_normalize.R Merge all matrices together, log normalize │ │ ├── 3_harmonize.R Run PCA, Harmony, test correction for different variables │ │ ├── 4_basic_plots.R Make basic QC plots │ │ └── 5_reharmonize_seeded.R Re-run Harmony with a seed set, cluster using optimized parameters from scclusteval ├── 3_scclusteval_round2 Methods sections: Clustering │ └── pyflow_seurat_parameter_custom Files corresponding to the scclusteval Snakemake workflow │ ├── cluster.json As per scclusteval Snakemake workflow, customized │ ├── config.yaml As per scclusteval Snakemake workflow, customized │ ├── post_snakemake.R Custom evaluation of clustering parameters after running the Snakemake workflow │ ├── post_snakemake.sh Bash submission script for post_snakemake.R │ ├── pyflow-scBoot.sh As per scclusteval Snakemake workflow, customized │ ├── scripts │ │ ├── gather_fullsample.R As per scclusteval Snakemake workflow, customized │ │ ├── gather_subsample.R As per scclusteval Snakemake workflow, customized │ │ ├── preprocess.R As per scclusteval Snakemake workflow, customized │ │ └── subsample.R As per scclusteval Snakemake workflow, customized │ ├── Snakefile As per scclusteval Snakemake workflow, customized ├── 4_Finalized_cluster_QC_annotation Methods sections: Cluster annotation, Comparison to other datasets │ ├── bash_files Contains the bash scripts used to submit R scripts. │ │ ├── Step1.sh │ │ ├── Step2.sh │ │ ├── Step3.sh │ │ ├── Step5_R4.1.sh │ │ └── Step6.sh │ ├── Finalized_scripts │ │ ├── 1_cluster_QC.R Basic QC of clustering results │ │ ├── 2_cluster_annotation.R Evaluation of marker genes, and other approaches for cluster annotation │ │ ├── 3_cluster_matching.R Matching of cluster with published data │ │ ├── 5_R4.1_spatial_matching.R Preparation of published spatial dataset │ │ └── 6_cluster_naming.R Naming of clusters based on annotation evaluation, additional house-keeping ├── 5_Finalized_downstream_analysis Methods sections: Cell type proportions comparison, Pseudotime trajectory analysis │ ├── bash_files Contains the bash scripts used to submit R scripts. │ │ ├── Step1.3.1.sh │ │ ├── Step1.3.sh │ │ ├── Step2.1.sh │ │ ├── Step2.sh │ │ ├── Step3.4.sh │ │ ├── Step3.5.sh │ │ └── Step4.sh │ ├── Finalized_scripts │ │ ├── 1.3.1_morabito_proportions.R Sub-sampling assessment of cell type proportion differences │ │ ├── 1.3_celltype_props_case_control.R Wilcoxon assesssment of cell type proportion differences │ │ ├── 2.1_pseudotime_evaluate_models.R Fit OL marker genes to pseudotime using different models, per sex │ │ ├── 2_pseudotime_comparison.R Create pseudotime trajectory for OL │ │ ├── 3.4_spatial_reverse_label_transfer.R Predict coritcal layer of nuclei based on spatial dataset │ │ ├── 3.5_readd_stability_info.R House-keeping │ │ └── 4_cluster_markers_plots.R Plots of cluster marker genes ├── 6_Differential_expression Methods sections: Differential expression analysis │ ├── 00_Scripts │ │ ├── 01_Male_pseudobulk_edgeR_cell_subtype.R Differential expression analysis with muscat for male clusters │ │ ├── 02_Male_pseudobulk_edgeR_cell_Broad.R Differential expression analysis with muscat for male broad cell types │ │ ├── 03_Female_pseudobulk_edgeR_cell_subtype.R Differential expression analysis with muscat for female clusters │ │ └── 04_Female_pseudobulk_edgeR_cell_broad.R Differential expression analysis with muscat for female broad cell types │ └── Table_loop_parameters_Male.csv Example file for selecting differential expression parameters ├── 7_RRHO_analysis Methods sections: Comparison of male and female results │ └── Redone_RRHO │ ├── run_RROH2_per_clusterID_sample_2022.04.17.R Rank-rank hypergeometric overlap analysis per cluster and broad cell type │ └── submission_RRHO.sh Bash submission script for RRHO ├── 8_Finalized_interpretation Methods sections: Differential expression analysis, Functional interpretation of female differential expression results, Comparison of male and female results │ ├── bash_files Contains the bash scripts used to submit R scripts. │ │ ├── Step1.1.sh │ │ ├── Step12.sh │ │ ├── Step15.sh │ │ ├── Step1.9.sh │ │ ├── Step1.sh │ │ ├── Step3.2.sh │ │ ├── Step4.2.sh │ │ ├── Step6.sh │ │ ├── Step7.sh │ │ ├── Step8.sh │ │ ├── Step9.sh │ │ └── Step1.7.sh │ └──Finalized_scripts │ ├── 1.1_fgsea.R Run Gene Set Enrichment Analysis on differential expression results │ ├── 12_CellChat_Mic_PV.R Run CellChat on female microglia and PV interneurons │ ├── 14_selected_heatmaps.R Heatmaps for DEGs in top female clusters │ ├── 15_combined_matrix_for_GEO.R Script to output GEO matrix and UCSC Cell rowser matrix │ ├── 1.9_PsyGeNET.R Script to run PsyGeNET analysis on DEGs │ ├── 1_refiltering_DEGs.R Refine list of DEGs │ ├── 3.2_logFC_corrs_old_data.R Compare male results to previous analysis of the same data │ ├── 4.2_microglia_thorough_reclustering.R Re-run differential expression after sub-clustering female microglia │ ├── 6_UMAP_QC.R Make good quality plots │ ├── 7_FGSEA_selected_stats.R Collapse gene set enrichment results (Reactome pathway) for top female clusters │ ├── 8_per_subject_per_cluster_stats.R Per subject (library) and cluster metrics │ ├── 9_p_combination.R Metanalysis of male and female differential expression │ └── 1.7_per_subject_DEG_violins.R Make histograms with distributions of DEGs per analysis, make boxplots (not included in publication) ├── 9_Local_plotting Figures: 2-6 │ ├── alternatives_to_pies.R Figures: 2a,3a-d,4a & 4c │ ├── make_graphs.R Figures: 5d, 6f │ └── pies.R Figures: 4b ├── 10_WGCNA Methods sections: Weighted gene co-expression network analysis (WGCNA) │ ├── Geneset_Moduleenrichment_InN.R │ ├── Geneset_Moduleenrichment_Mic.R │ ├── wgcna_InN.R │ └── wgcna_Mic.R ├── 11_Permutation_analysis Methods sections: Permutation analysis │ ├──bash_files Contains the bash scripts used to submit R scripts. │ │ ├── Step1.sh │ │ ├── Step2.sh │ │ ├── Step3.sh │ │ ├── Step4.sh │ │ ├── Step5.sh │ │ └── Step8.sh │ └──submisssion_male.sh │ └──submisssion_female.sh │ └──Finalized_scripts │ ├──1_male_subtype.R Script for running differential expression analysis on male clusters with permuted labels │ ├──2_male_broad.R Script for running differential expression analysis on male broad cell types with permuted labels │ ├──3_female_subtype.R Script for running differential expression analysis on female clusters with permuted labels │ ├──4_female_broad.R Script for running differential expression analysis on female broad cell types with permuted labels │ ├──5_Plot_dists_genes_clusters_overlaps.R Script for politting distributions of DEGs with real and permuted labels │ └──8_spearman_corrs_permuted.R Script for running Spearman correlations between male and female differential expression results with permuted labels ├── 12_Followup_analyses │ ├──bash_files Contains the bash scripts used to submit R scripts. │ │ ├── Step16.sh │ │ ├── Step17.sh │ │ └── Step2.sh │ └──Finalized_scripts │ ├──16_pseudotime_replots_source_data.R Generate the source data for regenerated pseudotime gene expression plots │ ├──17_scclust_eval_source_data.R Generate the source data for regenerated scclusteval and chooseR inspired plots │ └──2_spearman_corrs.R Scripts for running Spearman correlations between male and female differential results └── README
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