data.tar.gz contains all files from the data directory (except for sam outputs from STAR) associated with the 230926_EJ_Setbp1_AlternativeSplicing GitHub project and includes the following files: ./marvel: - This directory contains rds and Rdata objects that were created using the MARVEL R package cell_type_goresults.rds - This is the go results split by cell type marvel_04_split_counts.Rdata - This R data includes all environment objects from MARVEL script 04, and is used for downstream plotting normalized_sj_expression.Rds - This object is the normalized splice junction expression Setbp1_marvel_aligned.rds - Final prepared MARVEL object before any SJU analyses have been run significant_tables.RData - For those who do not want to load multiple massive files, this includes all significant SJU results for each cell type sj_usage_cell_type.rds - This data object has splice junction usage calculated for each cell type sj_usage_condition.rds - This data object has splice junction usage calculated for each cell type and also split by condition ./seurat: - This directory contains all intermediate and final Seurat single-cell gene expression objects annotated_brain_samples.rds - This is the final iteration of the processing in Seurat for a final annotated object. Please use this object for any Seurat or single-cell gene expression analyses. clustered_brain_samples.rds - This is the clustered Seurat object, before cell type annotation based on canonical markers. filtered_brain_samples_pca.rds - This is the filtered Seurat object, before clustering but after PCA. filtered_brain_samples.rds - This is the filtered Seurat object, before PCA. integrated_brain_samples.rds - This the integrated Seurat object, before other steps. ./star: - All files in the STAR directory are outputs from STARsolo, as described in our methods. Each output directory contains the same files, so only one example is included here for brevity. Intermediate SAM files were removed to optimize space. J1/ - This directory contains outputs for brain sample J1 J13/ - This directory contains outputs for brain sample J13 J15/ - This directory contains outputs for brain sample J15 J2/ - This directory contains outputs for brain sample J2 J3/ - This directory contains outputs for brain sample J3 J4/ - This directory contains outputs for brain sample J4 K1/ - This directory contains outputs for kidney sample K1 K2/ - This directory contains outputs for kidney sample K2 K3/ - This directory contains outputs for kidney sample K3 K4/ - This directory contains outputs for kidney sample K4 K5/ - This directory contains outputs for kidney sample K5 K6/ - This directory contains outputs for kidney sample K6 ./star/genome: - This directory contains outputs from running STAR genomeGenerate. Detailed file descriptions available from https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf chrLength.txt chrNameLength.txt chrName.txt chrStart.txt exonGeTrInfo.tab exonInfo.tab geneInfo.tab Genome genomeParameters.txt Log.out SA SAindex sjdbInfo.txt sjdbList.fromGTF.out.tab sjdbList.out.tab transcriptInfo.tab ./star/J1: - This is the head STAR directory for sample J1. It contains logs, basic QC, and gene and splice junction counts. For more information about the STAR pipeline and its outputs, please refer to the STAR documentation https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf Log.final.out Log.out Log.progress.out SJ.out.tab Solo.out/ STARgenome/ ./star/J1/Solo.out:- This directory contains the outputs used for downstream analysis Barcodes.stats GeneFull_Ex50pAS/ SJ/ ./star/J1/Solo.out/GeneFull_Ex50pAS: - This directory contains the filtered and raw barcodes, features, and matrix files for gene expression (including introns) Features.stats filtered/ raw/ Summary.csv UMIperCellSorted.txt ./star/J1/Solo.out/GeneFull_Ex50pAS/filtered: - This directory contains the filtered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages) barcodes.tsv.gz - This file contains filtered cell barcodes features.tsv.gz - This file contains filtered features (genes) matrix.mtx.gz - This file contains the filtered cell by gene expression count matrix ./star/J1/Solo.out/GeneFull_Ex50pAS/raw: - This directory contains the unfiltered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages). Files are the same as previously described for filtered. barcodes.tsv features.tsv matrix.mtx ./star/J1/Solo.out/SJ: - This directory contains the QC and raw barcodes, features, and matrix files for splice junction expression Features.stats raw/ Summary.csv ./star/J1/Solo.out/SJ/raw: - This directory contains the raw barcodes, features, and matrix files for splice junction expression barcodes.tsv - This file contains filtered cell barcodes features.tsv - This file contains filtered features (splice junctions) m...

ometiff: Imaging Data

Cell Masks: Masks generated with cellprofiler from ilastik segmentation training

cp-output_config: All relevant cellprofiler output and additional configuration files (for example clinical data) necessary to generate the single cell experiments.

IMC Data Objects: Single cell experiment RDS files.

scRNA-seq_dataobjects: .Rds files containing the clustered breast cancer, colon cancer, HNSCC, NSCLC and PDAC datasets as well as the integrated validation dataset.

Serialized R data files (.rds) associated with the inDrop single-cell RNA-seq analysis in Huang et al., 2019. Each file has a single Seurat object containing a subset of clusters from the full processed dataset, which were separated into different objects due to file size limitations. Raw data (UMIFM counts) are included in the corresponding slot in each Seurat object. Seurat objects can be re-merged into a single object containing the full dataset using the MergeSeurat function.

data.tar.gz contains all files from the data directory associated with the 230313_TS_CCCinHumanAD GitHub project and includes the following:

• CellRangerCounts/
  • GSE157827/
    • post_soupX/ : contains 21 directories for 21 samples, which each contain 3 files obtained from ambient RNA removal with soupX. Below is a representative example, but this repo contains 1 directory per sample:
      • SAMN16100290_S01_AD/
        • barcodes.tsv
        • genes.tsv
        • matrix.mtx
    • pre_soupX/ : contains 21 directories for 21 samples, which each contain 2 files obtained from Cell Ranger after aligning fastq files to the reference genome. Below is a representative example, but this repo contains 1 directory per sample:
      • SAMN16100290_S01_AD/
        • filtered_feature_ bc_matrix.h5
        • Raw_feature_bc_matrix.h5
  • GSE174367/ : contains 19 directories for 19 samples, which contain 3 files each from Cell Ranger alignment of fastq files to the reference genome. Below is a representative example, but this repo contains 1 directory per sample:
    • SAMN19128610_S1_CTRL/
      • barcodes.tsv
      • genes.tsv
      • Matrix.mtx
• ccc/
  • nichenet_grn/
    • gr_network_human_21122021.rds : accessed in October 2023, gene regulation network – gene regulatory information from MultiNicheNet
    • ligand_tf_matrix_nsga2r_final.rds: accessed in October 2023, ligand tf matrix for signaling path determination from MultiNicheNet
    • signaling_network_human_21122021.rds : accessed in October 2023, signaling network – protein-protein interaction information from MultiNicheNet
    • weighted_networks_nsga2r_final.rds : accessed in October 2023, networks weighted by literature evidence from MultiNicheNet
  • nichenet_prior/
    • ligand_target_matrix.rds : accessed in April 2023, ligand to target matrix from NicheNet
    • lr_network.rds : accessed in April 2023, ligand-receptor matrix from NicheNet
  • nichenet_v2_prior/
    • ligand_target_matrix_nsga2r_final.rds : accessed in June 2023, ligand to target matrix from MultiNicheNet used to predict target genes.
    • lr_network_human_21122021.rds : accessed in June 2023, ligand-receptor matrix from MultiNicheNet used to predict ligand-receptor pairs.
  • geo_multinichenet_output.rds : MultiNicheNet output for Morabito et al., 2021 data
  • geo_signaling_igraph_objects.rds : list of igraph objects for 17 overlapping LRTs and their signaling mediators in the Morabito et al., 2021 dataset.
  • gse_multinichenet_output.rds : MultiNicheNet output for Lau et al., 2020 data
  • gse_signaling_igraph_objects.rds : list of igraph objects for 17 overlapping LRTs and their signaling mediators in the Lau et al., 2020 dataset
• seurat_preprocessing/
  • geo_filtered_seurat.rds : merged and filtered seurat object of Morabito et al., 2021 data
  • geo_integrated_seurat.rds : seurat object integrated using harmony of Morabito et al., 2021 data
  • geo_clustered_seurat.rds : clustered seurat object of Morabito et al., 2021 data
  • geo_processed_seurat.rds : processed seurat object with final cell type assignments at specified resolution of Morabito et al., 2021 data
  • gse_filtered_seurat.rds : merged and filtered seurat object of Lau et al., 2020 data
  • gse_integrated_seurat.rds : seurat object integrated using harmony of Lau et al., 2020 data
  • gse_clustered_seurat.rds : clustered seurat object of Lau et al., 2020 data
  • gse_processed_seurat.rds : processed seurat object with final cell type assignments at specified resolution of Lau et al., 2020 data