Understand the output from cellranger

Cell Ranger is a popular software package developed by 10x Genomics for analyzing single-cell RNA sequencing (scRNA-seq) data. When you run cellranger count, it generates several output files and folders that contain processed data, analysis results, and quality control metrics. Here’s an overview of the main files and folders generated by cellranger count:

1. outs/ folder:
   • This is the main output folder where most of the analysis results are stored.
   • It contains subfolders such as filtered_feature_bc_matrix/, raw_feature_bc_matrix/, analysis/, etc.
2. outs/raw_feature_bc_matrix/ folder:
   • Contains the raw feature-barcode matrix (matrix of gene expression counts).
   • Each row corresponds to a gene, and each column corresponds to a cell barcode.
   • This matrix includes all detected genes and their counts for each cell barcode before any filtering or normalization.
3. outs/filtered_feature_bc_matrix/ folder:
   • Contains the filtered feature-barcode matrix.
   • This matrix typically represents the filtered and normalized gene expression data after quality control and filtering steps.
   • Each row corresponds to a gene, and each column corresponds to a cell barcode.
4. outs/analysis/ folder:
   • Contains various analysis outputs, including clustering results, dimensionality reduction plots, and other analysis artifacts.
   • For example, you might find files related to t-SNE plots, UMAP plots, Louvain clustering results, etc.
5. outs/metrics_summary.csv file:
   • Contains summary metrics for quality control and analysis.
   • This CSV file typically includes metrics such as the number of reads, number of cells detected, median genes per cell, etc.
6. outs/web_summary.html file:
   • Provides an interactive HTML summary of the analysis results.
   • This file can be viewed in a web browser and contains various plots and statistics summarizing the analysis.
7. outs/possorted_genome_bam.bam file:
   • Contains the aligned reads in BAM format.
   • This file includes information about the alignment of reads to the reference genome.
8. outs/possorted_genome_bam.bam.bai file:
   • Index file for the sorted BAM file.
   • It allows for efficient querying and retrieval of reads from the BAM file.

These are some of the main files and folders generated by cellranger count. The specific contents and structure may vary depending on the version of Cell Ranger and the parameters used during the analysis.

What can we do after this step?

Once you have processed scRNA-seq data using Cell Ranger and obtained the output files, there are several potential follow-up analyses and programs you can use to further explore and analyze the data. Some of the common analyses and tools include:

1. Quality Control (QC) and Data Exploration:
   • Tools: Seurat, Scanpy, scater
   • Analyses: QC metrics visualization, dimensionality reduction (PCA, t-SNE, UMAP), batch effect correction, visualization of gene expression patterns.
2. Clustering and Cell Type Identification:
   • Tools: Seurat, Scanpy, scran
   • Analyses: Cell clustering, identification of cell types and states, marker gene identification, cell type annotation.
3. Differential Expression Analysis:
   • Tools: Seurat, Scanpy, edgeR, DESeq2
   • Analyses: Identification of differentially expressed genes between cell types or conditions, gene set enrichment analysis.
4. Trajectory Inference and Pseudotime Analysis:
   • Tools: Monocle, Scanpy, Slingshot, destiny
   • Analyses: Reconstruction of developmental trajectories, identification of intermediate cell states, inference of pseudotime.
5. Spatial Analysis:
   • Tools: Seurat, SpatialDE, STViewer
   • Analyses: Spatial mapping of gene expression, identification of spatially associated cell types, visualization of spatial patterns.
6. Integration with Other Omics Data:
   • Tools: Seurat, Scanpy, scMerge
   • Analyses: Integration of scRNA-seq data with other omics data (e.g., scATAC-seq, spatial transcriptomics), multi-omics analysis.
7. Functional Annotation and Pathway Analysis:
   • Tools: clusterProfiler, EnrichR, gProfiler
   • Analyses: Functional enrichment analysis, pathway analysis, identification of enriched biological processes and pathways.
8. Single-Cell Genomic Data Visualization:
   • Tools: Loupe Cell Browser, CellBrowser, Cellxgene
   • Analyses: Interactive visualization of single-cell data, exploration of gene expression patterns, cell type distributions, and spatial relationships.

These are just a few examples of the many analyses and tools available for exploring and analyzing scRNA-seq data. The choice of analysis will depend on the specific research questions, experimental design, and characteristics of the dataset. It’s common to use a combination of different tools and analyses to gain comprehensive insights into single-cell transcriptomic data.