Single cell RNA-Seq to quantify gene levels and assay for differential expression

Create a matrix of gene counts by cells

• For 10x Genomics experiments, we use ​cell ranger to get this counts matrix.
  • The main command is ​cellranger count, which requires a reference transcriptome indexed specifically for cellranger.
  • ​Pre-built reference transcriptomes are available from 10x Genomics. Several of them are available at Whitehead on tak under /nfs/genomes/[ASSEMBLY]/10x where ASSEMBLY is specific to our nomenclature. Note that only certain gene types are included in these pre-built references.
  • Custom reference transcriptomes can be created with cellranger commands:
    • Filter the gtf to include only a subset of the annotated gene biotypes, for example,

      bsub cellranger mkgtf Homo_sapiens.GRCh38.93.gtf Homo_sapiens.GRCh38.93.filtered.gtf --attribute=gene_biotype:protein_coding
      

    • Create the cellranger index using a command such as

      bsub cellranger mkref --genome=MyGenome --fasta=genome.fa --genes=Genes.filtered.gtf --ref-version=1.0
      

  • Run the actual ​cellranger count command using syntax like

    bsub cellranger count –id=ID –fastqs=PATH –transcriptome=DIR –sample=SAMPLE_LIST –project=PROJECT
    

• The ​output of 'cellranger count' includes
  • An indexed BAM file of all mapped reads (possorted_genome_bam.bam)
  • A Loupe Browser visualization and analysis file (cloupe.cloupe)

• The quality control summary is "web_summary.html" in the 'outs' folder and has important quality metrics and graphs such as: Estimated Number of Cells, Mean Reads per Cell, Mean Reads per Cell, Sequencing Saturation, etc.

• The "matrix" output files are not in the usual matrix structure. To create a standard 2-dimensional matrix, one can use R commands such as

  library(monocle)
  library(cellrangerRkit)
  cellranger_data_path = "/path/to/dir/with/outs/dir"
  crm = load_cellranger_matrix(cellranger_data_path)
  crm.matrix = as.matrix(exprs(crm))
  write.table(crm.matrix, "My.cellranger.matrix.txt", sep="\t", quote=F)
  

Run quality control and filter cells

We typically use the ​Seurat R package for these steps.

• Start out by loading the counts matrix from cellranger:

  library("Seurat") 
  message("Loaded Seurat version", packageDescription("Seurat")$Version)
  # Load the barcodes*, features*, and matrix* files in your 10x Genomics directory
  counts.all <- Read10X(data.dir = input.counts.filename)
  

Export expression and dimensional analysis data for interactive viewing

We prefer using ​UCSC's Cell Browser environment for this task.

• Prerequisites. To make the most of this interactive viewing tool,
  • Run dimensional reduction (such as PCA, tSNE, UMAP).
  • Cluster/partition the cells (such as with Seurat's FindClusters()).
  • Identify cluster-specific marker genes (such as with Seurat'sFindAllMarkers()) and assemble/print information about them with commands such as

    all.markers.forCB = cbind(as.numeric(all.markers$cluster), all.markers$gene, all.markers$p_val_adj, all.markers$avg_logFC,
                          all.markers$pct.1, all.markers$pct.2)
    write.table(all.markers.forCB, file="all.markers.exported.txt", quote = FALSE, sep = "\t", row.names=F)
    

  • Add info/links about the marker genes with the CellBrowser command

    cbMarkerAnnotate all.markers.exported.txt markers.txt
    

• Export the key data from the Seurat object:

  ExportToCellbrowser(seurat, dir=export.dir, dataset.name=dataset.name, markers.file=markers.file, reductions=c("pca", "tsne", "umap"))
  

• Run Cell Browser's ​cbBuild to create the web-viewable directory of files.
• Move the cbBuild output to a web server, which creates a page that looks something like ​https://cells.ucsc.edu/

Links to recommended scRNA-seq analysis tutorials and resources

• ​Seurat vignettes and guided analysis
• ​Analysis of single cell RNA-seq data course, Hemberg Group.
• ​Analysis of single cell RNA-seq data workshop, Broad Institute
• ​2017/2018 Single Cell RNA Sequencing Analysis Workshop at UCD,UCB,UCSF
• ​Single cell RNA sequencing, NYU.
• ​Awesome-single-cell, Sean Davis