== 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 [https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger cell ranger] to get this counts matrix.
      * The main command is [https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/count 'cellranger count'], which requires a reference transcriptome indexed specifically for cellranger.
      * [https://support.10xgenomics.com/single-cell-gene-expression/software/release-notes/build#grch38_3.0.0 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 [https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/count 'cellranger count'] command using syntax like
{{{
bsub cellranger count –id=ID –fastqs=PATH –transcriptome=DIR –sample=SAMPLE_LIST –project=PROJECT
}}}

      * The [https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/output/overview 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 [https://satijalab.org/seurat/ 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)
}}}


=== Perform and visualize dimensional analysis === 

=== Partition cells into clusters === 

=== Identify genes that differentially expressed between samples or clusters === 

Years of research has led to effective algorithms to quantify differential expression between RNA-seq samples that have been assayed genome-wide.  Single cell expression profiles, however, typically assay only a small fraction of all genes, and this single property greatly complicates differential expression analysis.  Two general approaches exist for differential expression:

  * consider each cell as a sample
  * aggregate counts across all cells in a group/cluster, and treat them as one sample

=== Add biological annotations to cells or cell clusters  ===

Drawing biological conclusions from a single-cell experiment usually requires that one classify cells (or at least cell clusters) by type.  Traditionally this is a time-consuming process of exploring marker genes and manually assigning cell type to each numbered cluster.  Given that a number of public scRNA-seq experiments already have these annotations, one can leverage automated software with these ideally "gold standard" datasets to classify current experiments, either via expression profiles or marker genes.


=== Perform trajectory analysis === 

This step is relevant for projects that include cells at different stages of a developmental process or other change that is associated with a time course.  Specific methods/algorithms for dimensional reduction are available to do this, but they often give very different results.  Most of the methods have some concept of pseudotime,  metric that one expects is correlated with actual time, but given that they aren't identical, interpretation needs to be performed with caution.

=== Combine multiple scRNA-seq datasets === 

Many experiments are especially informative when compared to other experiments, either performed by the same or different laboratories.  This is challenging, however, especially when the different experiments profile different types of cells.  In these cases, biological and technical differences are confounded, and one needs to make thoughtful assumptions about how to perform batch correction and achieve "success" during dataset integration.

=== Export expression and dimensional analysis data for interactive viewing === 

We prefer using [https://cellbrowser.readthedocs.io/ 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 [https://cellbrowser.readthedocs.io/basic_usage.html 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  ===

    * [https://satijalab.org/seurat/get_started.html Seurat vignettes and guided analysis]
    * [https://github.com/hemberg-lab/scRNA.seq.course  Analysis of single cell RNA-seq data course, Hemberg Group].
    * [https://broadinstitute.github.io/2019_scWorkshop/ Analysis of single cell RNA-seq data workshop, Broad Institute]
    * [https://ucdavis-bioinformatics-training.github.io/2017_2018-single-cell-RNA-sequencing-Workshop-UCD_UCB_UCSF/ 2017/2018 Single Cell RNA Sequencing Analysis Workshop at UCD,UCB,UCSF]
    * [https://learn.gencore.bio.nyu.edu/single-cell-rnaseq/  Single cell RNA sequencing, NYU].
    * [https://github.com/seandavi/awesome-single-cell Awesome-single-cell, Sean Davis]