Single-cell Genomics

High-throughput “bulk”-omics techniques have advanced our understanding of the molecular states of biological systems. However, they can only capture ensemble averages of cell states and are poorly suited to understand cell types, states, transitions, and locations. Massively parallel single cell genomics assays that can profile hundreds of thousands of individual cells is rapidly emerging as a revolutionary technology.

Single-cell genomics methods have the potential to transform many areas of biological research such as physiology, developmental biology, and anatomy – in health and disease. The Human Cell Atlas Project, an international collaborative effort, aims to create a comprehensive reference map of the types and properties of all human cells.

Recently, a number of next-generation sequencing-based assays have been optimized to work at the level of individual cells. While the most widely used of these single-cell techniques is RNA-seq, other techniques that have been adapted for single-cell measurements from “bulk” cell assays include whole-genome bisulfite sequencing, DNase I hypersensitivity sequencing, and ATAC-seq to assay accessible DNA elements. These techniques allow researchers to characterize the genetic and functional properties of individual cells in their native conditions.

Analyzing single-cell RNA-seq data is substantially more difficult than analyzing data from typical RNA-seq experiments. Typical single-cell studies capture hundreds or even thousands of cells, and generate very large data sets. The size and scale of the data can considerably affect performance of most algorithms. Solvuu’s intelligent execution engine is fully scalable, automatically parallelizes jobs, and can implement workflows using open-source tools.


bowtie2Ultra-fast, sensitive gapped, short-read aligner.
STARUltra-fast universal RNA-seq aligner.
ScaterPre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R.
SeuratR package designed for QC, analysis, and exploration of single cell RNA-seq data.
MonocleToolkit for analyzing single cell gene expression experiments - clustering, classifying, and counting cells, Constructing single-cell trajectories and differential expression analysis.
MASTSupervised analyses about differential expression of genes and gene modules, unsupervised analyses of model residuals to generate hypotheses regarding co-expression of genes.
SCDESingle cell differential expression analysis using single cell RNA-seq data.
BPSCBeta-Poisson model for differential expression analysis of single-cell RNA-seq data.
HoneyBADGERIdentify and infer the presence of CNV and LOH events in single cells and reconstructs subclonal architecture using allele and expression information.
chromVARR package for the analysis of sparse chromatin accessibility data from single cell or bulk ATAC or DNAse-seq data.
MonovarStatistical method for detecting and genotyping single-nucleotide variants in single-cell data.

File types

Group 2983CSV
Group 2994TSV
Group 3005SAM
Group 3016BAM
Group 3027CRAM
Group 3049FASTA
Group 3038FASTQ
Group 3060BED
Group 3071Wig
Group 3082bigBED
Group 3093bigWig
Group 3137bedGraph
Group 3104GTF
Group 3126GFF3
Group 3148VCF

Visual analytics

t-SNE plotNon-linear, non-parametric dimensionality reduction method to visualize cluster structures.
HeatmapVisualize expression similarity of population markers that can be clustered and reordered to reveal patterns.
DendrogramTree-like represention of major cell types and subpopulations from hierarchical clustering.
Volcano plotVisualize DEG on a scatter-plot of fold change vs its significance (negative log of the p value).
Venn diagramCompare gene lists from various populations.
Parallel plotVisualize standardized expression data across developmental cell states.
Principal Component Analysis (PCA)Identify the principal components that explain a substantial amount of variance in the data.
Pathway mapVisualize maps of biochemical capability, or signal transduction of enriched gene lists.
NetworkUnderstand gene co-expression and gene-trait relationships.
Genome browserView your transcriptome alignments from a BAM file or as a graph along with reference annotation.
CytoscapeOpen source software platform for visualizing complex networks and integrating these with any type of attribute data.