Data analysis is a critical step in the single cell study, but the standard bioinformatics process is not enough to uncover all information from the complicated single cell data. Singleron not only offers customers with a standard analyses option and strictly evaluated advanced analyses, but also provide customized analysis services to meet different customer needs.
Singleron provides comprehensive analysis contents with the most up-to-date bioinformatics tools in the standard analysis package. A HTML report will also be generated to provide a concise summary of the results. In addition to basic QC results, the standard analysis package includes the following functions:
1. Cell Clustering
The cell clustering results can be displayed on the two-dimensional space, clearly demonstrating the separation of clusters.
After dimensionality reduction by PCA and graph-based SNN clustering, we use UMAP for visualization. The cells in this figure are divided into 15 different clusters, labeled by different colors.
2. Marker Gene Profiles
The expression levels of marker genes in each cluster can be visualized in different formats.
The heatmap (left) shows the relative expression levels of marker genes in each Cluster. The color from red to blue indicates high to low expression level. The Dotplot (right) also shows relative expression of marker genes in each cluster by red to blue colors. The dot size from small to large indicates the expression ratio in each cluster from low to high. Researchers can choose the most suitable method based on their needs.
3. Trajectory Analysis
Single cell data is helpful to investigate cellular dynamics, such as cell cycle, differentiation and development.
The figure above shows distribution of cells on a pseudo-time axis and the development relationship between clusters. Cluster4 is the initial state of the pseudotime and the other cells are ordered along the trajectory. Cell differentiation occurs at the branch points.
More Standard Analyses
Tailored to the needs of each project, Singleron provides strictly tested and evaluated advanced analyses as shown below, with the option to develop personalized analysis solutions based on the latest research.
1. Multiple Samples Integration Analysis
By integrating multiple samples, potential batch effects would be removed to reveal biological insights and compare different samples.
The figure above shows the results of the integration analysis of different gastric cancer samples. The left panel shows the clusters of all sample visualized by tSNE, and the middle panel is the tSNE visualization colored by sample. The right figure shows the different cell composition of samples.
2. Linking Phenotypes to Genotypes
CNV information obtained by single cell RNA sequencing reveals the heterogeneity of cell genome and facilitates the understanding of mechanisms of human disease on the single cell level.
Taking immune cells as normal cells as reference, the expression profile of cancer cells obtained by single cell technology was used to detect CNV. Red represents insertion on the chromosome, and blue represents deletion on the chromosome.
3. RNA Velocity Analysis
By simulating RNA transcription rates of each cell, RNA velocity analysis constructs developmental trajectories among cells. Combined with expression based pseudotime analysis such as monocle, the developmental relationship between different cells can be further verified.
In the figure above, different colors indicate cell types and arrows denote development directions.