Single Cell Sequencing reveals the full complexity of cellular diversity compared to bulk sequencing. It is primarily employed to solve complications related to the study of unusual cell types, cell-lineage associations, and samples of heterogeneous nature. By doing the in-depth single-cell sequencing, the cell functions of individual cells can be explored easily and efficiently.
By combining the 10x Genomics Chromium system and Illumina NGS platform, Novogene offers high-quality end-to-end single cell sequencing solutions and services, including Single Cell Gene Expression, Single Cell Immune Profiling, and Single Cell ATAC, etc. As a Certified Service Provider of 10x Genomics, Novogene provides efficient and accurate support for your research.
* : Only acceptable in AMEA (Asia pacific, Middle Eiddle East and Africa)
Note: 10x Single Cell sequencing service is only available in America and AMEA. Please refer to the service specifications of your region or contact your local sales.
From sample preparation, library preparation, sequencing and data quality control, to bioinformatics analysis, Novogene provides high-quality products and professional services. Each step is performed in agreement with a high scientific standard and meticulous design to ensure high-quality research results.
Onco-fetal Reprogramming of Endothelial Cells Drives Immunosuppressive Macrophages in Hepatocellular Carcinoma
Journal: CellIssue Date: Oct 15，2020IF: 31.398DOI: https://doi.org/10.1016/j.cell.2020.08.040
Single-cell RNA-sequencing atlas reveals an MDK-dependent immunosuppressive environment in ErbB pathway-mutated gallbladder cancer
Journal: Journal of HepatologyIssue date: Jun 22， 2021IF: 25.083DOI: https://doi.org/10.1016/j.jhep.2021.06.023
Single-cell transcriptomic analysis reveals disparate effector differentiation pathways in human Treg compartment
Journal: Nature CommunicationsIssue date:Jun 23, 2021IF:14.919DOI: https://doi.org/10.1038/s41467-021-24213-6
Figure 1 The t-SNE and UMAP plots
Note:Each point represents a cell, and each color designates a cluster.
For visualization purposes, dimensionality was further reduced to 2D using t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP). Both of them try to find a low-dimensional representation that preserves relationships between neighbors in high-dimensional space. Compared to t-SNE, the UMAP visualization tends to have more compact visual clusters with more empty spaces between them.
Figure 2 Expression Pattern of Marker Genes
Note: Darker red represents a higher expression level, and darker blue dictates a lower expression level.
Heatmaps show the expression of indicated marker genes for given cells and features. In this case, we plotted the top 10 markers (or all markers if less than 10) for each cluster.
Figure 3 Expression Level of Marker Genes
Note: The X-axis represents the cluster, and the Y-axis represents the expression level.
Violin plots show the relative expression levels of marker genes among all clusters. (Only six marker genes are shown here)
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