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Single-cell transcriptomics. Single-cell transcriptomics examines the gene expression level of individual cells in a given population by simultaneously measuring the RNA concentration (conventionally only messenger RNA (mRNA)) of hundreds to thousands of genes. [1] Single-cell transcriptomics makes it possible to unravel heterogeneous cell ...
Like typical next-generation sequencing experiments, single-cell sequencing protocols generally contain the following steps: isolation of a single cell, nucleic acid extraction and amplification, sequencing library preparation, sequencing, and bioinformatic data analysis. It is more challenging to perform single-cell sequencing than sequencing ...
The data produced by single-cell RNA-seq can consist of thousands of cells each with expression levels recorded across thousands of genes. [7] In order to efficiently process data with such high dimensionality many trajectory inference algorithms employ a dimensionality reduction procedure such as principal component analysis (PCA), independent component analysis (ICA), or t-SNE as their first ...
Single-cell RNA sequencing ... The main advantage of RNA-Seq data in this kind of analysis over the microarray platforms is the capability to cover the entire ...
CITE-Seq. CITE-Seq (C ellular I ndexing of T ranscriptomes and E pitopes by Seq uencing) is a method for performing RNA sequencing along with gaining quantitative and qualitative information on surface proteins with available antibodies on a single cell level. [1] So far, the method has been demonstrated to work with only a few proteins per cell.
This single cell shows the process of the central dogma of molecular biology, which are all steps researchers are interested to quantify (DNA, RNA, and Protein).. In the field of cellular biology, single-cell analysis and subcellular analysis [1] is the study of genomics, transcriptomics, proteomics, metabolomics and cell–cell interactions at the single cell level.
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