Single-cell transcriptomics

Single-cell transcriptomics refers to the quantification and analysis of the transcriptomes of individual cells. Single-cell transcriptomics makes it possible to unravel heterogeneous cell populations, reconstruct cellular developmental pathways, and model transcriptional dynamics.

Single-cell transcriptomics refers to the quantification and analysis of the transcriptomes of individual cells. Single-cell transcriptomics makes it possible to unravel heterogeneous cell populations, reconstruct cellular developmental pathways, and model transcriptional dynamics. === Dimensionality reduction=== Dimensionality reduction algorithms such as Principal component analysis (PCA), t-SNE and UMAP can be used to simplify data for visualisation and pattern detection by transforming cells from a high to a lower dimensional space. The result of this method produces graphs with each cell as a point in a 2D or 3D space. Dimensionality reduction is frequently used before clustering as cells in high dimensions can wrongly appear to be close due to distance metrics behaving non-intuitively. ====Principal component analysis==== The most frequently used technique is PCA, which identifies the directions of largest variance principal components and transforms the data so that the first principal component has the largest possible variance, and successive principle components in turn each have the highest variance possible while remaining orthogonal to the preceding components. The contribution each gene makes to each component is used to infer which genes are contributing the most to variance in the population and are involved in differentiating different subpopulations. ===Integration=== The presence or strength of technical effects and the types of cells observed often differ in single-cell transcriptomics datasets generated using different experimental protocols and under different conditions. This difference results in strong batch effects that may bias the findings of statistical methods applied across batches, particularly in the presence of confounding. As a result of the aforementioned properties of single-cell transcriptomic data, batch correction methods developed for bulk sequencing data were observed to perform poorly. Consequently, researchers developed statistical methods to correct for batch effects that are robust to the properties of single-cell transcriptomic data to integrate data from different sources or experimental batches. Laleh Haghverdi performed foundational work in formulating the use of mutual nearest neighbors between each batch to define batch correction vectors. With these vectors, one can merge datasets that each include at least one shared cell type. An orthogonal approach involves the projection of each dataset onto a shared low-dimensional space using canonical correlation analysis. Mutual nearest neighbors and canonical correlation analysis have also been combined to define integration "anchors" comprising reference cells in one dataset, to which query cells in another dataset are normalized. Another class of methods (e.g., scDREAMER ) uses deep generative models such as variational autoencoders for learning batch-invariant latent cellular representations which can be used for downstream tasks such as cell type clustering, denoising of single-cell gene expression vectors and trajectory inference. Cell-cell Communication Cell-cell communication tools leverage the expression of cognate ligand and receptor pairs in distinct cell pairs to predict putative interactions between cells. A pioneer tool is CellPhoneDB, which included a curate database of receptor complexes and ligand-receptor interactions. The method also implemented a statistical test by evaluating the expression of the ligand-receptor in the respective cell pair. One interesting advanced analysis is at scenarios when two conditions (disease vs. controls) are available. Here, one is interested in finding cell-cell interactions specific to disease or control conditions . Recent methods such as Renoir integrate single-cell and spatial transcriptomics data to infer context-specific cell-cell communication within tissues. Analysis of Disease Atlas With the lowering of single cell cost, it is possible to measure single cell data over large disease cohorts with up to 100 individual samples and million of cells. This brings several challenges such as individual variability and the multi-scale properties of the data, every single cell experiment represents a distribution of cells. MILO tackle individual variability by encoding single cell data as a k-nn graph followed by a differential abundance test. This allows to find cellular neighboorhoods associated to a particular disease condition. Optimal transport theory is another represents another framework to tackle multi-scale nature of single cell diseae atlas. By encoding single cell experiments of an patient as a distribution of cells, optimal transport based Wasserstein distance allows to measure a distance between two patient. This can be used for sample level clustering and trajectory analysis as explored by PILOT and QOT. Software frameworks An important pilar of single cell analysis are software packages providing functionally for quality check, normalization, clustering and down-stream analysis described above. They also provide data containers for the storage and representation of analysed single cell data. Seurat is the first and possibly most complete frameworks for single cell analysis. Scanpy is an alternative for python language. Additional software package offer specific data analysis taks. Notable examples is LIANA+ for cell-cell communication analysis, scVI as a package offering variational autoencoders for analysis of single cell data, and PILOT as a package for sample level analysis of disease atlas Dissecting Tumor Heterogeneity with Single-Cell Transcriptomics The ultimate single-cell RNA sequencing guide by single-cell RNA sequencing service provider Single Cell Discoveries.

Text derived from https://en.wikipedia.org/w/index.php?oldid=1377560935 under CC BY-SA 4.0; modifications have been made.