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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 cell biology, single-cell analysis and subcellular analysis [1] refer to the study of genomics, transcriptomics, proteomics, metabolomics, and cell–cell interactions at the level of an individual cell, as opposed to more ...
Single-cell RamDA-Seq is a method that achieves this by performing reverse transcription with ... Quality control covariates serve as a strategy to analyze the number ...
In systems biology, live single-cell imaging is a live-cell imaging technique that combines traditional live-cell imaging and time-lapse microscopy techniques with automated cell tracking and feature extraction, drawing many techniques from high-content screening. It is used to study signalling dynamics and behaviour in populations of ...
Single-cell omics technologies has extended beyond the transcriptome to profile diverse physical-chemical properties at single-cell resolution, including whole genomes/exomes, DNA methylation, chromatin accessibility, histone modifications, epitranscriptome (e.g., mRNAs, microRNAs, tRNAs, lncRNAs), proteome, phosphoproteome, metabolome, and more.
In cell biology, single-cell variability occurs when individual cells in an otherwise similar population differ in shape, size, position in the cell cycle, or molecular-level characteristics. Such differences can be detected using modern single-cell analysis techniques. [ 1 ]
A list of more than 100 different single cell sequencing (omics) methods have been published. [1] The large majority of methods are paired with short-read sequencing technologies, although some of them are compatible with long read sequencing.
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Nelson rules are a method in process control of determining whether some measured variable is out of control (unpredictable versus consistent). Rules for detecting "out-of-control" or non-random conditions were first postulated by Walter A. Shewhart [1] in the 1920s.
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