what is time course expression analysis

First, a time course captures the diverse kinetics of each gene’s expression, which is useful in characterizing complex responses that employ several signaling pathways and transcription factors. Second, transient expression changes, which may be important in the response, will be identified in a time course.

Full Answer

What's new in time-series expression data?

Several additional types of time-series expression data sets, including single-cell measurements and data from next-generation sequencing technologies, will provide new opportunities while also raising new computational analysis challenges. Biological processes are often dynamic, thus researchers must monitor their activity at multiple time points.

Why study time-series gene expression?

Studying time-series gene expression enables the identification of transient transcriptional changes, temporal patterns of a response and causal relationships between genes.

What is time history analysis in research?

Time history analysis is detailed analysis in which response is calculated for each time step. It requires more time but gives good results. In time history analyses the structural response is computed at a number of subsequent time instants. In other words, time histories of the structural response to a given input are obtained ad a result.

How is the differential expression analysis performed?

Finally, the differential expression analysis is performed using your tool of interest. The first step in the DE analysis workflow is count normalization, which is necessary to make accurate comparisons of gene expression between samples.

What is a time course experiment?

In a temporal experiment the arrays are collected over a time course, allowing one to study the dynamic behavior of gene expression. A large amount of work has been done on the problem of identifying differentially expressed genes in static experiments (2).

What is an expression analysis?

Gene expression analysis involves the determination of the pattern of genes expressed at the level of genetic transcription, under specific circumstances or in a specific cell.

What are expression studies?

The expression studies are directed to detect and quantify messenger RNA (mRNA) levels of a specific gene. The development of the RNA-based gene expression studies began with the Northern Blot by Alwine et al.

What is gene expression analysis used for?

Gene expression analysis simultaneously compares the RNA expression levels of multiple genes (profiling) and/or multiple samples (screening). This analysis can help scientists identify the molecular basis of phenotypic differences and to select gene expression targets for in-depth study.

Why is gene expression analysis important?

Gene expression profiling has been used extensively in biological research and has resulted in significant advances in the understanding of the molecular mechanisms of complex disorders, including cancer, heart disease, and metabolic disorders.

What is differential expression analysis?

Differential expression analysis means taking the normalised read count data and performing statistical analysis to discover quantitative changes in expression levels between experimental groups.

How do you analyze gene expression data?

A common approach to interpreting gene expression data is gene set enrichment analysis based on the functional annotation of the differentially expressed genes (Figure 13). This is useful for finding out if the differentially expressed genes are associated with a certain biological process or molecular function.

What are the two stages of gene expression?

It consists of two major steps: transcription and translation. Together, transcription and translation are known as gene expression. During the process of transcription, the information stored in a gene's DNA is passed to a similar molecule called RNA (ribonucleic acid) in the cell nucleus.

How are time series gene expression data used?

Time-series gene expression data are being increasingly used to monitor patient responses in clinical studies that are focused on human responses to injury and disease 9, 35, 104, as well as to treatments and preventive measures 46, 96, 105. Patient heterogeneity can render the analysis of absolute expression levels meaningless 104, and ethnicity can affect the transcriptional responses to therapy 105. Therefore, dynamic measurements that allow the assessment of within-patient expression changes are especially beneficial. Such studies often face a unique set of challenges. Ethical considerations may preclude certain types of sample collection that would be most relevant to the scientific hypotheses. For example, although the analysis of hepatocytes would have been most relevant in a study of patients chronically infected with hepatitis C virus, harvesting hepatocytes requires a liver biopsy, which cannot be carried out repeatedly on humans 105. Instead, many such studies examine peripheral blood mononuclear cells 46, 96, 105 or other peripheral blood cells 9 as an approximation, owing to their accessibility. It is difficult to assess how accurately this approximation reflects the actual gene expression dynamics in specific conditions, but an analysis of which genes are expressed in peripheral blood versus nine other tissues indicated that it may be an effective surrogate tissue 106. For infections by milder viruses (for example, certain influenza strains 9) it is possible to expose volunteers to the pathogen and to measure phenotypic changes directly, but studies of more lethal viruses cannot do so (although studies of responses to vaccines, for example yellow fever vaccination 96, have been carried out).

Why are time series experiments important?

As most biological processes are dynamic, time-series experiments are key to our ability to understand and model these processes. Although several types of genomics data can be measured over time, one of the most abundant and available types of such data is time-series gene expression data.

What is the MARA method?

(MARA). A method for inferring DNA-binding-motif activity and for linking motifs to promoters. MARA models promoter expression as a linear function of motif activity and the number of functional binding sites.

What are the advantages of time series data?

A key advantage of time-series data is the ability to infer causality without perturbing the system, using causal modelling. By observing the cascade of expression changes, their specific profiles and their temporal autocorrelations, researchers can derive several hypotheses regarding causal relationships between genes.

What are the categories of time series experiments?

Categories of time-series experiments include the response to an external signal, developmental processes and cyclic processes. Each type of experiment has a characteristic outcome: transient responses, fate switches and cyclic expression patterns, respectively.

Can heat maps show uneven time intervals?

However, heat maps are plotted with equal width for each time point, and cannot show whether the samples were taken at uneven time intervals. An alternative and very popular approach is to use piecewise linear curves, in which every two consecutive time points are connected by a line 32.

Can single cell sequencing be used to monitor temporal studies?

Currently, such experiments can only monitor the products of a few genes simultaneously in a cell. However, improvements in this approach and in single-cell sequencing techniques may enable high-throughput temporal studies at a single-cell level.

Transient Response

After applying input to the control system, output takes certain time to reach steady state. So, the output will be in transient state till it goes to a steady state. Therefore, the response of the control system during the transient state is known as transient response.

Steady state Response

The part of the time response that remains even after the transient response has zero value for large values of ‘t’ is known as steady state response. This means, the transient response will be zero even during the steady state.

Unit Parabolic Signal

We can write unit parabolic signal, p ( t) in terms of the unit step signal, u ( t) as,

Most recent answer

In time history analysis, the structural response is computed at a number of subsequent time instants. In other words, time histories of the structural response to a given input are obtained ad as a result. A full-time history will give the response of structure over time during and after the application of a load.

Popular Answers (1)

A full time history will give the response of a structure over time during and after the application of a load. To find the full time history of a structure's response, you must solve the structure's equation of motion.

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In time history analyses the structural response is computed at a number of subsequent time instants. In other words, time histories of the structural response to a given input are obtained ad a result. In response spectrum analyses the time evolution of response cannot be computed. Only the maximum response is estimated.

What is differential expression analysis?

The goal of differential expression testing is to determine which genes are expressed at different levels between conditions. These genes can offer biological insight into the processes affected by the condition (s) of interest.

What is the next step in differential expression workflow?

The next step in the differential expression workflow is QC, which includes sample-level and gene-level steps to perform QC checks on the count data to help us ensure that the samples/replicates look good.

What is PCA in math?

Principal Component Analysis (PCA) is a dimensionality reduction technique that finds the greatest amounts of variation in a dataset and assigns it to principal components . The principal component (PC) explaining the greatest amount of variation in the dataset is PC1, while the PC explaining the second greatest amount is PC2, and so on and so forth. For a more detailed explanation, please see additional materials here.