Gene Expression Profiling
Gene expression profiling contributes in measuring the activity or the expression in a literal manner of the extracted sample. In this study, the targeted samples are the Brown Adipose Tissue (BAT) of a sheep that are being centred for the gene expression profiling technique. The method creates profiles for analysing a number of genes at once for initiating the representation of the cellular function and related activities. In general, such gene profiles indicate the differences between different cells or their reaction towards a particular condition. Experiments that are conducted in this domain help in measuring an entire genome concurrently for the required purpose (HC & AA., 2001).
- Sheep Brown Adipose Tissue – Model for Biomedical Research
- BAT Tissue Extraction from the Sheep Model
The selected model was used for obtaining biopsy for analysing the expression. The model allows inserting the monitoring and sampling devices for collecting the BAT tissue in order to analyse the DNA, RNA, and protein factors in gene expression. Sheep has been utilised in particular sectors of the biomedical research because of high strength the sheep has as an animal model. Then again, there are certain limitations as well for sheep as an animal model (Ojha et al., 2013).
DNA Microarray Technology
The biopsy obtained from the sheep model were analysed by DNA microarray. The conducted expressions were further processed in the oligonucleotide microarrays Affymetrix GeneChip. This technology helps in measuring the activity of the genes that have been identified. The technique of microarrays in this study helps in measuring the expression levels of the obtained genes. Every DNA spot in this context comprises of picomoles (10-12 moles), which is of a particular DNA sequence addressed as probes. This smaller sections of the gene are utilised in hybridising cDNA or cRNA sample that processed under high stringency situations. In terms of spotted microarrays, the probes used are the oligonucleotides, which have been utilised for the conducted study (Tarca et al., 2008).
- Oligonucleotide Microarrays
- Oligonucleotide Microarrays Affymetrix GeneChip
This study has utilised the shorter sequences of having the 25-mer probes that have been formed through Affymetrix. In the Affymetrix technique, the method that has been utilised for producing the oligonucleotide arrays involves the photolithographic synthesis. The study has utilised the Affymetrix GeneChip in this regard to analyse the DNA Microarrays for immediately scanning the appearance of the specific genes that are present in the extracted biological sample. The study has been exacting in this area towards the oligonucleotide arrays of the Affymetrix GeneChip. These microarrays are utilised in this study for the gene expression profiling, for analysing the particular mRNA pieces. This research is using a single chip for observing the thousands of genes onto a single assay (Auer et al., 2009).
The GeneChip microarrays comprise of a distinctive characteristic of having the photochemical synthesis when it comes to their manufacturing. The manufacturing technology has provided with the ease of synthesising a million numbers of different probes, even more, on the presented array that is of a thumbnail size. This range of numbers allocates towards the inclusion of having multiple probes that are investigated in this study, and the targeted sequence that further relates to present the statistical course of interpreting the data.
- Investigating Gene Expression in Sheep BAT Tissues
The performed study has utilised the above-discussed Oligonucleotide microarrays Affymetrix GeneChip in order to analyse the gene expression profiling of the sheep BAT tissues. The GeneChip microarray is performed basing it on the present chemical attraction, which is between the DNA molecules. The process refers the strands matching to the complementary strands of the RNA. This depends on the basis being complementary, because if they are not, they will not fit together. The Affymetrix microarrays proceed depending on the basic principle of base pairing attraction, referring to the property of hybridisation.
The elements that are involved in the technical variation in terms of expression profiling, are indefinite, which is why in the performed research, technical variations that are present within the sample, would be processed further through statistical means. This indicates towards the differential expression, if there is any. By conducting the procedure in a parallel manner, it would contribute in using the identical equipment of hybridisation oven, washing station, and scanner, and would be used as a sample in this study. In different studies, limited cell numbers are present when it comes to gene profiling.
Applying the isothermal amplification would contribute in the condition of a small number of cells. This could start at a low range of 5ng total RNA and then can be utilised further, in terms of wide range of experiments. For this quantity of RNA, it can be isolated from different range of cells, with respect to many cell types. The gene expression microarray has been conducted on the Affymetrix GeneChip, which is based on the RNA extracted from the sample of brown adipose tissue. The research then utilises the R programming for the analysis procedure. The Affymetrix helps in generating the cDNA being as an amplified product. The cDNA hybridisation towards the microarrays contributes in generating more details and information regarding differential expression (Dalma-Weiszhausz et al., 2006).
For analysing the gene expression profiling that has been carried out in this study, it requires a course of procedures in order to indicate the pathway of the gene that has been highly expressed in the discussed-microarray. This would be performed by using the R program and further involves the Principal Competent Analysis (PCA) that would be used in the computation and visualisation procedures in the same program. The methodology would also require the clustering processing in the R program, with the addition of heat map and the volcano plot.
- R Programming and Gene Expression Profiling
- Principal Competent Analysis (PCA) for Gene Expression Profiling
- Static Type in R Programming
The performed research would involve the static type in R programming. In this context, it is necessary to check appropriately for the types that have been utilised. This is because; passing a wrong type in the used function would fail. By having, a ‘type system’ in the applied programming language is significant in terms of critical numerical computations. R language is a bit weekly typed programming language, or relatively to Python, Perl, or MatLab/Octave is dynamically typed similarity, as most of the R users exclude in order to place type checks in terms of their functions (Ojha et al., 2013).
If there were a function in the R program, which could take the argument of a matrix, a vector, and a function name, for instance, it would apply the named function towards every column of the matrix that has been listed in the presented vector. If the named function would be returning a single number, then the function would be returning a vector of numbers. For every argument in the function, the ‘if-statements’ can be placed. Now, the major issue that lies in this regard is that this technique would be a bit verbose and based on the gene expression profiling process, there would be a repeating pattern.
This indicates the copy and paste scenario that could be used for the code many times, because of the repeating pattern present, in terms of the arguments mention for different functions and arguments. This would not look good, with respect to the appearance of the coding environment, but would also waste the time for the methodology of the conducted research. However, the R programming does comprise of a mechanism that could contribute in addressing this part, the S-4 class system. This class system requires set of arguments, which are attained by rewriting the function that utilises the S4 class system. This would contribute in the type checking for passing the object only once.
- Clustering Gene Expression in R Programming with PCA
Clustering in gene expression has been a very useful technique when it comes to examining the data of gene expression. The course of clustering helps in finding the patterns, which are found in the data and remains unnoticed by the users. The study simply aims towards the gene expression profiling to be investigated according to the extraction and analysis made in the oligonucletide microarray of the Affymetrix GeneChip. This helps the study in not only contributing towards finding the patterns in the data that the researcher does not know existed, but also helps in indicating the outliers, inadequately annotated samples, and other problems that might be present in the entered data (Simpson, 2012).
Clustering involves different types, such as the hierarchical clustering including the binary tree grouping samples, and the K means, which involves the data to be properly organised into the K clusters. There are different computational techniques that are used for the process of clustering data, in terms of not being restricted to the gene expression data, but the clustering approach as a general technique. Different methods are involved in not only the R programming, but in MatLab as well and such related analysis software. The basic concept is to simply cluster the entered data and then visualise those clusters.
The hierarchical clustering only identifies that which samples are having a greater similarity to each other. Samples that are placed in different trees could be partially associated with one another, but that particular information is misplaced within the binary tree. For PCA analysis to be performed on the R program, the gene expression matrix needs to be saved first in the tab-delimited text file. This includes one exception; the value that is present in the very first row should be deleted. This is associated with other commands to be executed in an adequate manner.
There should be no spaces left between the names of the sample, otherwise, it could turn into a troubling effect while reading the file. Genes that comprise of low expression should be removed and the data should be log transformed prior to the process of saving the file. The current directory should be changed to the directory that contains the file after opening R. The data analysis procedure relates the clustering, heat map and volcano plotting processes in working on a similar track for identifying the highly expressed gene while visualising the microarray at every step.
- Drawing a Heat Map for Gene Expression Data
R analyses the microarray dataset and then produces the heat map of the genes. Having the topographical colours installed in the system would contribute in resulting automatically scaling the heat map with colours for each row. The distances in a heat map are visualised as it reflects the values of the gene expression for different conditions. For the study that has been carried out, it would focus on the pathway of that gene, which has been highly expressed in the microarrays.
- Volcano Plotting
The scatter plot is for examining the datasets of the microarray in order to provide with an overview of the highly expressed gene. The log fold change is the aspect plotted on the x-axis and the negative log 10 p-value would be plotted on the y-axis. The volcano plot function would be utilised in order to generate the volcano plot in R for the microarray data. The R environment also contributes in identifying the genes that are present above particular cut-offs (Gillespie, 2011).
- Identifying the Highly Expressed Gene Pathway in the Microarray
- Advantages of Microarray Analysis with R Program
- Disadvantages of Microarray Analysis with R Program
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Gillespie, C., 2011. Volcano plots of microarray data. The Bioinformatics Knowledgeblog, 21 June.
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Ojha, S. et al., 2013. Brown adipose tissue genes in pericardial adipose tissue of newborn sheep are downregulated by maternal nutrient restriction in late gestation. Pediatr Res. , 74(3), pp.246-51.
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