Data collection

[Virtual Presenter] Welcome everyone. Today, we are exploring the topic of Parkinson's Disease, or PD. We will delve into the symptomatic prevalence, causes, and data analysis of PD with the help of Gene Expression Omnibus Analysis. Put on your data science hats and let's get started!.

Parkinson's Disease (PD). Definition: Parkinson's disease (PD) is a neurodegenerative disorder that primarily affects movement control. Prevalence : PD ranks as the second most common neurodegenerative disorder globally, affecting an estimated 10 million people. Symptoms: Motor symptoms include resting tremors, slowness of movement stiffness of limbs, difficulty maintaining balance, mood disorders, and autonomic dysfunction. Pathology: The loss of dopamine-producing neurons in the substantia nigra, with an estimated 60-80% of these cells being lost before the onset of motor symptoms. Cause: It remains unknown, but it is thought to result from a combination of genetic and environmental factors..

Data collection. Figure 2. The flowchart representing the process of data collection, analysis, and validation..

[Audio] Our Volcano Plot from the Gene Expression Omnibus analysis displays 61 significant genes; 27 of them are upregulated (marked in red) and 34 are downregulated (marked in blue). To ensure accuracy, we used a default adjusted p-value cutoff of 0.01..

[Audio] Parkinson's Disease is a complex neurological disorder, and it's the second most common degenerative disorder after Alzheimer's Disease. In this presentation, we are exploring the disease with insights from the Gene Expression Omnibus Analysis. As part of this work, we have employed Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) techniques. PCA is a classical linear method for transforming original variables to produce uncorrelated components and effectively reduce dimensionality, making it easier to identify patterns in multivariate datasets. With UMAP, we are employing a non-linear dimensionality reduction technique which is used to visualize high-dimensional data, while preserving both local and global structures. The number of nearest neighbors parameter for UMAP further influences its granularity by balancing local relationships and capturing global structure..

Gene Expression Profile. Gene Expression Profile.

[Audio] The slide emphasizes the value of examining the gene expression of pathways connected to Parkinson's Disease. Genes such as TUBB2A, TUBB2B, and TUBB3, and pathways like Post-chaperonin Tubulin Folding Pathway, Cooperation Of Prefoldin And TriC'CCT In Actin And Tubulin Folding, Protein Folding, and Gap junction pathway, have a clear association with the disease. The adjusted p-value for these pathways was 0.0001172, 0.001638, 0.004856, 0.005682, 0.01467, 0.01417, 0.00005532 and 0.0005801 respectively. These results support the need for further exploration of gene expression to gain a better understanding of Parkinson's Disease..

[Audio] We conducted an exploration of gene expression data to identify genes associated with Parkinson's Disease. We analyzed 2023 genes and their expression across various biological processes, cellular components, and molecular functions. Our findings indicated that the genes P2RX7, TUBB2A, TUBB2B, TUBB3, KCNJ6, and CA2 were positively associated with Parkinson's Disease. Additionally, we identified eleven GO terms for biological processes, cellular components, and molecular functions with an adjusted p-value lower than 0.05..

[Audio] GEO Analysis is proving to be an invaluable asset in helping researchers better understand the mechanism of action of Parkinson's Disease. It has identified two key genes, P2RX7 and KCNJ6, which have been found to be associated with the development and progression of the condition. P2RX7 is important for cell survival and proliferation, while KCNJ6 controls the entry of potassium into cells. Both of these genes have been implicated in intellectual disability, developmental delays, and the release of pro-inflammatory cytokines, as well as the response of the body to cellular stress and damage. Through the use of GEO Analysis, we are gaining a better understanding of the complexity of this devastating disease..

[Audio] Research into Parkinson's Disease has uncovered that certain tubulin proteins, encoded by the TUBB2A, TUBB2B and TUBB3 genes, play a key role in the microtubule formation and assembly crucial for the transport of cellular components along axons. Such microtubule degenerations can prevent the transportation of proteins, such as alpha-synuclein, leading to the accumulation of abnormal proteins, a hallmark of the disease. The bar plots in Figure 8 of the GSE136666 data set, available at the National Center for Biotechnology Information, show the differential expression of these three genes..

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[Audio] Parkinson's disease is a complex neurological disorder that scientists are making progress in understanding the underlying genetic factors of. Through an analysis of public datasets on gene expression in Parkinson's disease from the Gene Expression Omnibus database, it was found that the most of the genes studied were not present in the validation set. However, certain singular genes were identified that may be relevant for developing treatments. Genes with divergent names, but convergent functions showed significant p-adjusted values, indicating their possible role in the mechanisms behind this chronic disorder. Further research is necessary to determine the precise underlying causes and potential treatments..

[Audio] Now that we've talked a little about what gene expression omnibus analysis can do, it's important to examine the limitations of this method. Although the technology is incredibly powerful, there are a few drawbacks that we must consider. As the datasets we work with cover a wide range of sources, there can be issues with heterogeneity and variability. This can lead to challenges during the data preprocessing stage as well as when dealing with sample size, as even the largest dataset had only 29 samples. Additionally, there is a lack of standardization when it comes to experimental protocols, data formats and analysis methods, which can lead to difficulties in comparing data. There are also issues with ethnic diversity, as this can potentially introduce biases, which can affect the generalizability of findings..

Power 0 0.00 0 0.25 075 1.00 P2RX7 CA2 KCNJ6 CCPIIO TUBB3 TUBB2A TUBB2B 0 5 0 O 10 2.87 2.14 1.95 m 1.75* 1.67 1.57 1.08 0 20 O 40 O 80 160 320 0 640 1280 2560 Sample Size.

[Audio] The analysis of gene expression data from the Gene Expression Omnibus repository has identified seven genes that are significant to Parkinson's Disease. These include TUBB2A, TUBB2B, TUBB3, P2RX7, CA2, KCNJ6, and CCP110; they affect the cellular structure, protein binding, ion channels and mitosis. Further research could uncover more genes that have a major influence on this condition, as well as more effective biomarkers that clinicians can utilize for better diagnosis and management of Parkinson's Disease..

[Audio] A careful analysis of six different scientific studies and papers highlighted the role of different genes and pathways in the development and progression of Parkinson's Disease. This list of references gives us a greater insight into the mechanisms of Parkinson's Disease and the development of new treatments..