Podcast
Questions and Answers
How is the performance of a model evaluated when using a random data division methodology?
How is the performance of a model evaluated when using a random data division methodology?
- Based on the total errors generated during training.
- Based on the number of features selected during modeling.
- Based on the percentage of correctly classified samples in the test set. (correct)
- Based on the percentage of correctly classified samples in the training set.
What is a key characteristic of the Nearest Template Prediction (NTP) classification algorithm?
What is a key characteristic of the Nearest Template Prediction (NTP) classification algorithm?
- It uses multiple samples to determine class probabilities.
- It requires extensive neural network architecture for classification.
- It relies solely on a training dataset without any flexibility.
- It provides a single-sample-based flexible class prediction. (correct)
Which of the following best describes gene expression profiling?
Which of the following best describes gene expression profiling?
- It quantifies protein levels in a sample.
- It analyzes gene expression patterns to predict survival rates. (correct)
- It uses genomic sequencing data to identify mutations.
- It focuses solely on DNA methylation patterns.
Which visualization technique is primarily used for integrated models of biomolecular interaction networks?
Which visualization technique is primarily used for integrated models of biomolecular interaction networks?
In which scenario would cross-validation be most beneficial in a machine learning context?
In which scenario would cross-validation be most beneficial in a machine learning context?
What does a high similarity score in classification tasks generally indicate?
What does a high similarity score in classification tasks generally indicate?
Which is a notable feature of the GSVA method in gene expression analyses?
Which is a notable feature of the GSVA method in gene expression analyses?
What is the main advantage of using hierarchical agglomerative clustering methods?
What is the main advantage of using hierarchical agglomerative clustering methods?
Which method is NOT used for hierarchical clustering?
Which method is NOT used for hierarchical clustering?
What is the primary purpose of calculating Euclidean distance in clustering?
What is the primary purpose of calculating Euclidean distance in clustering?
In hierarchical clustering, what is the result of using the agglomerative approach?
In hierarchical clustering, what is the result of using the agglomerative approach?
What is the function of the 'hclust' method in R?
What is the function of the 'hclust' method in R?
Which of the following best describes the distance matrix in the context of hierarchical clustering?
Which of the following best describes the distance matrix in the context of hierarchical clustering?
What does Ward's minimum variance method primarily aim to minimize during the merging of clusters?
What does Ward's minimum variance method primarily aim to minimize during the merging of clusters?
Which distance metric is specifically calculated before applying the hclust function in R?
Which distance metric is specifically calculated before applying the hclust function in R?
What does the dendrogram visualize in hierarchical clustering?
What does the dendrogram visualize in hierarchical clustering?
Why was the transcriptomic data from the CCLE used in the ATRA-scores correlation analysis?
Why was the transcriptomic data from the CCLE used in the ATRA-scores correlation analysis?
During which step of cluster analysis is the distance matrix created?
During which step of cluster analysis is the distance matrix created?
What analysis technique is used to correlate the basal gene-expression levels with the ATRA-scores?
What analysis technique is used to correlate the basal gene-expression levels with the ATRA-scores?
What is the role of the hclust function in the context of clustering?
What is the role of the hclust function in the context of clustering?
Which aspect of the data is primarily analyzed to predict response to ATRA treatment?
Which aspect of the data is primarily analyzed to predict response to ATRA treatment?
How many treated gastric lines had RNA-Sequencing-based transcriptomic profiling performed?
How many treated gastric lines had RNA-Sequencing-based transcriptomic profiling performed?
What feature of Ward's method makes it distinct in cluster analysis?
What feature of Ward's method makes it distinct in cluster analysis?
What is the primary purpose of calculating the S-Score in the analysis?
What is the primary purpose of calculating the S-Score in the analysis?
Which technique was used to visualize the final score obtained from the GSVA analysis?
Which technique was used to visualize the final score obtained from the GSVA analysis?
In the GSVA analysis, how are the GSVA scores for the Positive and Negative clusters rescaled?
In the GSVA analysis, how are the GSVA scores for the Positive and Negative clusters rescaled?
What gene signature template was used in the NTP classification algorithm for clustering?
What gene signature template was used in the NTP classification algorithm for clustering?
Which aspect of machine learning does cross-validation help to address?
Which aspect of machine learning does cross-validation help to address?
How many TCGA samples were analyzed in the GSVA analysis?
How many TCGA samples were analyzed in the GSVA analysis?
What is the outcome of applying the NTP classification algorithm?
What is the outcome of applying the NTP classification algorithm?
What kind of matrix is outputted after performing GSVA analysis on TCGA samples?
What kind of matrix is outputted after performing GSVA analysis on TCGA samples?
What is the purpose of normalizing data in the GSVA analysis?
What is the purpose of normalizing data in the GSVA analysis?
What benefit does gene expression profiling provide in gastric tumor studies?
What benefit does gene expression profiling provide in gastric tumor studies?
What is the primary purpose of calculating the ATRA-Score in the context of Retinoic Acid treatment?
What is the primary purpose of calculating the ATRA-Score in the context of Retinoic Acid treatment?
Which technique is particularly emphasized as invaluable for obtaining input data in assessing the effects of ATRA treatment?
Which technique is particularly emphasized as invaluable for obtaining input data in assessing the effects of ATRA treatment?
In the computational model predicting ATRA treatment response, what type of expression patterns are primarily utilized?
In the computational model predicting ATRA treatment response, what type of expression patterns are primarily utilized?
What is the objective of sample clustering methods in the analysis of gastric cancer concerning ATRA treatment?
What is the objective of sample clustering methods in the analysis of gastric cancer concerning ATRA treatment?
What is the ultimate aim of developing predictive models related to ATRA treatment?
What is the ultimate aim of developing predictive models related to ATRA treatment?
What is the role of transcriptomics techniques in the context of ATRA analysis?
What is the role of transcriptomics techniques in the context of ATRA analysis?
Which of the following is NOT a method discussed in the study of ATRA treatment effectiveness?
Which of the following is NOT a method discussed in the study of ATRA treatment effectiveness?
Which concept is related to validating the computational models established for ATRA sensitivity?
Which concept is related to validating the computational models established for ATRA sensitivity?
What computational method is applied to evaluate gene expression patterns in this analysis?
What computational method is applied to evaluate gene expression patterns in this analysis?
Which aspect of bioinformatics does the ATRA-Score primarily leverage to determine treatment response?
Which aspect of bioinformatics does the ATRA-Score primarily leverage to determine treatment response?
What is the primary function of the CPM normalization method in gene expression analysis?
What is the primary function of the CPM normalization method in gene expression analysis?
Which method would likely NOT be applicable for enhancing the accuracy of predictive models in gene expression analyses?
Which method would likely NOT be applicable for enhancing the accuracy of predictive models in gene expression analyses?
How does the False-Discovery-Rate (FDR) procedure benefit statistical analysis in this context?
How does the False-Discovery-Rate (FDR) procedure benefit statistical analysis in this context?
What is a potential limitation of using the Euclidean Distance Metric in transcriptomic clustering?
What is a potential limitation of using the Euclidean Distance Metric in transcriptomic clustering?
What is the main purpose of applying the DESeq2 pipeline in this analysis?
What is the main purpose of applying the DESeq2 pipeline in this analysis?
What is the implication of using GSEA (Gene-Set Enrichment Analysis) with the Limma package?
What is the implication of using GSEA (Gene-Set Enrichment Analysis) with the Limma package?
In the context of RNA-seq analysis, which method should be avoided for visualizing the results due to misleading interpretations?
In the context of RNA-seq analysis, which method should be avoided for visualizing the results due to misleading interpretations?
Which aspect of gene expression data is primarily utilized in the NTP classification algorithm?
Which aspect of gene expression data is primarily utilized in the NTP classification algorithm?
What role does the Euclidean distance play in the NTP classification algorithm?
What role does the Euclidean distance play in the NTP classification algorithm?
Which technique is primarily utilized to validate the performance of the NTP classification method?
Which technique is primarily utilized to validate the performance of the NTP classification method?
Which gene expression profiling method is best suited for categorizing samples into predefined classes using NTP?
Which gene expression profiling method is best suited for categorizing samples into predefined classes using NTP?
What is a common misconception about the application of Euclidean distance in gene expression studies?
What is a common misconception about the application of Euclidean distance in gene expression studies?
In the context of gene expression profiling, what does a high similarity score typically indicate?
In the context of gene expression profiling, what does a high similarity score typically indicate?
Which aspect of the NTP classification process is assessed through cross-validation?
Which aspect of the NTP classification process is assessed through cross-validation?
Which of the following is an important consideration when interpreting the results of gene expression profiling?
Which of the following is an important consideration when interpreting the results of gene expression profiling?
What limitation is often associated with using the NTP algorithm for gene classification?
What limitation is often associated with using the NTP algorithm for gene classification?
What is a principal purpose of visualizing gene expression data in the NTP context?
What is a principal purpose of visualizing gene expression data in the NTP context?
Which technique best complements the NTP classification in assessing gene expression patterns?
Which technique best complements the NTP classification in assessing gene expression patterns?
Which mathematical method is often utilized to calculate similarity scores in classification tasks?
Which mathematical method is often utilized to calculate similarity scores in classification tasks?
Which key feature distinguishes the Nearest Template Prediction (NTP) classification algorithm?
Which key feature distinguishes the Nearest Template Prediction (NTP) classification algorithm?
Which visualization technique is commonly used to represent integrated models of biomolecular interaction networks?
Which visualization technique is commonly used to represent integrated models of biomolecular interaction networks?
What is the primary purpose of cross-validation in machine learning?
What is the primary purpose of cross-validation in machine learning?
Which characteristic is most significant in gene expression profiling for cancer studies?
Which characteristic is most significant in gene expression profiling for cancer studies?
In the context of similarity score calculation, which of the following metrics is not normally applied?
In the context of similarity score calculation, which of the following metrics is not normally applied?
Which statement correctly describes the function of the NTP algorithm?
Which statement correctly describes the function of the NTP algorithm?
What kind of analysis does cross-validation primarily enhance in machine learning?
What kind of analysis does cross-validation primarily enhance in machine learning?
Which of the following factors does gene expression profiling typically assess in patients with gastric cancer?
Which of the following factors does gene expression profiling typically assess in patients with gastric cancer?
Which approach is NOT commonly associated with visualization techniques in bioinformatics?
Which approach is NOT commonly associated with visualization techniques in bioinformatics?
What is the primary purpose of calculating the S-Score in the analysis?
What is the primary purpose of calculating the S-Score in the analysis?
Which method was employed to classify gastric tumors into G-DIFF and G-INT sub-groups?
Which method was employed to classify gastric tumors into G-DIFF and G-INT sub-groups?
Which visualization techniques were used to represent the final score obtained from the GSVA analysis?
Which visualization techniques were used to represent the final score obtained from the GSVA analysis?
In the context of machine learning, what is the significance of cross-validation when applied to predictive models?
In the context of machine learning, what is the significance of cross-validation when applied to predictive models?
What primary aspect does gene expression profiling focus on in gastric cancer studies?
What primary aspect does gene expression profiling focus on in gastric cancer studies?
What is the significance of having a p-value < 0.01 in the correlation analysis of genes?
What is the significance of having a p-value < 0.01 in the correlation analysis of genes?
How does Cytoscape facilitate the understanding of molecular interactions?
How does Cytoscape facilitate the understanding of molecular interactions?
What does a high degree of interconnectedness (> 2) in the final gene selection imply?
What does a high degree of interconnectedness (> 2) in the final gene selection imply?
What is a primary characteristic of the STRING database in relation to gene and protein interactions?
What is a primary characteristic of the STRING database in relation to gene and protein interactions?
In the context of ATRA treatment response analysis, what does gene expression profiling primarily assess?
In the context of ATRA treatment response analysis, what does gene expression profiling primarily assess?
Why is it necessary to eliminate isolated genes when performing connectivity analysis?
Why is it necessary to eliminate isolated genes when performing connectivity analysis?
What is a key advantage of using the NTP classification algorithm in gene expression analysis?
What is a key advantage of using the NTP classification algorithm in gene expression analysis?
Which aspect of the similarity score calculation is crucial for gene interaction analysis?
Which aspect of the similarity score calculation is crucial for gene interaction analysis?
What technique is reinforced as effective for obtaining input data in assessing ATRA treatment effects?
What technique is reinforced as effective for obtaining input data in assessing ATRA treatment effects?
What role does cross-validation play in machine learning concerning gene analysis?
What role does cross-validation play in machine learning concerning gene analysis?
Which of the following is a primary purpose of calculating similarity scores in classification tasks?
Which of the following is a primary purpose of calculating similarity scores in classification tasks?
What is a key feature of the Nearest Template Prediction (NTP) classification algorithm?
What is a key feature of the Nearest Template Prediction (NTP) classification algorithm?
Which visualization technique is particularly useful for representing relationships in biomolecular interaction networks?
Which visualization technique is particularly useful for representing relationships in biomolecular interaction networks?
In gene expression profiling, which analysis is primarily performed to compare different gene expression levels across samples?
In gene expression profiling, which analysis is primarily performed to compare different gene expression levels across samples?
What is an essential advantage of using cross-validation in machine learning?
What is an essential advantage of using cross-validation in machine learning?
Which factor is NOT typically considered when calculating similarity scores between data points?
Which factor is NOT typically considered when calculating similarity scores between data points?
In the context of the NTP classification algorithm, what role does a 'gene signature template' play?
In the context of the NTP classification algorithm, what role does a 'gene signature template' play?
Which of the following statements best characterizes the use of cross-validation in gene expression analyses?
Which of the following statements best characterizes the use of cross-validation in gene expression analyses?
What mathematical operation is primarily used to combine the GSVA scores for the Positive and Negative clusters to calculate the S-Score?
What mathematical operation is primarily used to combine the GSVA scores for the Positive and Negative clusters to calculate the S-Score?
Which statement best describes the role of the Nearest Template Prediction (NTP) classification algorithm in the analysis?
Which statement best describes the role of the Nearest Template Prediction (NTP) classification algorithm in the analysis?
Which visualization techniques were utilized to represent the final score obtained from the GSVA analysis?
Which visualization techniques were utilized to represent the final score obtained from the GSVA analysis?
In the context of gene expression profiling within gastric cancer studies, which of the following statements is incorrect?
In the context of gene expression profiling within gastric cancer studies, which of the following statements is incorrect?
How does cross-validation contribute to the reliability of models in machine learning, particularly in the context of gene expression analysis?
How does cross-validation contribute to the reliability of models in machine learning, particularly in the context of gene expression analysis?
What does the calculation of the similarity score primarily aim to achieve in gene expression profiling?
What does the calculation of the similarity score primarily aim to achieve in gene expression profiling?
Which key characteristic distinguishes the NTP classification algorithm in the context of clustering?
Which key characteristic distinguishes the NTP classification algorithm in the context of clustering?
What is the primary advantage of visualizing data with a dendrogram in hierarchical clustering?
What is the primary advantage of visualizing data with a dendrogram in hierarchical clustering?
In what way does gene expression profiling contribute to treatment decisions in gastric cancer?
In what way does gene expression profiling contribute to treatment decisions in gastric cancer?
What is a fundamental purpose of cross-validation in the context of predictive modeling for ATRA treatment?
What is a fundamental purpose of cross-validation in the context of predictive modeling for ATRA treatment?
What is a significant limitation associated with using the NTP classification algorithm in predictive models?
What is a significant limitation associated with using the NTP classification algorithm in predictive models?
What aspect of visualization techniques is emphasized in the analysis of ATRA treatment responses?
What aspect of visualization techniques is emphasized in the analysis of ATRA treatment responses?
How does the use of cross-validation enhance the validity of predictive models in ATRA treatment?
How does the use of cross-validation enhance the validity of predictive models in ATRA treatment?
What is the primary criterion used to evaluate the performance of a classification model based on a random data division methodology?
What is the primary criterion used to evaluate the performance of a classification model based on a random data division methodology?
Which characteristic is essential for the Nearest Template Prediction (NTP) classification algorithm?
Which characteristic is essential for the Nearest Template Prediction (NTP) classification algorithm?
In gene expression profiling, which technique is primarily used to visualize relationships in biomolecular interaction networks?
In gene expression profiling, which technique is primarily used to visualize relationships in biomolecular interaction networks?
What is a significant advantage of utilizing cross-validation in machine learning?
What is a significant advantage of utilizing cross-validation in machine learning?
What does a high similarity score typically indicate in clustering tasks?
What does a high similarity score typically indicate in clustering tasks?
Which method is often used in gene expression profiling to assess overall classifications?
Which method is often used in gene expression profiling to assess overall classifications?
What is a characteristic feature of utilizing gene expression profiling in gastric cancer studies?
What is a characteristic feature of utilizing gene expression profiling in gastric cancer studies?
How does the NTP classification algorithm assess the confidence of its predictions?
How does the NTP classification algorithm assess the confidence of its predictions?
What is primarily measured by the ATRA-Score in the context of Retinoic Acid treatment?
What is primarily measured by the ATRA-Score in the context of Retinoic Acid treatment?
Which approach is utilized to validate computational models predicting ATRA sensitivity?
Which approach is utilized to validate computational models predicting ATRA sensitivity?
In which of the following contexts would the NTP classification algorithm primarily apply?
In which of the following contexts would the NTP classification algorithm primarily apply?
What key feature distinguishes gene expression profiling from other analytical techniques?
What key feature distinguishes gene expression profiling from other analytical techniques?
What benefit does cross-validation provide in the context of predictive model training?
What benefit does cross-validation provide in the context of predictive model training?
Which method is explicitly mentioned as a visualization technique in the analysis of gene expression data?
Which method is explicitly mentioned as a visualization technique in the analysis of gene expression data?
What is a potential misconception regarding the ATRA-Score's predictive capabilities?
What is a potential misconception regarding the ATRA-Score's predictive capabilities?
What is the primary purpose of the Spearman's approach in the correlation analysis of basal gene-expression levels with ATRA-scores?
What is the primary purpose of the Spearman's approach in the correlation analysis of basal gene-expression levels with ATRA-scores?
Which aspect of gene expression profiling is crucial for understanding ATRA treatment efficacy?
Which aspect of gene expression profiling is crucial for understanding ATRA treatment efficacy?
Which aspect does the Nearest Template Prediction (NTP) classification algorithm primarily focus on?
Which aspect does the Nearest Template Prediction (NTP) classification algorithm primarily focus on?
What statistical technique is commonly associated with ensuring robust gene expression analysis?
What statistical technique is commonly associated with ensuring robust gene expression analysis?
What statistical characteristic does the similarity score typically indicate in gene expression studies?
What statistical characteristic does the similarity score typically indicate in gene expression studies?
What visualization technique is commonly employed to represent the results of hierarchical clustering?
What visualization technique is commonly employed to represent the results of hierarchical clustering?
In the context of gene expression profiling, which method would be most unsuitable for assessing transcriptomic alterations?
In the context of gene expression profiling, which method would be most unsuitable for assessing transcriptomic alterations?
How does cross-validation commonly improve the predictive accuracy of machine learning models?
How does cross-validation commonly improve the predictive accuracy of machine learning models?
Which of the following describes a limitation of using the Euclidean Distance Metric in clustering?
Which of the following describes a limitation of using the Euclidean Distance Metric in clustering?
What is the role of the heatmap in visualizing gene expression data?
What is the role of the heatmap in visualizing gene expression data?
What statistical technique is most commonly applied before performing gene expression analysis to balance the data?
What statistical technique is most commonly applied before performing gene expression analysis to balance the data?
What key factor distinguishes the NTP classification algorithm from traditional clustering methods?
What key factor distinguishes the NTP classification algorithm from traditional clustering methods?
Before applying clustering techniques, what is crucial for analyzing transcriptomic data effectively?
Before applying clustering techniques, what is crucial for analyzing transcriptomic data effectively?
What does the S-Score represent in the GSVA analysis?
What does the S-Score represent in the GSVA analysis?
What is the primary function of the Nearest Template Prediction (NTP) algorithm in the analysis?
What is the primary function of the Nearest Template Prediction (NTP) algorithm in the analysis?
Which of the following visualization techniques is utilized to represent the final S-Score in the GSVA analysis?
Which of the following visualization techniques is utilized to represent the final S-Score in the GSVA analysis?
In gene expression profiling, which aspect is critical for obtaining a reliable analysis?
In gene expression profiling, which aspect is critical for obtaining a reliable analysis?
How does cross-validation contribute to machine learning model performance?
How does cross-validation contribute to machine learning model performance?
What mathematical operation is used to rescale the GSVA scores for both Positive and Negative clusters?
What mathematical operation is used to rescale the GSVA scores for both Positive and Negative clusters?
What is a notable feature of the clustering achieved through the NTP algorithm?
What is a notable feature of the clustering achieved through the NTP algorithm?
Which component is fundamental for the calculation of the S-Score?
Which component is fundamental for the calculation of the S-Score?
What does rescaling the GSVA scores achieve in the context of data analysis?
What does rescaling the GSVA scores achieve in the context of data analysis?
Which of the following best describes a potential challenge in gene expression profiling?
Which of the following best describes a potential challenge in gene expression profiling?
What is the significance of calculating the Euclidean distance in the context of hierarchical clustering?
What is the significance of calculating the Euclidean distance in the context of hierarchical clustering?
Which statement best characterizes the function of the NTP classification algorithm?
Which statement best characterizes the function of the NTP classification algorithm?
What is a primary limitation of visualization techniques used in analyzing gene expression data?
What is a primary limitation of visualization techniques used in analyzing gene expression data?
How does cross-validation enhance the reliability of predictive models in machine learning contexts?
How does cross-validation enhance the reliability of predictive models in machine learning contexts?
In the context of gene expression profiling, what role does calculating similarity scores play?
In the context of gene expression profiling, what role does calculating similarity scores play?
Study Notes
Methodology Overview
- Data is divided into training and test sets randomly for model evaluation.
- Performance is assessed through the percentage of correctly classified samples in the test set.
Ward's Method in Clustering
- Ward's minimum variance method merges clusters to minimize total variance increase.
- Selection of two clusters is based on minimizing changes in the sum of squared distances.
Clustering Process Steps
- Calculation of Euclidean distances between samples using the distance formula.
- Organization of distance values into a distance matrix.
- Hierarchical clustering input is derived from the distance matrix using R's hclust function.
- The Ward's method is employed within hclust to optimize cluster merging.
Data Visualization
- Hierarchical clustering results are illustrated using a dendrogram, reflecting cluster relationships.
ATRA-Score and Gene Expression Analysis
- ATRA-scores predict gastric cancer response using Spearman's correlation with basal gene-expression levels from the CCLE database.
- RNA-sequencing data was available for only 15 of the 27 treated gastric cell lines to ensure substantial data for analysis.
- GSVA analysis was conducted on both TCGA samples (373 patients) and lab samples (13 patients) to generate a matrix representing Positive and Negative clusters.
S-Score Calculation
- Rescaling techniques used to determine average values and generate the final S-Score.
- Visual presentation of results achieved through BoxPlot and ScatterPlot representations.
Nearest Template Prediction (NTP)
- NTP classification algorithm utilized for clustering TCGA samples based on a gene signature of 171 genes.
- Two primary gastric tumor sub-groups identified: G-DIFF and G-INT.
Computational Approaches
- Focus on quantifying the cellular response to Retinoic Acid through bioinformatics and transcriptomics.
- Emphasis on differential expression analysis post-ATRA treatment to enhance treatment personalization.
Euclidean Distance Metric
- Utilized to measure similarity or dissimilarity between points in n-dimensional space.
- Formula: d(p,q)=∑i=1n(qi−pi)2\mathbf{d}(\mathbf{p,q}) = \sqrt{\sum_{i=1}^n (q_{i} - p_{i})^{2}}d(p,q)=i=1∑n​(qi​−pi​)2​
- Provides a mathematical framework for hierarchical clustering calculations.
Hierarchical Clustering Techniques
- Hierarchical clustering can be either agglomerative (bottom-up) or divisive (top-down).
- Agglomerative method starts with individual points and merges clusters iteratively.
- Final clustering decisions are based on the Euclidean distance as the similarity measure.
Software Implementation
- R's hclust function is instrumental in performing agglomerative hierarchical clustering using the Ward's method, facilitating analysis of complex datasets.
RNA-Seq Analysis Overview
- Two-pass mode sequence alignment performed on total-RNA (stranded) using STAR (v.2.7.9a) aligned to reference human genome GRCh38.
- Gene expression quantified using Gencode's v38 GTF annotations.
- cpm (Counts Per Million) normalization method used to adjust and normalize samples for library size, facilitating comparisons of samples with different sizes.
CPM Normalization
- Formula: CPM=(Gene CountTotal Number of Reads Sequenced)×106CPM = \left( \frac{\text{Gene Count}}{\text{Total Number of Reads Sequenced}} \right) \times 10^{6}CPM=(Total Number of Reads SequencedGene Count​)×106
- Gene Count: Number of reads mapped to a specific gene.
- Total Number of Reads Sequenced: Total reads across all genes in the sample.
- Scaling Factor: The multiplication by 10^6 standardizes counts for better comparability across samples.
Differential Expression Analysis
- DESeq2 (v1.28.1) pipeline used for differential analysis.
- Gene-Set Enrichment Analysis (GSEA) conducted using the Limma (v.3.52.2) package and GSEABase (v3.19).
- Gene set collections sourced from Molecular-Signature-Database (MSigDB).
- P-values adjusted for multiple testing using the False-Discovery-Rate (FDR) method; significance threshold set at 0.1.
- Raw data accessible via EMBL-EBI Annotare database, accession numbers: E-MTAB-12387 (Cell-lines) and E-MTAB-12385 (Patients).
Transcriptomic Clustering
- Processed RNA-Seq data used for transcriptomic clustering of cell-lines in R programming environment.
- Pairwise distances calculated using the Euclidean Distance Metric.
Gene Signatures
- G-INT CLASS genes: Includes genes like TSPAN8, GPX2, ALDH3A1, and NRAP, totaling 171 genes.
- G-DIFF CLASS genes: Includes genes like RDX, MYO5A, and AURKB, totaling 85 genes.
NTP Classification Algorithm
- Samples classified based on gene expression profiles using predefined gene signatures.
- The NTP R-package calculates Euclidean distance from each sample's expression profile to the centroid of each template.
- Sample assigned to the template with the shortest distance; accuracy assessed via cross-validation with 100 permutations.
Correlation Analysis and Connectivity
- Initial correlation analysis identified 358 genes with p-value < 0.01 and rho correlation coefficient > 0.4, with 146 genes positively correlated and 211 negatively correlated.
- Connectivity analysis performed in Cytoscape to refine predictive gene clusters.
- Final gene signature comprises 42 interconnected genes selected for significance.
GSVA Analysis
- GSVA performed on 373 TCGA samples and 13 laboratory samples.
- Outputs generated a matrix contrasting Positive and Negative clusters.
- Rescaling method applied to derive final "S-Score" representing average similarity between clusters.
Visualization
- Final S-Score visualization achieved through BoxPlot and ScatterPlot representations.
NTP Algorithm Application
- NTP algorithm utilized to cluster 373 TCGA samples and lab samples based on gene expression signatures, classifying gastric tumors into G-DIFF and G-INT groups.
Methodology Overview
- Data is divided randomly into a training set and a test set.
- Model performance is assessed by the percentage of correctly classified samples from the test set.
Ward's Method and Clustering
- Ward's minimum variance method merges clusters to minimize total variance increase.
- Steps for clustering include:
- Calculation of Distances: Euclidean distances calculated between samples.
- Distance Matrix Creation: Organized distance values into a matrix.
- Hierarchical Clustering: Utilized the distance matrix as input for R's hclust function.
- Ward's Method Application: Merges clusters via the hclust function, focusing on minimizing variance.
Data Visualization
- Hierarchical clustering results are visualized using a dendrogram, depicting a tree-like structure of clusters.
ATRA-Score Fingerprint Analysis
- ATRA treatment response signature derived from correlating basal gene-expression levels with ATRA-scores using Spearman's method.
- Transcriptomic data sourced from Cancer Cell Line Encyclopedia (CCLE) to balance sample sizes and enhance data robustness.
GSVA Analysis
- Performed on 373 TCGA patient samples and 13 lab samples, defining two clusters (Positive and Negative).
- Output represented as a 2 x 373 matrix for TCGA samples and 2 x 13 for lab samples, with rows for clusters and columns for patient numbers.
- Rescaling of cluster values facilitates average calculation for final "S-Score":
- GSVA UP: Rescaled to range (0,1).
- GSVA DOWN: Rescaled to range (1,0).
- S-Score Calculation: Averaged from GSVA UP and GSVA DOWN.
Final Score Visualization
- Results displayed via BoxPlot and ScatterPlot for clearer interpretation.
Nearest Template Prediction (NTP)
- NTP algorithm clusters 373 TCGA samples and lab samples based on a 171-gene signature.
- Classifies gastric tumors into two sub-groups: G-DIFF and G-INT.
Methodology Overview
- Data is split into training and test sets randomly for model evaluation.
- Model performance is assessed by the percentage of correctly classified samples in the test set.
Distance Calculation and Clustering
- Euclidean distances between samples are calculated using the Euclidean distance formula.
- A distance matrix is created from these distance values.
- Hierarchical clustering is performed using the hclust function in R.
- Ward's method is applied to merge clusters, minimizing total variance at each step.
- Visualization of hierarchical clustering results is done using a dendrogram.
ATRA-Score Fingerprint
- ATRA-Score is derived from basal gene-expression levels correlated to ATRA treatment responsiveness.
- Transcriptomics data retrieved from the Cancer Cell Line Encyclopedia (CCLE) was used for correlation analysis.
- Out of 27 treated cell lines with experimental sensitivity scores, only 15 had RNA-seq data; data from all treated lines was analyzed for robustness.
- Gene set variation analysis (GSVA) was performed on 373 TCGA patient samples and 13 lab samples, leading to cluster generation.
- The final score, "S-Score," is derived from rescaled GSVA values representing positive and negative clusters.
- Visual outputs of the score include BoxPlot and ScatterPlot representations.
Sample Clustering
- The Nearest Template Prediction (NTP) algorithm is utilized to classify TCGA samples using a signature template of 171 genes.
- Two sub-groups of gastric tumors identified: G-DIFF and G-INT, guiding therapeutic decisions.
Computational Analysis Methods
- The ATRA-Score quantifies cellular responses to Retinoic Acid through bioinformatics and gene expression analyses.
- Transcriptomics techniques, especially RNA-Sequencing, are crucial for acquiring input data for further analyses.
- Emphasis on developing predictive models for personalized medicine in gastric cancer treatment.
- Hierarchical clustering is used to categorize genomic data, identifying molecular subgroups responsive to ATRA treatment.
Euclidean Distance Metric
- The Euclidean distance provides a metric for determining similarities or differences between data points in n-dimensional space.
- A generalized formula calculates distance between points, fundamental for clustering analysis.
Hierarchical Clustering Techniques
- Hierarchical clustering can be performed using agglomerative (bottom-up) or divisive (top-down) methods.
- The agglomerative approach initiates with single clusters, merging them until only one cluster remains.
- The hclust function utilizes the Ward's minimum variance method for merging clusters based on a distance matrix.
Final Goals
- To complete hierarchical clustering using distance metrics to determine cluster similarities and differences.
- The ultimate aim is to construct models that address personalized treatment strategies for gastric cancer patients through genomic insights.
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Description
This chapter will introduce all the computational approaches and methodologies used to study the effectiveness of ATRA treatment and its genomic implications. The first methodology covered is inherent to calculating the ATRA-Score, derived from experimental data in order to quantify the cellular response to treatment with Retinoic Acid. This calculation is carried out through a series of bioinformatic analyses investigating the gene expression resulting from ATRA treatment. There will be additional paragraphs discussing transcriptomics techniques (RNA-Sequencing), which are widely known for being useful in acquiring the input data required to carry out further analysis and in determining which genes exhibit differential expression after ATRA treatment. Subsequently, the method concerning the in-silico calculation of the ATRA-score fingerprint will be evaluated in depth. The ATRA-Score predicts the response to treatment with Retinoic Acid through computational models. In particular, this is performed by using gene expression patterns indicative of sensitivity to ATRA. Finally, the methods used to evaluate ATRA sensitivity in a set of patients affected by gastric cancer and allowing the validation of signatures/models previously obtained will be discussed. The final aim is to develop predictive models that are representative of the current clinical situation, meeting the needs of personalized medicine. As a final step, the sample clustering methods, which allowed the genomic differences/similarities relating to gastric cancer to be categorized to identify any molecular subgroups that may respond in an alternative or exclusive manner to ATRA treatment, will be explored.
