6 Computational Analysis Methods Employed

Questions and Answers

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?

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?

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?

Cytoscape for network visualization. Signup and view all the answers

In which scenario would cross-validation be most beneficial in a machine learning context?

When validating the model's performance on unseen data. Signup and view all the answers

What does a high similarity score in classification tasks generally indicate?

The samples belong to the same class or group. Signup and view all the answers

Which is a notable feature of the GSVA method in gene expression analyses?

It allows for gene set variation analysis. Signup and view all the answers

What is the main advantage of using hierarchical agglomerative clustering methods?

They do not require prior knowledge of the number of clusters. Signup and view all the answers

Which method is NOT used for hierarchical clustering?

K-means clustering Signup and view all the answers

What is the primary purpose of calculating Euclidean distance in clustering?

To measure the difference between pairs of objects Signup and view all the answers

In hierarchical clustering, what is the result of using the agglomerative approach?

Clusters are formed recursively by merging two nearest clusters Signup and view all the answers

What is the function of the 'hclust' method in R?

To perform agglomerative hierarchical clustering using a distance matrix Signup and view all the answers

Which of the following best describes the distance matrix in the context of hierarchical clustering?

A representation of distances between all pairs of data points Signup and view all the answers

What does Ward's minimum variance method primarily aim to minimize during the merging of clusters?

The increase in total variance within the clusters Signup and view all the answers

Which distance metric is specifically calculated before applying the hclust function in R?

Euclidean distance Signup and view all the answers

What does the dendrogram visualize in hierarchical clustering?

The tree-like structure of the clusters Signup and view all the answers

Why was the transcriptomic data from the CCLE used in the ATRA-scores correlation analysis?

It provides a balanced amount of data across multiple cell lines treated with retinoic acid. Signup and view all the answers

During which step of cluster analysis is the distance matrix created?

After calculating the distances Signup and view all the answers

What analysis technique is used to correlate the basal gene-expression levels with the ATRA-scores?

Spearman's approach Signup and view all the answers

What is the role of the hclust function in the context of clustering?

It merges clusters based on the minimum variance method. Signup and view all the answers

Which aspect of the data is primarily analyzed to predict response to ATRA treatment?

Basal gene-expression levels Signup and view all the answers

How many treated gastric lines had RNA-Sequencing-based transcriptomic profiling performed?

15 out of 27 Signup and view all the answers

What feature of Ward's method makes it distinct in cluster analysis?

It minimizes the increase in total variance through merging. Signup and view all the answers

What is the primary purpose of calculating the S-Score in the analysis?

To determine the average value of the samples in each cluster Signup and view all the answers

Which technique was used to visualize the final score obtained from the GSVA analysis?

BoxPlot and ScatterPlot Signup and view all the answers

In the GSVA analysis, how are the GSVA scores for the Positive and Negative clusters rescaled?

Positive is rescaled to (0,1) and Negative to (1,0) Signup and view all the answers

What gene signature template was used in the NTP classification algorithm for clustering?

171 genes that classify gastric tumors Signup and view all the answers

Which aspect of machine learning does cross-validation help to address?

Overfitting Signup and view all the answers

How many TCGA samples were analyzed in the GSVA analysis?

373 Signup and view all the answers

What is the outcome of applying the NTP classification algorithm?

Clustering of samples into G-DIFF and G-INT sub-groups Signup and view all the answers

What kind of matrix is outputted after performing GSVA analysis on TCGA samples?

A 2 x 373 matrix Signup and view all the answers

What is the purpose of normalizing data in the GSVA analysis?

To align the scaling of the data for visual representation Signup and view all the answers

What benefit does gene expression profiling provide in gastric tumor studies?

It assists in developing targeted therapies Signup and view all the answers

What is the primary purpose of calculating the ATRA-Score in the context of Retinoic Acid treatment?

To quantify the cellular response to treatment. Signup and view all the answers

Which technique is particularly emphasized as invaluable for obtaining input data in assessing the effects of ATRA treatment?

RNA-Sequencing Signup and view all the answers

In the computational model predicting ATRA treatment response, what type of expression patterns are primarily utilized?

Patterns indicative of sensitivity to ATRA. Signup and view all the answers

What is the objective of sample clustering methods in the analysis of gastric cancer concerning ATRA treatment?

To identify molecular subgroups that may respond differently. Signup and view all the answers

What is the ultimate aim of developing predictive models related to ATRA treatment?

To personalize medicine according to individual responses. Signup and view all the answers

What is the role of transcriptomics techniques in the context of ATRA analysis?

To investigate gene expression following treatment. Signup and view all the answers

Which of the following is NOT a method discussed in the study of ATRA treatment effectiveness?

Receptor binding assays for Retinoic Acid. Signup and view all the answers

Which concept is related to validating the computational models established for ATRA sensitivity?

Assessing models using additional patient data. Signup and view all the answers

What computational method is applied to evaluate gene expression patterns in this analysis?

Cross-validation methodologies. Signup and view all the answers

Which aspect of bioinformatics does the ATRA-Score primarily leverage to determine treatment response?

Gene expression profiling. Signup and view all the answers

What is the primary function of the CPM normalization method in gene expression analysis?

To convert raw gene counts into standardized values for comparison. Signup and view all the answers

Which method would likely NOT be applicable for enhancing the accuracy of predictive models in gene expression analyses?

Two-pass mode sequence alignment Signup and view all the answers

How does the False-Discovery-Rate (FDR) procedure benefit statistical analysis in this context?

It reduces the likelihood of identifying false positives in significance testing. Signup and view all the answers

What is a potential limitation of using the Euclidean Distance Metric in transcriptomic clustering?

It can exaggerate the influence of outliers on clustering results. Signup and view all the answers

What is the main purpose of applying the DESeq2 pipeline in this analysis?

To conduct differential analysis of gene expression between conditions. Signup and view all the answers

What is the implication of using GSEA (Gene-Set Enrichment Analysis) with the Limma package?

To analyze predefined sets of genes and their collective impact on biological processes. Signup and view all the answers

In the context of RNA-seq analysis, which method should be avoided for visualizing the results due to misleading interpretations?

Single-dimensional scaling plots of gene counts. Signup and view all the answers

Which aspect of gene expression data is primarily utilized in the NTP classification algorithm?

Unique gene signatures associated with specific conditions or treatments. Signup and view all the answers

What role does the Euclidean distance play in the NTP classification algorithm?

It calculates the similarity between sample profiles and template centroids. Signup and view all the answers

Which technique is primarily utilized to validate the performance of the NTP classification method?

Cross-validation with multiple permutations. Signup and view all the answers

Which gene expression profiling method is best suited for categorizing samples into predefined classes using NTP?

Predefined signature comparison against sample profiles. Signup and view all the answers

What is a common misconception about the application of Euclidean distance in gene expression studies?

It is effective for all classification problems regardless of data structure. Signup and view all the answers

In the context of gene expression profiling, what does a high similarity score typically indicate?

Close resemblance between the sample and a template gene expression profile. Signup and view all the answers

Which aspect of the NTP classification process is assessed through cross-validation?

The robustness and generalizability of the classification results. Signup and view all the answers

Which of the following is an important consideration when interpreting the results of gene expression profiling?

The biological relevance of the genes involved. Signup and view all the answers

What limitation is often associated with using the NTP algorithm for gene classification?

It cannot classify samples that do not fit existing templates. Signup and view all the answers

What is a principal purpose of visualizing gene expression data in the NTP context?

To simplify the interpretation of classification results. Signup and view all the answers

Which technique best complements the NTP classification in assessing gene expression patterns?

Principal Component Analysis for dimensionality reduction. Signup and view all the answers

Which mathematical method is often utilized to calculate similarity scores in classification tasks?

Cosine Similarity Signup and view all the answers

Which key feature distinguishes the Nearest Template Prediction (NTP) classification algorithm?

It works on single-sample metrics with confidence assessment. Signup and view all the answers

Which visualization technique is commonly used to represent integrated models of biomolecular interaction networks?

Network Graphs Signup and view all the answers

What is the primary purpose of cross-validation in machine learning?

To reduce overfitting by evaluating model performance on unseen data. Signup and view all the answers

Which characteristic is most significant in gene expression profiling for cancer studies?

Identifying the activation levels of specific oncogenes. Signup and view all the answers

In the context of similarity score calculation, which of the following metrics is not normally applied?

Normalized Cut Signup and view all the answers

Which statement correctly describes the function of the NTP algorithm?

It matches expression patterns of a single sample to templates for prediction. Signup and view all the answers

What kind of analysis does cross-validation primarily enhance in machine learning?

Robustness of model selection and tuning. Signup and view all the answers

Which of the following factors does gene expression profiling typically assess in patients with gastric cancer?

Patterns of gene expression associated with treatment responders and non-responders. Signup and view all the answers

Which approach is NOT commonly associated with visualization techniques in bioinformatics?

Logistic regression for predictive modeling. Signup and view all the answers

What is the primary purpose of calculating the S-Score in the analysis?

To evaluate the similarity between different gene expression profiles Signup and view all the answers

Which method was employed to classify gastric tumors into G-DIFF and G-INT sub-groups?

Nearest Template Prediction (NTP) Signup and view all the answers

Which visualization techniques were used to represent the final score obtained from the GSVA analysis?

BoxPlot and ScatterPlot Signup and view all the answers

In the context of machine learning, what is the significance of cross-validation when applied to predictive models?

It helps mitigate the risk of overfitting by validating model performance Signup and view all the answers

What primary aspect does gene expression profiling focus on in gastric cancer studies?

The variations in transcriptional activity among different tumor samples Signup and view all the answers

What is the significance of having a p-value < 0.01 in the correlation analysis of genes?

It suggests that the gene's effects may be statistically significant. Signup and view all the answers

How does Cytoscape facilitate the understanding of molecular interactions?

By visualizing complex molecular networks and pathways. Signup and view all the answers

What does a high degree of interconnectedness (> 2) in the final gene selection imply?

Genes have multiple interactions, enhancing their significance in the network. Signup and view all the answers

What is a primary characteristic of the STRING database in relation to gene and protein interactions?

It collects and integrates data on functional associations between genes and proteins. Signup and view all the answers

In the context of ATRA treatment response analysis, what does gene expression profiling primarily assess?

The pattern of gene expression related to treatment response. Signup and view all the answers

Why is it necessary to eliminate isolated genes when performing connectivity analysis?

They do not contribute to a meaningful analysis of gene interactions. Signup and view all the answers

What is a key advantage of using the NTP classification algorithm in gene expression analysis?

It aids in the identification and grouping of predictive gene signatures. Signup and view all the answers

Which aspect of the similarity score calculation is crucial for gene interaction analysis?

The score must account for the overlapping pathways among genes. Signup and view all the answers

What technique is reinforced as effective for obtaining input data in assessing ATRA treatment effects?

Gene expression analysis. Signup and view all the answers

What role does cross-validation play in machine learning concerning gene analysis?

It assesses how the results of a statistical analysis will generalize to an independent dataset. Signup and view all the answers

Which of the following is a primary purpose of calculating similarity scores in classification tasks?

To evaluate the resemblance between data points and clusters Signup and view all the answers

What is a key feature of the Nearest Template Prediction (NTP) classification algorithm?

It allows for confidence assessment in predictions Signup and view all the answers

Which visualization technique is particularly useful for representing relationships in biomolecular interaction networks?

Network graphs Signup and view all the answers

In gene expression profiling, which analysis is primarily performed to compare different gene expression levels across samples?

Fold change analysis Signup and view all the answers

What is an essential advantage of using cross-validation in machine learning?

It helps to avoid overfitting and assesses model performance Signup and view all the answers

Which factor is NOT typically considered when calculating similarity scores between data points?

The variance of data points within clusters Signup and view all the answers

In the context of the NTP classification algorithm, what role does a 'gene signature template' play?

It provides the reference against which new samples are compared Signup and view all the answers

Which of the following statements best characterizes the use of cross-validation in gene expression analyses?

It aids in the refinement of gene signatures for accuracy Signup and view all the answers

What mathematical operation is primarily used to combine the GSVA scores for the Positive and Negative clusters to calculate the S-Score?

Mean function Signup and view all the answers

Which statement best describes the role of the Nearest Template Prediction (NTP) classification algorithm in the analysis?

It clusters patient samples based on a set of defined gene signatures. Signup and view all the answers

Which visualization techniques were utilized to represent the final score obtained from the GSVA analysis?

BoxPlot and ScatterPlot Signup and view all the answers

In the context of gene expression profiling within gastric cancer studies, which of the following statements is incorrect?

It relies solely on RNA-Sampling methods. Signup and view all the answers

How does cross-validation contribute to the reliability of models in machine learning, particularly in the context of gene expression analysis?

It provides a means to assess model performance on unseen data. Signup and view all the answers

What does the calculation of the similarity score primarily aim to achieve in gene expression profiling?

To establish the genetic correlation between distinct samples Signup and view all the answers

Which key characteristic distinguishes the NTP classification algorithm in the context of clustering?

It uses a predetermined template for classification Signup and view all the answers

What is the primary advantage of visualizing data with a dendrogram in hierarchical clustering?

It illustrates the relationships between clusters and their hierarchies Signup and view all the answers

In what way does gene expression profiling contribute to treatment decisions in gastric cancer?

By correlating gene expressions to treatment responses Signup and view all the answers

What is a fundamental purpose of cross-validation in the context of predictive modeling for ATRA treatment?

To assess the predictive performance and avoid overfitting Signup and view all the answers

What is a significant limitation associated with using the NTP classification algorithm in predictive models?

It relies on static gene templates that may not represent dynamic changes Signup and view all the answers

What aspect of visualization techniques is emphasized in the analysis of ATRA treatment responses?

Static representations that simplify complex interactions Signup and view all the answers

How does the use of cross-validation enhance the validity of predictive models in ATRA treatment?

It helps to generalize findings across different datasets Signup and view all the answers

What is the primary criterion used to evaluate the performance of a classification model based on a random data division methodology?

Percentage of correctly classified samples Signup and view all the answers

Which characteristic is essential for the Nearest Template Prediction (NTP) classification algorithm?

It predicts classes by comparing new samples against predefined templates. Signup and view all the answers

In gene expression profiling, which technique is primarily used to visualize relationships in biomolecular interaction networks?

Cytoscape software environment Signup and view all the answers

What is a significant advantage of utilizing cross-validation in machine learning?

It ensures that the model is trained on every sample once. Signup and view all the answers

What does a high similarity score typically indicate in clustering tasks?

Closer proximity or higher relatedness between data points. Signup and view all the answers

Which method is often used in gene expression profiling to assess overall classifications?

Hierarchical agglomerative clustering Signup and view all the answers

What is a characteristic feature of utilizing gene expression profiling in gastric cancer studies?

It aids in identifying molecular signatures related to treatment response. Signup and view all the answers

How does the NTP classification algorithm assess the confidence of its predictions?

By evaluating the consistency across various templates. Signup and view all the answers

What is primarily measured by the ATRA-Score in the context of Retinoic Acid treatment?

The cellular response to ATRA treatment Signup and view all the answers

Which approach is utilized to validate computational models predicting ATRA sensitivity?

Using patient population data affected by gastric cancer Signup and view all the answers

In which of the following contexts would the NTP classification algorithm primarily apply?

Determining personalized treatment response signatures Signup and view all the answers

What key feature distinguishes gene expression profiling from other analytical techniques?

It compares expression levels of genes across different conditions Signup and view all the answers

What benefit does cross-validation provide in the context of predictive model training?

It reduces overfitting by ensuring models generalize well to unseen data Signup and view all the answers

Which method is explicitly mentioned as a visualization technique in the analysis of gene expression data?

Heatmaps with clustered dendrograms Signup and view all the answers

What is a potential misconception regarding the ATRA-Score's predictive capabilities?

It predicts treatment outcomes for all types of cancer Signup and view all the answers

What is the primary purpose of the Spearman's approach in the correlation analysis of basal gene-expression levels with ATRA-scores?

To rank the data based on their relative values Signup and view all the answers

Which aspect of gene expression profiling is crucial for understanding ATRA treatment efficacy?

The differential expression of genes post-treatment Signup and view all the answers

Which aspect does the Nearest Template Prediction (NTP) classification algorithm primarily focus on?

Creating templates for known classes to determine predictions Signup and view all the answers

What statistical technique is commonly associated with ensuring robust gene expression analysis?

Normalization and transformation of raw data Signup and view all the answers

What statistical characteristic does the similarity score typically indicate in gene expression studies?

The degree of concordance between different sample profiles Signup and view all the answers

What visualization technique is commonly employed to represent the results of hierarchical clustering?

Dendrogram Signup and view all the answers

In the context of gene expression profiling, which method would be most unsuitable for assessing transcriptomic alterations?

Data normalization Signup and view all the answers

How does cross-validation commonly improve the predictive accuracy of machine learning models?

By applying techniques to reduce overfitting Signup and view all the answers

Which of the following describes a limitation of using the Euclidean Distance Metric in clustering?

It fails to account for nonlinear relationships among features Signup and view all the answers

What is the role of the heatmap in visualizing gene expression data?

To visualize patterns of expression across samples Signup and view all the answers

What statistical technique is most commonly applied before performing gene expression analysis to balance the data?

Normalization of data Signup and view all the answers

What key factor distinguishes the NTP classification algorithm from traditional clustering methods?

It uses existing labels rather than forming new clusters Signup and view all the answers

Before applying clustering techniques, what is crucial for analyzing transcriptomic data effectively?

Normalize the data to adjust biases Signup and view all the answers

What does the S-Score represent in the GSVA analysis?

The average of GSVA scores for the Positive and Negative clusters Signup and view all the answers

What is the primary function of the Nearest Template Prediction (NTP) algorithm in the analysis?

To classify samples into differential gastric tumor sub-groups Signup and view all the answers

Which of the following visualization techniques is utilized to represent the final S-Score in the GSVA analysis?

Box plots and scatter plots Signup and view all the answers

In gene expression profiling, which aspect is critical for obtaining a reliable analysis?

The accuracy of gene quantification methods employed Signup and view all the answers

How does cross-validation contribute to machine learning model performance?

By helping to detect overfitting and assess generalization Signup and view all the answers

What mathematical operation is used to rescale the GSVA scores for both Positive and Negative clusters?

Normalization to a 0-1 range Signup and view all the answers

What is a notable feature of the clustering achieved through the NTP algorithm?

It uses a predefined gene signature template for classification Signup and view all the answers

Which component is fundamental for the calculation of the S-Score?

The average values of GSVA scores from both clusters Signup and view all the answers

What does rescaling the GSVA scores achieve in the context of data analysis?

Aligning scores for better comparability across samples Signup and view all the answers

Which of the following best describes a potential challenge in gene expression profiling?

Overcoming data sparsity issues in large datasets Signup and view all the answers

What is the significance of calculating the Euclidean distance in the context of hierarchical clustering?

It helps identify the similarity or dissimilarity between data points. Signup and view all the answers

Which statement best characterizes the function of the NTP classification algorithm?

It predicts outcomes by comparing gene expression profiles to predefined templates. Signup and view all the answers

What is a primary limitation of visualization techniques used in analyzing gene expression data?

They may obscure important relationships by oversimplifying complex data sets. Signup and view all the answers

How does cross-validation enhance the reliability of predictive models in machine learning contexts?

It ensures that the model is evaluated on multiple subsets, minimizing overfitting. Signup and view all the answers

In the context of gene expression profiling, what role does calculating similarity scores play?

It aids in grouping samples with related expression patterns for further analysis. Signup and view all the answers

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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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.