Week 7 Feedback: Unsupervised Learning Analysis

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Week 7 Feedback: Unsupervised Learning Analysis

Hey guys! Let's dive into the feedback for Week 7's work on unsupervised learning. We're talking about Principal Component Analysis (PCA), K-means clustering, and Gene Ontology (GO) analysis. The goal here is to help you all understand the feedback, learn from it, and level up your skills in these crucial areas of data analysis. I'll break down the feedback, offer some friendly explanations, and provide context to make sure everything clicks. This is all about making sure you can confidently tackle these techniques in your future projects. So, let's get started!

Overall Grade and Initial Feedback

First off, the overall grade for this assignment was 1/10. It's a bummer, but don't let it get you down! We're here to learn and improve. The initial feedback pointed out a great start with the R script, which is awesome. The most important thing here is to understand what went wrong and how to fix it. This is a journey, and every assignment is a step forward.

One key note was about running prcomp. Remember, because the data provided was already normalized, using scale=TRUE wasn't necessary. This is a common mistake, but the important takeaway is that understanding your data is key. Always check your data's state before applying any transformations. Always keep an eye on these details, as they can significantly influence your results.

Detailed Breakdown

Let's get into the specifics. The assignment was broken down into several parts:

  • R script for analysis and plotting: (4 points total)

    • Performs PCA (1 point)
    • Fixes data issues with tissue labels correct (1 point)
    • Performs k-means clustering and reordering based on clusters (1 point)
    • Saves gene names from selected clusters (1 point)

  • README.md file with answers to questions: (2 points total)

    • Answer to first question for part 1.3 (0.5 points)
    • Answer to second question for part 1.3 (0.5 points)
    • Answer to question for part 3 (1 point)

  • Plots: (3 points total)

    • PCA plot with point shape and color denoting tissues/replicates (1 point)
    • Scree plot (1 point)
    • Heatmap showing 12 clusters denoted by color (1 point)

  • GO analysis reports: (1 point)

    • GO analysis reports (1 point)

R Script: Deep Dive

Alright, let's break down the R script part, which was the core of the analysis. Remember, PCA, K-means, and heatmap generation are essential skills, so understanding each step is super important. We will look into the details for each exercise, providing clear explanations and practical tips. These exercises are the building blocks of your analysis. It's all about making sure you can properly use these techniques.

1. Performing PCA

Performing PCA is the very first step. It is crucial for dimensionality reduction and data visualization. The main goal here is to reduce a large number of variables into a smaller set of principal components while retaining important information. For this, prcomp() from the base R package is your best friend. Make sure you understand the output of prcomp() including the rotation matrix, the scores, and the standard deviations. These components explain the variance in your data, which is essential for further analysis. A PCA plot is the most common way to visualize this. It will help to identify the main sources of variation in your data, and look for patterns, and outliers.

2. Data Issue Fixing with Correct Tissue Labels

This is all about data cleaning. Data cleaning is the unsung hero of any data analysis task! It's super important to make sure your tissue labels are correct. Incorrect labels can lead to meaningless results and misleading conclusions. Go through the data and make sure everything is in place, and the data is accurate. Use this step as a chance to check your data and see if it makes sense. Correcting data issues is a crucial skill. If you encounter any problems, always double-check your data, and make sure that you have not missed any details.

3. K-means Clustering and Reordering

K-means clustering is used for grouping similar data points into clusters. The purpose is to group the dataset into k distinct clusters, each data point belonging to the cluster with the nearest mean. The output of K-means can be used to reorder your data. Also, cluster centers and the assignments are the main outputs you should pay attention to. The key is to select the correct number of clusters. Consider methods like the elbow method or the silhouette score to optimize your choice of k. Reordering the data based on clusters helps visualize the groupings better, especially when generating heatmaps. Understanding K-means clustering gives you the ability to identify hidden patterns.

4. Saving Gene Names from Selected Clusters

This is all about extracting valuable information for further investigation. This step involves saving the gene names from clusters that you think are important. The output is a list of genes. Make sure you can link these gene names back to your data so that you can see what is happening in the data. The next step is to understand the function of each gene. This helps you identify genes that are highly related, which can reveal biological insights. Saving gene names is super helpful for more in-depth exploration.

README.md: Answering Questions

The README file is a place to document your analysis and answer any questions. It is a very important part of the assignment. It helps you clarify your thoughts and demonstrate your understanding of the process. This section focuses on the parts where you answer the questions. It's not just about providing answers; it's about showing you understand the underlying concepts.

Part 1.3 Questions

Make sure your answers are clear, concise, and provide sufficient detail. Think about the question and try to answer with the proper technical details. Focus on what you did and why, rather than just what you found. When answering, be sure to demonstrate your comprehension of the concepts and techniques you used.

Part 3 Question

In this section, you need to provide a very comprehensive explanation of your findings and the results of the analysis. It is very important to make sure everything is perfect and accurate. Write about what you observed, what you learned, and any limitations of your analysis. The most important thing is to tell a story with your data.

Plots: Visualizing Your Findings

Plots are key for communicating your findings. They let you see patterns and communicate your results. Each plot has a specific purpose, so let's check them out.

1. PCA Plot

The PCA plot is essential for showing how your data varies. The most important things here are point shape and color. Use shape and color to show the different tissues and replicates. This will show you which variables contribute most to the principal components. It is a powerful way to visualize the high-dimensional data.

2. Scree Plot

The scree plot displays the variance explained by each principal component. It helps you to decide how many components to keep. This plot should look like a classic scree plot to show which components are important. If the plot is correct, it will show a curve that levels off, indicating that additional components explain very little variance.

3. Heatmap

This is where you visualize your clustered data. Use color to represent each cluster to represent the gene expression patterns. The heatmap will show clusters of genes with similar expression patterns. Make sure you carefully label your axes, and you have a legend that is easy to understand. It is a very effective way to show your results and provide insights. The heatmap will help to identify the genes.

GO Analysis Reports: Understanding Gene Functions

GO analysis helps you understand the biological functions of your genes. The goal of this analysis is to identify enriched biological processes within your selected gene clusters.

GO Analysis Reports

Make sure to interpret the results of your GO analysis. These reports will highlight the biological processes, cellular components, and molecular functions associated with the genes in your clusters. This is all about telling you the functions of each gene. This helps you to discover which biological pathways are active in your experiment. Always document your findings and give insightful conclusions, providing a complete story of your data.

Conclusion: Moving Forward

So there you have it, folks! The detailed feedback on your Week 7 work. It is okay if you didn't get a perfect score. The goal is to learn and improve. By carefully reviewing this feedback, understanding where you can improve, and asking questions, you'll be well on your way to mastering unsupervised learning techniques. Keep up the amazing work, and always remember: the best way to learn is by doing! Happy coding!