hands-on descriptions: Objectives, datasets and steps

Author: pubudu

.DS_Store

@@ -352,6 +352,29 @@ Understanding the distinctions between Python's general-purpose types and NumPy'
 
 ## Hands-on
 
+**Basic RNA Expression Analysis:**
+
+***Objective:***
+
+* Validate differential expression of immune-related genes between patient groups [previously classified as immunologically strong (`istrong`) and immunologically weak (`iweak`)](https://pubmed.ncbi.nlm.nih.gov/30393759/)
+* Apply an alternative analytical approach using Z-ratio methodology to complement standard differential expression tools like `EdgeR`
+* Confirm whether genes that contribute to the Immune-score show consistent expression patterns between the two patient groups
+* Provide a computational verification method that ranks immune-related genes based on their relative expression differences between the patient groups
+
+***Dataset description:***
+
+* `test_data/Sample_group_info.csv`
+  * Patients were divided into `istrong` (immunity-strong) and `iweak` (immunity-weak) groups based on their Immunity score
+  * The Immunity score was calculated by averaging the gene expression values of the 17 identified immune-genes
+* `test_data/count_matrix.csv`: Gene expression raw counts
+
+***Steps:***
+
+1. Data Loading
+   1. Load sample group information (`iweak` vs `istrong`)
+   2. Load gene expression count matrix
+2. Examine array information
+
 :::{exercise} Hands-on
 
 ```python

content/2.NumPy_Data_Types.md

@@ -351,6 +351,16 @@ print(f"A: {a_count}, T: {t_count}, G: {g_count}, C: {c_count}")
 
 ## Hands-on
 
+***Steps:***
+
+1. Data Loading and visualization
+   1. Load sample group information (`iweak` vs `istrong`)
+   2. Load gene expression count matrix
+   3. Examine array information
+2. Sample Identification
+   1. Filter samples by group (`iweak`/`istrong`)
+   2. Match count matrix columns with sample IDs
+
 :::{exercise} Hands-on
 
 ```python

content/4.Advance_indexing_filtering.md

@@ -372,6 +372,21 @@ print(f"Row sums: {row_sums}")  # [6 15]
 
 ## Hands-on
 
+***Steps:***
+
+1. Data Loading and visualization
+   1. Load sample group information (`iweak` vs `istrong`)
+   2. Load gene expression count matrix
+   3. Examine array information
+2. Sample Identification
+   1. Filter samples by group (`iweak`/`istrong`)
+   2. Match count matrix columns with sample IDs
+3. Data Preprocessing
+   1. Convert count matrix to numeric values
+   2. Apply log2 transformation: log2(counts + 1)
+4. Statistical Analysis
+   1. Calculate mean and std for each gene within each group
+
 :::{exercise} Hands-on
 
 ```python
@@ -461,7 +476,6 @@ istrong_std = cm[:,cm_strong_cols].std(1)   ## STD of istrong samples
 
 :::{solution} Visual representation
 
-**Visual representation - Calculating mean and STD of each gene in `iweak` group:**
+**Visual representation - Calculating mean and SD of each gene in `iweak` and `istrong` group:**
 ![alt text](image-15.png)
-
 :::

content/5.Essential_array_operations.md

@@ -615,6 +615,41 @@ Vectorized operations in NumPy are a powerful tool for data analysis in computat
 
 ## Hands-on
 
+***Objective:***
+
+* Validate differential expression of immune-related genes between patient groups [previously classified as immunologically strong (`istrong`) and immunologically weak (`iweak`)](https://pubmed.ncbi.nlm.nih.gov/30393759/)
+* Apply an alternative analytical approach using Z-ratio methodology to complement standard differential expression tools like `EdgeR`
+* Confirm whether genes that contribute to the Immune-score show consistent expression patterns between the two patient groups
+* Provide a computational verification method that ranks immune-related genes based on their relative expression differences between the patient groups
+
+***Dataset description:***
+
+* `test_data/Sample_group_info.csv`
+  * Patients were divided into `istrong` (immunity-strong) and `iweak` (immunity-weak) groups based on their Immunity score
+  * The Immunity score was calculated by averaging the gene expression values of the 17 identified immune-genes
+* `test_data/count_matrix.csv`: Gene expression raw counts
+
+***Steps:***
+
+1. Data Loading and visualization
+   1. Load sample group information (`iweak` vs `istrong`)
+   2. Load gene expression count matrix
+   3. Examine array information
+2. Sample Identification
+   1. Filter samples by group (`iweak`/`istrong`)
+   2. Match count matrix columns with sample IDs
+3. Data Preprocessing
+   1. Convert count matrix to numeric values
+   2. Apply log2 transformation: log2(counts + 1)
+4. Statistical Analysis
+   1. Calculate mean and std for each gene within each group
+   2. Compute Z-scores within each sample group
+   3. Calculate Z-score differences between groups
+   4. Compute standard deviation of all differences
+5. Differential Expression - verification
+   1. Calculate Z-ratio: difference / std_difference
+   2. Rank genes by Z-ratio (highest to lowest)
+
 :::{exercise} Hands-on
 
 ```python