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Unsupervised Learning (UL)

UL is a type of ML that helps us discover patterns in data without needing predefined labels or much human guidance.

Unlike supervised learning, which uses data with known outcomes, UL explores relationships within the data itself.

A common approach to UL is clustering –> where data points are grouped based on similarities. Methods include K-means clustering, Hierarchical clustering or Probabilistic clustering.

  • For example, if we are analysing mRNA expression data, we use clustering to group genes with similar expression profiles.

We also apply dimensionality reduction techniques: PCA (Principal Component Analysis) & t-SNE (t-Distributed Stochastic Neighbor Embedding) to simplify and visualize complex data.

  • This allows us to uncover hidden structures and gain insights into the genetic basis of various conditions.

Models trained using UNLABELLED data

  • Only INPUT provided

  • Discovers patterns, groupings, or relationships within data

library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Step 1: Prepare Data

Objective: Prepare and clean the data to ensure its quality and suitability for analysis.

Data Cleaning: Handle missing values, outliers, and ensure that data is formatted correctly. Normalization/Scaling: Standardize the data to bring features to a common scale, making it easier for clustering algorithms to work effectively.

# Example in R
library(dplyr)
data <- iris[,-5]
data_clean <- na.omit(data)  # Remove missing values
data_scaled <- scale(data_clean)  # Normalize data

Step 2: Data Filtering and Reduction

To enhance the quality of our clustering, we will only focus on variables that show significant variation, as they provide more meaningful insights for grouping.

This step involves filtering out variables with low variance and any missing values to ensure our data is clean and robust for analysis.

This step is crucial for preprocessing data before it’s used for downstream analysis, such as clustering or visualization.

The aim is to filter out variables that are unlikely to be informative for distinguishing between different samples.

Objective: Focus on genes or features that have significant variability to improve clustering results.

Filter Out Low Variance Genes: Genes with low variance are less informative for distinguishing between samples.

#
variances <- apply(data_scaled, 2, var)
SIDE:
  • Objective: Ensure that the analysis is purely unsupervised by removing any outcome or label variables from the data. Steps:
  • Identify and exclude outcome variables from the dataset.
  • Ensure the dataset contains only features used for unsupervised learning.

Step 3: Clustering Prep & Criterion Evaluation

Objective: Determine the number of clusters and evaluation criteria to ensure meaningful clusters.

Determine Number of Clusters: Use methods like the elbow method, silhouette score, or cross-validation to decide the optimal number of clusters.


sessionInfo()
R version 4.3.3 (2024-02-29)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.5

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Africa/Johannesburg
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] dplyr_1.1.4     workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] jsonlite_1.8.8    compiler_4.3.3    promises_1.3.0    tidyselect_1.2.1 
 [5] Rcpp_1.0.13       stringr_1.5.1     git2r_0.33.0      callr_3.7.6      
 [9] later_1.3.2       jquerylib_0.1.4   yaml_2.3.10       fastmap_1.2.0    
[13] R6_2.5.1          generics_0.1.3    knitr_1.48        tibble_3.2.1     
[17] rprojroot_2.0.4   bslib_0.8.0       pillar_1.9.0      rlang_1.1.4      
[21] utf8_1.2.4        cachem_1.1.0      stringi_1.8.4     httpuv_1.6.15    
[25] xfun_0.46         getPass_0.2-4     fs_1.6.4          sass_0.4.9       
[29] cli_3.6.3         magrittr_2.0.3    ps_1.7.7          digest_0.6.36    
[33] processx_3.8.4    rstudioapi_0.16.0 lifecycle_1.0.4   vctrs_0.6.5      
[37] evaluate_0.24.0   glue_1.7.0        whisker_0.4.1     fansi_1.0.6      
[41] rmarkdown_2.27    httr_1.4.7        tools_4.3.3       pkgconfig_2.0.3  
[45] htmltools_0.5.8.1