New visualization stuff

README.md

@@ -119,4 +119,3 @@ You can leave your comments and bug reports at our [GitHub repository tracker](h
         --data_type (assembly|pacbio|nanopore) -o OUTPUT_FOLDER
 
 * If multiple files are provided, IsoQuant will create a single output annotation and a single set of gene/transcript expression tables.
-

docs/visualization.md

@@ -4,35 +4,41 @@ IsoQuant provides a visualization tool to help interpret and explore the output
 
 ## Running the visualization tool
 
-To run the visualization tool, use the following command:
+To run the visualization tool, use one of the following commands:
 
 ```bash
-
+# Visualize a predefined list of genes
 python visualize.py <output_directory> --gene_list <gene_list> [options]
 
+# Automatically find the top N most differentially expressed genes
+python visualize.py <output_directory> --find_genes [N] [options]
 ```
 
 ## Command line options
 
 * `output_directory` (required): Directory containing IsoQuant output files.
-* * `--gene_list` (required): Path to a .txt file containing a list of genes, each on its own line.
-* `--viz_output`: Optional directory to save visualization output files. Defaults to the main output directory if not specified.
+* `--gene_list`: Path to a .txt file containing a list of genes, each on its own line. Mutually exclusive with `--find_genes`.
+* `--find_genes [N]`: Automatically select the top **N** genes with the highest combined differential-expression rank between chosen conditions (default 100 if *N* is omitted).
+* `--viz_output`: Optional directory to save visualization output files. Defaults to `<output_directory>/visualization`.
 * `--gtf`: Optional path to a GTF file if it cannot be extracted from the IsoQuant log.
-* `--counts`: Use counts instead of TPM files for visualization.
 * `--ref_only`: Use only reference transcript quantification instead of transcript model quantification.
-* `--filter_transcripts`: Filter transcripts by minimum value occurring in at least one condition.
+* `--filter_transcripts <float>`: Minimum expression value a transcript must reach in at least one condition to be included in plots (default 1.0).
+* `--gsea`: Perform Gene Set Enrichment Analysis on differential expression results (requires `--find_genes`).
+* `--technical_replicates`: Specify technical replicate groupings as a file (`sample,group`) or inline (`sample1:group1,sample2:group1`).
 
 
 ## Output
 
-The visualization tool generates the following plots based on the IsoQuant output:
+The visualization tool can generate the following outputs:
 
-1. Transcript usage profiles: For each gene specified in the gene list, a plot showing the relative usage of different transcripts across conditions or samples.
+1. Transcript usage profiles: For each gene, a plot showing the relative usage of different transcripts across conditions or samples.
 
 2. Gene-specific transcript maps: Visual representation of the different splicing patterns of transcripts for each gene, allowing easy comparison of exon usage and alternative splicing events.
 
-3. Global read assignment consistency: A summary plot showing the overall consistency of read assignments across all genes and transcripts analyzed.
+3. Global read assignment consistency: A summary plot showing the overall consistency of read assignments across all genes and transcripts analyzed (enabled interactively).
+
+4. Global transcript alignment classifications: A chart representing the distribution of different transcript alignment categories (e.g., full splice match, incomplete splice match, novel isoforms) across the entire dataset.
 
-4. Global transcript alignment classifications: A chart or plot representing the distribution of different transcript alignment categories (e.g., full splice match, incomplete splice match, novel isoforms) across the entire dataset.
+5. Differential expression tables and volcano plots when `--find_genes` is used, with optional GSEA pathway visualizations if `--gsea` is supplied.
 
-These visualizations provide valuable insights into transcript diversity, splicing patterns, and the overall quality of the IsoQuant analysis.
+These visualizations and reports provide valuable insights into transcript diversity, splicing patterns, differential expression, and the overall quality of the IsoQuant analysis.

install_r_packages.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+
+import rpy2.robjects.packages as rpackages
+from rpy2.robjects.vectors import StrVector
+
+# List of R packages to install
+r_package_names = ('DESeq2', 'ggplot2', 'ggrepel', 'RColorBrewer', 'clusterProfiler', 'org.Hs.eg.db')
+
+# Get R's utility package
+utils = rpackages.importr('utils')
+
+# Select CRAN mirror (optional, but recommended for reproducibility)
+utils.chooseCRANmirror(ind=1) # Select the first mirror in the list
+
+# Function to check if R package is installed
+def is_installed(package_name):
+    return package_name in rpackages.packages()
+
+# Install R packages if not already installed
+packages_to_install = [pkg for pkg in r_package_names if not is_installed(pkg)]
+
+if packages_to_install:
+    print(f"Installing R packages: {', '.join(packages_to_install)}")
+    utils.install_packages(StrVector(packages_to_install))
+else:
+    print("All required R packages are already installed.")