KSTAR

Overview:

• Overview
• Dependencies
• Preparing your Data
• Options for Running KSTAR

Galaxy Tutorial:

• Run KSTAR on Galaxy
• Analyze Results
  • Downloading Results from Galaxy
  • Inspect the summary PDF file
  • Plotting the Results
  • Additional Analysis in Python

Python Tutorial (Just the Basics):

• Getting Started
  • Installation
    • Pip
    • Conda Forge
    • Tarball
    • Git
  • Configuring your KSTAR environment
    • Download Networks
      • for KSTAR version 1.0.4 and newer
      • for KSTAR version 0.5.3 and older
      • If Using Self-generated Networks
    • Configure KSTAR to point to the correct network directory
  • Verify that KSTAR environment is ready
  • Follow the provided tutorial
• Map Dataset to Reference
  • Download and Process Dataset of Interest
  • Map the Dataset to Reference
  • Save Mapping Results
• Predict Kinase Activities
  • Determine Thresholds
  • KSTAR Run with Default Parameters
  • Saving KSTAR Results
  • Other Considerations
    • Using Pregenerated Experiments (or Not)
• Plotting/Analysis
  • Summary PDFs
    • Creating the PDF
    • Resulting PDF Files
  • DotPlots
    • With Interactive Web App
    • In Python
      • Load KSTAR Results
      • Plot Results
  • Additional Analyses
    • Load results and other necessary data (networks, binary evidence, etc.)
    • Identify relevant phosphorylation sites for a kinase of interest
    • Exploring Network and Experiment Coverage of Phosphorylation Sites
      • Number of unique phosphorylation sites in network(s)
      • Distribution of Study Bias in Phosphoproteome and in Experiments
      • Coverage of Experiment by Kinase-Substrate Network
      • Similarity between kinases in network

Example Vignettes:

• Analysis Examples
  • Activation of Kinases in Controlled Cell Lines
    • EGF Stimulation of 184A1 Cells (Wolf Yadlin, 2007)
      • Agglomerative clustering of kinases
      • Agglomerative clustering of only significant kinases
    • EGF and HRG Stimulation of ERBB2 Overexpressing HMEC Cells (Wolf Yadlin 2006)
      • Plot all samples and kinases
      • Plot just EGF HMEC data for comparison to MRM experiment
      • Reshape results and plot to compare kinase between conditions
    • TCR Activation (Chylek, 2014)
      • Agglomerative clustering of kinases
      • Agglomerative clustering of all significant kinases
  • Inhibition of Kinases in Controlled Cell Lines
    • BCR-ABL inhibition by dasatinib in K562 cells (Asmussen, 2014)
      • Plot kinases sorted by activity pre-treatment
      • Plot kinases sorted by activity pre-treatment, but with only significant kinases
    • Inhibition of AKT by Five Different Inhibitors in Breast Cancer (Weichmann, 2021)
      • Plot and cluster kinase activities
      • Plot and cluster kinase activities, but with only significant kinases
    • Paradoxical Activation of MAP-ERK pathway upon BRAF inhibition (Kubiniok, 2017)
      • Shared agglomerative clustering of kinases for both Colo205 and HCT116 cells
      • Shared agglomerative clustering of significant kinases for both Colo205 and HCT116 cells
  • Profiling HER2 therapy response in Breast Cancer
    • Analysis of CPTAC Breast Cancer Patient Samples (Mertens 2016)
      • Plot using agglomerative clustering on both kinases and patients, and add context to each sample
      • Plot ERBB2 activities with each samples associated HER2 status
      • Measure the number of cross-predictions between HER2-status and HER2/ERBB2 activity
    • Analysis of PDX Tumor Samples (Huang 2017)
      • Plot all samples and all kinases, with HER2 status context
      • Plot all samples and all significant kinases, with HER2 status context
    • Analysis of HER2 Positive Patients using Microbiopsies (Satpathy 2021)
      • Agglomerative clustering of all patients and kinases, with context
      • Agglomerative clustering of all patients and all significant kinases, with context
      • Plot only pre-treatment samples and provide treatment response context
      • Plot specific kinases for each patient (all samples)

Advanced Python Usage:

• KSTAR Reference
  • The “Config” Module
    • check_configuration()
    • find_available_networks()
    • install_network_files()
    • update_configuration()
    • update_network_directory()
  • The “Prune” Module
    • The “Pruner” Class
      • Pruner
    • Functions to Perform Pruning
      • run_pruning()
  • The “ExperimentMapper” class
    • ExperimentMapper
      • ExperimentMapper.align_sites()
      • ExperimentMapper.get_experiment()
      • ExperimentMapper.get_number_missed_peptides()
      • ExperimentMapper.get_number_missed_sites()
      • ExperimentMapper.get_reason_for_unmapped()
      • ExperimentMapper.get_sequence()
      • ExperimentMapper.save_experiment()
      • ExperimentMapper.set_data_columns()
  • Functions for Activity Calculation
    • The “KinaseActivity” class
      • KinaseActivity
    • Master Functions for Running KSTAR Pipeline
      • Mann_Whitney_analysis()
      • enrichment_analysis()
      • randomized_analysis()
      • run_kstar_analysis()
    • Functions for Saving and Loading KSTAR results
      • from_kstar()
      • from_kstar_nextflow()
      • save_kstar()
  • Plotting/Analysis Functions
    • The “DotPlot” class
      • DotPlot
    • The “KSTAR_PDF” class
      • KSTAR_PDF
    • Downstream Analysis Modules
      • getSubstrateInfluence()
      • getSubstrateInfluence_inExperiment()
      • averageUniqueSubstrates_KSTAR()
      • experimentCoverage()
      • getStudyBiasDistribution_InExperiment()
      • getStudyBiasDistribution_InPhosphoproteome()
      • getStudyBiasDistribution_InSample()
      • numUniqueSubstrates()
      • sampleCoverage()
      • kinase_mutual_information()
      • plot_kinase_heatmap()
  • Dataset Processing Functions
  • Other Helper Functions
    • agg_jaccard()
    • calculate_jaccard_by_binary()
    • calculate_jaccard_by_sets()
    • convert_acc_to_uniprot()
    • get_logger()
    • jaci_matrix_between_samples()
    • parse_network_information()
    • process_fasta_file()
    • string_to_boolean()
• Updating Configuration
• Network Generation
  • Download pre-generated networks
  • Generate your own networks
    • Required format of weighted kinase-substrate network
    • Determining Pruning Parameters to Use
    • Run pruning
      • Within python interpreter/IDE
      • Run pruning from the command line
• Pregenerated Random actiivities
  • Adjusting Pregenerated Experiments Use
  • Generating Additional Pregenerated Experiments
    • Automatically during KSTAR activity Calculation
    • Manually with the pregenerate module
• KSTAR in Parallel
  • Option 1: Running KSTAR using Multiprocessing
  • Option 2: Running Large Datasets with Nextflow
    • Requirements
    • Installation
    • Setting up KSTAR for Nextflow
      • Download Resource Files
      • Downloading or Generating Networks used in KSTAR
      • Mapping datasets
    • Running nextflow
      • Output of a nextflow run
      • Running nextflow on Singularity
• Frequently Asked Questions
  • Data Preparation
  • Choosing Parameter Values
  • Interpreting Results

Indices and tables

• Index

• Module Index

• Search Page