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Overview

• Data description

Microscopy image analysis

• Processing images - from images to intensities

• Visual inspection
  • Inspect images with multiple nuclei
  • Inspect images with no nucleus
• QC and preprocessing image intensities
  • CONFESS results
  • QC analysis including no. nuclei detected, DAPI, and intensity variation
  • Explore using log10 sum pixel intensity for signal metrics
  • Compare correction approaches using median versus mean background
• Normalize intensities by batch
  • Visualize signal variation by plate and individual identity
  • Visualize the structure of signal variation by individual identity
  • Quantile normalization for GFP, RFP and DAPI
• Shape metrics
  • Nucleus shape metrics versus intensities
• transgene count in sequencing data
  • Investigate zero-count transgenes
• Evalute categorical labeling
  • Cluster samples by Partition around medoids(PAM)
  • Cluster samples by Mixture modeling
  • Select a subset using silhouette index
  • Expression profiles of the “best” samples
  • Expression profiles of sorted cells in Leng et al. 2015

RNA-seq data

Model fitting

• Evaluating cellcycleR 0.1.6
  • Model convergence assessment
  • Fitting on intensities across plates and individuals
  • Fitting on Leng data)
  • Fitting on fucci-seq RNA-seq data)

Qualty Control

• Sample QC and filtering
  • Sample QC criteria
    
  • Sequencing depth
    
  • Reads versus molecules
• Gene QC and filtering
  • gene filtering
  • PCA with technical fators

One-time investigations

• Why some gene symbols (genes) correspond to multiple Ensembl IDs?