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Overview

• Data description

Microscopy image analysis

• Processing images - from images to intensities

We evaluated and pre-processed the results of image analysis as follows:

1. We visually inspect images deteced to have none or more than one nucleus. For cases that are inconsistent with visual inspection, we correct the number of nuclei detected.
   • Inspect images with multiple nuclei
   • Inspect images with no nucleus
2. We applied background correction to the intensity measurements of GFP, RFP and DAPI based on the following analyses.
   • 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
   • Explore associations between nucleus shape metrics vs intensities
3. We analyzed intensity variation across individuals and batches and determined on an approach that removes batch effect in the data.
   • Visualize signal variation by plate and individual identity
   • Visualize the structure of signal variation by individual identity
   • Quantile normalization for GFP, RFP and DAPI
   • Estimate variance explained in IBD and correct for batch effects in intensities

RNA-seq data

The first steps in preprocessing RNA-seq data consists of QC and filtering.

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

We then analyzed and corrected for batch effect due to C1 plate in the sequencing data

• Estimate variance explained in IBD and correct for batch effects

Other information:

• transgene count in sequencing data
  • Investigate zero-count transgenes

Intensity-based sample classification/ordering

We explored the possiblities of using intensities to learn cell cycle phases/genes in RNA-seq data.

1. We considered 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
2. We considered continuous ordering
   • Obtain sample ordering on a unit circle based on GFP and RFP

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)

One-time investigations

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