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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 preprcessing

1. The first step 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
2. 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

RNA-seq data cell cycle gene signal

We investigated cell cycle signals in the sequencing data.

1. Investigate transgene count in sequencing data

2. Select cell cycle genes for training data

3. Gene expression levels and intensities

4. PCA and intensities

Intensity-based cell cycle labeling

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

1. Consider 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 using intensities before confounding correction and after confounding correction
   • Expression profiles of sorted cells in Leng et al. 2015
2. Consider continuous ordering
   • 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?