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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

Cell cycle signal in gene expression data

1. We investigated cell cycle signals in the sequencing data alone.

• Consider transgene count in sequencing data
• Compute linear correlation between gene expression levels with DAPI and FUCCI intensities

2. We then assign categorical labels of cell cycle and explored the expresson profiles of these categories.

• Cluster samples by Partition around medoids(PAM)
• Cluster samples by Guassian mixture modeling
• Select a subset of samples that are closet to the cluster centers (cluster representives) using silhouette index
• Examine gene pression scores defined by the Macosko paper in the selected cluster representatives before confounding correction and after confounding correction
• Examine gene expression scores defined by the Macosk paer in the sorted cells of the Leng et al. 2015 paper

3. We ordered cells on a circle using FUCCI intensities alone.

• First, I used GFP and RFP intensities to estimate a least-square fit of unit circle which approximate the relative ordering of cells on cell cycle
• I computed the PCs of GFP and RFP and used these to infer the relative ordering on a circle (i.e., polar coordinates), and to evaluate the circle fit, I computed circular-circular and circular-linear correlation with DAPI and gene expression
• Here I put together lists of genes identified as signfiicantly correlatd with the PC-based fit by linear correlation and circular-linear correlation, also cyclical genes by smash

Model fitting

• 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)

• Correlation for circular response variable: some simulations to check its property and also relations with Fisher’s z transformation

• Nonparametric circular regression
  • First, evaluate smash for smoothing gene expression data and compare results of smash with NPCirc

One-time investigations

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

• I selected a set of cell cycle genes that belong to GO term Cell Cycle and looked at the overlap with the detected genes in our data.