Overview


Summary analysis


Model training and prediction results


Explore aproaches to fitting cyclical trend


Cell cycle signal in gene expression data

  1. We investigated cell cycle signals in the sequencing data alone.
  2. We then assign categorical labels of cell cycle and explored the expresson profiles of these categories.
  3. We ordered cells on a circle using FUCCI intensities alone.
  4. I used nonparametric methods to identify genes that may be cyclical along cell cycle phases.
    • Fit smash and kernel regression on circular variables on a subset of genes with detection rate > .8.
    • Fit trendfilter on a subset of genes (5) that are observed (visually) to have cyclical pattern. trendfilter is robust to small proportion of undetected cells, approx 2 or 3%. In cases of simulation when increasing proportion of undetected cells to 20%, we observed a flat line in gene expression for genes previously identified to tend to a cyclical pattern.
    • Next, we fit trendfilter on all genes after transforming the data to follow standard normal distribution, permutation-based p-values for PVE are used to select 101 significant cyclical genes.

RNA-seq data preprcessing

  1. The first step in preprocessing RNA-seq data consists of QC and filtering.
  2. We then analyzed and corrected for batch effect due to C1 plate in the sequencing data

Microscopy image analysis

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.
  2. We applied background correction to the intensity measurements of GFP, RFP and DAPI based on the following analyses.
  3. We analyzed intensity variation across individuals and batches and considers approaches for removing batch effects in the data.
  4. We investigated the cell time estimates based on FUCCI intensities.

One-time investigations


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