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Overview

• Data description

Summary analysis

• Seminar talk 2018-04-16
• Some code for generating plots for the paper
  • code/paper.plot.images.R

Model training and prediction results

• Early analysis using trendfilter (to be updated…)

• Investigate the property and quality of the cell time labels derived from GFP and RFP: PVE of intensities by cell times, comparing PVEs by cell times from DAPI and FUCCI vs from FUCCI; comparing prediction before and after removing PC outliers (after is slightly better), and in different sets of genes.

• Investigate the property of Seurat classes and cell time

• Analyze the training datasets
  • Prediction results from trendfilter order 2 and order 3 with npcirc.nw on the odd-numbered genes in the top 101 cyclical genes
  • Prediction results of the various classifers (supervised, PC, unsupervised) on top 101 cyclical genes, and on top 10 cyclial genes.
  • Finalizing results of all classifiers and the top X number of genes.
• Analye the withheld samples
  • Evaluate the withheld samples on top 101 cyclical genes, and on top 10 cyclial genes
  • Explore method performances: compare peco with Seurat

• Analyze the public datasets, including Leng et al., Bottcher et al., and Buettner et al..

method-train-classifiers-non101.html

Explore aproaches to fitting cyclical trend

• Apply spherically projected multivariate linear model
  • First trying out spml package
  • To predictd cell times using traing/test samples set-up

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

• 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)

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 significantly correlatd with the PC-based fit by linear correlation and circular-linear correlation, also cyclical genes by smash
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.
   • 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

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 considers approaches for removing batch effects 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
4. We investigated the cell time estimates based on FUCCI intensities.
   • Consider the mean-adjusted FUCCI intensities, what’s the relationship between cell time estimates and DAPI and sample molecule count?
   • Consider the quantile-normalized FUCCI intensities versus the mean-adjusted intensities (for adjusting batch effect). What’s the differences between cell time estimates from the two normalization approaches?

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.

• Replicate Seurat example of scoring cell cycle phases