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Website to share results of fucci-seq project.

## Overview

* [Data description](data-overview.html)

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## Microscopy image analysis

* [Processing images - from images to intensities](images-process.html)

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](images-multiple-nuclei.html)
    * [Inspect images with no nucleus](images-zero-nuclei.html)
        
2. We applied background correction to the intensity measurements of GFP, RFP and DAPI based on the following analyses.  
    * [CONFESS results](confess-prelim.html)
    * [QC analysis including no. nuclei detected, DAPI, and intensity variation](images-qc.html)
    * [Explore using log10 sum pixel intensity for signal metrics](images-qc-followup.html)
    * [Compare correction approaches using median versus mean background](images-metrics.html)
    * [Explore associations between nucleus shape metrics vs intensities](images-metrics-cell-shape.html)

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](images-qc-labels.html)
    * [Visualize the structure of signal variation by individual identity](images-qc-variation.html)
    * [Quantile normalization for GFP, RFP and DAPI](images-normalize-quantile.html)
    * [Estimate variance explained in IBD and correct for batch effects in intensities](images-normalize-anova.html)
    
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## 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](sampleqc.html)  
        * [Sequencing depth](totals.html)  
        * [Reads versus molecules](reads-v-molecules.html)  
    
    * Gene QC and filtering
        * [gene filtering](gene-filtering.html)
        * [PCA with technical fators](pca-tf.html)

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](seqdata-batch-correction.html)


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## RNA-seq data cell cycle gene signal

We investigated cell cycle signals in the sequencing data.

1. [Investigate transgene count in sequencing data](images-transgene.html)

2. [Select cell cycle genes for training data](seqdata-select-cellcyclegenes.html)

3. [Gene expression levels and intensities](images-seq-correlation.Rmd)

4. PCA and intensities





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## 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)](images-pam.html)
    * [Cluster samples by Mixture modeling](images-mclust.html)
    * [Select a subset using silhouette index](images-subset-silhouette.html)
    * Expression profiles of the "best" samples using intensities [before confounding correction](images-classify-fucci.html) and [after confounding correction](images-classify-fucci-adjusted.html)
    * [Expression profiles of sorted cells in Leng et al. 2015](images-classify-leng.html)

2. Consider continuous ordering  
    * [Ordering on a unit circle based on GFP and RFP](images-circle-ordering.html)
    


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## Model fitting

* Evaluating cellcycleR 0.1.6
    * [Model convergence assessment](images-cellcycleR-convergence.html)
    * [Fitting on intensities across plates and individuals](images-cellcycleR.html)
    * [Fitting on Leng data)](cellcycler-seqdata-leng.html)
    * [Fitting on fucci-seq RNA-seq data)](cellcycler-seqdata-fucci.html)
    

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## One-time investigations

* [Why some gene symbols (genes) correspond to multiple Ensembl IDs?](ensembl.html)