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index.Rmd
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index.Rmd
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---
title: "Home"
output:
html_document:
toc: false
---
Website to share results of fucci-seq project.
## Overview
* [Data description](data-overview.html)
---
## 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)
---
## RNA-seq data
The first steps 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)
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)
Other information:
* transgene count in sequencing data
* [Investigate zero-count transgenes](images-transgene.html)
---
## Intensity-based sample classification/ordering
We explored the possiblities of using intensities to learn cell cycle phases/genes in RNA-seq data.
1. We considered 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](images-classify-fucci.html)
* [Expression profiles of sorted cells in Leng et al. 2015](images-classify-leng.html)
2. We considered continuous ordering
* [Obtain sample ordering on a unit circle based on GFP and RFP](images-circle-ordering.html)
---
## 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)
---
---
## One-time investigations
* [Why some gene symbols (genes) correspond to multiple Ensembl IDs?](ensembl.html)