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index.Rmd
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index.Rmd
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---
title: "Home"
output:
html_document:
toc: TRUE
toc_float: FALSE
---
---
## Overview
* [Data description](data-overview.html)
---
## Summary analysis
* [Seminar talk 2018-04-16](talk-20180416.html)
* Some code for generating plots for the paper
* `code/paper.plot.images.R`
---
## Model training and prediction results
* [Early analysis using trendfilter](method-npreg.html) (to be updated...)
* Investigate the property and quality of [the cell time labels derived from GFP and RFP](method-labels.html): 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](method-label-seurat.html)
* 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](method-npreg-prelim-results.html)
* Prediction results of the various classifers (supervised, PC, unsupervised) [on top 101 cyclical genes](method-train-classifiers.html), and on [top 10 cyclial genes](method-train-classifiers-top10.html).
* Finalizing results of all classifiers and the [top X number of genes](method-train-classifiers-all.html).
* Analye the withheld samples
* Evaluate the withheld samples [on top 101 cyclical genes](method-eval-withheld.html), and on [top 10 cyclial genes](method-eval-withheld-top10.html)
* Explore method performances: [compare peco with Seurat](method-eval-withheld-explore.html)
* Analyze the public datasets, including [Leng et al.](method-eval-leng.html), [Bottcher et al.](method-eval-bottcher.html), and [Buettner et al.](method-eval-buettner.html).
---
##
method-train-classifiers-non101.html
---
## Explore aproaches to fitting cyclical trend
* Apply spherically projected multivariate linear model
* [First trying out spml package](model-spml.pilot.html)
* [To predictd cell times using traing/test samples set-up](method-spml.html)
* Learning about correlation for circular response variable: some simulations to check [its property and also relations with Fisher's z transformation](circ-simulation-correlation.html)
* 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)
---
## Cell cycle signal in gene expression data
1. We investigated cell cycle signals in the sequencing data alone.
* Consider [transgene count in sequencing data](images-transgene.html)
* Compute linear correlation between gene expression levels [with DAPI and FUCCI intensities](images-seq-correlation.html)
2. We then assign categorical labels of cell cycle and explored the expresson profiles of these categories.
* Cluster samples by [Partition around medoids(PAM)](images-pam.html)
* Cluster samples by [Guassian mixture modeling](images-mclust.html)
* Select a subset of samples that are closet to the cluster centers (cluster representives) [using silhouette index](images-subset-silhouette.html)
* Examine gene pression scores defined by the Macosko paper in the selected cluster representatives [before confounding correction](images-classify-fucci.html) and [after confounding correction](images-classify-fucci-adjusted.html)
* Examine gene expression scores defined by the Macosk paer [in the sorted cells of the Leng et al. 2015 paper](images-classify-leng.html)
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](images-circle-ordering.html)
* 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](images-circle-ordering-eval.html)
* 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](images-circle-ordering-sigcorgenes.html)
4. I used nonparametric methods to identify genes that may be cyclical along cell cycle phases.
* Fit [smash and kernel regression on circular variables](npreg-methods.html) on a subset of genes with detection rate > .8.
* Fit [trendfilter](npreg-trendfilter-prelim.html) 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](npreg-trendfilter-quantile.html).
---
## 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)
---
## 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 considers approaches for removing batch effects 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)
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](images-time-eval.html)?
* 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](images-time-compare-normalize.html)?
---
## One-time investigations
* Why some gene symbols (genes) correspond to [multiple Ensembl IDs?](ensembl.html)
* 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.](seqdata-select-cellcyclegenes.html)
* [Replicate Seurat example of scoring cell cycle phases](seurat-cellcycle.Rmd)