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Last updated: 2022-01-06

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Knit directory: fucci-seq/analysis/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/index.Rmd) and HTML (docs/index.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
html	8f88d99	John Blischak	2020-10-03	Build site.
Rmd	6ce99e4	John Blischak	2020-10-03	Update citation.
html	8aa2d71	jhsiao999	2019-09-17	Build site.
Rmd	05f661f	jhsiao999	2019-09-17	wflow_publish("analysis/index.Rmd")
html	05f661f	jhsiao999	2019-09-17	wflow_publish("analysis/index.Rmd")
html	237fdf1	John Blischak	2019-09-05	Build site.
Rmd	e39d12e	John Blischak	2019-09-05	Update URLs on homepage.
html	d848f8e	Joyce Hsiao	2019-02-12	Build site.
Rmd	391ed92	Joyce Hsiao	2019-02-12	add John’s changes
Rmd	435d656	GitHub	2019-02-12	Merge branch ‘master’ into master
html	984f9a9	Joyce Hsiao	2019-02-12	Build site.
Rmd	c4f411f	Joyce Hsiao	2019-02-12	move TOC
Rmd	a0a3d31	John Blischak	2019-02-12	Document how to download data files. Close #56.
Rmd	5b3779c	John Blischak	2019-02-01	Update workflowr to 1.0.1 with wflow_update().
html	9471094	Joyce Hsiao	2019-01-31	Build site.
Rmd	162f192	Joyce Hsiao	2019-01-31	build homepage
html	0544e3c	Joyce Hsiao	2018-09-25	Build site.
Rmd	49ad6fd	Joyce Hsiao	2018-09-25	build homepage
html	e3e6dbf	Joyce Hsiao	2018-07-17	Build site.
Rmd	ff67833	Joyce Hsiao	2018-07-17	update homepage
html	453044f	Joyce Hsiao	2018-07-12	Build site.
Rmd	1d8f214	Joyce Hsiao	2018-07-12	update homepage
html	6a46a6b	Joyce Hsiao	2018-07-02	Build site.
Rmd	efaea97	Joyce Hsiao	2018-07-02	update homepage
html	c526f3f	Joyce Hsiao	2018-06-29	Build site.
Rmd	78ce6d8	Joyce Hsiao	2018-06-29	update homepage
html	6a93de3	Joyce Hsiao	2018-06-28	Build site.
Rmd	9c0cc13	Joyce Hsiao	2018-06-28	update hompage
html	63fd7ff	Joyce Hsiao	2018-06-21	Build site.
Rmd	5387a9c	Joyce Hsiao	2018-06-21	update hompage
html	3dc9b89	Joyce Hsiao	2018-05-22	Build site.
Rmd	b4cdf0a	Joyce Hsiao	2018-05-22	update homepage
html	dea875a	Joyce Hsiao	2018-05-09	Build site.
Rmd	fec5c5c	Joyce Hsiao	2018-05-09	update homepage
html	a625a22	Joyce Hsiao	2018-04-10	Build site.
Rmd	067340b	Joyce Hsiao	2018-04-10	update hompage
html	688a3ba	Joyce Hsiao	2018-03-15	Build site.
Rmd	0fc96fd	Joyce Hsiao	2018-03-15	re-organize homepage
html	7205420	Joyce Hsiao	2018-03-09	Build site.
Rmd	94d2849	Joyce Hsiao	2018-03-09	update hompage
html	e3ecf83	Joyce Hsiao	2018-02-26	Build site.
Rmd	920c732	Joyce Hsiao	2018-02-26	update homepage
html	d2d3c37	Joyce Hsiao	2018-02-22	Build site.
Rmd	e74c417	Joyce Hsiao	2018-02-22	update homepage
html	76f055a	Joyce Hsiao	2018-02-14	Build site.
Rmd	b04ee99	Joyce Hsiao	2018-02-14	update homepage
Rmd	af2ac8b	John Blischak	2018-02-08	update index
html	7d55333	John Blischak	2018-02-08	Build site.
Rmd	e0ef1bd	John Blischak	2018-02-08	minor edits
html	be18b2f	John Blischak	2018-02-08	Build site.
Rmd	34c35b1	John Blischak	2018-02-08	update homepage
html	3ae09b6	John Blischak	2018-02-08	Build site.
html	2a026d4	Joyce Hsiao	2018-02-08	Build site.
Rmd	86e0a90	Joyce Hsiao	2018-02-08	update index
html	6991a4f	Joyce Hsiao	2018-02-08	Build site.
Rmd	e4747bc	Joyce Hsiao	2018-02-08	minor edits
html	59d28df	Joyce Hsiao	2018-02-07	Build site.
Rmd	5991f68	Joyce Hsiao	2018-02-07	update homepage
html	25e1a9f	Joyce Hsiao	2018-02-01	Build site.
Rmd	f6c8d2d	Joyce Hsiao	2018-02-01	update homepage
Rmd	5a1ad9c	John Blischak	2018-01-31	Merge remote-tracking branch ‘jhsiao999/master’
html	9fb49f9	Joyce Hsiao	2018-01-31	Build site.
Rmd	36af913	Joyce Hsiao	2018-01-31	update homepage
Rmd	4b1a660	pytung	2018-01-31	add analysis of PCA vs techinical factors
Rmd	3909c4c	Joyce Hsiao	2018-01-30	update index
html	f422ce2	Joyce Hsiao	2018-01-25	Build site.
Rmd	e5682e3	Joyce Hsiao	2018-01-25	wflow_publish("analysis/index.Rmd")
html	bde6496	Joyce Hsiao	2017-12-21	Build site.
Rmd	556d50e	Joyce Hsiao	2017-12-21	update index
html	913f2d1	Joyce Hsiao	2017-12-14	Build site.
Rmd	6fbaf59	Joyce Hsiao	2017-12-14	wflow_publish("analysis/index.Rmd")
html	cff51ca	Joyce Hsiao	2017-12-13	Build site.
Rmd	16d22e3	Joyce Hsiao	2017-12-13	wflow_publish("analysis/index.Rmd")
html	189df68	Joyce Hsiao	2017-12-12	Build site.
Rmd	95e8dda	Joyce Hsiao	2017-12-12	wflow_publish("analysis/index.Rmd")
Rmd	6e3c938	Joyce Hsiao	2017-12-11	Build site.
html	3a4dd7c	Joyce Hsiao	2017-12-02	Build site.
Rmd	fbca340	Joyce Hsiao	2017-12-02	update index
Rmd	ab2ad0c	John Blischak	2017-11-29	Fix merge conflict in index.Rmd.
html	ab2ad0c	John Blischak	2017-11-29	Fix merge conflict in index.Rmd.
Rmd	0c360f7	Chiaowen Hsiao	2017-11-29	update index
html	0c360f7	Chiaowen Hsiao	2017-11-29	update index
html	4fcf7d1	John Blischak	2017-11-29	Build site.
Rmd	2582fb0	John Blischak	2017-11-29	Investigate effect of C1 chip and individual on molecule conversion
html	6898149	John Blischak	2017-11-29	Build site.
Rmd	3d5d193	John Blischak	2017-11-29	Visualize sequencing depth per C1 chip.
Rmd	f41cb62	Chiaowen Hsiao	2017-11-20	update index
html	f41cb62	Chiaowen Hsiao	2017-11-20	update index
Rmd	cbaf910	Chiaowen Hsiao	2017-11-01	add image QC follow-up to index page
html	cbaf910	Chiaowen Hsiao	2017-11-01	add image QC follow-up to index page
Rmd	b238da6	Chiaowen Hsiao	2017-10-31	update index
html	620ad02	Chiaowen Hsiao	2017-10-31	preliminary image anaysis, mainly QC
Rmd	bd252b5	jhsiao999	2017-10-12	update index
html	bd252b5	jhsiao999	2017-10-12	update index
html	f6b94c1	jhsiao999	2017-10-12	Build site.
Rmd	c8c2654	jhsiao999	2017-10-12	wflow_publish(all = TRUE)
Rmd	e7f5236	John Blischak	2017-10-11	Start workflowr project.

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We used this site to collaborate and share our results. Please feel free to explore. The results that made it into the final paper are in the section Finalizing below. Here are some useful links:

• Characterizing and inferring quantitative cell-cycle phase in single-cell RNA-seq data analysis
• GEO record GSE121265 for the raw FASTQ files
• GitHub repo for code and data
• peco package for our supervised approach predicting cell cycle phase in induced pluripotent stem cells.

Finalizing

• Data description
• Quality control of scRNA-seq data
• Process images - from images to intensities
• Inspect and correct for the C1 plate effect in fluorescence intensities
• Infer an angel for each cell based on FUCCI fluorescence intensities
• Estimate the cyclic trends of gene expression levels

Analysis

Supervised method learning: finalizing on data holiding out validation samples

• Perform model training on cell times computed in different ways
  • Based on FUCCI only
    • Cells from mixed individuals: Prediction error, and functional annotations of genes selected
    • Cells from one individual: Prediction error, and functional annotations of genes selected
  • Based on FUCCI and DAPI and derived from trigonometric transformation
    • Cells from mixed individuals: Prediction error
    • Cells from one individual: Prediction error, and functional annotations of genes selected
  • Based on FUCCI and DAPI and derived from an algebraic approach
    • Cells from one individual
• Compare model training results
  • Compute and compare cell time estimates derived under two different assumptions: assuming equal variance in PC1 and PC2 (circle) vs. unequal variances in PC1 and PC2.
  • Prediction error for cells from mixed individuals: Prediction error smaller when based on fucci time only than based on fucci and dapi time, though only a very small difference. However, the genes selected in these two are similar, both found to have 37 genes enriched for the Cell Cycle GO (0007049).
• Compiling results
  • Compile training results for both training scenarios and Plotting prediction error margin and genes selected
  • Compile results on validation samples

Supervised method learning: building the analysis approach

• Early analysis using trendfilter and evaluating model performance

• 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 cyclical trends for each individual

• Investigate the property of Seurat classes and cell time

• Noisy label analysis

  • PVE in DAPI, GFP, and RFP before vs after removing noisy labels
  • Comparing results excluding/including noisy training labels on top 10 and top 101 cyclical genes, when excluding noisy labels from each fold validation, hence unequal sample numbers per fold as opposed to excluding noisy labels before making fold valdiation, hence equal nubmer per fold

• Analyze the training datasets

  • Comparing trendfilter order 2 and order 3 with npcirc.nw on the odd-numbered genes in the top 101 cyclical genes
  • Comparing resuls of the various classifers (supervised, PC, unsupervised) on top 101 cyclical genes, and on top 10 cyclial genes.

• Analyze the withheld samples

  • Evaluate the withheld sample including noisy labels on the top 10 and top 101 cyclical genes] and comparing peco methods with seurat predictions.
  • Evaluate the withheld samples removing noisy labels on the top 10 and top 101 cyclical genes and comparing peco methods with seurat predictions.

Approaches to fitting cyclical trend in gene expression data

• 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 inferred an angle for each cell on a unit circle using FUCCI intensities alone.
   • First, I used GFP and RFP intensities to estimate a least-square fit of unit circle
   • I computed the PCs of GFP and RFP and used these to infer an angle for each cell on a unit circle (i.e., polar coordinates), and to evaluate these results, 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.
5. Additional analysis done to identify top cyclical genes in each individual. The top 5 are not shared across the six individuals. Results

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

• Reproducing some QC analysis and making plots for a seminar talk