Assess Poisson NMF fits in pulse-seq data
Peter Carbonetto
Last updated: 2020-10-26
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Knit directory: single-cell-topics/analysis/
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| Rmd | 042c50d | Peter Carbonetto | 2020-10-26 | workflowr::wflow_publish(“assess_fits_pulseseq.Rmd”) |
| html | ea2a346 | Peter Carbonetto | 2020-08-31 | Re-ran assess_fits_pulseseq analysis with fix to timings reported in X |
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| html | 647ca81 | Peter Carbonetto | 2020-08-10 | Revised text in assess_fits_pulseseq analysis. |
| Rmd | 3b6d17f | Peter Carbonetto | 2020-08-10 | wflow_publish(“assess_fits_pulseseq.Rmd”) |
| html | 147da1f | Peter Carbonetto | 2020-08-10 | First build of assess_fits_pulseseq analysis. |
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| Rmd | 897e38f | Peter Carbonetto | 2020-08-10 | Created assess_fits_pulseseq.Rmd. |
Here we compare the quality of the fits obtained from the different updates (EM, CCD, SCD, with and without extrapolation), and with different \(k\).
Load the packages used in the analysis below, as well as some additional functions for creating the plots.
library(fastTopics)
library(ggplot2)
library(cowplot)
source("../code/functions_for_assessing_fits.R")Load the results of running fit_poisson_nmf on the pulse-seq data, with different algorithms, and for various choices of \(k\) (the number of “topics”).
load("../output/pulseseq/fits-pulseseq.RData")This plot shows the improvement in the log-likelihood as the rank, \(k\), is increased. The log-likelihoods are shown relative to the log-likelihood at \(k = 2\).
plot_loglik_vs_rank(fits) +
theme_cowplot(font_size = 12)
As expected, the likelihood consistently improves as the rank, \(k\), is increased.
The next set of plots shows the improvement in the fit over time, for \(k = 2--13\), and for each of the three optimization methods (EM, CCD, SCD), with and without the extrapolation scheme. The quality of the fit is measured by the log-likelihood relative to the best log-likelihood that was identified among all methods compared.
create_progress_plots(dat,fits,"loglik")
The last set of plots shows the evolution of the KKT residuals over time; the KKT residuals should vanish near a stationary point of the log-likelihood, so looking at the largest KKT residual can be used to assess how close we are to a solution. Note that, unlike the log-likelihood, the KKT residuals are not expected to decrease monotonically over time.
create_progress_plots(dat,fits,"res")
sessionInfo()
# R version 3.6.2 (2019-12-12)
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