Last updated: 2021-02-24

Checks: 7 0

Knit directory: scATACseq-topics/

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Rmd 96b63a9 kevinlkx 2020-09-09 assess fits

Here we compare the quality of the fits obtained from the different updates (EM, CCD, SCD, with and without extrapolation), and with different \(k\).

Assess Poisson NMF fits in Cusanovich_2018 scATAC-seq data in all tissues

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 Cusanovich_2018 data, with different algorithms, and for various choices of \(k\) (the number of "topics").

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018"
load(file.path(out.dir, "/compiled.fits.Cusanovich2018.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)

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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 all choices of \(k\), 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, x="iter", y="loglik", numiter.prefit=200)

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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, x="iter", y="res", numiter.prefit=200)

Version Author Date
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Assess Poisson NMF fits in all tissues using larger number of topics (\(k\)).

k = 20

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018"
fit <- readRDS(file.path(out.dir, "fit-Cusanovich2018-scd-ex-k=20.rds"))$fit
p1 <- plot_progress_poisson_nmf(fit,x = "iter",y = "loglik",e = 1e-6)
p2 <- plot_progress_poisson_nmf(fit,x = "iter",y = "res")
plot_grid(p1,p2)

k = 25

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018"
fit <- readRDS(file.path(out.dir, "fit-Cusanovich2018-scd-ex-k=25.rds"))$fit
p1 <- plot_progress_poisson_nmf(fit,x = "iter",y = "loglik",e = 1e-6)
p2 <- plot_progress_poisson_nmf(fit,x = "iter",y = "res")
plot_grid(p1,p2)

k = 30

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018"
fit <- readRDS(file.path(out.dir, "fit-Cusanovich2018-scd-ex-k=30.rds"))$fit
p1 <- plot_progress_poisson_nmf(fit,x = "iter",y = "loglik",e = 1e-6)
p2 <- plot_progress_poisson_nmf(fit,x = "iter",y = "res")
plot_grid(p1,p2)

k = 35

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018"
fit <- readRDS(file.path(out.dir, "fit-Cusanovich2018-scd-ex-k=35.rds"))$fit
p1 <- plot_progress_poisson_nmf(fit,x = "iter",y = "loglik",e = 1e-6)
p2 <- plot_progress_poisson_nmf(fit,x = "iter",y = "res")
plot_grid(p1,p2)

Assess Poisson NMF fits in bone marrow

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018_BoneMarrow/"
load(file.path(out.dir, "/compiled.fits.Cusanovich2018.RData"))

Plot the improvement of log-likelihood as the rank, \(k\), is increased.

plot_loglik_vs_rank(fits) +
  theme_cowplot(font_size = 12)

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9f68ea1 kevinlkx 2021-02-02

Plots the improvement in the fit over time, for all choices of \(k\).

create_progress_plots(dat, fits, x="iter", y="loglik", numiter.prefit=200)

Version Author Date
9f68ea1 kevinlkx 2021-02-02

Plots the evolution of the KKT residuals over time.

create_progress_plots(dat, fits, x="iter", y="res", numiter.prefit=200)

Version Author Date
9f68ea1 kevinlkx 2021-02-02

Assess Poisson NMF fits in whole brain

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018_WholeBrain/"
load(file.path(out.dir, "/compiled.fits.Cusanovich2018.RData"))

Plot the improvement of log-likelihood as the rank, \(k\), is increased.

plot_loglik_vs_rank(fits) +
  theme_cowplot(font_size = 12)

Version Author Date
9f68ea1 kevinlkx 2021-02-02

Plots the improvement in the fit over time, for all choices of \(k\).

create_progress_plots(dat, fits, x="iter", y="loglik", numiter.prefit=200)

Version Author Date
9f68ea1 kevinlkx 2021-02-02

Plots the evolution of the KKT residuals over time.

create_progress_plots(dat, fits, x="iter", y="res", numiter.prefit=200)

Version Author Date
9f68ea1 kevinlkx 2021-02-02

Assess Poisson NMF fits in Cusanovich_2018 scATAC-seq data in PreFrontalCortex

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018_PreFrontalCortex/"
load(file.path(out.dir, "/compiled.fits.Cusanovich2018.RData"))

Plot the improvement of log-likelihood as the rank, \(k\), is increased.

plot_loglik_vs_rank(fits) +
  theme_cowplot(font_size = 12)

Plots the improvement in the fit over time, for all choices of \(k\).

create_progress_plots(dat, fits, x="iter", y="loglik", numiter.prefit=200)

Plots the evolution of the KKT residuals over time.

create_progress_plots(dat, fits, x="iter", y="res", numiter.prefit=200)

Assess Poisson NMF fits in kidney

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018_Kidney/"
load(file.path(out.dir, "/compiled.fits.Cusanovich2018.RData"))

Plot the improvement of log-likelihood as the rank, \(k\), is increased.

plot_loglik_vs_rank(fits) +
  theme_cowplot(font_size = 12)

Plots the improvement in the fit over time, for all choices of \(k\).

create_progress_plots(dat, fits, x="iter", y="loglik", numiter.prefit=200)

Plots the evolution of the KKT residuals over time.

create_progress_plots(dat, fits, x="iter", y="res", numiter.prefit=200)


sessionInfo()
# R version 3.6.1 (2019-07-05)
# Platform: x86_64-pc-linux-gnu (64-bit)
# Running under: Scientific Linux 7.4 (Nitrogen)
# 
# Matrix products: default
# BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
# 
# locale:
#  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
# [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
# 
# attached base packages:
# [1] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
# [1] cowplot_1.1.1     ggplot2_3.3.3     fastTopics_0.5-18 workflowr_1.6.2  
# 
# loaded via a namespace (and not attached):
#  [1] httr_1.4.2         tidyr_1.1.2        jsonlite_1.7.2     viridisLite_0.3.0 
#  [5] RcppParallel_5.0.2 mixsqp_0.3-43      yaml_2.2.1         progress_1.2.2    
#  [9] ggrepel_0.9.1      pillar_1.5.0       lattice_0.20-41    quantreg_5.83     
# [13] glue_1.4.2         quadprog_1.5-8     digest_0.6.27      promises_1.2.0.1  
# [17] colorspace_2.0-0   htmltools_0.5.1.1  httpuv_1.5.4       Matrix_1.2-18     
# [21] conquer_1.0.2      pkgconfig_2.0.3    invgamma_1.1       SparseM_1.81      
# [25] purrr_0.3.4        scales_1.1.1       whisker_0.4        later_1.1.0.1     
# [29] Rtsne_0.15         MatrixModels_0.4-1 git2r_0.27.1       tibble_3.0.6      
# [33] farver_2.0.3       generics_0.1.0     ellipsis_0.3.1     withr_2.4.1       
# [37] ashr_2.2-47        lazyeval_0.2.2     magrittr_2.0.1     crayon_1.4.1      
# [41] mcmc_0.9-7         evaluate_0.14      fs_1.3.1           fansi_0.4.2       
# [45] MASS_7.3-53        truncnorm_1.0-8    tools_3.6.1        data.table_1.14.0 
# [49] prettyunits_1.1.1  hms_1.0.0          lifecycle_1.0.0    matrixStats_0.58.0
# [53] stringr_1.4.0      MCMCpack_1.5-0     plotly_4.9.3       munsell_0.5.0     
# [57] irlba_2.3.3        compiler_3.6.1     rlang_0.4.10       debugme_1.1.0     
# [61] grid_3.6.1         htmlwidgets_1.5.3  labeling_0.4.2     rmarkdown_2.6     
# [65] gtable_0.3.0       DBI_1.1.0          R6_2.5.0           knitr_1.30        
# [69] dplyr_1.0.4        utf8_1.1.4         rprojroot_2.0.2    stringi_1.5.3     
# [73] SQUAREM_2021.1     Rcpp_1.0.6         vctrs_0.3.6        tidyselect_1.1.0  
# [77] xfun_0.19          coda_0.19-4