Deng et al. dataset: nonnegative fits

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Last updated: 2022-04-01

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Overview

library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
#> ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
#> ✓ tibble  3.1.6     ✓ dplyr   1.0.8
#> ✓ tidyr   1.2.0     ✓ stringr 1.4.0
#> ✓ readr   2.0.0     ✓ forcats 0.5.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag()    masks stats::lag()
library(flashier)
#> Loading required package: magrittr
#> 
#> Attaching package: 'magrittr'
#> The following object is masked from 'package:purrr':
#> 
#>     set_names
#> The following object is masked from 'package:tidyr':
#> 
#>     extract
library(singleCellRNASeqMouseDeng2014)
#> Loading required package: Biobase
#> Loading required package: BiocGenerics
#> Loading required package: parallel
#> 
#> Attaching package: 'BiocGenerics'
#> The following objects are masked from 'package:parallel':
#> 
#>     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
#>     clusterExport, clusterMap, parApply, parCapply, parLapply,
#>     parLapplyLB, parRapply, parSapply, parSapplyLB
#> The following objects are masked from 'package:dplyr':
#> 
#>     combine, intersect, setdiff, union
#> The following objects are masked from 'package:stats':
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
#> 
#>     anyDuplicated, append, as.data.frame, basename, cbind, colMeans,
#>     colnames, colSums, dirname, do.call, duplicated, eval, evalq,
#>     Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply,
#>     lengths, Map, mapply, match, mget, order, paste, pmax, pmax.int,
#>     pmin, pmin.int, Position, rank, rbind, Reduce, rowMeans, rownames,
#>     rowSums, sapply, setdiff, sort, table, tapply, union, unique,
#>     unsplit, which, which.max, which.min
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
library(Rtsne)
library(ggrepel)
# library(fastTopics)

counts <- exprs(Deng2014MouseESC)
meta_data <- pData(Deng2014MouseESC)
gene_names <- rownames(counts)

preprocess <- function(dat, min.nzcts = 10) {
  size.factors <- colSums(dat)
  size.factors <- size.factors / mean(size.factors)
  gene_cts <- rowSums(dat > 0)
  dat <- dat[gene_cts >= min.nzcts, ]

  lunpc <- max(1 / min(size.factors) - 1 / max(size.factors), 1)
  fl.dat <- log1p(t(t(dat) / size.factors) / lunpc)

  return(list(
    dat = dat,
    fl.dat = fl.dat,
    size.factors = size.factors,
    excluded.genes = gene_cts < min.nzcts)
  )
}
Deng <- preprocess(counts)

I give semi-nonnegative and non-negative flashier fits to the Deng et al. dataset (see here for an introduction). For both, I added factors greedily. I used point-exponential priors for factors and point-Laplace or point-exponential priors for loadings. I also fix a “mean factor” (shown as factor 1 below).

snmf <- readRDS("./output/deng/snmf.rds")
nmf <- readRDS("./output/deng/nmf.rds")

snmf$fl <- flash.reorder.factors(snmf$fl, c(1, order(snmf$fl$pve[-1], decreasing = TRUE) + 1))
#> Warning in ebnm_workhorse(x = x, s = s, mode = mode, scale = scale, g_init =
#> g_init, : Since they're not well defined for nonzero modes, local false sign
#> rates won't be returned.
nmf$fl <- flash.reorder.factors(nmf$fl, c(1, order(nmf$fl$pve[-1], decreasing = TRUE) + 1))
#> Warning in ebnm_workhorse(x = x, s = s, mode = mode, scale = scale, g_init =
#> g_init, : Since they're not well defined for nonzero modes, local false sign
#> rates won't be returned.

get.factors <- function(res, colnames.prefix) {
  FF <- ldf(res$fl, type = "I")$F
  colnames(FF) <- paste0(colnames.prefix, 1:ncol(FF))
  return(FF)
}

snmf.F <- get.factors(snmf, "SNMF")
nmf.F <- get.factors(nmf, "NMF")
tib <- cbind(snmf.F, nmf.F)

tsne_res <- Rtsne(
    tib,
    dims = 1,
    perplexity = pmax(1, floor((nrow(tib) - 1) / 3) - 1),
    pca = FALSE,
    normalize = FALSE,
    theta = 0.1,
    check_duplicates = FALSE,
    verbose = FALSE
  )$Y[, 1]

tib <- as_tibble(tib) %>%
  mutate(tsne_res = unlist(tsne_res)) %>%
  mutate(Cell.type = fct_relevel(meta_data$cell_type, c(
    "zy",
    "early2cell", "mid2cell", "late2cell",
    "4cell", "8cell", "16cell",
    "earlyblast", "midblast", "lateblast"
  )))

tsne.zy <- tib %>% filter(Cell.type == "zy") %>% summarize(mean(tsne_res))
tsne.late <- tib %>% filter(Cell.type == "lateblast") %>% summarize(mean(tsne_res))
if (tsne.zy < tsne.late) {
  tib <- tib %>%
    arrange(Cell.type, tsne_res)
} else {
  tib <- tib %>%
    arrange(Cell.type, -tsne_res)
}

tib <- tib %>%
  mutate(Cell.idx = row_number()) %>%
  select(-tsne_res)
  
tib <- tib %>%
  pivot_longer(
    -c(Cell.idx, Cell.type),
    names_to = "Factor",
    values_to = "Loading",
    values_drop_na = TRUE
  ) %>%
  mutate(
    Fit = factor(str_remove(Factor, "[0-9]+"), levels = c("SNMF", "NMF")),
    Factor = as.numeric(str_extract(Factor, "[0-9]+"))
  )

cell_type <- tib %>% group_by(Cell.idx) %>% summarize(Cell.type = Cell.type[1]) %>% pull(Cell.type)
cell_type_breaks <- c(-4, which(cell_type[2:nrow(tib)] != cell_type[1:(nrow(tib) - 1)]))
  
ggplot(tib, aes(x = Factor, y = -Cell.idx, fill = Loading)) +
  geom_tile() +
  scale_fill_gradient(low = "white", high = "red") +
  labs(x = "Factor", y = "") +
  scale_y_continuous(breaks = -cell_type_breaks,
                     minor_breaks = NULL,
                     labels = levels(tib$Cell.type)) +
  theme_minimal() +
  theme(axis.text.y=element_text(angle = 45, size = 6)) +
  facet_wrap(~Fit, nrow = 1)

Past versions of snmf-1.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

Factors

I’ve tried to group together factors that are very similar for ease of comparison. The headers roughly indicate the embryonic stage where the factors are active.

do.volcano.plot <- function(nmf.k = 0, snmf.k = 0, plt.title = "") {
  make.tib <- function(fit, fl, k) {
    if (k == 0) {
      return(tibble())
    }
    
    tib <- tibble(
      fit = fit,
      pm = ldf(fl, type = "I")$L[, k],
      z = abs(fl$L.pm[, k]) / pmax(sqrt(.Machine$double.eps), fl$L.psd[, k]),
      exprmean = log10(rowMeans(Deng$dat)),
      SYMBOL = rownames(fl$L.pm)
    ) %>%
      mutate(SYMBOL = ifelse(
        z * (abs(pm) > 0.1) > sort(z * (abs(pm) > 0.1), decreasing = TRUE)[16] |
          (pm > 0.1 & pm > sort(pm, decreasing = TRUE)[11]) |
          (pm < -0.1 & pm < sort(pm, decreasing = FALSE)[11]), 
        SYMBOL, 
        ""
      )) %>%
      mutate(fit = paste(fit, "Factor", k))
    return(tib)
  }
  nmf.tib <- make.tib("NMF", nmf$fl, nmf.k)
  snmf.tib <- make.tib("SNMF", snmf$fl, snmf.k)
  tib <- nmf.tib %>% bind_rows(snmf.tib)
    
  plt <- ggplot(tib, aes(x = pm, y = z, color = exprmean, label = SYMBOL)) +
    geom_point() +
    scale_color_gradient2(low = "deepskyblue", mid = "gold", high = "orangered",
                          na.value = "gainsboro",
                          midpoint = mean(range(tib$exprmean))) +
    scale_y_sqrt() +
    geom_text_repel(color = "darkgray",size = 2.25, fontface = "italic",
                    segment.color = "darkgray", segment.size = 0.25,
                    min.segment.length = 0, na.rm = TRUE, max.overlaps = 20) +
    theme_minimal() +
    labs(
      x = "Factor Loading (posterior mean)",
      y = "|z-score|",
      color = "Mean Expression (log10)",
      title = plt.title
    ) +
    theme(legend.position = "bottom") +
    facet_wrap(~fit, scales = "free", ncol = 1)

  return(plt)
}

Zygote to mid 2cell

do.volcano.plot(0, 2)
#> Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

Past versions of v1-1.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

do.volcano.plot(6, 0)

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Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

do.volcano.plot(2, 0, "(All but zygote to mid 2cell)")

Past versions of v1-3.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

do.volcano.plot(3, 0, "(All but zygote to mid 2cell)")

Past versions of v1-4.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

Mid to late 2cell

do.volcano.plot(0, 9)
#> Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

Past versions of v2-1.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

do.volcano.plot(10, 0)

Past versions of v2-2.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

Late 2cell / 4cell

do.volcano.plot(0, 8)
#> Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

Past versions of v3-1.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

do.volcano.plot(8, 0)

Past versions of v3-2.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

8cell / 16cell

do.volcano.plot(0, 6)

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Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

do.volcano.plot(4, 0)

Past versions of v4-2.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

Early blastocyte

do.volcano.plot(0, 12)
#> Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

Past versions of v5-1.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

do.volcano.plot(13, 0, "All but early blastocyte")

Past versions of v5-2.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

Blastocytes

do.volcano.plot(0, 3)
#> Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

Past versions of v6-1.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

do.volcano.plot(0, 5)
#> Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

Past versions of v6-2.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

do.volcano.plot(0, 4)
#> Warning: ggrepel: 14 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

Past versions of v6-3.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

do.volcano.plot(7, 0)

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Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

do.volcano.plot(5, 0)

Past versions of v6-5.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

All over

do.volcano.plot(0, 10)
#> Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

Past versions of v7-1.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

do.volcano.plot(9, 0)

Past versions of v7-2.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

NMF-only factors

do.volcano.plot(12, 0, "Zygote / Early 2cell")

Past versions of v8-1.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

do.volcano.plot(11, 0, "Late Blastocytes")

Past versions of v8-2.png

Version	Author	Date
1e055ba	Jason Willwerscheid	2022-04-01

Session information

sessionInfo()
#> R version 3.5.3 (2019-03-11)
#> Platform: x86_64-apple-darwin15.6.0 (64-bit)
#> Running under: macOS Mojave 10.14.6
#> 
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> attached base packages:
#> [1] parallel  stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] ggrepel_0.9.1                        Rtsne_0.15                          
#>  [3] singleCellRNASeqMouseDeng2014_0.99.0 Biobase_2.42.0                      
#>  [5] BiocGenerics_0.28.0                  flashier_0.2.32                     
#>  [7] magrittr_2.0.2                       forcats_0.5.1                       
#>  [9] stringr_1.4.0                        dplyr_1.0.8                         
#> [11] purrr_0.3.4                          readr_2.0.0                         
#> [13] tidyr_1.2.0                          tibble_3.1.6                        
#> [15] ggplot2_3.3.5                        tidyverse_1.3.1                     
#> [17] workflowr_1.6.2                     
#> 
#> loaded via a namespace (and not attached):
#>  [1] fs_1.5.0          lubridate_1.7.10  httr_1.4.2        rprojroot_2.0.2  
#>  [5] tools_3.5.3       backports_1.1.3   bslib_0.3.1       utf8_1.2.2       
#>  [9] R6_2.5.1          irlba_2.3.3       DBI_1.0.0         colorspace_2.0-3 
#> [13] withr_2.5.0       tidyselect_1.1.2  compiler_3.5.3    git2r_0.28.0     
#> [17] cli_3.2.0         rvest_1.0.0       xml2_1.3.2        labeling_0.4.2   
#> [21] horseshoe_0.2.0   sass_0.4.0        scales_1.1.1      SQUAREM_2021.1   
#> [25] mixsqp_0.3-43     digest_0.6.29     rmarkdown_2.11    deconvolveR_1.2-1
#> [29] pkgconfig_2.0.3   htmltools_0.5.2   highr_0.9         dbplyr_2.1.1     
#> [33] fastmap_1.1.0     invgamma_1.1      rlang_1.0.2       readxl_1.3.1     
#> [37] rstudioapi_0.13   farver_2.1.0      jquerylib_0.1.4   generics_0.1.2   
#> [41] jsonlite_1.8.0    Matrix_1.3-4      Rcpp_1.0.8        munsell_0.5.0    
#> [45] fansi_1.0.2       lifecycle_1.0.1   stringi_1.4.6     whisker_0.3-2    
#> [49] yaml_2.3.5        grid_3.5.3        promises_1.2.0.1  crayon_1.5.0     
#> [53] lattice_0.20-38   haven_2.3.1       splines_3.5.3     hms_1.1.1        
#> [57] knitr_1.33        pillar_1.7.0      softImpute_1.4-1  reprex_2.0.0     
#> [61] glue_1.6.2        evaluate_0.14     trust_0.1-8       modelr_0.1.8     
#> [65] vctrs_0.3.8       tzdb_0.1.1        httpuv_1.5.2      cellranger_1.1.0 
#> [69] gtable_0.3.0      ebnm_1.0-11       assertthat_0.2.1  ashr_2.2-54      
#> [73] xfun_0.29         broom_0.7.6       later_1.3.0       truncnorm_1.0-8  
#> [77] ellipsis_0.3.2