Last updated: 2022-03-29

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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(ggrepel)
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(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)
do.heatmap <- function(res) {
  fl <- res$fl
  
  FF <- ldf(fl, type = "I")$F
  FF <- FF[, -1]
  FF <- FF[, order(res$fl$pve[-1], decreasing = TRUE)]
  colnames(FF) <- 1:ncol(FF) 
  
  cell_type <- meta_data$cell_type
  
  tib <- as_tibble(FF) %>%
    mutate(Cell.type = cell_type) %>%
    mutate(Cell.type = fct_relevel(Cell.type, c(
      "zy",
      "early2cell", "mid2cell", "late2cell",
      "4cell", "8cell", "16cell",
      "earlyblast", "midblast", "lateblast"
    )))
  
  tsne_res <- Rtsne(
    as.matrix(tib %>% select(-Cell.type)),
    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 <- tib %>%
    mutate(tsne_res = unlist(tsne_res)) %>%
    arrange(Cell.type, tsne_res) %>%
    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(Factor = as.numeric(Factor))
  
  cell_type_breaks <- c(1, 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(y = "") +
    scale_y_continuous(breaks = -cell_type_breaks,
                       minor_breaks = NULL,
                       labels = levels(tib$Cell.type)) +
    theme_minimal() +
    geom_hline(yintercept = -cell_type_breaks, size = 0.1)
}

do.struct.plot <- function(fl, kset, group_by_embryo = FALSE) {
  tm <- init_poisson_nmf(X = t(Deng$dat), k = fl$n.factors, init.method = "random")
  tm <- poisson2multinom(tm)
  L <- ldf(fl)$F %*% diag(ldf(fl)$D)
  L <- L[, order(fl$pve, decreasing = TRUE)]
  colnames(L) <- paste0("k", 1:ncol(L))
  L[, setdiff(1:fl$n.factors, kset)] <- 0
  tm$L <- L
  topic_colors <- rep("black", fl$n.factors)
  topic_colors[kset] <- c("gainsboro", "forestgreen", "tomato", "skyblue", "royalblue",
                          "darkorange", "peru", "gold", "limegreen", "darkmagenta")[1:length(kset)]
  cell_type <- factor(
    meta_data$cell_type,
    levels = c("zy", "early2cell", "mid2cell", "late2cell", "4cell", "8cell",
               "16cell", "earlyblast", "midblast", "lateblast")
  )
  embryo <- factor(
    paste0(meta_data$cell_type, "/", meta_data$embryo_id),
    levels = paste0(rep(levels(cell_type), each = length(levels(meta_data$embryo_id))),
                   "/", levels(meta_data$embryo_id))
  )
  embryo <- droplevels(embryo)
  embed_with_pca <- function (fit, ...) {
    drop(pca_from_topics(fit, dims = 1,...))
  }
  if (group_by_embryo) {
    grp <- embryo
  } else {
    grp <- cell_type
  }
  set.seed(1)
  structure_plot(tm, grouping = grp, colors = topic_colors,
                 gap = 2, topics = kset, embed_method = embed_with_pca) +
    labs(y = "loading", color = "factor", fill = "factor")
}

I give a semi-nonnegative and three non-negative flashier fits to the Deng et al. dataset (see here for an introduction). The nonnegative fits were obtained by backfitting from a greedy nonnegative fit and from a NNMF obtained via NNLM.

Semi-nonnegative fit

smnf <- readRDS("./output/deng/smnf.rds")
do.heatmap(smnf)

Version Author Date
c322aa6 Jason Willwerscheid 2022-03-28

Nonnegative fit (from greedy)

bf2 <- readRDS("./output/deng/bf2.rds")
do.heatmap(bf2)

Version Author Date
c322aa6 Jason Willwerscheid 2022-03-28

Nonnegative fit (from NNLM)

nnlmbf <- readRDS("./output/deng/nnlmbf.rds")
do.heatmap(nnlmbf)

Version Author Date
c322aa6 Jason Willwerscheid 2022-03-28

Nonnegative fit (from NNLM, shifted point-exponential priors)

nzpe <- readRDS("./output/deng/nzpe.rds")
shifts <- sapply(nzpe$fl$F.ghat[-1], function(k) k$shift[1])
nzpe$fl$flash.fit$EF[[2]][, -1] <- nzpe$fl$flash.fit$EF[[2]][, -1] - rep(shifts, each = ncol(Deng$fl.dat))
do.heatmap(nzpe)

Version Author Date
c322aa6 Jason Willwerscheid 2022-03-28

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] fastTopics_0.6-101                   Rtsne_0.15                          
#>  [3] singleCellRNASeqMouseDeng2014_0.99.0 Biobase_2.42.0                      
#>  [5] BiocGenerics_0.28.0                  ggrepel_0.9.1                       
#>  [7] flashier_0.2.29                      magrittr_2.0.2                      
#>  [9] forcats_0.5.1                        stringr_1.4.0                       
#> [11] dplyr_1.0.8                          purrr_0.3.4                         
#> [13] readr_2.0.0                          tidyr_1.2.0                         
#> [15] tibble_3.1.6                         ggplot2_3.3.5                       
#> [17] tidyverse_1.3.1                      workflowr_1.6.2                     
#> 
#> loaded via a namespace (and not attached):
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#>  [5] rstudioapi_0.13    farver_2.1.0       MatrixModels_0.4-1 fansi_1.0.2       
#>  [9] lubridate_1.7.10   xml2_1.3.2         splines_3.5.3      knitr_1.33        
#> [13] jsonlite_1.8.0     mcmc_0.9-7         broom_0.7.6        ashr_2.2-54       
#> [17] dbplyr_2.1.1       uwot_0.1.11        compiler_3.5.3     httr_1.4.2        
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