Dorsal Horn and Ventral Lateral White DE analysis

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Last updated: 2024-10-19

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Knit directory: DEanalysis/

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---

source("./code/hippo_all_proportion.R")
source("./code/DE_methods.R")
source("./code/functions_in_rmd.R")
# load DE result generated from "analysis_humanspine.Rmd"
load("./data/human_spine_DEresult.RData")
load("./data/human_spine.RData")
load("./data/fallopian_tubes.RData")
library(ggplot2)
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(ggpubr)
library(SingleCellExperiment)

Loading required package: SummarizedExperiment

Loading required package: MatrixGenerics

Loading required package: matrixStats

Attaching package: 'matrixStats'

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    count

Attaching package: 'MatrixGenerics'

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    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
    colWeightedMeans, colWeightedMedians, colWeightedSds,
    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
    rowWeightedSds, rowWeightedVars

Loading required package: GenomicRanges

Loading required package: stats4

Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

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    combine, intersect, setdiff, union

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    anyDuplicated, aperm, append, as.data.frame, basename, cbind,
    colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
    get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    Position, rank, rbind, Reduce, rownames, sapply, setdiff, table,
    tapply, union, unique, unsplit, which.max, which.min

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Loading required package: GenomeInfoDb

Loading required package: Biobase

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Attaching package: 'Biobase'

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library(pheatmap)
library(gridExtra)

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library(reshape)

Attaching package: 'reshape'

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library(clusterProfiler)

clusterProfiler v4.12.6 Learn more at https://yulab-smu.top/contribution-knowledge-mining/

Please cite:

Guangchuang Yu, Li-Gen Wang, Yanyan Han and Qing-Yu He.
clusterProfiler: an R package for comparing biological themes among
gene clusters. OMICS: A Journal of Integrative Biology. 2012,
16(5):284-287

Attaching package: 'clusterProfiler'

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library(enrichplot)

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library("org.Hs.eg.db")

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library(pathview)

##############################################################################
Pathview is an open source software package distributed under GNU General
Public License version 3 (GPLv3). Details of GPLv3 is available at
http://www.gnu.org/licenses/gpl-3.0.html. Particullary, users are required to
formally cite the original Pathview paper (not just mention it) in publications
or products. For details, do citation("pathview") within R.

The pathview downloads and uses KEGG data. Non-academic uses may require a KEGG
license agreement (details at http://www.kegg.jp/kegg/legal.html).
##############################################################################

group1 = "Dors_Horn"
group2 = "Vent_Lat_White"
pair = c(group1, group2)
subgroupsce = sce_human_spine_filtered[, sce_human_spine_filtered$region%in%pair]
subgroup_Seurat = Seurat_human_spine_integrate[, Seurat_human_spine_integrate$region%in%pair]
cellgroup = ifelse(subgroupsce$region%in% group1, 1, 2)
  genemean1 = rowMeans(counts(subgroupsce)[,cellgroup==1])
  genemean2 = rowMeans(counts(subgroupsce)[,cellgroup==2])
  subgroupsce@metadata$log2mean = log2(genemean1*genemean2)/2
  subgroupsce@metadata$log2meandiff = log2(abs(genemean1-genemean2))

Wilcox_Seurat_df$avg_log2FC = - Wilcox_Seurat_df$avg_log2FC

region_palette = c(
  "#A0522D", "#6A00FF", "#A4C400", "#F472D0",
  "#0050EF", "#00ABA9", "#F0A30A", "#008A00",
  "#647687", "#1BA1E2", "#FA6800", "#E3C800")
donors_palette <- c("#FDA4AF", "#FCD34D", "#BEF264", "#67E8F9", "#A5B4FC", 
                    "#FF6F61", "#6A4C93", "#001688", "#009A33")
names(region_palette) = levels(as.factor(sce_human_spine_filtered$region))
names(donors_palette) = levels(as.factor(sce_human_spine_filtered$patient_ID))
annotation_colors = list(Region = region_palette[c(group1, group2)], Donor = donors_palette)
remove_y = theme(
  axis.text.y = element_blank(),
  axis.ticks.y = element_blank(),
  axis.title.y = element_blank()
)

Data summary

df = data.frame(Region = subgroupsce$region, Donor = subgroupsce$patient_ID, Count = 1)
df = aggregate(df$Count, list(df$Donor, df$Region), FUN = sum)
colnames(df) = c("Donor", "Region", "Count")
df = df[nrow(df):1,]
df$cumcount= c(t(aggregate(df$Count, list(rev(df$Region)), FUN = cumsum)[,-1]))
p_donor = ggplot(df, aes(x = Region, y = Count, fill = Donor)) + 
  geom_bar(stat = "identity") + 
  xlab("Region") + 
  ylab("Number of cells") + 
  scale_fill_manual(values = donors_palette) + 
  geom_text(aes(label=Count, y = cumcount), vjust = -0.2, size = 2) + 
  theme_bw() + 
  theme(panel.grid = element_blank())

# gene count per cell by region
count_cell_df = data.frame(totalcount = apply(assays(subgroupsce)$counts, 2, sum), Region = as.factor(subgroupsce$region), Donor = as.factor(subgroupsce$patient_ID), Slide = as.factor(subgroupsce$Slide))

p_countbygroup = ggplot(count_cell_df, aes(x = totalcount, color = Region)) +
  geom_histogram(aes(y=..density..), fill= "white", alpha=0.5, position="identity") +
  geom_density(alpha = 0.2) +
  xlab("Library size") + 
  ylab("Density") +
  theme_bw() + 
  theme(panel.grid = element_blank()) +
  scale_colour_manual(values = region_palette[c(group1, group2)]) +
  xlim(c(0,5000))

# gene count per cell by donor
p_countbydonor = ggplot(count_cell_df, aes(x = totalcount, color = Donor)) +
  geom_histogram(aes(y=..density..),fill="white", alpha=0.5, position="identity") + 
  geom_density(alpha = 0.2) + 
  xlab("Library size") + 
  ylab("Density") +
  theme_bw() + 
  theme(panel.grid = element_blank()) + 
  scale_color_manual(values = donors_palette) + 
  xlim(c(0,5000))

# gene count per cell by slide
p_countbyslide = ggplot(count_cell_df, aes(x = totalcount, color = Slide)) +
  geom_histogram(aes(y=..density..),fill="white", alpha=0.5, position="identity") + 
  geom_density(alpha = 0.2) + 
  xlab("Library size") + 
  ylab("Density") +
  theme_bw() + 
  theme(panel.grid = element_blank()) + 
  xlim(c(0,5000))

# zero proportion per gene
zeroprop_df = rbind(data.frame(zero_prop = rowMeans(assays(subgroupsce)$counts[, subgroupsce$region %in% group1]== 0), genemean = rowMeans(assays(subgroupsce)$counts[, subgroupsce$region %in% group1]), Region = group1),
                data.frame(zero_prop = rowMeans(assays(subgroupsce)$counts[, subgroupsce$region %in% group2]== 0), genemean = rowMeans(assays(subgroupsce)$counts[, subgroupsce$region %in% group2]), Region = group2),
                data.frame(zero_prop = rowMeans(assays(subgroupsce)$counts == 0), genemean = rowMeans(assays(subgroupsce)$counts), Region = "Combined")
                )
zeroprop_df = zeroprop_df[which(zeroprop_df$genemean>0),]
zeroprop_df$Region =factor(zeroprop_df$Region, levels = c(group1, group2, "Combined"))

p_zeroprop = ggplot(zeroprop_df, aes(x = genemean, y = zero_prop)) + 
  geom_point(aes(shape = Region, color = Region), alpha=0.5, size = 1.2) + 
  scale_color_manual(values = c(region_palette[c(group1, group2)],Combined = "darkmagenta")) +
  scale_shape_manual(values = c(16,16,3)) +
  xlab("Gene mean") + 
  ylab("Zero proportion") +
  xlim(c(0,10)) + 
  stat_function(fun = function(x) exp(-x), colour = "black") +
  theme_bw() + theme(panel.grid = element_blank())

ggarrange(p_zeroprop, p_countbygroup, p_countbyslide, p_countbydonor, nrow = 2, ncol = 2)

Warning: Removed 17 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(density)` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

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(`stat_bin()`).

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`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

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`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

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(`stat_bin()`).

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(`stat_density()`).

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(`geom_bar()`).

Difference in library size

ks_result = ks.test(subset(count_cell_df, Region == group1, totalcount)$totalcount, subset(count_cell_df, Region == group2, totalcount)$totalcount)

Warning in ks.test.default(subset(count_cell_df, Region == group1,
totalcount)$totalcount, : p-value will be approximate in the presence of ties

t_test = t.test(subset(count_cell_df, Region == group1, totalcount)$totalcount, subset(count_cell_df, Region == group2, totalcount)$totalcount)
print(ks_result)

    Asymptotic two-sample Kolmogorov-Smirnov test

data:  subset(count_cell_df, Region == group1, totalcount)$totalcount and subset(count_cell_df, Region == group2, totalcount)$totalcount
D = 0.12804, p-value < 2.2e-16
alternative hypothesis: two-sided

print(t_test)

    Welch Two Sample t-test

data:  subset(count_cell_df, Region == group1, totalcount)$totalcount and subset(count_cell_df, Region == group2, totalcount)$totalcount
t = 17.059, df = 7783.8, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 235.2685 296.3577
sample estimates:
mean of x mean of y 
 1549.826  1284.013 

Number of DEGs from each method

dflist = list(pois_glmm_df, 
              binom_glmm_df, 
              DESeq2.muscat,
              edgeR.cpm,
              MAST_donor_df, 
              Wilcox_Seurat_df,
              # MMdream,
              MMvst
              # MMpoisson
              )
titlelist = c("Poisson-glmm_UMI", 
              "Binomial-glmm_UMI",
              "pb-DESeq2_UMI", 
              "pb-edgeR_CPM",
              "MAST_CPM",
              "Wilcox_int",
              # "MMdream",
              "MMvst_VST"
              # "MMpoisson_UMI"
              )


number_input_hits = data.frame(DEGs = integer(0), Input = integer(0))
pois_glmm_inputgene = pois_glmm_df$genes[!is.na(pois_glmm_df$hits)]
for (df in dflist){
  number_input_hits = rbind(number_input_hits, 
                            c(sum(!is.na(df$hits)), 
                              sum(df$hits, na.rm = T))) 
}
colnames(number_input_hits) = c("Inputs", "DEGs")
number_input_hits$method = titlelist

number_input_hits = melt(number_input_hits, id = "method")
colnames(number_input_hits)[2:3] = c("Gene", "Number_of_genes")

# pdf("../submission preparation/graph_panels/panel4/input_hits.pdf", width = 8, height = 8)
ggplot(data=number_input_hits, aes(x=factor(method, 
                                            levels = titlelist), y=Number_of_genes, fill = Gene)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  geom_text(aes(label=Number_of_genes), vjust=-0.2, position=position_dodge(0.9), size = 2) +
  theme_bw() + theme(panel.grid = element_blank()) +
  theme(axis.text.x = element_text(angle = 20, margin = margin(t = 15))) +
  scale_fill_manual(values = c("#BCAAA4", "#5D4037")) +
  xlab("") +
  ylab("Number of genes") +
  ggtitle(paste("DEGs summary"))

# dev.off()

Volcano plot

pvalcutoff = 0.05
v = 1.5
log2FCcutoff = log2(v)

p = list()
for (i in 1:length(dflist)){
  if(i %in% 6){
    xvar = 'avg_log2FC'
    yvar = 'p_val_adj'
  }else{
    xvar = 'log2FC'
    yvar = 'BH'
  }
  if (nrow(dflist[[i]])==0){next}
  p[[i]] = ggplot(na.omit(data.frame(dflist[[i]])), aes_string(x = xvar, y = sprintf("-log10(%s)", yvar), colour = "hits")) +
    geom_point(alpha = 0.5, size = 0.5) +
    labs(color = "hits") +
    geom_hline(yintercept=-log10(pvalcutoff),linetype=2) +
    geom_vline(xintercept=log2FCcutoff,linetype=2) + 
    geom_vline(xintercept=-log2FCcutoff,linetype=2) +
    ggtitle(titlelist[i]) + 
    theme_bw() + 
    theme(panel.grid = element_blank()) + 
    scale_color_manual(values = c("gray", "blue")) +
    labs(color = "DEGs") +
    xlim(c(-3,3)) + 
    ylim(c(0,50)) + 
    xlab("Log2 Fold Change") + 
    ylab("-Log10 (adj.pval)")
}

Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.

figure = ggarrange(plotlist = p, common.legend = TRUE, legend="bottom", nrow = 2, ncol = 4)

Warning: Removed 121 rows containing missing values or values outside the scale range
(`geom_point()`).

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# Annotate the figure by adding a common labels
annotate_figure(figure,
                top = paste("Volcano plot"),
                bottom = text_grob(paste( "log2FC cutoff ", paste0("log2(",v,")"),"; adj.pval cutoff", pvalcutoff)
                                   , x = 0.99, just = "right")
)

Histogram of p-value/adj.p-value

p = list()
for (i in 1:length(dflist)){
  if (nrow(dflist[[i]])==0){next}
  df = data.frame(genes = dflist[[i]]$genes)
  
  if(i %in% 6){
    df = cbind(df, dflist[[i]][,c("p_val", "p_val_adj")])
  }else{
    df = cbind(df, dflist[[i]][,c("pval", "BH")])
  }
  
  p[[i]] = ggplot(melt(df,id = "genes"), aes(x=value, color=variable)) +
geom_histogram(fill="white", alpha=0.5, position="identity") + theme_bw() + theme(panel.grid = element_blank()) + xlab(NULL) + ylab("Count") + 
coord_cartesian(ylim = c(0, 3000)) +
    ggtitle(titlelist[i]) + scale_colour_discrete(guide = guide_legend(title = NULL),labels = c("p-value", "adj.p-value"))
}
figure = ggarrange(plotlist = p, common.legend = TRUE, legend="bottom", nrow = 2, ncol = 4)

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Warning: Removed 5010 rows containing non-finite outside the scale range
(`stat_bin()`).

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Warning: Removed 5010 rows containing non-finite outside the scale range
(`stat_bin()`).

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Warning: Removed 4970 rows containing non-finite outside the scale range
(`stat_bin()`).

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

annotate_figure(figure)

P-Value comparison across different methods

value = "pval"
p = list()
  for (i in 2:length(dflist)){
    if (nrow(dflist[[i]])==0){next}
    if (value == "pval"){
      xlimit = c(0,1)
      ylimit = c(0,1)
      if (is.null(dflist[[i]]$pval)){
        x = dflist[[i]]$p_val
      }else
        x = dflist[[i]]$pval
    }
    if (value == "log2FC"){
      xlimit = c(-3,3)
      ylimit = c(-3,3)
      if (is.null(dflist[[i]]$log2FC)){
        x = dflist[[i]]$avg_log2FC
      }else
        x = dflist[[i]]$log2FC
    }
    y = dflist[[1]][match(dflist[[i]]$genes, dflist[[1]]$genes),value]
    p[[i-1]] = ggplot(na.omit(data.frame(x = x, y = y)), aes(x = x, y = y)) +
      geom_point(alpha = 0.5, size = 0.5) + theme_minimal() + xlim(xlimit) + ylim(ylimit)+ 
      xlab(titlelist[i]) + ylab(titlelist[1])
  }
  figure = ggarrange(plotlist = p,nrow = 2, ncol = 4)
  
  # Annotate the figure by adding a common labels
  annotate_figure(figure,
                  top = paste(value, "comparison"))

Log2 fold change comparison across different methods

value = "log2FC"
p = list()
  for (i in 2:length(dflist)){
    if (nrow(dflist[[i]])==0){next}
    if (value == "pval"){
      xlimit = c(0,1)
      ylimit = c(0,1)
      if (is.null(dflist[[i]]$pval)){
        x = dflist[[i]]$p_val
      }else
        x = dflist[[i]]$pval
    }
    if (value == "log2FC"){
      xlimit = c(-3,3)
      ylimit = c(-3,3)
      if (is.null(dflist[[i]]$log2FC)){
        x = dflist[[i]]$avg_log2FC
      }else
        x = dflist[[i]]$log2FC
    }
    y = dflist[[1]][match(dflist[[i]]$genes, dflist[[1]]$genes),value]
    p[[i-1]] = ggplot(na.omit(data.frame(x = x, y = y)), aes(x = x, y = y)) +
      geom_point(alpha = 0.5, size = 0.5) + theme_minimal() + xlim(xlimit) + ylim(ylimit)+ 
      xlab(titlelist[i]) + ylab(titlelist[1])
  }
  figure = ggarrange(plotlist = p,nrow = 2, ncol = 4)

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 5 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

  # Annotate the figure by adding a common labels
  annotate_figure(figure,
                  top = paste(value, "comparison"))

Violin plot of log2mean of DEGs

p = list()
df = data.frame()
clustername = pair
for (i in 1:length(dflist)){
  if (nrow(dflist[[i]])==0){next}
  if (sum(dflist[[i]]$hits, na.rm = T)==0){next}
  hits_gene = dflist[[i]]$genes[which(dflist[[i]]$hits)]
  if(length(hits_gene)>1){fun = rowMeans}else{fun = mean}
  for (j in 1:2){
    cellind = subgroupsce$region %in% clustername[j]
    df = rbind(df, data.frame(Log2mean = log2(fun(assays(subgroupsce)$counts[hits_gene,cellind])), Group = clustername[j], method = titlelist[i]))
  }
}

ggplot(df, aes(x = factor(method,levels = titlelist), y=Log2mean, fill = factor(Group))) +
  geom_violin() + 
  theme_bw() + 
  theme(panel.grid = element_blank()) + 
  ylim(c(-3,4)) + 
  xlab("") + 
  theme(axis.text.x = element_text(angle = 20, margin = margin(t = 15))) +
  labs(fill = "Region") +
  scale_fill_manual(values = region_palette[c(group1, group2)])

Warning: Removed 2993 rows containing non-finite outside the scale range
(`stat_ydensity()`).

Violin plot of gene expression frequency of DEGs

p = list()
df = data.frame()
clusterlist = list(unique(sce_human_spine_filtered$region), group1, group2)
clustername = c("All region", group1, group2)
for (i in 1:length(dflist)){
  if (nrow(dflist[[i]])==0){next}
  if (sum(dflist[[i]]$hits, na.rm = T)==0){next}
  hits_gene = dflist[[i]]$genes[which(dflist[[i]]$hits)]

  if(length(hits_gene)>1){fun = rowMeans}else{fun = mean}
  for (j in 1:3){
    cellind = sce_human_spine_filtered$region %in% clusterlist[[j]]
    df = rbind(df, data.frame(freq = fun(assays(sce_human_spine_filtered)$counts[hits_gene,cellind]>0), cluster = clustername[j], method = titlelist[i]))
  }
}
levels(df$cluster) = clustername

ggplot(df, aes(x = factor(method,levels = titlelist), y=freq, fill = factor(cluster, levels =  levels(cluster)))) + 
  geom_violin() + 
  theme_bw() + 
  theme(panel.grid = element_blank()) + 
  ylab("Gene expression frequency") + 
  ylim(c(0,1)) + 
  xlab("") + 
  scale_fill_manual(values = c("All region" = "gray", region_palette[c(group1, group2)]), breaks = levels(df$cluster)) +
  labs(fill = "Region") +
  theme(axis.text.x = element_text(angle = 20, margin = margin(t = 15))) 

Upset plot

library(UpSetR)
all_genes = c()
for(df in dflist){
  all_genes = c(all_genes, df$genes[which(df$hits)])
}

all_genes = unique(all_genes)

# Create a binary matrix where rows are genes and columns represent methods
upset_data = data.frame(gene = all_genes)
for (i in seq_along(dflist)){
  title = titlelist[i]
  upset_data[[title]] = as.numeric(all_genes %in% dflist[[i]]$genes[which(dflist[[i]]$hits)])
}

# Generate the upset plot
upset(upset_data[,-1], 
      sets = rev(titlelist), 
      keep.order = TRUE, 
      order.by = "freq")

Heatmap of top DEGs

Poisson-glmm DEGs

sort_log2FC = sort(abs(pois_glmm_df$log2FC[pois_glmm_df$hits]), index.return = T, decreasing = T)
hitgenes = pois_glmm_df$genes[which(pois_glmm_df$hits)][sort_log2FC$ix]
mat = subgroupsce@assays@data$counts[hitgenes,]
#row annotation
annotation_log2FC = data.frame(abs_log2FC = abs(pois_glmm_df$log2FC[match(hitgenes,pois_glmm_df$genes)]))
rownames(annotation_log2FC) = rownames(mat)
#col annotation
annotation_df = data.frame(Donor = subgroupsce$patient_ID, Region = subgroupsce$region)
rownames(annotation_df) = colnames(mat)
annotation_df = annotation_df[with(annotation_df, order(Region, Donor)), ]
# cell level
pheatmap(mat[,rownames(annotation_df)],
         main = paste0("Poisson-glmm DEGs\nin UMI counts (", nrow(mat)," DEGs)"),
         annotation_col = annotation_df, 
         annotation_row = annotation_log2FC,
         annotation_colors = annotation_colors,
         cluster_rows=F, cluster_cols=F, 
         show_colnames = F, show_rownames = F,
         annotation_names_row = F,
         color=colorRampPalette(c("navy", "white", "red"))(10), breaks = seq(0,3, length.out = 10))

Additional DEGs from other methods

Wilcox

sort_log2FC = sort(abs(Wilcox_Seurat_df$avg_log2FC[Wilcox_Seurat_df$hits]), index.return = T, decreasing = T)
hitgenes_wilcox = Wilcox_Seurat_df$genes[which(Wilcox_Seurat_df$hits)][sort_log2FC$ix]
hits_notin_glmm = na.omit(hitgenes_wilcox[!hitgenes_wilcox%in%hitgenes])
mat = subgroupsce@assays@data$counts[hits_notin_glmm,]

# cell level
pheatmap(mat[,rownames(annotation_df)], 
         main = paste0("Wilcox DEGs \nnot identified by Poisson-glmm (",nrow(mat)," DEGs)"),
         annotation_col = annotation_df, 
         annotation_colors = annotation_colors,
         cluster_rows=F, cluster_cols=F, 
         show_colnames = F, show_rownames = F,
         annotation_names_row = F,
         color=colorRampPalette(c("navy", "white", "red"))(10), breaks = seq(0,3, length.out = 10))

pb-DESeq2

sort_log2FC = sort(abs(DESeq2.muscat$log2FC[DESeq2.muscat$hits]), index.return = T, decreasing = T)
hitgenes_deseq2 = DESeq2.muscat$genes[which(DESeq2.muscat$hits)][sort_log2FC$ix]
hits_notin_glmm = na.omit(hitgenes_deseq2[!hitgenes_deseq2%in%hitgenes][1:600])
mat = subgroupsce@assays@data$counts[hits_notin_glmm,]

# cell level
pheatmap(mat[,rownames(annotation_df)], 
         main = paste0("pb-DESeq2 DEGs \nnot identified by Poisson-glmm (",nrow(mat)," DEGs)"),
         annotation_col = annotation_df, 
         annotation_colors = annotation_colors,
         cluster_rows=F, cluster_cols=F, 
         show_colnames = F, show_rownames = F,
         annotation_names_row = F,
         color=colorRampPalette(c("navy", "white", "red"))(10), breaks = seq(0,3, length.out = 10))

pdf("~/Google Drive/My Drive/spatial/10X/graph_panels/panel7/heatmap_deseq2.pdf", width = 5, height = 5)
pheatmap(mat[,rownames(annotation_df)], 
         main = paste0("pb-DESeq2 DEGs \nnot identified by Poisson-glmm (",nrow(mat)," DEGs)"),
         annotation_col = annotation_df, 
         annotation_row = annotation_log2FC,
         annotation_colors = annotation_colors,
         cluster_rows=F, cluster_cols=F, 
         show_colnames = F, show_rownames = F,
         annotation_names_row = F,
         color=colorRampPalette(c("navy", "white", "red"))(10), breaks = seq(0,3, length.out = 10))
dev.off()

MA plot

xvar = subgroupsce@metadata$log2mean
xlimits = c(-6,6)
xLabel = "Log2 mean"
p = list()
  for (i in 1:length(dflist)){
    if(is.null(dflist[[i]]$genes)){
      x = xvar[match(rownames(dflist[[i]]), names(xvar))]
    }else{
      x = xvar[match(dflist[[i]]$genes,names(xvar))]
    }
    if(is.null(dflist[[i]]$log2FC)){
      y = dflist[[i]]$avg_log2FC
    }else{
      y = dflist[[i]]$log2FC
    }
    p[[i]] = ggplot(na.omit(data.frame(x = x, y = y, DEGs = dflist[[i]]$hits)), aes(x = x, y = y, colour = DEGs)) +
      geom_point(alpha = 0.5, size = 0.5) +
      geom_hline(yintercept=log2(1.5),linetype=2) +
      geom_hline(yintercept=-log2(1.5),linetype=2) +
      ggtitle(titlelist[i]) + theme_minimal() + xlim(xlimits) + ylim(c(-4,4)) + 
      scale_color_manual(values = c("gray", "blue")) +
      xlab(xLabel) + ylab("Log2 Fold Change") + theme(legend.position = "bottom")
  }
  fig_FC_mean = ggarrange(plotlist = p,common.legend = TRUE, legend = "right", ncol = 4, nrow = 2)

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 176 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 134 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 175 rows containing missing values or values outside the scale range
(`geom_point()`).

  annotate_figure(fig_FC_mean, top = paste("Log2FC vs. Log2 mean "))

Enrichment analysis

GO object

original_gene_list = pois_glmm_df$log2FC
names(original_gene_list) = pois_glmm_df$genes
gene_list = na.omit(original_gene_list)
# sort the list in decreasing order (required for clusterProfiler)
gene_list = sort(gene_list, decreasing = TRUE)
genes = pois_glmm_df$genes[which(pois_glmm_df$hits)]
# create GO object
go_enrich = enrichGO(gene = genes,
                      universe = names(gene_list),
                      OrgDb = "org.Hs.eg.db", 
                      keyType = 'SYMBOL',
                      readable = T,
                      ont = "BP",
                      pvalueCutoff = 0.05, 
                      qvalueCutoff = 0.10)

dotplot(go_enrich)

cnetplot(go_enrich, categorySize="pvalue", color.params = list(foldChange = gene_list))

enrichKEGG object

# Convert gene IDs for enrichKEGG function
# We will lose some genes here because not all IDs will be converted
ids = bitr(names(original_gene_list), fromType = "SYMBOL", toType = "ENTREZID", OrgDb="org.Hs.eg.db") 

'select()' returned 1:1 mapping between keys and columns

Warning in bitr(names(original_gene_list), fromType = "SYMBOL", toType =
"ENTREZID", : 5.09% of input gene IDs are fail to map...

dedup_ids = ids[!duplicated(ids[c("SYMBOL")]),]

# Create a vector of the gene universe
match_logic = pois_glmm_df$genes %in% dedup_ids$SYMBOL
kegg_gene_list <- pois_glmm_df$log2FC[match_logic]
names(kegg_gene_list) <- dedup_ids$ENTREZID
kegg_gene_list<-na.omit(kegg_gene_list)
kegg_gene_list = sort(kegg_gene_list, decreasing = TRUE)
# hits
kegg_genes = na.omit(dedup_ids$ENTREZID[pois_glmm_df$hits[match_logic]])
# create enrichKEGG object
kk = enrichKEGG(gene=kegg_genes,
                universe=names(kegg_gene_list),
                organism="hsa", pvalueCutoff = 0.05, keyType = "ncbi-geneid")

Reading KEGG annotation online: "https://rest.kegg.jp/link/hsa/pathway"...

Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/hsa"...

Reading KEGG annotation online: "https://rest.kegg.jp/conv/ncbi-geneid/hsa"...

dotplot(kk, 
        showCategory = 10, 
        title = "Enriched Pathways",
        font.size = 8)

cnetplot(kk, categorySize="pvalue", foldChange=kegg_gene_list)

Warning in cnetplot.enrichResult(x, ...): Use 'color.params = list(foldChange = your_value)' instead of 'foldChange'.
 The foldChange parameter will be removed in the next version.

Session information

sessionInfo()

R version 4.4.1 (2024-06-14)
Platform: x86_64-apple-darwin20
Running under: macOS Monterey 12.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Chicago
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] UpSetR_1.4.0                pathview_1.44.0            
 [3] org.Hs.eg.db_3.19.1         AnnotationDbi_1.66.0       
 [5] enrichplot_1.24.4           clusterProfiler_4.12.6     
 [7] reshape_0.8.9               gridExtra_2.3              
 [9] pheatmap_1.0.12             SingleCellExperiment_1.26.0
[11] SummarizedExperiment_1.34.0 Biobase_2.64.0             
[13] GenomicRanges_1.56.2        GenomeInfoDb_1.40.1        
[15] IRanges_2.38.1              S4Vectors_0.42.1           
[17] BiocGenerics_0.50.0         MatrixGenerics_1.16.0      
[19] matrixStats_1.4.1           ggpubr_0.6.0               
[21] dplyr_1.1.4                 ggplot2_3.5.1              

loaded via a namespace (and not attached):
  [1] bitops_1.0-9            fs_1.6.4                spatstat.sparse_3.1-0  
  [4] httr_1.4.7              RColorBrewer_1.1-3      Rgraphviz_2.48.0       
  [7] tools_4.4.1             sctransform_0.4.1       backports_1.5.0        
 [10] utf8_1.2.4              R6_2.5.1                lazyeval_0.2.2         
 [13] uwot_0.2.2              withr_3.0.1             sp_2.1-4               
 [16] progressr_0.14.0        cli_3.6.3               spatstat.explore_3.3-2 
 [19] fastDummies_1.7.4       scatterpie_0.2.4        labeling_0.4.3         
 [22] sass_0.4.9              KEGGgraph_1.64.0        Seurat_5.1.0           
 [25] spatstat.data_3.1-2     ggridges_0.5.6          pbapply_1.7-2          
 [28] yulab.utils_0.1.7       gson_0.1.0              DOSE_3.30.5            
 [31] R.utils_2.12.3          parallelly_1.38.0       rstudioapi_0.16.0      
 [34] RSQLite_2.3.7           generics_0.1.3          gridGraphics_0.5-1     
 [37] ica_1.0-3               spatstat.random_3.3-2   car_3.1-3              
 [40] GO.db_3.19.1            Matrix_1.7-0            fansi_1.0.6            
 [43] abind_1.4-8             R.methodsS3_1.8.2       lifecycle_1.0.4        
 [46] whisker_0.4.1           yaml_2.3.10             carData_3.0-5          
 [49] qvalue_2.36.0           SparseArray_1.4.8       Rtsne_0.17             
 [52] grid_4.4.1              blob_1.2.4              promises_1.3.0         
 [55] crayon_1.5.3            miniUI_0.1.1.1          lattice_0.22-6         
 [58] cowplot_1.1.3           KEGGREST_1.44.1         pillar_1.9.0           
 [61] knitr_1.48              fgsea_1.30.0            future.apply_1.11.2    
 [64] codetools_0.2-20        fastmatch_1.1-4         leiden_0.4.3.1         
 [67] glue_1.8.0              ggfun_0.1.6             spatstat.univar_3.0-1  
 [70] data.table_1.16.2       vctrs_0.6.5             png_0.1-8              
 [73] treeio_1.28.0           spam_2.11-0             gtable_0.3.5           
 [76] cachem_1.1.0            xfun_0.48               S4Arrays_1.4.1         
 [79] mime_0.12               tidygraph_1.3.1         survival_3.7-0         
 [82] fitdistrplus_1.2-1      ROCR_1.0-11             nlme_3.1-166           
 [85] ggtree_3.12.0           bit64_4.5.2             RcppAnnoy_0.0.22       
 [88] rprojroot_2.0.4         bslib_0.8.0             irlba_2.3.5.1          
 [91] KernSmooth_2.23-24      colorspace_2.1-1        DBI_1.2.3              
 [94] tidyselect_1.2.1        bit_4.5.0               compiler_4.4.1         
 [97] git2r_0.33.0            graph_1.82.0            httr2_1.0.5            
[100] DelayedArray_0.30.1     plotly_4.10.4           shadowtext_0.1.4       
[103] scales_1.3.0            lmtest_0.9-40           rappdirs_0.3.3         
[106] stringr_1.5.1           digest_0.6.37           goftest_1.2-3          
[109] spatstat.utils_3.1-0    rmarkdown_2.28          XVector_0.44.0         
[112] htmltools_0.5.8.1       pkgconfig_2.0.3         highr_0.11             
[115] fastmap_1.2.0           rlang_1.1.4             htmlwidgets_1.6.4      
[118] UCSC.utils_1.0.0        shiny_1.9.1             farver_2.1.2           
[121] jquerylib_0.1.4         zoo_1.8-12              jsonlite_1.8.9         
[124] BiocParallel_1.38.0     GOSemSim_2.30.2         R.oo_1.26.0            
[127] RCurl_1.98-1.16         magrittr_2.0.3          Formula_1.2-5          
[130] GenomeInfoDbData_1.2.12 ggplotify_0.1.2         dotCall64_1.2          
[133] patchwork_1.3.0         munsell_0.5.1           Rcpp_1.0.13            
[136] ggnewscale_0.5.0        ape_5.8                 viridis_0.6.5          
[139] reticulate_1.39.0       stringi_1.8.4           ggraph_2.2.1           
[142] zlibbioc_1.50.0         MASS_7.3-61             plyr_1.8.9             
[145] parallel_4.4.1          listenv_0.9.1           ggrepel_0.9.6          
[148] deldir_2.0-4            Biostrings_2.72.1       graphlayouts_1.2.0     
[151] splines_4.4.1           tensor_1.5              igraph_2.0.3           
[154] spatstat.geom_3.3-3     ggsignif_0.6.4          RcppHNSW_0.6.0         
[157] reshape2_1.4.4          XML_3.99-0.17           evaluate_1.0.1         
[160] SeuratObject_5.0.2      tweenr_2.0.3            httpuv_1.6.15          
[163] RANN_2.6.2              tidyr_1.3.1             purrr_1.0.2            
[166] polyclip_1.10-7         future_1.34.0           scattermore_1.2        
[169] ggforce_0.4.2           broom_1.0.7             xtable_1.8-4           
[172] RSpectra_0.16-2         tidytree_0.4.6          rstatix_0.7.2          
[175] later_1.3.2             viridisLite_0.4.2       tibble_3.2.1           
[178] aplot_0.2.3             memoise_2.0.1           cluster_2.1.6          
[181] workflowr_1.7.1         globals_0.16.3