## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
    echo = TRUE,
    message = FALSE,
    warning = FALSE
)

## -----------------------------------------------------------------------------
library(jazzPanda)
library(SpatialExperiment)
library(ggplot2)
library(grid)
library(data.table)
library(dplyr)
library(glmnet)
library(caret)
library(corrplot)
library(igraph)
library(ggraph)
library(ggrepel)
library(gridExtra)
library(utils)
library(spatstat)
library(tidyr)
library(ggpubr)

## -----------------------------------------------------------------------------
# ggplot style
defined_theme <- theme(strip.text = element_text(size = rel(1)),
                        strip.background = element_rect(fill = NA, 
                                                    colour = "black"),
                        axis.line=element_blank(),
                        axis.text.x=element_blank(),
                        axis.text.y=element_blank(),
                        axis.ticks=element_blank(),
                        axis.title.x=element_blank(),
                        axis.title.y=element_blank(),
                        legend.position="none",
                        panel.background=element_blank(),
                        panel.border=element_blank(),
                        panel.grid.major=element_blank(),
                        panel.grid.minor=element_blank(),
                        plot.background=element_blank())

## ----load subset of rep1------------------------------------------------------
data(rep1_sub, rep1_clusters, rep1_neg)
rep1_clusters$cluster<-factor(rep1_clusters$cluster,
                            levels=paste("c",1:8, sep=""))
# record the transcript coordinates for rep1
rep1_transcripts <-BumpyMatrix::unsplitAsDataFrame(molecules(rep1_sub))
rep1_transcripts <- as.data.frame(rep1_transcripts)
colnames(rep1_transcripts) <- c("feature_name","cell_id","x","y")
# record the negative control transcript coordinates for rep1
rep1_nc_data <- BumpyMatrix::unsplitAsDataFrame(molecules(rep1_neg))
rep1_nc_data <- as.data.frame(rep1_nc_data)
colnames(rep1_nc_data) <- c("feature_name","cell_id","x","y","category")

# record all real genes in the data
all_real_genes <- unique(as.character(rep1_transcripts$feature_name))
all_celltypes <- unique(rep1_clusters[,c("anno","cluster")])

## ----rep clusters vis, fig.height=5,  fig.width=6,warning=FALSE---------------
p1<-ggplot(data = rep1_clusters,
        aes(x = x, y = y, color=cluster))+
        geom_point(position=position_jitterdodge(jitter.width=0,
                                        jitter.height=0), size=0.1)+
        scale_y_reverse()+
        theme_classic()+
        facet_wrap(~sample)+
        scale_color_manual(values = c("#FC8D62","#66C2A5" ,"#8DA0CB","#E78AC3",
                        "#A6D854","skyblue","purple3","#E5C498"))+
        guides(color=guide_legend(title="cluster", nrow = 2,
                        override.aes=list(alpha=1, size=2)))+
        
        theme(axis.text.x=element_blank(),
                axis.text.y=element_blank(),
                axis.ticks=element_blank(),
                panel.spacing = grid::unit(0.5, "lines"),
                axis.title.x=element_blank(),
                axis.title.y=element_blank(),
                legend.position="none",
                strip.text = element_text(size = rel(1)))+
        xlab("")+
        ylab("")
p2<-ggplot(data = rep1_clusters,
        aes(x = x, y = y, color=cluster))+
        geom_point(position=position_jitterdodge(jitter.width=0, 
                                    jitter.height=0), size=0.1)+
        facet_wrap(~cluster, nrow = 2)+
        scale_y_reverse()+
        theme_classic()+
        scale_color_manual(values = c("#FC8D62","#66C2A5" ,"#8DA0CB","#E78AC3",
                        "#A6D854","skyblue","purple3","#E5C498"))+
        guides(color=guide_legend(title="cluster", nrow = 1,
        override.aes=list(alpha=1, size=4)))+
        theme(axis.text.x=element_blank(),
            axis.text.y=element_blank(),
            axis.ticks=element_blank(),
            axis.title.x=element_blank(),
            axis.title.y=element_blank(),
            panel.spacing = grid::unit(0.5, "lines"), 
            legend.text = element_text(size=10),
            legend.position="none",
            legend.title = element_text(size=10),
            strip.text = element_text(size = rel(1)))+
        xlab("")+
        ylab("")

spacer <- patchwork::plot_spacer()
layout_design <- (p1 / spacer) | p2

layout_design <- layout_design + 
                    patchwork::plot_layout(widths = c(1, 4), heights = c(1, 1)) 

print(layout_design)

## ----plot spatial genes, fig.width=6, fig.height=4----------------------------

w_x <- c(min(floor(min(rep1_transcripts$x)),
        floor(min(rep1_clusters$x))), 
        max(ceiling(max(rep1_transcripts$x)),
        ceiling(max(rep1_clusters$x))))
w_y <- c(min(floor(min(rep1_transcripts$y)),
        floor(min(rep1_clusters$y))), 
        max(ceiling(max(rep1_transcripts$y)),
        ceiling(max(rep1_clusters$y))))

# plot transcript detection coordinates
selected_genes <- rep1_transcripts$feature_name == "EPCAM"
loc_mt <- as.data.frame(rep1_transcripts[selected_genes,
                        c("x","y","feature_name")]%>%distinct())
colnames(loc_mt)<-c("x","y","feature_name")
layout(matrix(c(1, 2, 3), 1, 3, byrow = TRUE))
par(mar=c(5,3,6,3))
plot(loc_mt$x, loc_mt$y, main = "", xlab = "", ylab = "", 
        pch = 20, col = "maroon4", cex = 0.1,xaxt='n', yaxt='n')   
title(main = "EPCAM transcript detection", line = 3)
box() 

# plot square binning 
curr<-loc_mt[loc_mt[,"feature_name"]=="EPCAM",c("x","y")] %>% distinct()
curr_ppp <- ppp(curr$x,curr$y,w_x, w_y)
vec_quadrat <- quadratcount(curr_ppp, 10,10)
vec_its <- intensity(vec_quadrat, image=TRUE)
par(mar=c(0.01,1, 1, 2))
plot(vec_its, main = "")
title(main = "square binning", line = -2)

# plot hex binning 
w <- owin(xrange=w_x, yrange=w_y)
H <- hextess(W=w, 20)
bin_length <- length(H$tiles)
curr<-loc_mt[loc_mt[,"feature_name"]=="EPCAM",c("x","y")] %>% distinct()
curr_ppp <- ppp(curr$x,curr$y,w_x, w_y)
vec_quadrat <- quadratcount(curr_ppp, tess=H)
vec_its <- intensity(vec_quadrat, image=TRUE)
par(mar=c(0.1,1, 1, 2))
plot(vec_its, main = "")
title(main = "hex binning", line = -2)


## ----plot spatial clusters, fig.width=6, fig.height=4-------------------------
w_x <- c(min(floor(min(rep1_transcripts$x)),
            floor(min(rep1_clusters$x))), 
        max(ceiling(max(rep1_transcripts$x)),
            ceiling(max(rep1_clusters$x))))
w_y <-  c(min(floor(min(rep1_transcripts$y)),
            floor(min(rep1_clusters$y))), 
        max(ceiling(max(rep1_transcripts$y)),
            ceiling(max(rep1_clusters$y))))

# plot cell coordinates
loc_mt <- as.data.frame(rep1_clusters[rep1_clusters$cluster=="c1",
            c("x","y","cluster")])
colnames(loc_mt)=c("x","y","cluster")
layout(matrix(c(1, 2, 3), 1, 3, byrow = TRUE))
par(mar=c(5,3,6,3))
plot(loc_mt$x, loc_mt$y, main = "", xlab = "", ylab = "", 
        pch = 20, col = "maroon4", cex = 0.1,xaxt='n', yaxt='n')   
title(main = "cell coordinates in cluster c1", line = 3)
box() 

# plot square binning 
curr<-loc_mt[loc_mt[,"cluster"]=="c1", c("x","y")]%>%distinct()
curr_ppp <- ppp(curr$x,curr$y,w_x, w_y)
vec_quadrat <- quadratcount(curr_ppp, 10,10)
vec_its <- intensity(vec_quadrat, image=TRUE)
par(mar=c(0.1,1, 1, 2))
plot(vec_its, main = "")
title(main = "square binning", line = -2)

# plot hex binning 
w <- owin(xrange=w_x, yrange=w_y)
H <- hextess(W=w, 20)
bin_length <- length(H$tiles)
curr<-loc_mt[loc_mt[,"cluster"]=="c1",c("x","y")] %>%distinct()
curr_ppp <- ppp(curr$x,curr$y,w_x, w_y)
vec_quadrat <- quadratcount(curr_ppp, tess=H)
vec_its <- intensity(vec_quadrat, image=TRUE)
par(mar=c(0.1,1, 1, 2))
plot(vec_its, main = "")
title(main = "hex binning", line = -2)


## ----create rep1 all vectors--------------------------------------------------
seed_number<- 589

grid_length <- 10
# get spatial vectors
rep1_sq10_vectors <- get_vectors(x= rep1_sub,
                                sample_names = "rep1",
                                cluster_info = rep1_clusters,
                                bin_type="square",
                                bin_param=c(grid_length,grid_length), 
                                test_genes = all_real_genes)

## ----create rep1 different cluster-vec and gene vec---------------------------
seed_number<- 589
grid_length <- 10

# get spatial vectors for clusters only
cluster_sq10_vectors <- get_vectors(x= NULL,
                                sample_names = "rep1",
                                cluster_info = rep1_clusters,
                                bin_type="square",
                                bin_param=c(grid_length,grid_length), 
                                test_genes = all_real_genes)
# the gene vectors can be accessed as:
head(cluster_sq10_vectors$cluster_mt) 
# cluster_sq10_vectors$cluster_mt is equivalent to rep1_sq10_vectors$cluster_mt

#######################################################
# get spatial vectors for genes only 
gene_sq10_vectors <- get_vectors(x= rep1_sub,
                                sample_names = "rep1",
                                cluster_info = NULL,
                                bin_type="square",
                                bin_param=c(grid_length,grid_length), 
                                test_genes = all_real_genes)
# the gene vectors can be accessed as:
head(gene_sq10_vectors$gene_mt)
# gene_sq10_vectors$gene_mt is equivalent to rep1_sq10_vectors$gene_mt


## ----fig.width=6, fig.height=6------------------------------------------------
exp_ord <- paste("c", 1:8, sep="")
rep1_sq10_vectors$cluster_mt <- rep1_sq10_vectors$cluster_mt[,exp_ord]
cor_cluster_mt <- cor(rep1_sq10_vectors$cluster_mt,
                rep1_sq10_vectors$cluster_mt, method = "pearson")
# Calculate pairwise correlations
cor_gene_mt <- cor(rep1_sq10_vectors$gene_mt, rep1_sq10_vectors$gene_mt,
                    method = "pearson")

col <- grDevices::colorRampPalette(c("#4477AA", "#77AADD", 
                                    "#FFFFFF","#EE9988", "#BB4444"))

corrplot::corrplot(cor_cluster_mt, method="color", col=col(200), diag=TRUE,
                addCoef.col = "black",type="upper",
                tl.col="black", tl.srt=45, mar=c(0,0,5,0),sig.level = 0.05, 
                insig = "blank", 
                title = "cluster-cluster correlation (square bin = 40x40)"
                )

## ----gene_cluster_corr, warning=FALSE, message=FALSE,fig.width=6,fig.height=6----

cor_genecluster_mt <- cor(x=rep1_sq10_vectors$gene_mt, 
                        y=rep1_sq10_vectors$cluster_mt, method = "pearson")

gg_correlation <- as.data.frame(cbind(apply(cor_gene_mt, MARGIN=1, 
                                            FUN = mean, na.rm=TRUE),
                                        apply(cor_genecluster_mt, MARGIN=1, 
                                            FUN = mean, na.rm=TRUE)))
colnames(gg_correlation) <- c("mean_correlation","mean_cluster")  
gg_correlation$gene<-row.names(gg_correlation)

plot(ggplot(data = gg_correlation, 
    aes(x= mean_correlation, y=mean_cluster))+
        geom_point()+
        geom_text_repel(aes(label=gg_correlation$gene), size=1.8, hjust=1)+
    theme_bw()+
    theme(legend.title=element_blank(),
            axis.text.y = element_text(size=20),
            axis.text.x = element_text(size=20),
            axis.title.x=element_text(size=20),
            axis.title.y=element_text(size=20), 
            panel.spacing = grid::unit(0.5, "lines"), 
            legend.position="none",
            legend.text=element_blank())+
    xlab("Average gene-gene correlation")+
    ylab("Average gene-cluster correlation"))


## ----gene_graph,warning=FALSE, message=FALSE, fig.width=6, fig.height=6-------

vector_graph<- igraph::graph_from_adjacency_matrix(cor_gene_mt,
                                                mode = "undirected", 
                                                weighted = TRUE, 
                                                diag = FALSE)

vector_graph<-igraph::simplify(igraph::delete_edges(vector_graph, 
                        E(vector_graph)[abs(E(vector_graph)$weight) <= 0.7]))

layout<-igraph::layout_with_kk(vector_graph)

# Plot the graph
ggraph::ggraph(vector_graph, layout = layout) +
    geom_edge_link(aes(edge_alpha = weight), show.legend = FALSE) +
    geom_node_point(color = "lightblue", size = 5) +
    geom_node_text(aes(label = name), 
                    vjust = 1, hjust = 1,size=2,color="orange", repel = TRUE) 


## -----------------------------------------------------------------------------

seed_number<- 589
grid_length <- 10
# get spatial vectors
rep1_sq10_vectors_lst <- get_vectors(x= list("rep1" = rep1_transcripts),
                                sample_names = "rep1",
                                cluster_info = rep1_clusters,
                                bin_type="square",
                                bin_param=c(grid_length,grid_length), 
                                test_genes = all_real_genes)
# the created spatial vectors will be the same as from other input structure 
table(rep1_sq10_vectors$gene_mt[,"DST"]==rep1_sq10_vectors_lst$gene_mt[,"DST"])
# spatial vector for every cluster 
head(rep1_sq10_vectors_lst$cluster_mt)

# spatial vector for every gene 
head(rep1_sq10_vectors_lst$gene_mt)

## ----eval=TRUE----------------------------------------------------------------
library(SpatialFeatureExperiment)
library(SingleCellExperiment)
library(TENxXeniumData)
library(ExperimentHub)
library(scran)
library(scater)
eh <- ExperimentHub()
q <- query(eh, "TENxXenium")
spe_example <- q[["EH8547"]]
colData(spe_example)$cell_id <- colnames(spe_example)
set.seed(123)
# use a subset data
subset_cells <- sample(colnames(spe_example), size = 100, replace = FALSE)
spe_sub <- spe_example[, colnames(spe_example) %in% subset_cells]
# -----------------------------------------------------------------------------

# calculate logcounts and store in object
spe_sub <- logNormCounts(spe_sub)
rm(spe_example)
# compute PCA
set.seed(123)
spe_sub <- runPCA(spe_sub, subset_row = row.names(spe_sub))

# example Non-spatial clustering (for illustration purpose only)
set.seed(123)
k <- 10
g <- buildSNNGraph(spe_sub, k = k, use.dimred = "PCA")
g_walk <- igraph::cluster_walktrap(g)
clusters <- paste("c",g_walk$membership,sep="")
scran_clusters <- as.data.frame(cbind(cluster = clusters, 
                                        cell_id = colnames(spe_sub)))

# -----------------------------------------------------------------------------
# combine cluster labels and the coordinates 
# make sure the cluster information contains column names: 
# cluster, x, y, sample and cell_id
clusters_info <- as.data.frame(spatialCoords(spe_sub))
colnames(clusters_info) <- c("x","y")
clusters_info$cell_id <- row.names(clusters_info)
clusters_info$sample <- spe_sub$sample_id
clusters_info <- merge(clusters_info, scran_clusters, by="cell_id")
# -----------------------------------------------------------------------------
# build spatial vectors from count matrix and cluster coordinates 
example_vectors_cm <- get_vectors(x= spe_sub,
                                sample_names = "sample01",
                                cluster_info = clusters_info,
                                bin_type="square",
                                bin_param=c(5,5), 
                                test_genes = row.names(spe_sub)[1:5],
                                use_cm=TRUE)
# spatial vector for every cluster 
head(example_vectors_cm$cluster_mt)

# spatial vector for every gene 
head(example_vectors_cm$gene_mt)
# -----------------------------------------------------------------------------
# build spatial vectors from transcript coordinates and cluster coordinates 
example_vectors_tr <- get_vectors(x= spe_sub,
                                sample_names = "sample01",
                                cluster_info = clusters_info,
                                bin_type="square",
                                bin_param=c(5,5), 
                                test_genes = row.names(spe_sub)[1:5],
                                use_cm=FALSE)
# spatial vector for every cluster 
# example_vectors_tr$cluster_mt

# spatial vector for every gene 
# example_vectors_tr$gene_mt

## ----eval=FALSE---------------------------------------------------------------
# sfe_example <-SpatialFeatureExperiment(
#     list(counts = spe_sub@assays@data$counts),
#     colData = spe_sub@colData,
#     spatialCoords = spatialCoords(spe_sub),
#     spatialCoordsNames = c("x_centroid", "y_centroid"))
# # -----------------------------------------------------------------------------
# # build spatial vectors from count matrix and cluster coordinates
# # make sure the cluster information contains column names:
# # cluster, x, y, sample and cell_id
# example_vectors_cm <- get_vectors(x= sfe_example,
#                                 sample_names = "sample01",
#                                 cluster_info = clusters_info,
#                                 bin_type="square",
#                                 bin_param=c(5,5),
#                                 test_genes = row.names(spe_sub)[1:3],
#                                 use_cm = TRUE)
# 
# # spatial vector for every cluster
# head(example_vectors_cm$cluster_mt)
# 
# # spatial vector for every gene
# head(example_vectors_cm$gene_mt)
# # -----------------------------------------------------------------------------
# # build spatial vectors from transcript coordinates and cluster coordinates
# example_vectors_tr <- get_vectors(x= sfe_example,
#                                 sample_names = "sample01",
#                                 cluster_info = clusters_info,
#                                 bin_type="square",
#                                 bin_param=c(5,5),
#                                 test_genes = row.names(spe_sub)[1:3],
#                                 use_cm = FALSE)
# 
# # spatial vector for every cluster
# # example_vectors_tr$cluster_mt
# 
# # spatial vector for every gene
# # example_vectors_tr$gene_mt

## ----eval=FALSE---------------------------------------------------------------
# sce_example <- SingleCellExperiment(list(sample01 = cm))
# # -----------------------------------------------------------------------------
# # build spatial vectors from count matrix and cluster coordinates
# # make sure the cluster information contains column names:
# # cluster, x, y, sample and cell_id
# example_vectors_cm <- get_vectors(x= sce_example,
#                                 sample_names = "sample01",
#                                 cluster_info = clusters_info,
#                                 bin_type="square",
#                                 bin_param=c(5,5),
#                                 test_genes = row.names(spe_sub)[1:3],
#                                 use_cm=TRUE)
# 
# # spatial vector for every cluster
# head(example_vectors_cm$cluster_mt)
# 
# # spatial vector for every gene
# head(example_vectors_cm$gene_mt)
# 
# # -----------------------------------------------------------------------------
# # If the transcript coordinate information is available
# # make sure the transcript information contains column names:
# # feature_name, x, y
# spe <- SpatialExperiment(
#     assays = list(molecules = molecules(sfe_example)),
#     sample_id ="sample01")
# 
# # build spatial vectors from transcript coordinates and cluster coordinates
# example_vectors_tr <- get_vectors(x= spe, sample_names = "sample01",
#                                 cluster_info = clusters_info,
#                                 bin_type="square",
#                                 bin_param=c(5,5),
#                                 test_genes = row.names(spe_sub)[1:3])
# # spatial vector for every cluster
# # example_vectors_tr$cluster_mt
# 
# # spatial vector for every gene
# # example_vectors_tr$gene_mt
# 

## ----eval=FALSE---------------------------------------------------------------
# library(Seurat)
# # suppose cm is the count matrix
# seu_obj <- Seurat::CreateSeuratObject(counts = cm)
# sce <- SingleCellExperiment(list(sample01 =seu_obj@assays$RNA$counts ))
# # we will use the previously defined cluster_info for illustration here
# # make sure the clusters information contains column names:
# # cluster, x, y and sample
# clusters_info = clusters_info
# 
# # -----------------------------------------------------------------------------
# # build spatial vectors from count matrix and cluster coordinates
# # make sure the cluster information contains column names:
# # cluster, x, y, sample and cell_id
# example_vectors_cm <- get_vectors(x= sce, sample_names = "sample01",
#                                 cluster_info = clusters_info,
#                                 bin_type="square",
#                                 bin_param=c(10,10),
#                                 test_genes = test_genes,
#                                 use_cm=TRUE)
# # spatial vector for every cluster
# example_vectors_cm$cluster_mt
# 
# # spatial vector for every gene
# example_vectors_cm$gene_mt
# 
# # -----------------------------------------------------------------------------
# # If the transcript coordinate information is available
# # make sure the transcript information contains column names:
# # feature_name, x, y
# spe <- SpatialExperiment(
#     assays = list(
#         molecules = molecules(sfe_example)),
#     sample_id ="sample01" )
# 
# # build spatial vectors from transcript coordinates and cluster coordinates
# example_vectors_tr <- get_vectors(x= spe, sample_names = "sample01",
#                                 cluster_info = clusters_info,
#                                 bin_type="square",
#                                 bin_param=c(10,10),
#                                 test_genes = test_genes)
# # spatial vector for every cluster
# example_vectors_tr$cluster_mt
# 
# # spatial vector for every gene
# example_vectors_tr$gene_mt

## ----linear vector to vector plot, fig.height=2, fig.width=7------------------
genes_lst <- c("ERBB2","AQP1","LUM","IL7R","MZB1")

for (i_cluster in c("c1","c8","c3","c6","c7")){
    cluster_vector<-rep1_sq10_vectors$cluster_mt[,i_cluster]
    
    data_vis<-as.data.frame(cbind("cluster", cluster_vector,
                            rep1_sq10_vectors$gene_mt[, genes_lst]))
    
    colnames(data_vis)<-c("cluster","cluster_vector",genes_lst)
    data_vis<-reshape2::melt(data_vis,variable.name = "genes",
                            value.name = "gene_vector",
                            id= c("cluster","cluster_vector" ))
    data_vis$cluster_vector<-as.numeric(data_vis$cluster_vector)
    data_vis$genes<-factor(data_vis$genes)
    data_vis$gene_vector<-as.numeric(data_vis$gene_vector)
    
    plot(ggplot(data = data_vis, 
            aes(x= cluster_vector, y=gene_vector))+
            geom_point(size=0.1)+
            facet_wrap(~genes,scales = "free_y", ncol=10)+
            theme_bw()+
            theme(legend.title=element_blank(),
                axis.text.y = element_text(size=6),
                axis.text.x = element_text(size=6,angle=0),
                axis.title.x=element_text(size=10),
                axis.title.y=element_text(size=10), 
                panel.spacing = grid::unit(0.5, "lines"),
                legend.position="none",
                legend.text=element_blank(),
                strip.text = element_text(size = rel(1)))+
                xlab(paste(i_cluster," - cluster vector", sep=""))+
                ylab("gene vector"))
}

## ----rep1 permutation---------------------------------------------------------

set.seed(seed_number)
perm_p <- compute_permp(x=rep1_sub,
                        cluster_info=rep1_clusters, 
                        perm.size=1000,
                        bin_type="square",
                        bin_param=c(10,10),
                        test_genes= all_real_genes,
                        correlation_method = "pearson", 
                        n_cores=1, 
                        correction_method="BH")

# observed correlation for every pair of gene and cluster vector
obs_corr <- get_cor(perm_p)
head(obs_corr)

# permutation adjusted p-value for every pair of gene and cluster vector
perm_res <- get_perm_adjp(perm_p)
head(perm_res)

## ----rep1 permutation vis, fig.width=6, fig.height=3--------------------------
res_df_1000<-as.data.frame(perm_p$perm.pval.adj)
res_df_1000$gene<-row.names(res_df_1000)
cluster_names <- unique(as.character(rep1_clusters$cluster))
for (cl in cluster_names){
    perm_sig <- res_df_1000[res_df_1000[,cl]<0.05,]
    # define a cutoff value based on 75% quantile 
    obs_cutoff <- quantile(obs_corr[, cl], 0.75)
    perm_cl<-intersect(row.names(perm_res[perm_res[,cl]<0.05,]),
                        row.names(obs_corr[obs_corr[, cl]>obs_cutoff,]))
    inters<-perm_cl
    rounded_val<-signif(as.numeric(obs_corr[inters,cl]), digits = 3)
    inters_df<- as.data.frame(cbind(gene=inters, value=rounded_val))
    inters_df$value<- as.numeric(inters_df$value)
    inters_df<-inters_df[order(inters_df$value, decreasing = TRUE),]
    inters_df<-inters_df[1:min(nrow(inters_df),2),]
    inters_df$text<- paste(inters_df$gene,inters_df$value,sep=": ")
    curr_genes <- rep1_transcripts$feature_name %in% inters_df$gene
    data_vis <- rep1_transcripts[curr_genes, c("x","y","feature_name")]
    data_vis$text <- inters_df[match(data_vis$feature_name,inters_df$gene),
                                "text"]
    data_vis$text <- factor(data_vis$text, levels=inters_df$text)
    p1<-ggplot(data = data_vis,
                aes(x = x, y = y))+
                geom_point(size=0.01,color="maroon4")+
                facet_wrap(~text,ncol=10, scales="free")+
                scale_y_reverse()+
                guides(fill = guide_colorbar(height= grid::unit(5, "cm")))+
                defined_theme
    cl_pt<-ggplot(data = rep1_clusters[rep1_clusters$cluster==cl, ],
                    aes(x = x, y = y, color=cluster))+
                    geom_point(position=position_jitterdodge(jitter.width=0, 
                                            jitter.height=0), size=0.2)+
                    facet_wrap(~cluster)+
                    scale_y_reverse()+
                    theme_classic()+
                    scale_color_manual(values = "black")+
                    defined_theme
    lyt <- cl_pt | p1
    layout_design <- lyt + patchwork::plot_layout(widths = c(1,3)) 
    print(layout_design)
}

## ----vvplot_corr, fig.width=8, fig.height=6-----------------------------------

cluster_names <- paste("c", 1:8, sep="")
plot_lst<-list()
for (cl in cluster_names){
    perm_sig<- res_df_1000[res_df_1000[,cl]<0.05,]
    curr_cell_type <- all_celltypes[all_celltypes$cluster==cl,"anno"]
    obs_cutoff <- quantile(obs_corr[, cl], 0.75)
    perm_cl<-intersect(row.names(perm_res[perm_res[,cl]<0.05,]),
                        row.names(obs_corr[obs_corr[, cl]>obs_cutoff,]))
    inters<-perm_cl
    rounded_val<-signif(as.numeric(obs_corr[inters,cl]), digits = 3)
    inters_df <- as.data.frame(cbind(gene=inters, value=rounded_val))
    inters_df$value<- as.numeric(inters_df$value)
    inters_df<-inters_df[order(inters_df$value, decreasing = TRUE),]
    inters_df$text<- paste(inters_df$gene,inters_df$value,sep=": ")
    mk_gene<- inters_df[1:min(2, nrow(inters_df)),"gene"]
    if (length(mk_gene > 0)){
        dff <- as.data.frame(cbind(rep1_sq10_vectors$cluster_mt[,cl],
                                    rep1_sq10_vectors$gene_mt[,mk_gene]))
        colnames(dff) <- c("cluster", mk_gene)
        
        dff$vector_id <- c(1:(grid_length * grid_length))
        long_df <- dff %>% 
        pivot_longer(cols = -c(cluster, vector_id), names_to = "gene", 
                                values_to = "vector_count")
        long_df$gene <- factor(long_df$gene, levels=mk_gene)
        
        p<-ggplot(long_df, aes(x = cluster, y = vector_count )) +
            geom_point( size=0.01) +
            facet_wrap(~gene, scales = "free_y", nrow=1) +
            labs(x = paste("cluster vector ", curr_cell_type, sep=""), 
                    y = "marker gene vectors") +
            theme_minimal()+
            guides(color=guide_legend(nrow = 1,
                        override.aes=list(alpha=1, size=2)))+
            theme(panel.grid = element_blank(),legend.position = "none",
                    strip.text = element_text(size = rel(1)),
                    axis.line=element_blank(),
                    legend.title = element_blank(),
                    legend.key.size = grid::unit(0.5, "cm"),
                    legend.text = element_text(size=10),
                    axis.text=element_blank(),
                    axis.ticks=element_blank(),
                    axis.title=element_text(size = 10),
                    panel.border =element_rect(colour = "black", 
                                                fill=NA, linewidth=0.5)
            )
        plot_lst[[cl]] = p
    }
}
combined_plot <- ggarrange(plotlist = plot_lst, 
                            ncol = 2, nrow = 4,
                            common.legend = FALSE, legend = "none")

combined_plot 


## ----rep1 lasso_markers with background---------------------------------------
probe_nm <- unique(rep1_nc_data[rep1_nc_data$category=="probe","feature_name"])
codeword_nm <- unique(rep1_nc_data[rep1_nc_data$category=="codeword",
                                    "feature_name"])
rep1_nc_vectors <- create_genesets(x=rep1_neg,sample_names="rep1",
                                    name_lst=list(probe=probe_nm, 
                                                codeword=codeword_nm), 
                                    bin_type="square",
                                    bin_param=c(10, 10), 
                                    cluster_info = NULL)

set.seed(seed_number)

rep1_lasso_with_nc <- lasso_markers(gene_mt=rep1_sq10_vectors$gene_mt,
                                    cluster_mt = rep1_sq10_vectors$cluster_mt,
                                    sample_names=c("rep1"),
                                    keep_positive=TRUE, 
                                    background=rep1_nc_vectors)

rep1_top_df_nc <- get_top_mg(rep1_lasso_with_nc, coef_cutoff=0.2)
# the top result table 
head(rep1_top_df_nc)

# the full result table 
rep1_full_df <- get_full_mg(rep1_lasso_with_nc)
head(rep1_full_df)

## ----lasso-rep1 vis, fig.width=6, fig.height=3--------------------------------

cluster_names <- paste("c", 1:8, sep="")
for (cl in setdiff(cluster_names,"NoSig")){
    inters<-rep1_top_df_nc[rep1_top_df_nc$top_cluster==cl,"gene"]
    rounded_val<-signif(as.numeric(rep1_top_df_nc[inters,"glm_coef"]),
                                    digits = 3)
    inters_df <- as.data.frame(cbind(gene=inters, value=rounded_val))
    inters_df$value <- as.numeric(inters_df$value)
    inters_df<-inters_df[order(inters_df$value, decreasing = TRUE),]
    inters_df$text<- paste(inters_df$gene,inters_df$value,sep=": ")
    
    if (length(inters > 0)){
        inters_df<-inters_df[1:min(2, nrow(inters_df)),]
        inters <-inters_df$gene
        iters_rep1<-rep1_transcripts$feature_name %in% inters
        vis_r1<-rep1_transcripts[iters_rep1,
                                c("x","y","feature_name")]
        vis_r1$value<-inters_df[match(vis_r1$feature_name,inters_df$gene),
                                "value"]
        #vis_r1=vis_r1[order(vis_r1$value,decreasing = TRUE),]
        vis_r1$text_label<- paste(vis_r1$feature_name,
                                    vis_r1$value,sep=": ")
        vis_r1$text_label<-factor(vis_r1$text_label, levels = inters_df$text)
        vis_r1$sample<-"rep1"
        p1<- ggplot(data = vis_r1,
                    aes(x = x, y = y))+ 
                    geom_point(size=0.01,color="maroon4")+
                    facet_wrap(~text_label,ncol=10, scales="free")+
                    scale_y_reverse()+
                    guides(fill = guide_colorbar(height= grid::unit(5, "cm")))+
                    defined_theme
        cl_pt<-ggplot(data = rep1_clusters[rep1_clusters$cluster==cl, ],
                    aes(x = x, y = y, color=cluster))+
                    geom_point(position=position_jitterdodge(jitter.width=0, 
                                            jitter.height=0), size=0.2)+
                    facet_wrap(~cluster)+
                    scale_y_reverse()+
                    theme_classic()+
                    scale_color_manual(values = "black")+
                    defined_theme
        lyt <- cl_pt | p1
        layout_design <- lyt + patchwork::plot_layout(widths = c(1,3)) 
        print(layout_design)
}}

## ----vvplot_glm, fig.width=8, fig.height=6------------------------------------

cluster_names <- paste("c", 1:8, sep="")
plot_lst=list()
for (cl in cluster_names){
    curr_cell_type<-all_celltypes[all_celltypes$cluster==cl,"anno"]
    inters<-rep1_top_df_nc[rep1_top_df_nc$top_cluster==cl,"gene"]
    if (length(inters > 0)){
        rounded_val<-signif(as.numeric(rep1_top_df_nc[inters,"glm_coef"]),
                                digits = 3)
        inters_df<-as.data.frame(cbind(gene=inters, value=rounded_val))
        inters_df$value<-as.numeric(inters_df$value)
        inters_df<-inters_df[order(inters_df$value, decreasing = TRUE),]
        inters_df$text<-paste(inters_df$gene,inters_df$value,sep=": ")
    
        inters_df<-inters_df[1:min(2, nrow(inters_df)),]
        mk_gene<-inters_df$gene

        dff<- as.data.frame(cbind(rep1_sq10_vectors$cluster_mt[,cl],
                                rep1_sq10_vectors$gene_mt[,mk_gene]))
        colnames(dff)<-c("cluster", mk_gene)
        dff$vector_id<-c(1:(grid_length * grid_length))
        long_df <- dff %>% 
        pivot_longer(cols = -c(cluster, vector_id), names_to = "gene", 
                        values_to = "vector_count")
        long_df$gene<-factor(long_df$gene, levels=mk_gene)
        p<-ggplot(long_df, aes(x = cluster, y = vector_count )) +
            geom_point( size=0.01) +
            facet_wrap(~gene, scales = "free_y", nrow=1) +
            labs(x = paste("cluster vector ", curr_cell_type, sep=""), 
                    y = "marker gene vectors") +
            theme_minimal()+
            guides(color=guide_legend(nrow = 1,
                        override.aes=list(alpha=1, size=2)))+
            theme(panel.grid = element_blank(),legend.position = "none",
                    strip.text = element_text(size = rel(1)),
                    axis.line=element_blank(),
                    legend.title = element_blank(),
                    legend.key.size = grid::unit(0.5, "cm"),
                    legend.text = element_text(size=10),
                    axis.text=element_blank(),
                    axis.ticks=element_blank(),
                    axis.title=element_text(size = 10),
                    panel.border =element_rect(colour = "black", 
                                                fill=NA, linewidth=0.5)
        )
        plot_lst[[cl]] = p
    }
}
combined_plot <- ggarrange(plotlist = plot_lst, 
                            ncol = 2, nrow = 4,
                            common.legend = FALSE, legend = "none")

combined_plot 


## ----load rep2 data-----------------------------------------------------------
data(rep2_sub, rep2_clusters, rep2_neg)
rep2_clusters$cluster<-factor(rep2_clusters$cluster,
                            levels=paste("c",1:8, sep=""))
rep1_clusters$cells<-paste(row.names(rep1_clusters),"_1",sep="")
rep2_clusters$cells<-paste(row.names(rep2_clusters),"_2",sep="")
rep_clusters<-rbind(rep1_clusters,rep2_clusters)
rep_clusters$cluster<-factor(rep_clusters$cluster,
                            levels=paste("c",1:8, sep=""))
table(rep_clusters$sample, rep_clusters$cluster)


# record the transcript coordinates for rep2
rep2_transcripts <-BumpyMatrix::unsplitAsDataFrame(molecules(rep2_sub))
rep2_transcripts <- as.data.frame(rep2_transcripts)
colnames(rep2_transcripts) <- c("feature_name","cell_id","x","y")
# record the negative control transcript coordinates for rep2
rep2_nc_data <-BumpyMatrix::unsplitAsDataFrame(molecules(rep2_neg))
rep2_nc_data <- as.data.frame(rep2_nc_data)
colnames(rep2_nc_data) <- c("feature_name","cell_id","x","y","category")


## ----tworep cluster vis, fig.height=4,  fig.width=8, warning=FALSE------------

ggplot(data = rep_clusters,
        aes(x = x, y = y, color=cluster))+
        geom_point(position=position_jitterdodge(jitter.width=0, 
                                                jitter.height=0),size=0.1)+
        facet_grid(sample~cluster)+
        scale_y_reverse()+
        theme_classic()+
        scale_color_manual(values = c("#FC8D62","#66C2A5" ,"#8DA0CB","#E78AC3",
                                "#A6D854","skyblue","purple3","#E5C498"))+
        guides(color=guide_legend(title="cluster", nrow = 1,
        override.aes=list(alpha=1, size=7)))+
        theme(
            axis.line=element_blank(),
            axis.text.x=element_blank(),
            axis.text.y=element_blank(),
            axis.ticks=element_blank(),
            axis.title.x=element_blank(),
            axis.title.y=element_blank(),
            panel.background=element_blank(),
            panel.border=element_blank(),
            panel.grid.major=element_blank(),
            panel.grid.minor=element_blank(),
            plot.background=element_blank(),
            legend.text = element_text(size=10),
            legend.position="none",
            legend.title = element_text(size=10),
            strip.text = element_text(size = rel(1)))+
        xlab("")+
        ylab("")

## ----tworep no background-----------------------------------------------------
grid_length<-10
twosample_spe<-cbind(rep1_sub, rep2_sub)
# get spatial vectors
two_rep_vectors<- get_vectors(x= twosample_spe,
                                sample_names=c("rep1","rep2"),
                                cluster_info = rep_clusters, bin_type="square",
                                bin_param=c(grid_length, grid_length),
                                test_genes = all_real_genes)

twosample_neg_spe<-cbind(rep1_neg, rep2_neg)

probe_nm<-unique(c(rep1_nc_data[rep1_nc_data$category=="probe",
                                "feature_name"],
                rep2_nc_data[rep2_nc_data$category=="probe","feature_name"]))
codeword_nm<-unique(c(rep1_nc_data[rep1_nc_data$category=="codeword",
                                "feature_name"],
            rep2_nc_data[rep2_nc_data$category=="codeword","feature_name"]))

two_rep_nc_vectors<-create_genesets(x=twosample_neg_spe,
                                    sample_names=c("rep1","rep2"),
                                    name_lst=list(probe=probe_nm, 
                                                codeword=codeword_nm),
                                    bin_type="square",
                                    bin_param=c(10,10), 
                                    cluster_info = NULL)
set.seed(seed_number)
two_rep_lasso_with_nc<-lasso_markers(gene_mt=two_rep_vectors$gene_mt,
                                    cluster_mt = two_rep_vectors$cluster_mt,
                                    sample_names=c("rep1","rep2"),
                                    keep_positive=TRUE, 
                                    background=two_rep_nc_vectors,n_fold = 5)

tworep_res<-get_top_mg(two_rep_lasso_with_nc, coef_cutoff=0.2) 
tworep_res$celltype<-rep_clusters[match(tworep_res$top_cluster,
                                            rep_clusters$cluster),"anno"]
table(tworep_res$top_cluster)
head(tworep_res)

## ----top3 marker gene, fig.height=6, fig.width=6------------------------------

for (cl in all_celltypes$anno){
    inters<-tworep_res[tworep_res$celltype==cl,"gene"]
    rounded_val<-signif(as.numeric(tworep_res[inters,"glm_coef"]),
                        digits = 3)
    inters_df<-as.data.frame(cbind(gene=inters, value=rounded_val))
    inters_df$value<-as.numeric(inters_df$value)
    inters_df<-inters_df[order(inters_df$value, decreasing = TRUE),]
    inters_df$text<-paste(inters_df$gene,inters_df$value,sep=": ")
    if (length(inters > 0)){
        inters_df<-inters_df[1:min(2, nrow(inters_df)),]
        inters<-inters_df$gene
        iters_rep1<-rep1_transcripts$feature_name %in% inters
        vis_r1<-rep1_transcripts[iters_rep1,
                                c("x","y","feature_name")]
        vis_r1$value<-inters_df[match(vis_r1$feature_name,inters_df$gene),
                                "value"]
        vis_r1<-vis_r1[order(vis_r1$value,decreasing = TRUE),]
        vis_r1$text_label<- paste(vis_r1$feature_name,
                                    vis_r1$value,sep=": ")
        vis_r1$text_label<-factor(vis_r1$text_label)
        vis_r1$sample<-"rep1"
        iters_rep2<- rep2_transcripts$feature_name %in% inters
        vis_r2<-rep2_transcripts[iters_rep2,
                                c("x","y","feature_name")]
        vis_r2$value<-inters_df[match(vis_r2$feature_name,inters_df$gene),
                                "value"]
        vis_r2<-vis_r2[order(vis_r2$value, decreasing = TRUE),]
        vis_r2$text_label<-paste(vis_r2$feature_name,
                                vis_r2$value,sep=": ")
        vis_r2$text_label<-factor(vis_r2$text_label)
        vis_r2$sample<-"rep2"
        p1<- ggplot(data = vis_r1,
                    aes(x = x, y = y))+ 
                    geom_point(size=0.01,color="maroon4")+
                    facet_grid(sample~text_label, scales="free")+
                    scale_y_reverse()+
                    guides(fill = guide_colorbar(height= grid::unit(5, "cm")))+
                    defined_theme
        p2<- ggplot(data = vis_r2,
                    aes(x = x, y = y))+ 
                    geom_point(size=0.01,color="maroon4")+
                    facet_grid(sample~text_label,scales="free")+
                    scale_y_reverse()+
                    guides(fill = guide_colorbar(height= grid::unit(5, "cm")))+
                    defined_theme
        
        cl_pt<-ggplot(data = rep_clusters[rep_clusters$anno==cl, ],
                    aes(x = x, y = y, color=cluster))+
                    geom_point(position=position_jitterdodge(jitter.width=0, 
                                            jitter.height=0), size=0.2)+
                    facet_grid(sample~cluster)+
                    scale_y_reverse()+
                    theme_classic()+
                    scale_color_manual(values = "black")+
                    defined_theme
        lyt <- cl_pt | (p1 / p2) 
        # if (cl %in% c("c2","c5","c6","c7")){
        #     lyt <- cl_pt | ((p1 / p2) | patchwork::plot_spacer())   
        # }
        layout_design <- lyt + patchwork::plot_layout(widths = c(1,2)) 

        print(layout_design)
}}

## ----vvplot_twosample, fig.width=8, fig.height=6------------------------------
cluster_names<-paste("c", 1:8, sep="")
plot_lst<-list()
for (cl in cluster_names){
    inters<-tworep_res[tworep_res$top_cluster==cl,"gene"]
    curr_cell_type<- all_celltypes[all_celltypes$cluster==cl,"anno"]
    rounded_val<-signif(as.numeric(tworep_res[inters,"glm_coef"]),
                            digits = 3)
    inters_df<-as.data.frame(cbind(gene=inters, value=rounded_val))
    inters_df$value<-as.numeric(inters_df$value)
    inters_df<-inters_df[order(inters_df$value, decreasing = TRUE),]
    inters_df$text<-paste(inters_df$gene,inters_df$value,sep=": ")
        
    mk_gene<-inters_df[1:min(2, nrow(inters_df)),"gene"]
    if (length(inters > 0)){
        dff<-as.data.frame(cbind(two_rep_vectors$cluster_mt[,cl],
                                    two_rep_vectors$gene_mt[,mk_gene]))
        colnames(dff) <- c("cluster", mk_gene)
        total_tiles <- grid_length * grid_length
        dff$vector_id <- c(1:total_tiles)
        dff$sample<- "Replicate1"
        dff[(total_tiles+1):(total_tiles*2),"sample"] <- "Replicate2"
        dff$vector_id <- c(1:total_tiles, 1:total_tiles)
        long_df <- dff %>% 
        pivot_longer(cols = -c(cluster, sample, vector_id), names_to = "gene", 
                        values_to = "vector_count")
        long_df$gene <- factor(long_df$gene, levels=mk_gene)
        p<-ggplot(long_df, 
                        aes(x = cluster, y = vector_count, color =sample)) +
            geom_point( size=0.01) +
            facet_wrap(~gene, scales = "free_y", nrow=1) +
            labs(x = paste("cluster vector ", curr_cell_type, sep=""), 
                    y = "marker gene vectors") +
            theme_minimal()+
            guides(color=guide_legend(nrow = 1,
                        override.aes=list(alpha=1, size=2)))+
            theme(panel.grid = element_blank(),legend.position = "bottom",
                    strip.text = element_text(size = rel(1)),
                    axis.line=element_blank(),
                    legend.title = element_blank(),
                    legend.key.size = grid::unit(0.5, "cm"),
                    legend.text = element_text(size=10),
                    axis.text=element_blank(),
                    axis.ticks=element_blank(),
                    axis.title=element_text(size = 10),
                    panel.border =element_rect(colour = "black", 
                                                fill=NA, linewidth=0.5)
            )
        plot_lst[[cl]] <- p
    }
}
combined_plot <- ggarrange(plotlist = plot_lst, 
                            ncol = 2, nrow = 4,
                            common.legend = TRUE, legend = "top")

combined_plot 

## ----session info-------------------------------------------------------------
sessionInfo()