analysis/04_overall_results_summary_figures.md
In lulizou/spASE: Detecting Allele-Specific Expression in Spatial Transcriptomics
Overall results summary figures
- Overall p
- Estimated p
pairs
- Overall p bias
- Within cell type p bias
- Overall spatial patterns
- Within cell type spatial
patterns
- Table 1
- Table 2
- Previously reported
imprinted genes
- Supp table
samples
- Supp
table overall bias
- Supp table ct
bias
- Number of
santini et al genes imprinted in ours
- Number of
geneimprint genes imprinted in ours
- Supp table
spatial
- Supp
table ct spatial
library(spacexr)
library(spASE)
Registered S3 method overwritten by 'spASE':
method from
merge.RCTD.objects spacexr
Attaching package: 'spASE'
The following objects are masked from 'package:spacexr':
aggregate_cell_types, build.designmatrix.intercept,
build.designmatrix.nonparam, build.designmatrix.regions,
build.designmatrix.single, choose_sigma_c, convert.old.RCTD,
count_cell_types, create_RCTD_plots, create.RCTD,
create.RCTD.replicates, CSIDE.population.inference,
exvar.celltocell.interactions, exvar.point.density, fitBulk,
fitPixels, get_cell_type_info, get_de_genes, get_doublet_weights,
get_norm_ref, get_standard_errors, import_weights,
make_all_de_plots, make_de_plots_genes, make_de_plots_quant,
make_de_plots_regions, make_de_plots_replicates,
make_de_plots_spatial, normalize_weights, plot_all_cell_types,
plot_class, plot_cond_occur, plot_doub_occur_stack, plot_doublets,
plot_doublets_type, plot_gene_raw, plot_gene_regions,
plot_gene_two_regions, plot_occur_unthreshold,
plot_prediction_gene, plot_puck_continuous, plot_puck_wrapper,
plot_weights, plot_weights_doublet, plot_weights_unthreshold,
process_beads_batch, process_data, read.SpatialRNA,
read.VisiumSpatialRNA, Reference, restrict_counts, restrict_puck,
run.CSIDE, run.CSIDE.general, run.CSIDE.intercept,
run.CSIDE.nonparam, run.CSIDE.regions, run.CSIDE.replicates,
run.CSIDE.single, run.RCTD, run.RCTD.replicates,
save.CSIDE.replicates, set_cell_types_assigned,
set_likelihood_vars, SpatialRNA, write_de_summary
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(ggplot2)
library(data.table)
Attaching package: 'data.table'
The following objects are masked from 'package:dplyr':
between, first, last
library(viridis)
Loading required package: viridisLite
library(gplots)
Attaching package: 'gplots'
The following object is masked from 'package:stats':
lowess
library(rtracklayer)
Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics
Attaching package: 'BiocGenerics'
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, 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, sort,
table, tapply, union, unique, unsplit, which.max, which.min
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following object is masked from 'package:gplots':
space
The following objects are masked from 'package:data.table':
first, second
The following objects are masked from 'package:dplyr':
first, rename
The following object is masked from 'package:utils':
findMatches
The following objects are masked from 'package:base':
expand.grid, I, unname
Loading required package: IRanges
Attaching package: 'IRanges'
The following object is masked from 'package:data.table':
shift
The following objects are masked from 'package:dplyr':
collapse, desc, slice
Loading required package: GenomeInfoDb
library(Matrix)
Attaching package: 'Matrix'
The following object is masked from 'package:S4Vectors':
expand
THRESHOLD = 500
# Read in gencode to grab xchr genes
gencode <- import('results/gencode.vM10.annotation.gff3.gz')
xchr_genes <- unique(gencode$gene_name[which(seqnames(gencode)=='chrX')])
xchr_genes <- c(xchr_genes, 'Bex3')
Overall p
hippo_1 <- readRDS('results/results_overall_bias_hippo_1.rds')
hippo_2 <- readRDS('results/results_overall_bias_hippo_2.rds')
hippo_3 <- readRDS('results/results_overall_bias_hippo_3.rds')
cere_3 <- readRDS('results/results_overall_bias_cere_3.rds')
cere_4 <- readRDS('results/results_overall_bias_cere_4_visium.rds')
df <- hippo_1 |>
filter(totalUMI > THRESHOLD) |>
mutate(hippo_1 = p) |>
select(gene, hippo_1) |>
left_join(hippo_2 |>
filter(totalUMI > THRESHOLD) |>
mutate(hippo_2 = p) |>
select(gene, hippo_2)) |>
left_join(hippo_3 |>
filter(totalUMI > THRESHOLD) |>
mutate(hippo_3 = p) |>
select(gene, hippo_3)) |>
left_join(cere_3 |>
filter(totalUMI > THRESHOLD) |>
mutate(cere_3 = p) |>
select(gene, cere_3)) |>
left_join(cere_4 |>
filter(totalUMI > THRESHOLD) |>
mutate(cere_4 = p) |>
select(gene, cere_4)) |>
filter(!is.na(hippo_1), !is.na(hippo_2), !is.na(hippo_3), !is.na(cere_3), !is.na(cere_4)) |>
mutate(xchr = ifelse(gene %in% xchr_genes, 1, 0)) |>
filter(!grepl('mt-', gene))
Joining with `by = join_by(gene)`
Joining with `by = join_by(gene)`
Joining with `by = join_by(gene)`
Joining with `by = join_by(gene)`
mm <- as.matrix(df[,2:6])
rownames(mm) <- df$gene
colnames(mm) <- c('Hippo 1', 'Hippo 2', 'Hippo 3', 'Cere 3', 'Cere 4')
pdf('figures/04_heatmap.pdf', height=8, width=4)
heatmap.2(mm, scale='none', col= bluered(100), trace='none', density.info='none', breaks = seq(0,1,length.out=101), lhei=c(1,6), cexRow = 0.5, cexCol = 0.5)
dev.off()
quartz_off_screen
2
Estimated p pairs
panel.hist <- function(x, ...)
{
usr <- par("usr")
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE, breaks = 50)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col = "cyan", ...)
}
panel.cor <- function(x, y, digits = 2, prefix = "", cex.cor, ...)
{
par(usr = c(0, 1, 0, 1))
r <- abs(cor(x, y))^2
txt <- format(c(r, 0.123456789), digits = digits)[1]
txt <- paste0(prefix, txt)
if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
text(0.5, 0.5, txt, cex = cex.cor)
}
pdf('figures/04_pair_scatter.pdf', height=6, width=6)
pairs(df |> filter(xchr == 0) |> select(-gene, -xchr) |> dplyr::rename(`Hippo 1` = hippo_1, `Hippo 2` = hippo_2, `Hippo 3` = hippo_3, `Cere 3` = cere_3, `Cere 4` = cere_4) |> as.matrix(),
upper.panel = panel.cor,
diag.panel = panel.hist)
dev.off()
quartz_off_screen
2
Overall p bias
extract_mat_pat_overall <- function(spase_res, qthresh = 0.01) {
dd <- spase_res |>
filter(qval < qthresh | grepl('monoallelic', flag)) |>
mutate(bias = case_when(
p > 0.6 ~ 'maternal',
p < 0.4 ~ 'paternal',
flag == 'monoallelic1' ~ 'maternal',
flag == 'monoallelic2' ~ 'paternal',
T ~ 'low_bias'
))
dd$xchr <- ifelse(dd$gene %in% xchr_genes, T, F)
return(dd)
}
hippo1_overall <- extract_mat_pat_overall(hippo_1)
hippo2_overall <- extract_mat_pat_overall(hippo_2)
hippo3_overall <- extract_mat_pat_overall(hippo_3)
cere4_overall <- extract_mat_pat_overall(cere_4)
cere3_overall <- extract_mat_pat_overall(cere_3)
Within cell type p bias
hippo1_intercept <- readRDS('results/results_celltype_intercept_hippo_1_df_5.rds')
hippo2_intercept <- readRDS('results/results_celltype_intercept_hippo_2_df_5.rds')
hippo3_intercept <- readRDS('results/results_celltype_intercept_hippo_3_df_5.rds')
cere3_intercept <- readRDS('results/results_celltype_intercept_cere_3_df_5.rds')
cere4_intercept <- readRDS('results/results_celltype_intercept_cere_4_visium_df_5.rds')
extract_ct_genes <- function(sig_gene_list) {
return(unique(unname(unlist(lapply(sig_gene_list, rownames)))))
}
extract_mat_pat_ct <- function(sig_gene_list) {
res <- NULL
for (i in seq_along(sig_gene_list)) {
dd <- sig_gene_list[[i]]
if (nrow(dd)==0) {
next
}
ct <- names(sig_gene_list)[i]
dd$gene <- rownames(dd)
rownames(dd) <- NULL
dd$cell_type <- ct
dd$bias <- ifelse(dd$log_fc > 0, 'maternal', 'paternal')
dd$xchr <- ifelse(dd$gene %in% xchr_genes, T, F)
if (is.null(res)) {
res <- dd
} else {
res <- bind_rows(res, dd)
}
}
res <- res |> select(gene, xchr, cell_type, bias, Z_score, log_fc, se, p_val, mean_0, mean_1, sd_0, sd_1)
return(res)
}
hippo1_ct <- extract_mat_pat_ct(hippo1_intercept@spase_results$sig_gene_list)
hippo2_ct <- extract_mat_pat_ct(hippo2_intercept@spase_results$sig_gene_list)
hippo3_ct <- extract_mat_pat_ct(hippo3_intercept@spase_results$sig_gene_list)
cere3_ct <- extract_mat_pat_ct(cere3_intercept@spase_results$sig_gene_list)
cere4_ct <- extract_mat_pat_ct(cere4_intercept@spase_results$sig_gene_list)
rm(hippo1_intercept, hippo2_intercept, hippo3_intercept, cere3_intercept, cere4_intercept)
Overall spatial patterns
hippo_1_sp <- readRDS('results/results_overall_spatial_hippo_1.rds')
hippo_2_sp <- readRDS('results/results_overall_spatial_hippo_2.rds')
hippo_3_sp <- readRDS('results/results_overall_spatial_hippo_3.rds')
cere_3_sp <- readRDS('results/results_overall_spatial_cere_3.rds')
cere_4_sp <- readRDS('results/results_overall_spatial_cere_4_visium.rds')
extract_spatial_overall <- function(spase_res, qthresh = 0.01) {
dd <- spase_res |> filter(qval < qthresh)
dd$xchr <- ifelse(dd$gene %in% xchr_genes, T, F)
return(dd)
}
hippo1_sp_overall <- extract_spatial_overall(hippo_1_sp$result)
hippo2_sp_overall <- extract_spatial_overall(hippo_2_sp$result)
hippo3_sp_overall <- extract_spatial_overall(hippo_3_sp$result)
cere3_sp_overall <- extract_spatial_overall(cere_3_sp$result)
cere4_sp_overall <- extract_spatial_overall(cere_4_sp$result)
Within cell type spatial patterns
hippo_1_spct_res <- readRDS('results/results_celltype_hippo_1_df_5.rds')
hippo_2_spct_res <- readRDS('results/results_celltype_hippo_2_df_5.rds')
hippo_3_spct_res <- readRDS('results/results_celltype_hippo_3_df_5.rds')
cere_3_spct_res <- readRDS('results/results_celltype_cere_3_df_5.rds')
cere_4_spct_res <- readRDS('results/results_celltype_cere_4_visium_df_5.rds')
extract_spatial_ct <- function(sig_gene_list) {
res <- NULL
for (i in seq_along(sig_gene_list)) {
dd <- sig_gene_list[[i]]
if (nrow(dd) == 0) {
next
}
ct <- names(sig_gene_list)[i]
dd$gene <- rownames(dd)
rownames(dd) <- NULL
dd$cell_type <- ct
dd$xchr <- ifelse(dd$gene %in% xchr_genes, T, F)
if (is.null(res)) {
res <- dd
} else {
res <- bind_rows(res, dd)
}
}
return(res)
}
hippo1_sp_ct <- extract_spatial_ct(hippo_1_spct_res@spase_results$sig_gene_list)
hippo2_sp_ct <- extract_spatial_ct(hippo_2_spct_res@spase_results$sig_gene_list)
hippo3_sp_ct <- extract_spatial_ct(hippo_3_spct_res@spase_results$sig_gene_list)
cere3_sp_ct <- extract_spatial_ct(cere_3_spct_res@spase_results$sig_gene_list)
cere4_sp_ct <- extract_spatial_ct(cere_4_spct_res@spase_results$sig_gene_list)
hippo1_sp_ct_all <- extract_spatial_ct(hippo_1_spct_res@spase_results$all_gene_list)
hippo2_sp_ct_all <- extract_spatial_ct(hippo_2_spct_res@spase_results$all_gene_list)
hippo3_sp_ct_all <- extract_spatial_ct(hippo_3_spct_res@spase_results$all_gene_list)
cere3_sp_ct_all <- extract_spatial_ct(cere_3_spct_res@spase_results$all_gene_list)
cere4_sp_ct_all <- extract_spatial_ct(cere_4_spct_res@spase_results$all_gene_list)
format_lengths <- function(c1, c2) {
return(paste0(prettyNum(length(c1), big.mark=','), ' (', prettyNum(length(c2), big.mark=','), ')'))
}
extract_info_for_table <- function(all, p_bias, ct_p_bias, sp, sp_ct, sp_ct_all) {
all$xchr <- ifelse(all$gene %in% xchr_genes, T, F)
all_genes_autosome <- all |> filter(!xchr) |> pull(gene)
rn <- rownames(sp_ct_all@originalSpatialRNA@maternalCounts[rowSums(sp_ct_all@originalSpatialRNA@maternalCounts) + rowSums(sp_ct_all@originalSpatialRNA@paternalCounts) > 128,])
rn_autosome <- rn[!rn %in% xchr_genes]
rn_xchr <- rn[rn %in% xchr_genes]
all_genes_autosome <- unique(c(all_genes_autosome, rn_autosome))
all_genes_xchr <- all |> filter(xchr) |> pull(gene)
all_genes_xchr <- unique(c(all_genes_xchr, rn_xchr))
mat_genes_autosome <- p_bias |> filter(!xchr, bias == 'maternal') |> pull(gene)
pat_genes_autosome <- p_bias |> filter(!xchr, bias == 'paternal') |> pull(gene)
mat_genes_xchr <- p_bias |> filter(xchr, bias == 'maternal') |> pull(gene)
pat_genes_xchr <- p_bias |> filter(xchr, bias == 'paternal') |> pull(gene)
mat_genes_ct_autosome <- ct_p_bias |> filter(!xchr, bias == 'maternal') |> pull(gene) |> unique()
pat_genes_ct_autosome <- ct_p_bias |> filter(!xchr, bias == 'paternal') |> pull(gene)|> unique()
mat_genes_ct_xchr <- ct_p_bias |> filter(xchr, bias == 'maternal') |> pull(gene)|> unique()
pat_genes_ct_xchr <- ct_p_bias |> filter(xchr, bias == 'paternal') |> pull(gene)|> unique()
all_spatial_autosome <- sp |> filter(!xchr) |> pull(gene)
all_spatial_xchr <- sp |> filter(xchr) |> pull(gene)
if (is.null(sp_ct)) {
ct_spatial_autosome <- c()
ct_spatial_xchr <- c()
} else {
ct_spatial_autosome <- sp_ct |> filter(!xchr) |> pull(gene) |> unique()
ct_spatial_xchr <- sp_ct |> filter(xchr) |> pull(gene) |> unique()
}
no_sig_ase_autosome <- all_genes_autosome[!all_genes_autosome %in% c(mat_genes_autosome, pat_genes_autosome, mat_genes_ct_autosome, pat_genes_ct_autosome, all_spatial_autosome, ct_spatial_autosome)]
no_sig_ase_xchr <- all_genes_xchr[!all_genes_xchr %in% c(mat_genes_xchr, pat_genes_xchr, mat_genes_ct_xchr, pat_genes_ct_xchr, all_spatial_xchr, ct_spatial_xchr)]
return(list(
col = c(
format_lengths(no_sig_ase_autosome, no_sig_ase_xchr),
format_lengths(mat_genes_autosome, mat_genes_xchr),
format_lengths(pat_genes_autosome, pat_genes_xchr),
format_lengths(mat_genes_ct_autosome, mat_genes_ct_xchr),
format_lengths(pat_genes_ct_autosome, pat_genes_ct_xchr),
format_lengths(all_spatial_autosome, all_spatial_xchr),
format_lengths(ct_spatial_autosome, ct_spatial_xchr),
format_lengths(all_genes_autosome, all_genes_xchr)
),
nsa = no_sig_ase_autosome, nsx = no_sig_ase_xchr,
mga = mat_genes_autosome, mgx = mat_genes_xchr, pga = pat_genes_autosome, pgx = pat_genes_xchr, mgca = mat_genes_ct_autosome, mgcx = mat_genes_ct_xchr, pgca = pat_genes_ct_autosome, pgcx = pat_genes_ct_xchr, asa = all_spatial_autosome, asx = all_spatial_xchr, cta = ct_spatial_autosome, ctx = ct_spatial_xchr,
aga = all_genes_autosome, agx = all_genes_xchr
))
}
h1 <- extract_info_for_table(hippo_1, hippo1_overall, hippo1_ct, hippo1_sp_overall, hippo1_sp_ct, hippo_1_spct_res)
h2 <- extract_info_for_table(hippo_2, hippo2_overall, hippo2_ct, hippo2_sp_overall, hippo2_sp_ct, hippo_2_spct_res)
h3 <- extract_info_for_table(hippo_3, hippo3_overall, hippo3_ct, hippo3_sp_overall, hippo3_sp_ct, hippo_3_spct_res)
c3 <- extract_info_for_table(cere_3, cere3_overall, cere3_ct, cere3_sp_overall, cere3_sp_ct, cere_3_spct_res)
c4 <- extract_info_for_table(cere_4, cere4_overall, cere4_ct, cere4_sp_overall, cere4_sp_ct, cere_4_spct_res)
# make the overlap columns
c_overlap <- c(
format_lengths(c3$nsa[c3$nsa %in% c4$nsa], c3$nsx[c3$nsx %in% c4$nsx]),
format_lengths(c3$mga[c3$mga %in% c4$mga], c3$mgx[c3$mgx %in% c4$mgx]),
format_lengths(c3$pga[c3$pga %in% c4$pga], c3$pgx[c3$pgx %in% c4$pgx]),
format_lengths(c3$mgca[c3$mgca %in% c4$mgca], c3$mgcx[c3$mgcx %in% c4$mgcx]),
format_lengths(c3$pgca[c3$pgca %in% c4$pgca], c3$pgcx[c3$pgcx %in% c4$pgcx]),
format_lengths(c3$asa[c3$asa %in% c4$asa], c3$asx[c3$asx %in% c4$asx]),
format_lengths(c3$cta[c3$cta %in% c4$cta], c3$ctx[c3$ctx %in% c4$ctx]),
format_lengths(c3$aga[c3$aga %in% c4$aga], c3$agx[c3$agx %in% c4$agx])
)
h_overlap12 <- c(
format_lengths(h1$nsa[h1$nsa %in% h2$nsa], h1$nsx[h1$nsx %in% h2$nsx]),
format_lengths(h1$mga[h1$mga %in% h2$mga], h1$mgx[h1$mgx %in% h2$mgx]),
format_lengths(h1$pga[h1$pga %in% h2$pga], h1$pgx[h1$pgx %in% h2$pgx]),
format_lengths(h1$mgca[h1$mgca %in% h2$mgca], h1$mgcx[h1$mgcx %in% h2$mgcx]),
format_lengths(h1$pgca[h1$pgca %in% h2$pgca], h1$pgcx[h1$pgcx %in% h2$pgcx]),
format_lengths(h1$asa[h1$asa %in% h2$asa], h1$asx[h1$asx %in% h2$asx]),
format_lengths(h1$cta[h1$cta %in% h2$cta], h1$ctx[h1$ctx %in% h2$ctx]),
format_lengths(h1$aga[h1$aga %in% h2$aga], h1$agx[h1$agx %in% h2$agx])
)
h_overlap13 <- c(
format_lengths(h1$nsa[h1$nsa %in% h3$nsa], h1$nsx[h1$nsx %in% h3$nsx]),
format_lengths(h1$mga[h1$mga %in% h3$mga], h1$mgx[h1$mgx %in% h3$mgx]),
format_lengths(h1$pga[h1$pga %in% h3$pga], h1$pgx[h1$pgx %in% h3$pgx]),
format_lengths(h1$mgca[h1$mgca %in% h3$mgca], h1$mgcx[ h1$mgcx %in% h3$mgcx]),
format_lengths(h1$pgca[h1$pgca %in% h3$pgca], h1$pgcx[h1$pgcx %in% h3$pgcx]),
format_lengths(h1$asa[h1$asa %in% h3$asa], h1$asx[h1$asx %in% h3$asx]),
format_lengths(h1$cta[h1$cta %in% h3$cta], h1$ctx[h1$ctx %in% h3$ctx]),
format_lengths(h1$aga[h1$aga %in% h3$aga], h1$agx[h1$agx %in% h3$agx])
)
Table 1
t1 <- data.frame(Category = c('No significant ASE', 'Overall maternal bias', 'Overall paternal bias', 'Within cell type maternal bias', 'Within cell type paternal bias', 'Overall spatial pattern', 'Within cell type spatial pattern', 'Total n genes'),
`Slide-seq (Mouse 3)` = c3$col, `Visium (Mouse 4)` = c4$col, Overlap = c_overlap)
print(xtable::xtable(t1))
% latex table generated in R 4.3.1 by xtable 1.8-4 package
% Fri Mar 8 23:32:05 2024
\begin{table}[ht]
\centering
\begin{tabular}{rllll}
\hline
& Category & Slide.seq..Mouse.3. & Visium..Mouse.4. & Overlap \\
\hline
1 & No significant ASE & 6,235 (53) & 8,971 (162) & 5,425 (30) \\
2 & Overall maternal bias & 720 (157) & 502 (112) & 196 (81) \\
3 & Overall paternal bias & 947 (7) & 672 (12) & 300 (5) \\
4 & Within cell type maternal bias & 306 (62) & 9 (2) & 5 (2) \\
5 & Within cell type paternal bias & 416 (4) & 14 (0) & 7 (0) \\
6 & Overall spatial pattern & 8 (19) & 2 (0) & 1 (0) \\
7 & Within cell type spatial pattern & 0 (6) & 0 (0) & 0 (0) \\
8 & Total n genes & 8,304 (225) & 10,147 (286) & 7,599 (204) \\
\hline
\end{tabular}
\end{table}
Table 2
t2 <- data.frame(Category = c('No significant ASE', 'Overall maternal bias', 'Overall paternal bias', 'Within cell type maternal bias', 'Within cell type paternal bias', 'Overall spatial pattern', 'Within cell type spatial pattern', 'Total n genes'),
`Mouse 1` = h1$col, `Mouse 2` = h2$col, `Mouse 3` = h3$col, Overlap12 = h_overlap12, Overlap13 = h_overlap13)
print(xtable::xtable(t2))
% latex table generated in R 4.3.1 by xtable 1.8-4 package
% Fri Mar 8 23:32:05 2024
\begin{table}[ht]
\centering
\begin{tabular}{rllllll}
\hline
& Category & Mouse.1 & Mouse.2 & Mouse.3 & Overlap12 & Overlap13 \\
\hline
1 & No significant ASE & 4,961 (104) & 2,242 (5) & 6,530 (16) & 2,008 (3) & 4,249 (4) \\
2 & Overall maternal bias & 349 (30) & 176 (55) & 623 (208) & 93 (11) & 179 (29) \\
3 & Overall paternal bias & 456 (9) & 180 (1) & 834 (1) & 109 (1) & 254 (1) \\
4 & Within cell type maternal bias & 104 (3) & 24 (15) & 250 (61) & 18 (1) & 64 (1) \\
5 & Within cell type paternal bias & 151 (9) & 22 (0) & 307 (0) & 19 (0) & 101 (0) \\
6 & Overall spatial pattern & 18 (17) & 1 (0) & 67 (0) & 1 (0) & 9 (0) \\
7 & Within cell type spatial pattern & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) \\
8 & Total n genes & 5,866 (159) & 2,609 (61) & 8,309 (225) & 2,560 (59) & 5,698 (150) \\
\hline
\end{tabular}
\end{table}
Previously reported imprinted genes
santini_et_al <- read.delim('../inst/extdata/santini_et_al_2021_genomic_imprinting_mouse.tsv')
# with bias
geneimprint <- read.delim('../inst/extdata/geneimprint.com-mouse.tsv')
Supp table samples
mix_5 <- readRDS('results/rctd_mix_5_visium.rds')
get_stats <- function(cside_spase_obj) {
tot <- cside_spase_obj@originalSpatialRNA@counts
tot_ase <- cside_spase_obj@originalSpatialRNA@maternalCounts + cside_spase_obj@originalSpatialRNA@paternalCounts
nspots <- ncol(tot)
ngenes <- nrow(tot)
readsspot <- mean(colSums(tot))
readsase <- mean(colSums(tot_ase))
return(c(prettyNum(nspots, big.mark=','),prettyNum(ngenes, big.mark=','), prettyNum(round(readsspot),big.mark=','), prettyNum(round(readsase),big.mark=',')))
}
rr <- rbind(get_stats(hippo_1_spct_res), get_stats(hippo_2_spct_res), get_stats(hippo_3_spct_res), get_stats(cere_3_spct_res), get_stats(cere_4_spct_res), get_stats(mix_5))
colnames(rr) <- c('N spots', 'N genes', 'Avg. reads/spot', 'Allele-resolved')
rr <- data.frame(rr)
dd <- bind_cols(
data.frame(Mouse = c(1,2,3,3,4,5),
Tissue = c('Hippocampus', 'Hippocampus', 'Hippocampus', 'Cerebellum', 'Cerebellum','Mix'),
Platform = c('Slide-seq', 'Slide-seq', 'Slide-seq', 'Slide-seq', 'Visium','Visium'),
`Read length` = c(160, 56, 250, 250, 91,91)),
rr
)
xtable::xtable(dd)
% latex table generated in R 4.3.1 by xtable 1.8-4 package
% Fri Mar 8 23:32:19 2024
\begin{table}[ht]
\centering
\begin{tabular}{rrllrllll}
\hline
& Mouse & Tissue & Platform & Read.length & N.spots & N.genes & Avg..reads.spot & Allele.resolved \\
\hline
1 & 1.00 & Hippocampus & Slide-seq & 160.00 & 26,429 & 22,049 & 459 & 207 \\
2 & 2.00 & Hippocampus & Slide-seq & 56.00 & 13,680 & 23,541 & 623 & 145 \\
3 & 3.00 & Hippocampus & Slide-seq & 250.00 & 78,806 & 29,411 & 552 & 218 \\
4 & 3.00 & Cerebellum & Slide-seq & 250.00 & 60,942 & 30,658 & 622 & 240 \\
5 & 4.00 & Cerebellum & Visium & 91.00 & 4,315 & 19,860 & 2,219 & 834 \\
6 & 5.00 & Mix & Visium & 91.00 & 4,150 & 20,095 & 2,143 & 853 \\
\hline
\end{tabular}
\end{table}
Supp table overall bias
st1 <- hippo1_overall |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 1', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, totalUMI, totalCells, p, ci.low, ci.high, z, pval, qval, bias, xchr) |>
bind_rows(
hippo2_overall |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 2', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, totalUMI, totalCells, p, ci.low, ci.high, z, pval, qval, bias, xchr)
) |>
bind_rows(
hippo3_overall |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 3', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, totalUMI, totalCells, p, ci.low, ci.high, z, pval, qval, bias, xchr)
) |>
bind_rows(
cere3_overall |>
mutate(tissue = 'Cerebellum', sample = 'Mouse 3', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, totalUMI, totalCells, p, ci.low, ci.high, z, pval, qval, bias, xchr)
) |>
bind_rows(
cere4_overall |>
mutate(tissue = 'Cerebellum', sample = 'Mouse 4', technology = 'Visium') |>
select(tissue, sample, technology, gene, totalUMI, totalCells, p, ci.low, ci.high, z, pval, qval, bias, xchr)
)
# Overlap with previously known imprinted
st1$previously_reported_imprinted <- ifelse(st1$gene %in% santini_et_al$Gene.symbol, T, F)
st1 <- st1 |> left_join(geneimprint |> select(Gene, Status, Expressed.Allele) |> dplyr::rename(gene = Gene, geneimprint_status = Status, geneimprint_allele = Expressed.Allele), by = 'gene')
write.csv(st1, file = 'tables/04_supp_table_overall_bias.csv')
st1 |> select(gene, previously_reported_imprinted) |> distinct() |> pull(previously_reported_imprinted) |> table(useNA='always')
FALSE TRUE <NA>
5279 90 0
st1 |> filter(geneimprint_status == 'Imprinted') |> select(gene, bias, geneimprint_allele) |> distinct() |> arrange(gene)
gene bias geneimprint_allele
1 Axl paternal Maternal
2 Begain paternal Isoform Dependent
3 Blcap maternal Isoform Dependent
4 Cd81 paternal Maternal
5 Cd81 low_bias Maternal
6 Cdkn1c maternal Maternal
7 Chrac1 low_bias Maternal
8 Commd1 maternal Maternal
9 Dhcr7 maternal Maternal
10 Dlk1 paternal Paternal
11 Ftx maternal Paternal
12 Gatm low_bias Maternal
13 Gatm maternal Maternal
14 H13 paternal Maternal
15 Igf2 maternal Paternal
16 Impact paternal Paternal
17 Jade1 low_bias Paternal
18 Jpx maternal Paternal
19 Kcnq1ot1 paternal Paternal
20 Magel2 paternal Paternal
21 Mcts2 paternal Paternal
22 Mirg maternal Maternal
23 Mkrn3 paternal Paternal
24 Nap1l5 paternal Paternal
25 Peg13 paternal Paternal
26 Peg3 paternal Paternal
27 Plagl1 paternal Paternal
28 Qpct maternal Maternal
29 Rasgrf1 paternal Paternal
30 Rian maternal Maternal
31 Sgce paternal Paternal
32 Smoc1 paternal Paternal
33 Snrpn paternal Paternal
34 Th paternal Maternal
35 Trappc9 low_bias Maternal
36 Ube3a paternal Maternal
37 Ube3a maternal Maternal
38 Usp29 paternal Paternal
39 Xist paternal Paternal
40 Zdbf2 paternal Paternal
41 Zrsr1 paternal Paternal
Supp table ct bias
st2 <- hippo1_ct |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 1', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, cell_type, Z_score, log_fc, se, p_val, bias, xchr) |>
bind_rows(
hippo2_ct |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 2', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, cell_type, Z_score, log_fc, se, p_val, bias, xchr)
) |>
bind_rows(
hippo3_ct |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 3', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, cell_type, Z_score, log_fc, se, p_val, bias, xchr
)
) |>
bind_rows(
cere3_ct |>
mutate(tissue = 'Cerebellum', sample = 'Mouse 3', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, cell_type, Z_score, log_fc, se, p_val, bias, xchr)
)|>
bind_rows(
cere4_ct |>
mutate(tissue = 'Cerebellum', sample = 'Mouse 4', technology = 'Visium') |>
select(tissue, sample, technology, gene, cell_type, Z_score, log_fc, se, p_val, bias, xchr)
)
st2$previously_reported_imprinted <- ifelse(st2$gene %in% santini_et_al$Gene.symbol, T, F)
st2 <- st2 |> left_join(geneimprint |> select(Gene, Status, Expressed.Allele) |> dplyr::rename(gene = Gene, geneimprint_status = Status, geneimprint_allele = Expressed.Allele), by = 'gene')
write.csv(st2, file = 'tables/04_supp_table_celltype_bias.csv')
Number of santini et al genes imprinted in ours
sum(santini_et_al$Gene.symbol %in% c(st1$gene, st2$gene))
[1] 91
Number of geneimprint genes imprinted in ours
length(geneimprint$Gene[geneimprint$Status=='Imprinted' & geneimprint$Expressed.Allele %in% c('Maternal', 'Paternal')])
[1] 138
dd <- geneimprint |>
left_join(st1, by = c('Gene' = 'gene')) |>
select(Gene, bias, Expressed.Allele) |>
filter(!is.na(bias) & bias != 'low_bias' & Expressed.Allele %in% c('Paternal', 'Maternal')) |>
distinct() |>
mutate(Expressed.Allele = tolower(Expressed.Allele))
table(dd$bias, dd$Expressed.Allele)
maternal paternal
maternal 8 3
paternal 6 18
Supp table spatial
st3 <- hippo1_sp_overall |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 1', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, totalUMI, totalSpots, chisq.p, qval, xchr) |>
bind_rows(
hippo2_sp_overall |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 2', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, totalUMI, totalSpots, chisq.p, qval, xchr)
) |>
bind_rows(
hippo3_sp_overall |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 3', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, totalUMI, totalSpots, chisq.p, qval, xchr)
) |>
bind_rows(
cere3_sp_overall |>
mutate(tissue = 'Cerebellum', sample = 'Mouse 3', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, totalUMI, totalSpots, chisq.p, qval, xchr)
) |>
bind_rows(
cere4_sp_overall |>
mutate(tissue = 'Cerebellum', sample = 'Mouse 4', technology = 'Visium') |>
select(tissue, sample, technology, gene, totalUMI, totalSpots, chisq.p, qval, xchr)
)
write.csv(st3, file = 'tables/04_supp_table_spatial_overall_bias.csv')
print(xtable::xtable(st3 |> select(-chisq.p) |> arrange(sample, tissue, qval), display = c('s', 's', 's', 's', 's', 'd', 'd', 'E', 's')), include.rownames=F)
% latex table generated in R 4.3.1 by xtable 1.8-4 package
% Fri Mar 8 23:32:19 2024
\begin{table}[ht]
\centering
\begin{tabular}{llllrrrl}
\hline
tissue & sample & technology & gene & totalUMI & totalSpots & qval & xchr \\
\hline
Hippocampus & Mouse 1 & Slide-seq & Tspan7 & 9612 & 6667 & 0.00E+00 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Nrip3 & 4213 & 3060 & 9.29E-12 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Atrx & 2351 & 1893 & 1.67E-11 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Sst & 1429 & 745 & 1.67E-11 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Ptgds & 2374 & 1321 & 4.01E-10 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Arhgef9 & 1857 & 1556 & 1.09E-09 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Ids & 1002 & 919 & 1.09E-09 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Rgs4 & 2712 & 2097 & 1.54E-09 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Chgb & 7898 & 3923 & 5.53E-08 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Gprasp1 & 2665 & 2177 & 6.03E-08 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Cpe & 22568 & 11861 & 7.72E-08 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Nrsn1 & 5220 & 4092 & 1.60E-06 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Pcsk1n & 3208 & 2851 & 2.27E-06 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Maged1 & 1364 & 1235 & 3.04E-06 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Fam193a & 558 & 533 & 6.47E-06 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Crym & 1241 & 940 & 4.61E-05 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Gpm6b & 2203 & 1963 & 9.37E-05 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Sparcl1 & 18578 & 10638 & 9.37E-05 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Meg3 & 18494 & 5727 & 1.47E-04 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Bex2 & 1570 & 1391 & 2.41E-04 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Magee1 & 1698 & 1444 & 2.61E-04 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Gap43 & 2920 & 2282 & 4.71E-04 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Map1b & 14912 & 7333 & 5.28E-04 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Armcx3 & 646 & 600 & 7.98E-04 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Malat1 & 72124 & 12172 & 1.49E-03 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Rprm & 1107 & 796 & 1.49E-03 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Prps1 & 879 & 790 & 1.50E-03 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Tceal5 & 2117 & 1781 & 1.50E-03 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Frrs1l & 2992 & 2321 & 5.80E-03 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Mageh1 & 673 & 634 & 5.80E-03 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Xist & 537 & 449 & 6.37E-03 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Atp1a2 & 8717 & 5701 & 6.76E-03 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Fgf13 & 954 & 869 & 6.76E-03 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Napa & 1299 & 1184 & 6.76E-03 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Tceal3 & 1491 & 1271 & 7.25E-03 & TRUE \\
Hippocampus & Mouse 2 & Slide-seq & Ptgds & 910 & 635 & 2.96E-05 & FALSE \\
Cerebellum & Mouse 3 & Slide-seq & Meg3 & 28544 & 14574 & 0.00E+00 & FALSE \\
Cerebellum & Mouse 3 & Slide-seq & Ptgds & 6457 & 3277 & 0.00E+00 & FALSE \\
Cerebellum & Mouse 3 & Slide-seq & Rps4x & 12261 & 9019 & 3.32E-13 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Bex2 & 9202 & 7044 & 2.70E-09 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Tceal3 & 9503 & 6693 & 2.03E-08 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Cdr1 & 2255 & 1945 & 4.74E-08 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Tspan7 & 11409 & 8731 & 4.74E-08 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Bex3 & 12213 & 8369 & 7.34E-08 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Ncald & 2311 & 1804 & 1.92E-07 & FALSE \\
Cerebellum & Mouse 3 & Slide-seq & Htatsf1 & 1277 & 1068 & 1.64E-05 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Ndufb11 & 13950 & 9732 & 9.11E-05 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Tceal5 & 5929 & 4637 & 9.11E-05 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Uba1 & 7773 & 5728 & 1.73E-04 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Nkap & 962 & 783 & 2.35E-04 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Idh3g & 7591 & 5758 & 3.97E-04 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Gprasp1 & 3373 & 2746 & 7.26E-04 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Atrx & 4767 & 3617 & 7.33E-04 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Lpar1 & 1311 & 1021 & 9.02E-04 & FALSE \\
Cerebellum & Mouse 3 & Slide-seq & Mageh1 & 3337 & 2566 & 1.13E-03 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Xist & 1678 & 1373 & 1.22E-03 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Cdk16 & 3542 & 3133 & 1.22E-03 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Psat1 & 1507 & 1237 & 1.82E-03 & FALSE \\
Cerebellum & Mouse 3 & Slide-seq & Aldoc & 89897 & 26422 & 2.16E-03 & FALSE \\
Cerebellum & Mouse 3 & Slide-seq & Plp1 & 17084 & 5957 & 3.61E-03 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Shank1 & 3427 & 2879 & 3.61E-03 & FALSE \\
Cerebellum & Mouse 3 & Slide-seq & Hprt & 2848 & 2429 & 7.30E-03 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Kcnj9 & 3493 & 2655 & 7.30E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Chgb & 17790 & 8654 & 0.00E+00 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Nrip3 & 12045 & 8028 & 0.00E+00 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Ptgds & 4863 & 3318 & 0.00E+00 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Rasgrf1 & 4462 & 3399 & 0.00E+00 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Snca & 20846 & 10276 & 0.00E+00 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Sst & 6781 & 3676 & 0.00E+00 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Stmn1 & 68985 & 30606 & 0.00E+00 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & A830018L16Rik & 1831 & 1483 & 9.99E-13 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Uchl1 & 37040 & 20455 & 2.05E-12 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Lypd1 & 2073 & 1585 & 3.00E-12 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Pcp4l1 & 13914 & 9034 & 7.69E-11 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Akap5 & 196564 & 62287 & 3.71E-10 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Nrsn1 & 10799 & 8182 & 3.88E-10 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Gstm7 & 1150 & 1018 & 7.29E-10 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Mrps11 & 2140 & 1715 & 3.25E-09 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Calb1 & 1604 & 1327 & 2.49E-08 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Grb14 & 2591 & 2119 & 7.86E-08 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Sparcl1 & 40302 & 25100 & 1.21E-07 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Spon1 & 1941 & 1554 & 1.36E-07 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Bok & 2728 & 2336 & 1.03E-06 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Car12 & 502 & 396 & 2.36E-06 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Rasgrp1 & 11580 & 7368 & 2.36E-06 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Selenop & 21213 & 14535 & 2.36E-06 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Rhpn2 & 862 & 782 & 2.76E-06 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Ccng1 & 2158 & 1806 & 5.14E-06 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Gaa & 4307 & 3332 & 1.01E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Cck & 15170 & 10001 & 1.34E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Chst2 & 8951 & 6587 & 1.43E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Rprm & 2537 & 1678 & 1.50E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Nefl & 17453 & 10783 & 1.66E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Actr2 & 8748 & 6525 & 2.05E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Cnih3 & 733 & 641 & 3.18E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Ncald & 16168 & 9706 & 6.65E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Cpe & 54353 & 30714 & 7.63E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Stum & 3774 & 3000 & 9.24E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Aldh1a1 & 5190 & 4006 & 1.42E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Etv1 & 2097 & 1582 & 1.55E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Syt13 & 5708 & 4378 & 1.57E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Zfp365 & 5896 & 4907 & 1.69E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Thrsp & 1440 & 1210 & 1.74E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & 2310058D17Rik & 534 & 505 & 5.21E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Myo5b & 1480 & 1160 & 5.44E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Nudt4 & 8909 & 6478 & 6.03E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Sphkap & 2039 & 1653 & 8.62E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Synpr & 1001 & 831 & 1.14E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Cdo1 & 976 & 810 & 1.32E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Rps20 & 36917 & 23266 & 1.34E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Resp18 & 4095 & 3182 & 1.69E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Trim9 & 1997 & 1646 & 1.71E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Prkcq & 1487 & 1014 & 2.65E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Tunar & 1083 & 916 & 2.93E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Pik3r4 & 1370 & 1106 & 3.27E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Msh2 & 1565 & 1236 & 3.87E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Efhd2 & 4295 & 3551 & 4.35E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Slc35c2 & 902 & 785 & 6.24E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Golm1 & 665 & 565 & 6.83E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Igfbp4 & 2781 & 1787 & 7.46E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Rtn4 & 11088 & 8257 & 7.46E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Sema5a & 1319 & 1059 & 7.46E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Rgs4 & 6248 & 4578 & 7.54E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Ripor2 & 842 & 724 & 8.58E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Itpr1 & 3409 & 2816 & 8.70E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Ppp3ca & 38830 & 13587 & 8.70E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Elp6 & 2587 & 2431 & 9.49E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Klc1 & 1864 & 1553 & 9.72E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Gng5 & 1020 & 958 & 9.73E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Lmo7 & 2167 & 1661 & 9.73E-03 & FALSE \\
Cerebellum & Mouse 4 & Visium & Rps8 & 5145 & 2173 & 2.14E-03 & FALSE \\
Cerebellum & Mouse 4 & Visium & Meg3 & 5375 & 2175 & 5.57E-03 & FALSE \\
\hline
\end{tabular}
\end{table}
Supp table ct spatial
st4 <- cere3_sp_ct |>
mutate(tissue = 'Cerebellum', sample = 'Mouse 3', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, cell_type,p_val, xchr)
write.csv(st4, file = 'tables/04_supp_table_spatial_celltype_bias.csv')
lulizou/spASE documentation built on May 22, 2024, 5:24 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Overall results summary figures
- Overall p
- Estimated p pairs
- Overall p bias
- Within cell type p bias
- Overall spatial patterns
- Within cell type spatial patterns
- Table 1
- Table 2
- Previously reported imprinted genes
- Supp table samples
- Supp table overall bias
- Supp table ct bias
- Number of santini et al genes imprinted in ours
- Number of geneimprint genes imprinted in ours
- Supp table spatial
- Supp table ct spatial
library(spacexr)
library(spASE)
Registered S3 method overwritten by 'spASE':
method from
merge.RCTD.objects spacexr
Attaching package: 'spASE'
The following objects are masked from 'package:spacexr':
aggregate_cell_types, build.designmatrix.intercept,
build.designmatrix.nonparam, build.designmatrix.regions,
build.designmatrix.single, choose_sigma_c, convert.old.RCTD,
count_cell_types, create_RCTD_plots, create.RCTD,
create.RCTD.replicates, CSIDE.population.inference,
exvar.celltocell.interactions, exvar.point.density, fitBulk,
fitPixels, get_cell_type_info, get_de_genes, get_doublet_weights,
get_norm_ref, get_standard_errors, import_weights,
make_all_de_plots, make_de_plots_genes, make_de_plots_quant,
make_de_plots_regions, make_de_plots_replicates,
make_de_plots_spatial, normalize_weights, plot_all_cell_types,
plot_class, plot_cond_occur, plot_doub_occur_stack, plot_doublets,
plot_doublets_type, plot_gene_raw, plot_gene_regions,
plot_gene_two_regions, plot_occur_unthreshold,
plot_prediction_gene, plot_puck_continuous, plot_puck_wrapper,
plot_weights, plot_weights_doublet, plot_weights_unthreshold,
process_beads_batch, process_data, read.SpatialRNA,
read.VisiumSpatialRNA, Reference, restrict_counts, restrict_puck,
run.CSIDE, run.CSIDE.general, run.CSIDE.intercept,
run.CSIDE.nonparam, run.CSIDE.regions, run.CSIDE.replicates,
run.CSIDE.single, run.RCTD, run.RCTD.replicates,
save.CSIDE.replicates, set_cell_types_assigned,
set_likelihood_vars, SpatialRNA, write_de_summary
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(ggplot2)
library(data.table)
Attaching package: 'data.table'
The following objects are masked from 'package:dplyr':
between, first, last
library(viridis)
Loading required package: viridisLite
library(gplots)
Attaching package: 'gplots'
The following object is masked from 'package:stats':
lowess
library(rtracklayer)
Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics
Attaching package: 'BiocGenerics'
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, 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, sort,
table, tapply, union, unique, unsplit, which.max, which.min
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following object is masked from 'package:gplots':
space
The following objects are masked from 'package:data.table':
first, second
The following objects are masked from 'package:dplyr':
first, rename
The following object is masked from 'package:utils':
findMatches
The following objects are masked from 'package:base':
expand.grid, I, unname
Loading required package: IRanges
Attaching package: 'IRanges'
The following object is masked from 'package:data.table':
shift
The following objects are masked from 'package:dplyr':
collapse, desc, slice
Loading required package: GenomeInfoDb
library(Matrix)
Attaching package: 'Matrix'
The following object is masked from 'package:S4Vectors':
expand
THRESHOLD = 500
# Read in gencode to grab xchr genes
gencode <- import('results/gencode.vM10.annotation.gff3.gz')
xchr_genes <- unique(gencode$gene_name[which(seqnames(gencode)=='chrX')])
xchr_genes <- c(xchr_genes, 'Bex3')
Overall p
hippo_1 <- readRDS('results/results_overall_bias_hippo_1.rds')
hippo_2 <- readRDS('results/results_overall_bias_hippo_2.rds')
hippo_3 <- readRDS('results/results_overall_bias_hippo_3.rds')
cere_3 <- readRDS('results/results_overall_bias_cere_3.rds')
cere_4 <- readRDS('results/results_overall_bias_cere_4_visium.rds')
df <- hippo_1 |>
filter(totalUMI > THRESHOLD) |>
mutate(hippo_1 = p) |>
select(gene, hippo_1) |>
left_join(hippo_2 |>
filter(totalUMI > THRESHOLD) |>
mutate(hippo_2 = p) |>
select(gene, hippo_2)) |>
left_join(hippo_3 |>
filter(totalUMI > THRESHOLD) |>
mutate(hippo_3 = p) |>
select(gene, hippo_3)) |>
left_join(cere_3 |>
filter(totalUMI > THRESHOLD) |>
mutate(cere_3 = p) |>
select(gene, cere_3)) |>
left_join(cere_4 |>
filter(totalUMI > THRESHOLD) |>
mutate(cere_4 = p) |>
select(gene, cere_4)) |>
filter(!is.na(hippo_1), !is.na(hippo_2), !is.na(hippo_3), !is.na(cere_3), !is.na(cere_4)) |>
mutate(xchr = ifelse(gene %in% xchr_genes, 1, 0)) |>
filter(!grepl('mt-', gene))
Joining with `by = join_by(gene)`
Joining with `by = join_by(gene)`
Joining with `by = join_by(gene)`
Joining with `by = join_by(gene)`
mm <- as.matrix(df[,2:6])
rownames(mm) <- df$gene
colnames(mm) <- c('Hippo 1', 'Hippo 2', 'Hippo 3', 'Cere 3', 'Cere 4')
pdf('figures/04_heatmap.pdf', height=8, width=4)
heatmap.2(mm, scale='none', col= bluered(100), trace='none', density.info='none', breaks = seq(0,1,length.out=101), lhei=c(1,6), cexRow = 0.5, cexCol = 0.5)
dev.off()
quartz_off_screen
2
Estimated p pairs
panel.hist <- function(x, ...)
{
usr <- par("usr")
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE, breaks = 50)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col = "cyan", ...)
}
panel.cor <- function(x, y, digits = 2, prefix = "", cex.cor, ...)
{
par(usr = c(0, 1, 0, 1))
r <- abs(cor(x, y))^2
txt <- format(c(r, 0.123456789), digits = digits)[1]
txt <- paste0(prefix, txt)
if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
text(0.5, 0.5, txt, cex = cex.cor)
}
pdf('figures/04_pair_scatter.pdf', height=6, width=6)
pairs(df |> filter(xchr == 0) |> select(-gene, -xchr) |> dplyr::rename(`Hippo 1` = hippo_1, `Hippo 2` = hippo_2, `Hippo 3` = hippo_3, `Cere 3` = cere_3, `Cere 4` = cere_4) |> as.matrix(),
upper.panel = panel.cor,
diag.panel = panel.hist)
dev.off()
quartz_off_screen
2
Overall p bias
extract_mat_pat_overall <- function(spase_res, qthresh = 0.01) {
dd <- spase_res |>
filter(qval < qthresh | grepl('monoallelic', flag)) |>
mutate(bias = case_when(
p > 0.6 ~ 'maternal',
p < 0.4 ~ 'paternal',
flag == 'monoallelic1' ~ 'maternal',
flag == 'monoallelic2' ~ 'paternal',
T ~ 'low_bias'
))
dd$xchr <- ifelse(dd$gene %in% xchr_genes, T, F)
return(dd)
}
hippo1_overall <- extract_mat_pat_overall(hippo_1)
hippo2_overall <- extract_mat_pat_overall(hippo_2)
hippo3_overall <- extract_mat_pat_overall(hippo_3)
cere4_overall <- extract_mat_pat_overall(cere_4)
cere3_overall <- extract_mat_pat_overall(cere_3)
Within cell type p bias
hippo1_intercept <- readRDS('results/results_celltype_intercept_hippo_1_df_5.rds')
hippo2_intercept <- readRDS('results/results_celltype_intercept_hippo_2_df_5.rds')
hippo3_intercept <- readRDS('results/results_celltype_intercept_hippo_3_df_5.rds')
cere3_intercept <- readRDS('results/results_celltype_intercept_cere_3_df_5.rds')
cere4_intercept <- readRDS('results/results_celltype_intercept_cere_4_visium_df_5.rds')
extract_ct_genes <- function(sig_gene_list) {
return(unique(unname(unlist(lapply(sig_gene_list, rownames)))))
}
extract_mat_pat_ct <- function(sig_gene_list) {
res <- NULL
for (i in seq_along(sig_gene_list)) {
dd <- sig_gene_list[[i]]
if (nrow(dd)==0) {
next
}
ct <- names(sig_gene_list)[i]
dd$gene <- rownames(dd)
rownames(dd) <- NULL
dd$cell_type <- ct
dd$bias <- ifelse(dd$log_fc > 0, 'maternal', 'paternal')
dd$xchr <- ifelse(dd$gene %in% xchr_genes, T, F)
if (is.null(res)) {
res <- dd
} else {
res <- bind_rows(res, dd)
}
}
res <- res |> select(gene, xchr, cell_type, bias, Z_score, log_fc, se, p_val, mean_0, mean_1, sd_0, sd_1)
return(res)
}
hippo1_ct <- extract_mat_pat_ct(hippo1_intercept@spase_results$sig_gene_list)
hippo2_ct <- extract_mat_pat_ct(hippo2_intercept@spase_results$sig_gene_list)
hippo3_ct <- extract_mat_pat_ct(hippo3_intercept@spase_results$sig_gene_list)
cere3_ct <- extract_mat_pat_ct(cere3_intercept@spase_results$sig_gene_list)
cere4_ct <- extract_mat_pat_ct(cere4_intercept@spase_results$sig_gene_list)
rm(hippo1_intercept, hippo2_intercept, hippo3_intercept, cere3_intercept, cere4_intercept)
Overall spatial patterns
hippo_1_sp <- readRDS('results/results_overall_spatial_hippo_1.rds')
hippo_2_sp <- readRDS('results/results_overall_spatial_hippo_2.rds')
hippo_3_sp <- readRDS('results/results_overall_spatial_hippo_3.rds')
cere_3_sp <- readRDS('results/results_overall_spatial_cere_3.rds')
cere_4_sp <- readRDS('results/results_overall_spatial_cere_4_visium.rds')
extract_spatial_overall <- function(spase_res, qthresh = 0.01) {
dd <- spase_res |> filter(qval < qthresh)
dd$xchr <- ifelse(dd$gene %in% xchr_genes, T, F)
return(dd)
}
hippo1_sp_overall <- extract_spatial_overall(hippo_1_sp$result)
hippo2_sp_overall <- extract_spatial_overall(hippo_2_sp$result)
hippo3_sp_overall <- extract_spatial_overall(hippo_3_sp$result)
cere3_sp_overall <- extract_spatial_overall(cere_3_sp$result)
cere4_sp_overall <- extract_spatial_overall(cere_4_sp$result)
Within cell type spatial patterns
hippo_1_spct_res <- readRDS('results/results_celltype_hippo_1_df_5.rds')
hippo_2_spct_res <- readRDS('results/results_celltype_hippo_2_df_5.rds')
hippo_3_spct_res <- readRDS('results/results_celltype_hippo_3_df_5.rds')
cere_3_spct_res <- readRDS('results/results_celltype_cere_3_df_5.rds')
cere_4_spct_res <- readRDS('results/results_celltype_cere_4_visium_df_5.rds')
extract_spatial_ct <- function(sig_gene_list) {
res <- NULL
for (i in seq_along(sig_gene_list)) {
dd <- sig_gene_list[[i]]
if (nrow(dd) == 0) {
next
}
ct <- names(sig_gene_list)[i]
dd$gene <- rownames(dd)
rownames(dd) <- NULL
dd$cell_type <- ct
dd$xchr <- ifelse(dd$gene %in% xchr_genes, T, F)
if (is.null(res)) {
res <- dd
} else {
res <- bind_rows(res, dd)
}
}
return(res)
}
hippo1_sp_ct <- extract_spatial_ct(hippo_1_spct_res@spase_results$sig_gene_list)
hippo2_sp_ct <- extract_spatial_ct(hippo_2_spct_res@spase_results$sig_gene_list)
hippo3_sp_ct <- extract_spatial_ct(hippo_3_spct_res@spase_results$sig_gene_list)
cere3_sp_ct <- extract_spatial_ct(cere_3_spct_res@spase_results$sig_gene_list)
cere4_sp_ct <- extract_spatial_ct(cere_4_spct_res@spase_results$sig_gene_list)
hippo1_sp_ct_all <- extract_spatial_ct(hippo_1_spct_res@spase_results$all_gene_list)
hippo2_sp_ct_all <- extract_spatial_ct(hippo_2_spct_res@spase_results$all_gene_list)
hippo3_sp_ct_all <- extract_spatial_ct(hippo_3_spct_res@spase_results$all_gene_list)
cere3_sp_ct_all <- extract_spatial_ct(cere_3_spct_res@spase_results$all_gene_list)
cere4_sp_ct_all <- extract_spatial_ct(cere_4_spct_res@spase_results$all_gene_list)
format_lengths <- function(c1, c2) {
return(paste0(prettyNum(length(c1), big.mark=','), ' (', prettyNum(length(c2), big.mark=','), ')'))
}
extract_info_for_table <- function(all, p_bias, ct_p_bias, sp, sp_ct, sp_ct_all) {
all$xchr <- ifelse(all$gene %in% xchr_genes, T, F)
all_genes_autosome <- all |> filter(!xchr) |> pull(gene)
rn <- rownames(sp_ct_all@originalSpatialRNA@maternalCounts[rowSums(sp_ct_all@originalSpatialRNA@maternalCounts) + rowSums(sp_ct_all@originalSpatialRNA@paternalCounts) > 128,])
rn_autosome <- rn[!rn %in% xchr_genes]
rn_xchr <- rn[rn %in% xchr_genes]
all_genes_autosome <- unique(c(all_genes_autosome, rn_autosome))
all_genes_xchr <- all |> filter(xchr) |> pull(gene)
all_genes_xchr <- unique(c(all_genes_xchr, rn_xchr))
mat_genes_autosome <- p_bias |> filter(!xchr, bias == 'maternal') |> pull(gene)
pat_genes_autosome <- p_bias |> filter(!xchr, bias == 'paternal') |> pull(gene)
mat_genes_xchr <- p_bias |> filter(xchr, bias == 'maternal') |> pull(gene)
pat_genes_xchr <- p_bias |> filter(xchr, bias == 'paternal') |> pull(gene)
mat_genes_ct_autosome <- ct_p_bias |> filter(!xchr, bias == 'maternal') |> pull(gene) |> unique()
pat_genes_ct_autosome <- ct_p_bias |> filter(!xchr, bias == 'paternal') |> pull(gene)|> unique()
mat_genes_ct_xchr <- ct_p_bias |> filter(xchr, bias == 'maternal') |> pull(gene)|> unique()
pat_genes_ct_xchr <- ct_p_bias |> filter(xchr, bias == 'paternal') |> pull(gene)|> unique()
all_spatial_autosome <- sp |> filter(!xchr) |> pull(gene)
all_spatial_xchr <- sp |> filter(xchr) |> pull(gene)
if (is.null(sp_ct)) {
ct_spatial_autosome <- c()
ct_spatial_xchr <- c()
} else {
ct_spatial_autosome <- sp_ct |> filter(!xchr) |> pull(gene) |> unique()
ct_spatial_xchr <- sp_ct |> filter(xchr) |> pull(gene) |> unique()
}
no_sig_ase_autosome <- all_genes_autosome[!all_genes_autosome %in% c(mat_genes_autosome, pat_genes_autosome, mat_genes_ct_autosome, pat_genes_ct_autosome, all_spatial_autosome, ct_spatial_autosome)]
no_sig_ase_xchr <- all_genes_xchr[!all_genes_xchr %in% c(mat_genes_xchr, pat_genes_xchr, mat_genes_ct_xchr, pat_genes_ct_xchr, all_spatial_xchr, ct_spatial_xchr)]
return(list(
col = c(
format_lengths(no_sig_ase_autosome, no_sig_ase_xchr),
format_lengths(mat_genes_autosome, mat_genes_xchr),
format_lengths(pat_genes_autosome, pat_genes_xchr),
format_lengths(mat_genes_ct_autosome, mat_genes_ct_xchr),
format_lengths(pat_genes_ct_autosome, pat_genes_ct_xchr),
format_lengths(all_spatial_autosome, all_spatial_xchr),
format_lengths(ct_spatial_autosome, ct_spatial_xchr),
format_lengths(all_genes_autosome, all_genes_xchr)
),
nsa = no_sig_ase_autosome, nsx = no_sig_ase_xchr,
mga = mat_genes_autosome, mgx = mat_genes_xchr, pga = pat_genes_autosome, pgx = pat_genes_xchr, mgca = mat_genes_ct_autosome, mgcx = mat_genes_ct_xchr, pgca = pat_genes_ct_autosome, pgcx = pat_genes_ct_xchr, asa = all_spatial_autosome, asx = all_spatial_xchr, cta = ct_spatial_autosome, ctx = ct_spatial_xchr,
aga = all_genes_autosome, agx = all_genes_xchr
))
}
h1 <- extract_info_for_table(hippo_1, hippo1_overall, hippo1_ct, hippo1_sp_overall, hippo1_sp_ct, hippo_1_spct_res)
h2 <- extract_info_for_table(hippo_2, hippo2_overall, hippo2_ct, hippo2_sp_overall, hippo2_sp_ct, hippo_2_spct_res)
h3 <- extract_info_for_table(hippo_3, hippo3_overall, hippo3_ct, hippo3_sp_overall, hippo3_sp_ct, hippo_3_spct_res)
c3 <- extract_info_for_table(cere_3, cere3_overall, cere3_ct, cere3_sp_overall, cere3_sp_ct, cere_3_spct_res)
c4 <- extract_info_for_table(cere_4, cere4_overall, cere4_ct, cere4_sp_overall, cere4_sp_ct, cere_4_spct_res)
# make the overlap columns
c_overlap <- c(
format_lengths(c3$nsa[c3$nsa %in% c4$nsa], c3$nsx[c3$nsx %in% c4$nsx]),
format_lengths(c3$mga[c3$mga %in% c4$mga], c3$mgx[c3$mgx %in% c4$mgx]),
format_lengths(c3$pga[c3$pga %in% c4$pga], c3$pgx[c3$pgx %in% c4$pgx]),
format_lengths(c3$mgca[c3$mgca %in% c4$mgca], c3$mgcx[c3$mgcx %in% c4$mgcx]),
format_lengths(c3$pgca[c3$pgca %in% c4$pgca], c3$pgcx[c3$pgcx %in% c4$pgcx]),
format_lengths(c3$asa[c3$asa %in% c4$asa], c3$asx[c3$asx %in% c4$asx]),
format_lengths(c3$cta[c3$cta %in% c4$cta], c3$ctx[c3$ctx %in% c4$ctx]),
format_lengths(c3$aga[c3$aga %in% c4$aga], c3$agx[c3$agx %in% c4$agx])
)
h_overlap12 <- c(
format_lengths(h1$nsa[h1$nsa %in% h2$nsa], h1$nsx[h1$nsx %in% h2$nsx]),
format_lengths(h1$mga[h1$mga %in% h2$mga], h1$mgx[h1$mgx %in% h2$mgx]),
format_lengths(h1$pga[h1$pga %in% h2$pga], h1$pgx[h1$pgx %in% h2$pgx]),
format_lengths(h1$mgca[h1$mgca %in% h2$mgca], h1$mgcx[h1$mgcx %in% h2$mgcx]),
format_lengths(h1$pgca[h1$pgca %in% h2$pgca], h1$pgcx[h1$pgcx %in% h2$pgcx]),
format_lengths(h1$asa[h1$asa %in% h2$asa], h1$asx[h1$asx %in% h2$asx]),
format_lengths(h1$cta[h1$cta %in% h2$cta], h1$ctx[h1$ctx %in% h2$ctx]),
format_lengths(h1$aga[h1$aga %in% h2$aga], h1$agx[h1$agx %in% h2$agx])
)
h_overlap13 <- c(
format_lengths(h1$nsa[h1$nsa %in% h3$nsa], h1$nsx[h1$nsx %in% h3$nsx]),
format_lengths(h1$mga[h1$mga %in% h3$mga], h1$mgx[h1$mgx %in% h3$mgx]),
format_lengths(h1$pga[h1$pga %in% h3$pga], h1$pgx[h1$pgx %in% h3$pgx]),
format_lengths(h1$mgca[h1$mgca %in% h3$mgca], h1$mgcx[ h1$mgcx %in% h3$mgcx]),
format_lengths(h1$pgca[h1$pgca %in% h3$pgca], h1$pgcx[h1$pgcx %in% h3$pgcx]),
format_lengths(h1$asa[h1$asa %in% h3$asa], h1$asx[h1$asx %in% h3$asx]),
format_lengths(h1$cta[h1$cta %in% h3$cta], h1$ctx[h1$ctx %in% h3$ctx]),
format_lengths(h1$aga[h1$aga %in% h3$aga], h1$agx[h1$agx %in% h3$agx])
)
Table 1
t1 <- data.frame(Category = c('No significant ASE', 'Overall maternal bias', 'Overall paternal bias', 'Within cell type maternal bias', 'Within cell type paternal bias', 'Overall spatial pattern', 'Within cell type spatial pattern', 'Total n genes'),
`Slide-seq (Mouse 3)` = c3$col, `Visium (Mouse 4)` = c4$col, Overlap = c_overlap)
print(xtable::xtable(t1))
% latex table generated in R 4.3.1 by xtable 1.8-4 package
% Fri Mar 8 23:32:05 2024
\begin{table}[ht]
\centering
\begin{tabular}{rllll}
\hline
& Category & Slide.seq..Mouse.3. & Visium..Mouse.4. & Overlap \\
\hline
1 & No significant ASE & 6,235 (53) & 8,971 (162) & 5,425 (30) \\
2 & Overall maternal bias & 720 (157) & 502 (112) & 196 (81) \\
3 & Overall paternal bias & 947 (7) & 672 (12) & 300 (5) \\
4 & Within cell type maternal bias & 306 (62) & 9 (2) & 5 (2) \\
5 & Within cell type paternal bias & 416 (4) & 14 (0) & 7 (0) \\
6 & Overall spatial pattern & 8 (19) & 2 (0) & 1 (0) \\
7 & Within cell type spatial pattern & 0 (6) & 0 (0) & 0 (0) \\
8 & Total n genes & 8,304 (225) & 10,147 (286) & 7,599 (204) \\
\hline
\end{tabular}
\end{table}
Table 2
t2 <- data.frame(Category = c('No significant ASE', 'Overall maternal bias', 'Overall paternal bias', 'Within cell type maternal bias', 'Within cell type paternal bias', 'Overall spatial pattern', 'Within cell type spatial pattern', 'Total n genes'),
`Mouse 1` = h1$col, `Mouse 2` = h2$col, `Mouse 3` = h3$col, Overlap12 = h_overlap12, Overlap13 = h_overlap13)
print(xtable::xtable(t2))
% latex table generated in R 4.3.1 by xtable 1.8-4 package
% Fri Mar 8 23:32:05 2024
\begin{table}[ht]
\centering
\begin{tabular}{rllllll}
\hline
& Category & Mouse.1 & Mouse.2 & Mouse.3 & Overlap12 & Overlap13 \\
\hline
1 & No significant ASE & 4,961 (104) & 2,242 (5) & 6,530 (16) & 2,008 (3) & 4,249 (4) \\
2 & Overall maternal bias & 349 (30) & 176 (55) & 623 (208) & 93 (11) & 179 (29) \\
3 & Overall paternal bias & 456 (9) & 180 (1) & 834 (1) & 109 (1) & 254 (1) \\
4 & Within cell type maternal bias & 104 (3) & 24 (15) & 250 (61) & 18 (1) & 64 (1) \\
5 & Within cell type paternal bias & 151 (9) & 22 (0) & 307 (0) & 19 (0) & 101 (0) \\
6 & Overall spatial pattern & 18 (17) & 1 (0) & 67 (0) & 1 (0) & 9 (0) \\
7 & Within cell type spatial pattern & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) \\
8 & Total n genes & 5,866 (159) & 2,609 (61) & 8,309 (225) & 2,560 (59) & 5,698 (150) \\
\hline
\end{tabular}
\end{table}
Previously reported imprinted genes
santini_et_al <- read.delim('../inst/extdata/santini_et_al_2021_genomic_imprinting_mouse.tsv')
# with bias
geneimprint <- read.delim('../inst/extdata/geneimprint.com-mouse.tsv')
Supp table samples
mix_5 <- readRDS('results/rctd_mix_5_visium.rds')
get_stats <- function(cside_spase_obj) {
tot <- cside_spase_obj@originalSpatialRNA@counts
tot_ase <- cside_spase_obj@originalSpatialRNA@maternalCounts + cside_spase_obj@originalSpatialRNA@paternalCounts
nspots <- ncol(tot)
ngenes <- nrow(tot)
readsspot <- mean(colSums(tot))
readsase <- mean(colSums(tot_ase))
return(c(prettyNum(nspots, big.mark=','),prettyNum(ngenes, big.mark=','), prettyNum(round(readsspot),big.mark=','), prettyNum(round(readsase),big.mark=',')))
}
rr <- rbind(get_stats(hippo_1_spct_res), get_stats(hippo_2_spct_res), get_stats(hippo_3_spct_res), get_stats(cere_3_spct_res), get_stats(cere_4_spct_res), get_stats(mix_5))
colnames(rr) <- c('N spots', 'N genes', 'Avg. reads/spot', 'Allele-resolved')
rr <- data.frame(rr)
dd <- bind_cols(
data.frame(Mouse = c(1,2,3,3,4,5),
Tissue = c('Hippocampus', 'Hippocampus', 'Hippocampus', 'Cerebellum', 'Cerebellum','Mix'),
Platform = c('Slide-seq', 'Slide-seq', 'Slide-seq', 'Slide-seq', 'Visium','Visium'),
`Read length` = c(160, 56, 250, 250, 91,91)),
rr
)
xtable::xtable(dd)
% latex table generated in R 4.3.1 by xtable 1.8-4 package
% Fri Mar 8 23:32:19 2024
\begin{table}[ht]
\centering
\begin{tabular}{rrllrllll}
\hline
& Mouse & Tissue & Platform & Read.length & N.spots & N.genes & Avg..reads.spot & Allele.resolved \\
\hline
1 & 1.00 & Hippocampus & Slide-seq & 160.00 & 26,429 & 22,049 & 459 & 207 \\
2 & 2.00 & Hippocampus & Slide-seq & 56.00 & 13,680 & 23,541 & 623 & 145 \\
3 & 3.00 & Hippocampus & Slide-seq & 250.00 & 78,806 & 29,411 & 552 & 218 \\
4 & 3.00 & Cerebellum & Slide-seq & 250.00 & 60,942 & 30,658 & 622 & 240 \\
5 & 4.00 & Cerebellum & Visium & 91.00 & 4,315 & 19,860 & 2,219 & 834 \\
6 & 5.00 & Mix & Visium & 91.00 & 4,150 & 20,095 & 2,143 & 853 \\
\hline
\end{tabular}
\end{table}
Supp table overall bias
st1 <- hippo1_overall |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 1', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, totalUMI, totalCells, p, ci.low, ci.high, z, pval, qval, bias, xchr) |>
bind_rows(
hippo2_overall |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 2', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, totalUMI, totalCells, p, ci.low, ci.high, z, pval, qval, bias, xchr)
) |>
bind_rows(
hippo3_overall |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 3', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, totalUMI, totalCells, p, ci.low, ci.high, z, pval, qval, bias, xchr)
) |>
bind_rows(
cere3_overall |>
mutate(tissue = 'Cerebellum', sample = 'Mouse 3', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, totalUMI, totalCells, p, ci.low, ci.high, z, pval, qval, bias, xchr)
) |>
bind_rows(
cere4_overall |>
mutate(tissue = 'Cerebellum', sample = 'Mouse 4', technology = 'Visium') |>
select(tissue, sample, technology, gene, totalUMI, totalCells, p, ci.low, ci.high, z, pval, qval, bias, xchr)
)
# Overlap with previously known imprinted
st1$previously_reported_imprinted <- ifelse(st1$gene %in% santini_et_al$Gene.symbol, T, F)
st1 <- st1 |> left_join(geneimprint |> select(Gene, Status, Expressed.Allele) |> dplyr::rename(gene = Gene, geneimprint_status = Status, geneimprint_allele = Expressed.Allele), by = 'gene')
write.csv(st1, file = 'tables/04_supp_table_overall_bias.csv')
st1 |> select(gene, previously_reported_imprinted) |> distinct() |> pull(previously_reported_imprinted) |> table(useNA='always')
FALSE TRUE <NA>
5279 90 0
st1 |> filter(geneimprint_status == 'Imprinted') |> select(gene, bias, geneimprint_allele) |> distinct() |> arrange(gene)
gene bias geneimprint_allele
1 Axl paternal Maternal
2 Begain paternal Isoform Dependent
3 Blcap maternal Isoform Dependent
4 Cd81 paternal Maternal
5 Cd81 low_bias Maternal
6 Cdkn1c maternal Maternal
7 Chrac1 low_bias Maternal
8 Commd1 maternal Maternal
9 Dhcr7 maternal Maternal
10 Dlk1 paternal Paternal
11 Ftx maternal Paternal
12 Gatm low_bias Maternal
13 Gatm maternal Maternal
14 H13 paternal Maternal
15 Igf2 maternal Paternal
16 Impact paternal Paternal
17 Jade1 low_bias Paternal
18 Jpx maternal Paternal
19 Kcnq1ot1 paternal Paternal
20 Magel2 paternal Paternal
21 Mcts2 paternal Paternal
22 Mirg maternal Maternal
23 Mkrn3 paternal Paternal
24 Nap1l5 paternal Paternal
25 Peg13 paternal Paternal
26 Peg3 paternal Paternal
27 Plagl1 paternal Paternal
28 Qpct maternal Maternal
29 Rasgrf1 paternal Paternal
30 Rian maternal Maternal
31 Sgce paternal Paternal
32 Smoc1 paternal Paternal
33 Snrpn paternal Paternal
34 Th paternal Maternal
35 Trappc9 low_bias Maternal
36 Ube3a paternal Maternal
37 Ube3a maternal Maternal
38 Usp29 paternal Paternal
39 Xist paternal Paternal
40 Zdbf2 paternal Paternal
41 Zrsr1 paternal Paternal
Supp table ct bias
st2 <- hippo1_ct |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 1', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, cell_type, Z_score, log_fc, se, p_val, bias, xchr) |>
bind_rows(
hippo2_ct |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 2', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, cell_type, Z_score, log_fc, se, p_val, bias, xchr)
) |>
bind_rows(
hippo3_ct |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 3', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, cell_type, Z_score, log_fc, se, p_val, bias, xchr
)
) |>
bind_rows(
cere3_ct |>
mutate(tissue = 'Cerebellum', sample = 'Mouse 3', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, cell_type, Z_score, log_fc, se, p_val, bias, xchr)
)|>
bind_rows(
cere4_ct |>
mutate(tissue = 'Cerebellum', sample = 'Mouse 4', technology = 'Visium') |>
select(tissue, sample, technology, gene, cell_type, Z_score, log_fc, se, p_val, bias, xchr)
)
st2$previously_reported_imprinted <- ifelse(st2$gene %in% santini_et_al$Gene.symbol, T, F)
st2 <- st2 |> left_join(geneimprint |> select(Gene, Status, Expressed.Allele) |> dplyr::rename(gene = Gene, geneimprint_status = Status, geneimprint_allele = Expressed.Allele), by = 'gene')
write.csv(st2, file = 'tables/04_supp_table_celltype_bias.csv')
Number of santini et al genes imprinted in ours
sum(santini_et_al$Gene.symbol %in% c(st1$gene, st2$gene))
[1] 91
Number of geneimprint genes imprinted in ours
length(geneimprint$Gene[geneimprint$Status=='Imprinted' & geneimprint$Expressed.Allele %in% c('Maternal', 'Paternal')])
[1] 138
dd <- geneimprint |>
left_join(st1, by = c('Gene' = 'gene')) |>
select(Gene, bias, Expressed.Allele) |>
filter(!is.na(bias) & bias != 'low_bias' & Expressed.Allele %in% c('Paternal', 'Maternal')) |>
distinct() |>
mutate(Expressed.Allele = tolower(Expressed.Allele))
table(dd$bias, dd$Expressed.Allele)
maternal paternal
maternal 8 3
paternal 6 18
Supp table spatial
st3 <- hippo1_sp_overall |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 1', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, totalUMI, totalSpots, chisq.p, qval, xchr) |>
bind_rows(
hippo2_sp_overall |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 2', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, totalUMI, totalSpots, chisq.p, qval, xchr)
) |>
bind_rows(
hippo3_sp_overall |>
mutate(tissue = 'Hippocampus', sample = 'Mouse 3', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, totalUMI, totalSpots, chisq.p, qval, xchr)
) |>
bind_rows(
cere3_sp_overall |>
mutate(tissue = 'Cerebellum', sample = 'Mouse 3', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, totalUMI, totalSpots, chisq.p, qval, xchr)
) |>
bind_rows(
cere4_sp_overall |>
mutate(tissue = 'Cerebellum', sample = 'Mouse 4', technology = 'Visium') |>
select(tissue, sample, technology, gene, totalUMI, totalSpots, chisq.p, qval, xchr)
)
write.csv(st3, file = 'tables/04_supp_table_spatial_overall_bias.csv')
print(xtable::xtable(st3 |> select(-chisq.p) |> arrange(sample, tissue, qval), display = c('s', 's', 's', 's', 's', 'd', 'd', 'E', 's')), include.rownames=F)
% latex table generated in R 4.3.1 by xtable 1.8-4 package
% Fri Mar 8 23:32:19 2024
\begin{table}[ht]
\centering
\begin{tabular}{llllrrrl}
\hline
tissue & sample & technology & gene & totalUMI & totalSpots & qval & xchr \\
\hline
Hippocampus & Mouse 1 & Slide-seq & Tspan7 & 9612 & 6667 & 0.00E+00 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Nrip3 & 4213 & 3060 & 9.29E-12 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Atrx & 2351 & 1893 & 1.67E-11 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Sst & 1429 & 745 & 1.67E-11 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Ptgds & 2374 & 1321 & 4.01E-10 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Arhgef9 & 1857 & 1556 & 1.09E-09 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Ids & 1002 & 919 & 1.09E-09 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Rgs4 & 2712 & 2097 & 1.54E-09 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Chgb & 7898 & 3923 & 5.53E-08 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Gprasp1 & 2665 & 2177 & 6.03E-08 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Cpe & 22568 & 11861 & 7.72E-08 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Nrsn1 & 5220 & 4092 & 1.60E-06 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Pcsk1n & 3208 & 2851 & 2.27E-06 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Maged1 & 1364 & 1235 & 3.04E-06 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Fam193a & 558 & 533 & 6.47E-06 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Crym & 1241 & 940 & 4.61E-05 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Gpm6b & 2203 & 1963 & 9.37E-05 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Sparcl1 & 18578 & 10638 & 9.37E-05 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Meg3 & 18494 & 5727 & 1.47E-04 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Bex2 & 1570 & 1391 & 2.41E-04 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Magee1 & 1698 & 1444 & 2.61E-04 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Gap43 & 2920 & 2282 & 4.71E-04 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Map1b & 14912 & 7333 & 5.28E-04 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Armcx3 & 646 & 600 & 7.98E-04 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Malat1 & 72124 & 12172 & 1.49E-03 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Rprm & 1107 & 796 & 1.49E-03 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Prps1 & 879 & 790 & 1.50E-03 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Tceal5 & 2117 & 1781 & 1.50E-03 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Frrs1l & 2992 & 2321 & 5.80E-03 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Mageh1 & 673 & 634 & 5.80E-03 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Xist & 537 & 449 & 6.37E-03 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Atp1a2 & 8717 & 5701 & 6.76E-03 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Fgf13 & 954 & 869 & 6.76E-03 & TRUE \\
Hippocampus & Mouse 1 & Slide-seq & Napa & 1299 & 1184 & 6.76E-03 & FALSE \\
Hippocampus & Mouse 1 & Slide-seq & Tceal3 & 1491 & 1271 & 7.25E-03 & TRUE \\
Hippocampus & Mouse 2 & Slide-seq & Ptgds & 910 & 635 & 2.96E-05 & FALSE \\
Cerebellum & Mouse 3 & Slide-seq & Meg3 & 28544 & 14574 & 0.00E+00 & FALSE \\
Cerebellum & Mouse 3 & Slide-seq & Ptgds & 6457 & 3277 & 0.00E+00 & FALSE \\
Cerebellum & Mouse 3 & Slide-seq & Rps4x & 12261 & 9019 & 3.32E-13 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Bex2 & 9202 & 7044 & 2.70E-09 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Tceal3 & 9503 & 6693 & 2.03E-08 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Cdr1 & 2255 & 1945 & 4.74E-08 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Tspan7 & 11409 & 8731 & 4.74E-08 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Bex3 & 12213 & 8369 & 7.34E-08 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Ncald & 2311 & 1804 & 1.92E-07 & FALSE \\
Cerebellum & Mouse 3 & Slide-seq & Htatsf1 & 1277 & 1068 & 1.64E-05 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Ndufb11 & 13950 & 9732 & 9.11E-05 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Tceal5 & 5929 & 4637 & 9.11E-05 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Uba1 & 7773 & 5728 & 1.73E-04 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Nkap & 962 & 783 & 2.35E-04 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Idh3g & 7591 & 5758 & 3.97E-04 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Gprasp1 & 3373 & 2746 & 7.26E-04 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Atrx & 4767 & 3617 & 7.33E-04 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Lpar1 & 1311 & 1021 & 9.02E-04 & FALSE \\
Cerebellum & Mouse 3 & Slide-seq & Mageh1 & 3337 & 2566 & 1.13E-03 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Xist & 1678 & 1373 & 1.22E-03 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Cdk16 & 3542 & 3133 & 1.22E-03 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Psat1 & 1507 & 1237 & 1.82E-03 & FALSE \\
Cerebellum & Mouse 3 & Slide-seq & Aldoc & 89897 & 26422 & 2.16E-03 & FALSE \\
Cerebellum & Mouse 3 & Slide-seq & Plp1 & 17084 & 5957 & 3.61E-03 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Shank1 & 3427 & 2879 & 3.61E-03 & FALSE \\
Cerebellum & Mouse 3 & Slide-seq & Hprt & 2848 & 2429 & 7.30E-03 & TRUE \\
Cerebellum & Mouse 3 & Slide-seq & Kcnj9 & 3493 & 2655 & 7.30E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Chgb & 17790 & 8654 & 0.00E+00 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Nrip3 & 12045 & 8028 & 0.00E+00 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Ptgds & 4863 & 3318 & 0.00E+00 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Rasgrf1 & 4462 & 3399 & 0.00E+00 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Snca & 20846 & 10276 & 0.00E+00 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Sst & 6781 & 3676 & 0.00E+00 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Stmn1 & 68985 & 30606 & 0.00E+00 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & A830018L16Rik & 1831 & 1483 & 9.99E-13 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Uchl1 & 37040 & 20455 & 2.05E-12 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Lypd1 & 2073 & 1585 & 3.00E-12 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Pcp4l1 & 13914 & 9034 & 7.69E-11 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Akap5 & 196564 & 62287 & 3.71E-10 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Nrsn1 & 10799 & 8182 & 3.88E-10 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Gstm7 & 1150 & 1018 & 7.29E-10 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Mrps11 & 2140 & 1715 & 3.25E-09 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Calb1 & 1604 & 1327 & 2.49E-08 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Grb14 & 2591 & 2119 & 7.86E-08 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Sparcl1 & 40302 & 25100 & 1.21E-07 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Spon1 & 1941 & 1554 & 1.36E-07 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Bok & 2728 & 2336 & 1.03E-06 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Car12 & 502 & 396 & 2.36E-06 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Rasgrp1 & 11580 & 7368 & 2.36E-06 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Selenop & 21213 & 14535 & 2.36E-06 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Rhpn2 & 862 & 782 & 2.76E-06 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Ccng1 & 2158 & 1806 & 5.14E-06 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Gaa & 4307 & 3332 & 1.01E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Cck & 15170 & 10001 & 1.34E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Chst2 & 8951 & 6587 & 1.43E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Rprm & 2537 & 1678 & 1.50E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Nefl & 17453 & 10783 & 1.66E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Actr2 & 8748 & 6525 & 2.05E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Cnih3 & 733 & 641 & 3.18E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Ncald & 16168 & 9706 & 6.65E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Cpe & 54353 & 30714 & 7.63E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Stum & 3774 & 3000 & 9.24E-05 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Aldh1a1 & 5190 & 4006 & 1.42E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Etv1 & 2097 & 1582 & 1.55E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Syt13 & 5708 & 4378 & 1.57E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Zfp365 & 5896 & 4907 & 1.69E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Thrsp & 1440 & 1210 & 1.74E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & 2310058D17Rik & 534 & 505 & 5.21E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Myo5b & 1480 & 1160 & 5.44E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Nudt4 & 8909 & 6478 & 6.03E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Sphkap & 2039 & 1653 & 8.62E-04 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Synpr & 1001 & 831 & 1.14E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Cdo1 & 976 & 810 & 1.32E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Rps20 & 36917 & 23266 & 1.34E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Resp18 & 4095 & 3182 & 1.69E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Trim9 & 1997 & 1646 & 1.71E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Prkcq & 1487 & 1014 & 2.65E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Tunar & 1083 & 916 & 2.93E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Pik3r4 & 1370 & 1106 & 3.27E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Msh2 & 1565 & 1236 & 3.87E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Efhd2 & 4295 & 3551 & 4.35E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Slc35c2 & 902 & 785 & 6.24E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Golm1 & 665 & 565 & 6.83E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Igfbp4 & 2781 & 1787 & 7.46E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Rtn4 & 11088 & 8257 & 7.46E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Sema5a & 1319 & 1059 & 7.46E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Rgs4 & 6248 & 4578 & 7.54E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Ripor2 & 842 & 724 & 8.58E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Itpr1 & 3409 & 2816 & 8.70E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Ppp3ca & 38830 & 13587 & 8.70E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Elp6 & 2587 & 2431 & 9.49E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Klc1 & 1864 & 1553 & 9.72E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Gng5 & 1020 & 958 & 9.73E-03 & FALSE \\
Hippocampus & Mouse 3 & Slide-seq & Lmo7 & 2167 & 1661 & 9.73E-03 & FALSE \\
Cerebellum & Mouse 4 & Visium & Rps8 & 5145 & 2173 & 2.14E-03 & FALSE \\
Cerebellum & Mouse 4 & Visium & Meg3 & 5375 & 2175 & 5.57E-03 & FALSE \\
\hline
\end{tabular}
\end{table}
Supp table ct spatial
st4 <- cere3_sp_ct |>
mutate(tissue = 'Cerebellum', sample = 'Mouse 3', technology = 'Slide-seq') |>
select(tissue, sample, technology, gene, cell_type,p_val, xchr)
write.csv(st4, file = 'tables/04_supp_table_spatial_celltype_bias.csv')
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.