## ----setup, echo = FALSE------------------------------------------------------
knitr::opts_chunk$set(tidy = FALSE,
dpi = 72,
fig.width = 6,
fig.height = 6,
width=80,
cache = FALSE,
dev = "png",
message = TRUE,
error = TRUE,
warning = TRUE)
## ----installation, eval = FALSE-----------------------------------------------
# devtools::install_github("dunbarlabNIH/barcodetrackR")
## ----load packages required, eval = TRUE, warning=FALSE, message=FALSE--------
require("magrittr")
require("barcodetrackR")
require("SummarizedExperiment")
## ----load data, eval = TRUE, results="hold"-----------------------------------
system.file("extdata", "/WuC_etal_appdata/sample_data_ZJ31.txt", package = "barcodetrackR") %>%
read.delim(row.names = 1) -> wu_dataframe
system.file("extdata", "/WuC_etal_appdata/sample_metadata_ZJ31.txt", package = "barcodetrackR") %>%
read.delim() -> wu_metadata
wu_SE <- create_SE(your_data = wu_dataframe,
meta_data = wu_metadata,
threshold = 0)
system.file("extdata", "/BelderbosME_etal/count_matrix_mouse_C21.txt", package = "barcodetrackR") %>%
read.delim(row.names = 1) -> belderbos_dataframe
system.file("extdata", "/BelderbosME_etal/metadata_mouse_C21.txt", package = "barcodetrackR") %>%
read.delim() -> belderbos_metadata
belderbos_metadata$weeks <- factor(belderbos_metadata$weeks, levels = c("9", "14", "20", "22", "sac"))
belderbos_SE <- create_SE(your_data = belderbos_dataframe,
meta_data = belderbos_metadata,
threshold = 0)
system.file("extdata", "/SixE_etal/WAS5_reads.txt", package = "barcodetrackR") %>%
read.delim(row.names = 1) -> six_dataframe
system.file("extdata", "/SixE_etal/WAS5_metadata.txt", package = "barcodetrackR") %>%
read.delim() -> six_metadata
six_SE <- create_SE(your_data = six_dataframe,
meta_data = six_metadata,
threshold = 0)
## ----list assays, eval = TRUE-------------------------------------------------
assays(six_SE)
## ----estimate threshold, eval = TRUE------------------------------------------
my_thresh <- estimate_barcode_threshold(capture_efficiency = 0.4,
population_size = 500000,
proportion_labeled = 0.3,
confidence_level = 0.95,
verbose = TRUE)
## ----apply thresholding, eval = TRUE------------------------------------------
wu_thresh <- threshold_SE(your_SE = wu_SE,
threshold_value = 0.005,
threshold_type = "relative",
verbose = TRUE)
## ----launch_app, eval=FALSE---------------------------------------------------
# barcodetrackR::launchApp()
## ----scatter_plot, eval=TRUE, fig.width = 7, fig.height=4---------------------
Gr_B_20 <- c("ZJ31_20m_Gr", "ZJ31_20m_B")
Gr_T_20 <- c("ZJ31_20m_Gr", "ZJ31_20m_T")
wu_scatterplot_1 <- scatter_plot(wu_SE[,Gr_B_20], your_title = "Gr vs B", assay = "proportions")
wu_scatterplot_2 <- scatter_plot(wu_SE[,Gr_T_20], your_title = "Gr vs T", assay = "proportions")
cowplot::plot_grid(wu_scatterplot_1, wu_scatterplot_2, ncol = 2)
## ----cor_plot 1, eval = TRUE, fig.width = 6, fig.height = 5-------------------
wu_cor_plot_sample_selection <- colData(wu_SE)$SAMPLENAME[1:20]
cor_plot(wu_SE[,wu_cor_plot_sample_selection],
method_corr = "pearson",
plot_type = "color",
assay = "proportions")
## ----cor_plot 2, eval = TRUE, fig.width = 6, fig.height = 5-------------------
wu_cor_plot_sample_selection <- colData(wu_SE)$SAMPLENAME[1:20]
cor_plot(wu_SE[,wu_cor_plot_sample_selection],
method_corr = "pearson",
plot_type = "color",
assay = "logs")
## ----cor_plot 3, eval = TRUE--------------------------------------------------
cor_plot(wu_SE[,wu_cor_plot_sample_selection],
method_corr = "pearson",
assay = "proportions",
return_table = TRUE) %>% head
## ----cor_plot 4, eval = TRUE, fig.width = 4, fig.height = 3, warning=FALSE, message=FALSE----
belderbos_wk_samples_PB <- c("wk9_U", "wk14_U", "wk20_U", "wk22_U")
cor_plot(your_SE = belderbos_SE[,belderbos_wk_samples_PB],
method_corr = "pearson",
plot_type = "number",
assay = "proportions",
number_size = 3)
## ----summary proxy, eval = TRUE-----------------------------------------------
summary(proxy::pr_DB)
## ----dist plot 1, eval = TRUE, fig.width = 7, fig.height = 5------------------
dist_plot(wu_SE[,wu_cor_plot_sample_selection],
dist_method = "manhattan",
plot_type = "color",
assay = "logs")
## ----dist plot 2, eval = TRUE, fig.width = 7, fig.height = 5------------------
dist_plot(wu_SE[,wu_cor_plot_sample_selection],
dist_method = "cosine",
plot_type = "color",
assay = "logs",
color_pal = "Greens",
cluster_tree = TRUE)
## ----barcode_ggheatmap wu_SE 1, echo=TRUE, warning = FALSE, fig.width = 8, fig.height=8, eval = TRUE----
barcode_ggheatmap(your_SE = wu_SE[,wu_cor_plot_sample_selection],
n_clones = 10,
grid = FALSE,
label_size = 14,
cellnote_size = 4)
## ----barcode_ggheatmap six_SE, echo=TRUE, warning = FALSE, fig.width = 8, fig.height=6, eval = TRUE----
six_celltype_order <- c("m13_TCELLS", "m36_TCELLS", "m43_TCELLS",
"m55_TCELLS","m13_BCELLS", "m36_BCELLS",
"m43_BCELLS", "m55_BCELLS", "m13_NKCELLS",
"m36_NKCELLS", "m43_NKCELLS","m55_NKCELLS",
"m13_GRANULOCYTES", "m36_GRANULOCYTES",
"m43_GRANULOCYTES", "m55_GRANULOCYTES",
"m13_MONOCYTES", "m36_MONOCYTES",
"m43_MONOCYTES","m55_MONOCYTES")
barcode_ggheatmap(your_SE = six_SE[,six_celltype_order],
n_clones = 5,
cellnote_assay = "stars",
cellnote_size = 3,
label_size = 14,
dendro = TRUE,
grid = FALSE,
clusters = 4,
distance_method = "euclidean")
## ----barcode_ggheatmap_stat wu_SE, echo=TRUE, warning = FALSE, fig.width = 8, fig.height=6, eval = TRUE----
wu_CD16_NK_order <- colnames(wu_SE)[17:20]
barcode_ggheatmap_stat(your_SE = wu_SE[,wu_CD16_NK_order],
sample_size = rep(40000,length(wu_CD16_NK_order)),
stat_test = "chi-squared",
stat_option = "subsequent",
p_threshold = 0.05,
n_clones = 10,
cellnote_assay = "stars",
cellnote_size = 6)
## ----barcode_stat_test wu_SE, eval = TRUE, echo=TRUE, warning = FALSE---------
sample_size <- rep(40000,length(wu_CD16_NK_order))
wu_CD16_NK_statistics <- barcode_stat_test(your_SE = wu_SE[,wu_CD16_NK_order],
sample_size = sample_size,
stat_test = "chi-squared",
stat_option = "subsequent",
bc_threshold = 0.01)
head(wu_CD16_NK_statistics$p_val)
## ----binary_heat_map, echo=TRUE, warning = FALSE, eval = TRUE-----------------
barcode_binary_heatmap(your_SE = belderbos_SE[,belderbos_wk_samples_PB],
label_size = 12,
threshold = 0.01)
## ----clonal_contribution wu_SE, echo=TRUE, warning = FALSE, eval = TRUE-------
clonal_contribution(your_SE = wu_SE,
SAMPLENAME_choice = "ZJ31_20m_NK_CD56n_CD16p",
n_clones = 10,
graph_type = "line",
plot_over = "months",
filter_by = "celltype",
filter_selection = "NK_16",
plot_non_selected = FALSE)
## ----bias_histogram wu_SE, echo=TRUE, warning = FALSE, eval = TRUE------------
wu_bias_plot_sample_selection <- colData(wu_SE)$SAMPLENAME[1:20]
bias_histogram(your_SE = wu_SE[,wu_bias_plot_sample_selection],
split_bias_on = "celltype",
bias_1 = "B",
bias_2 = "Gr",
split_bias_over = "months",
ncols = 2)
## ----bias_histogram six_SE, echo=TRUE, warning = FALSE, eval = TRUE-----------
bias_histogram(your_SE = six_SE,
split_bias_on = "celltype",
bias_1 = "B",
bias_2 = "T",
split_bias_over = "months",
ncols = 2)
## ----bias_ridge_plot wu_SE 1, echo=TRUE, warning = FALSE, message=FALSE, eval = TRUE----
bias_ridge_plot(your_SE = wu_SE,
split_bias_on = "celltype",
bias_1 = "B",
bias_2 = "Gr",
split_bias_over = "months",
bias_over = c("6","9.5","12","20"),
weighted = TRUE,
add_dots = TRUE)
## ----bias_ridge_plot wu_SE 2, echo=TRUE, warning = FALSE, message=FALSE, eval = TRUE----
bias_ridge_plot(your_SE = wu_SE,
split_bias_on = "celltype",
bias_1 = "B",
bias_2 = "Gr",
split_bias_over = "months",
bias_over = c("6","9.5","12","20"),
weighted = FALSE,
add_dots = TRUE)
## ----bias_lineplot six_SE, echo=TRUE, warning = FALSE, eval = TRUE------------
bias_lineplot(your_SE = six_SE,
split_bias_on = "celltype",
bias_1 = "Gr",
bias_2 = "Mo",
split_bias_over = "months",
remove_unique = TRUE)
## ----clone counts 1, eval = TRUE----------------------------------------------
clonal_count(your_SE = six_SE,
plot_over = "months",
group_by = "celltype")
## ----clone counts cumulative, eval = TRUE-------------------------------------
clonal_count(your_SE = six_SE,
plot_over = "months",
group_by = "celltype",
cumulative = TRUE)
## ----belderbos rank_abundance, eval = TRUE------------------------------------
rank_abundance_plot(belderbos_SE[,1:8],
scale_rank = TRUE,
point_size = 2)
## ----belderbos rank_abundance stat_test, eval = TRUE, warning = FALSE---------
stat_result <- rank_abundance_stat_test(belderbos_SE[,1:8])
print(stat_result[["p_value"]])
## ----wu shannon, eval = TRUE--------------------------------------------------
clonal_diversity(wu_SE[,1:20],
plot_over = "months",
group_by = "celltype",
index_type = "shannon")
## ----belderbos shannon, eval = TRUE-------------------------------------------
clonal_diversity(belderbos_SE[,1:6],
plot_over = "weeks",
group_by = "celltype",
index_type = "shannon")
## ----six simpson, eval = TRUE-------------------------------------------------
clonal_diversity(six_SE,
plot_over = "months",
group_by = "celltype",
index_type = "simpson")
## ----wu bray diversity, eval = TRUE-------------------------------------------
mds_plot(wu_SE, group_by = "celltype",
method_dist = "bray", assay = "proportions")
## ----six bray diversity, eval = TRUE------------------------------------------
mds_plot(six_SE, group_by = "celltype",
method_dist = "bray", assay = "proportions")
## ----chord_diagram wu_SE 1, echo=TRUE, warning = FALSE, eval = TRUE-----------
wu_circos_selection <- c("ZJ31_12m_T","ZJ31_12m_B","ZJ31_12m_Gr")
chord_diagram(wu_SE[,wu_circos_selection], plot_label = "celltype")
## ----chord_diagram wu_SE 2, echo=TRUE, warning = FALSE, eval = TRUE-----------
chord_diagram(wu_SE[,wu_circos_selection],
plot_label = "celltype",
weighted = TRUE)
## ----chord_diagram wu_SE 3, echo=TRUE, warning = FALSE, eval = TRUE-----------
wu_circos_selection2 <- c("ZJ31_12m_T",
"ZJ31_12m_B",
"ZJ31_12m_Gr",
"ZJ31_12m_NK_CD56n_CD16p")
chord_diagram(wu_SE[,wu_circos_selection2],
plot_label = "celltype",
alpha = 0.9)
## ----chord_diagram belderbos_SE 1, echo=TRUE, warning = FALSE, eval = TRUE----
belderbos_wk_samples_PB <- c("wk9_U", "wk14_U", "wk20_U", "wk22_U")
chord_diagram(belderbos_SE[,belderbos_wk_samples_PB])
## ----chord_diagram belderbos_SE 2, echo=TRUE, warning = FALSE, eval=TRUE------
chord_diagram(belderbos_SE[,belderbos_wk_samples_PB],
weighted = TRUE)
## ----session info, eval=TRUE, width=6-----------------------------------------
sessionInfo()