Check bridging datasets
First, confirm that there are overlapping sample IDs within the study. It is important that the sample IDs are the same in both NPX files.
| SampleID |
|---|
| A13 |
| A29 |
| A30 |
| A36 |
| A45 |
| A46 |
| A52 |
| A63 |
| A71 |
| A73 |
| B3 |
| B4 |
| B37 |
| B45 |
| B63 |
| B75 |
Then, gain an overview of the datasets that are going to be bridged. For example, plot and compare NPX distribution between datasets. By having a sense of how the studies compared to each other before bridging, we can then determine the success of the bridging process post bridging.
# Load datasets
npx_1 <- npx_data1 %>%
mutate(dataset = "data1")
npx_2 <- npx_data2 %>%
mutate(dataset = "data2")
npx_df <- bind_rows(npx_1, npx_2)
# Plot NPX density before bridging normalization
npx_df %>%
mutate(Panel = gsub("Olink ", "", Panel)) %>%
ggplot(aes(x = NPX, fill = dataset)) +
geom_density(alpha = 0.4) +
facet_grid(~Panel) +
olink_fill_discrete(coloroption = c("red", "darkblue")) +
set_plot_theme() +
ggtitle("Before bridging normalization: NPX distribution") +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
strip.text = element_text(size = 16),
legend.title = element_blank(),
legend.position = "top")
Use a PCA plot to visualize sample-to-sample distance before bridging. Typically the project dataset accounts for most of the observed variation within the combined datasets at this point.
## before bridging
#### Extract bridging samples
overlapping_samples <- data.frame(SampleID = intersect(npx_1$SampleID, npx_2$SampleID)) %>%
filter(!str_detect(SampleID, "CONTROL_SAMPLE")) %>% #Remove control samples
pull(SampleID)
npx_before_br <- npx_data1 %>%
dplyr::filter(!str_detect(SampleID, "CONTROL_SAMPLE")) %>% #Remove control samples
dplyr::mutate(Type = if_else(SampleID %in% overlapping_samples,
paste0("20200001 Bridge"),
paste0("20200001 Sample"))) %>%
rbind({
npx_data2 %>%
filter(!str_detect(SampleID, "CONTROL_SAMPLE")) %>% #Remove control samples %>%
mutate(Type = if_else(SampleID %in% overlapping_samples,
paste0("20200002 Bridge"),
paste0("20200002 Sample"))) %>%
mutate(SampleID = if_else(SampleID %in% overlapping_samples,
paste0(SampleID, "_new"),
SampleID))
})
### PCA plot
OlinkAnalyze::olink_pca_plot(df = npx_before_br,
color_g = "Type",
byPanel = TRUE)
PCA plot of combined datasets before bridging
