counts_scaled %>% reduce_dimensions(method = "PCA", .dims = 3)
de_all %>%
# Subset transcript information
pivot_transcript() %>%
# Reshape for nesting
pivot_longer(
cols = -c(feature, symbol, .abundant, group:exon_name),
names_sep = "_",
names_to = c("method", "statistic"),
values_to = "value"
) %>%
# Filter statistic
filter(statistic %in% c("FDR", "adj.P.Val", "padj")) %>%
filter(value < 0.05) %>%
# Counting
count(method) %>%
# Sort
arrange(desc(n))
What is the most abundant cell type overall in BRCA samples?
BRCA_cell_type_long %>% group_by(cell_type) %>% summarise(m = median(proportion)) %>% arrange(desc(m))
mito_info_all_datasets <- pbmc_nested %>% mutate(mitochondrion_info = map( data, ~ # Calculate mitochondrial statistics perCellQCMetrics(.x, subsets = list(Mito = which(location == "MT"))) %>% # Convert to tibble as_tibble(rownames = "cell") %>% # Label cells with high mitochondrial content mutate(high_mitochondrion = isOutlier(subsets_Mito_percent, type = "higher")) )) mito_info_all_datasets
UMAP 1 of 2 components has more variability than 3 components
left_join(
pbmc %>%
runUMAP(ncomponents = 2, dimred="corrected") %>%
as_tibble() %>%
select(cell, UMAP1),
pbmc %>%
runUMAP(ncomponents = 3, dimred="corrected") %>%
as_tibble() %>%
select(cell, UMAP1),
by="cell"
) %>%
summarise(sd(UMAP1.x), sd(UMAP1.y))
Skeletal muscle
pbmc %>% count(label, first.labels) %>% arrange(desc(n))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.