FastQDesign helps investigators plan scRNA-seq experiments by optimizing the number of cells and sequencing depth given a budget constraint.
Data Source
Power analysis data (df_power) is included with the
package:
# Load power analysis data from package
df_power <- read.csv(system.file("data", "AIBM_power.csv", package = "FastQDesign"))Basic Usage
library(FastQDesign)
# Load power analysis data
df_power <- read.csv(system.file("data", "AIBM_power.csv", package = "FastQDesign"))
# Define experiment parameters
budget <- 7500 # Total budget in USD
power_threshold <- 0.7 # Statistical power target
flowcell_capacities <- c(10^7, 5 * 10^7, 2 * 10^8) # Reads per flowcell
flowcell_costs <- c(1000, 2000, 3000) # Cost per flowcell
library_costs <- 5000 # Library preparation cost
# Run experiment design
rst <- FastQDesign(
df_power = df_power,
budget = budget,
power_threshold = power_threshold,
flowcell_capacities = flowcell_capacities,
flowcell_costs = flowcell_costs,
library_costs = library_costs,
reads_valid_rate = 0.9
)Output
The FastQDesign() function returns a list
containing:
- $p_ind: Individual sample design plot
-
$p_share: Shared sample design plot
- $design: Design results table
Parameters
| Parameter | Description |
|---|---|
df_power |
DataFrame with n_cells, expected_reads_per_cell, power columns |
budget |
Total experiment budget |
power_threshold |
Target statistical power |
flowcell_capacities |
Vector of flowcell read capacities |
flowcell_costs |
Vector of flowcell costs |
library_costs |
Library preparation cost |
reads_valid_rate |
Expected valid reads rate |
Plotting
Save the generated plots:
# Save individual plot
ggsave("design_individual.pdf", rst$p_ind, width = 8, height = 6)
# Save combined plot
combined <- patchwork::wrap_plots(rst$p_ind, rst$p_share, ncol = 1)
ggsave("design_combined.pdf", combined, width = 10, height = 10)