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PPT.jl
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PPT.jl
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using DataFrames, Distributions, HypothesisTests, MultipleTesting, CSV
# Abstract type for input strategies
abstract type InputStrategy end
# Strategy for reading from file
struct FileInputStrategy <: InputStrategy
file_path::String
end
# Strategy for using pre-loaded data
struct PreloadedDataStrategy <: InputStrategy
data::DataFrame
end
# Function to read count matrix based on strategy
function read_count_matrix(strategy::InputStrategy)
return _read_count_matrix(strategy)
end
# Implementation for FileInputStrategy
function _read_count_matrix(strategy::FileInputStrategy)
# Read count matrix from file
data = CSV.read(strategy.file_path, DataFrame)
return preprocess_data(data)
end
# Implementation for PreloadedDataStrategy
function _read_count_matrix(strategy::PreloadedDataStrategy)
return preprocess_data(strategy.data)
end
# Common preprocessing function
function preprocess_data(data::DataFrame)
# Implement data quality control
# Perform normalization
return data
end
# Rest of the code remains the same...
# Statistical model
function fit_model(count_data, design_matrix)
# Implement negative binomial model or linear model
# Estimate dispersion
# Perform variance stabilizing transformation
end
# Differential expression analysis
function differential_expression(fit, contrast)
# Perform hypothesis testing (e.g., Wald test)
# Calculate p-values
# Adjust p-values for multiple testing
# Calculate fold changes
end
# Result output
function generate_results(de_results)
# Create DataFrame with results
# Implement visualization functions (e.g., volcano plot)
end
# Updated main analysis function
function analyze_differential_expression(count_input::InputStrategy, design_file, contrast)
count_data = read_count_matrix(count_input)
design_matrix = read_design_matrix(design_file)
fit = fit_model(count_data, design_matrix)
de_results = differential_expression(fit, contrast)
return generate_results(de_results)
end
# Example usage
# For file input
file_strategy = FileInputStrategy("counts.csv")
results_file = analyze_differential_expression(file_strategy, "design.csv", [:treatment, :control])
# For pre-loaded data
preloaded_data = DataFrame(/* your preloaded data here */)
data_strategy = PreloadedDataStrategy(preloaded_data)
results_preloaded = analyze_differential_expression(data_strategy, "design.csv", [:treatment, :control])