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Project description
Coral (c-core)
Docker image build
Building your own docker image is optional. You can use the pre-built image from docker hub.
docker build . -t coral -f dockerfile/Dockerfile
Running from built docker image
docker run --rm -v ".\data:/data" coral -u "/data/For_Curtain_Raw_PPM1H- PROTAC_TP.txt" -a "/data/annotation.txt"-o "/data/output.txt" -c "/data/comparison.txt" -x "T: Index,T: Gene"
Running from docker hub image
docker run --rm -v ".\data:/data" noatgnu/coral:0.0.1 -u "/data/For_Curtain_Raw_PPM1H- PROTAC_TP.txt" -a "/data/annotation.txt"-o "/data/output.txt" -c "/data/comparison.txt" -x "T: Index,T: Gene"
Pip install
Install R and set R_HOME
environment variable to the R installation directory as well as install the QFeatures package and its dependencies.
pip install ccore-coral
Running from pip install
coral -u "/data/For_Curtain_Raw_PPM1H- PROTAC_TP.txt" -a "/data/annotation.txt"-o "/data/output.txt" -c "/data/comparison.txt" -x "T: Index,T: Gene"
CLI Usage
usage: coral [-h] [-u unprocessed] [-a annotation] [-o output] [-c comparison] [-x index] [-f column_na_filter_threshold] [-r row_na_filter_threshold] [-i imputation_method] [-n normalization_method] [-g aggregation_method] [-t aggregation_column]
-u unprocessed, --unprocessed unprocessed
Filepath to the unprocessed data file.
-a annotation, --annotation annotation
Filepath to the annotation file.
-o output, --output output
Filepath to the output file.
-c comparison, --comparison comparison
Filepath to the comparison file.
-x index, --index index
Column names to be used as index.
-f column_na_filter_threshold, --column_na_filter_threshold column_na_filter_threshold
Threshold for column-wise NA filtering.
-r row_na_filter_threshold, --row_na_filter_threshold row_na_filter_threshold
Threshold for row-wise NA filtering.
-i imputation_method, --imputation_method imputation_method
Method for imputation.
-n normalization_method, --normalization_method normalization_method
Method for normalization.
-g aggregation_method, --aggregation_method aggregation_method
Method for aggregation.
-t aggregation_column, --aggregation_column aggregation_column
Column name to be used for aggregation.
Usage as a module
import pandas as pd
from coral.data import Coral
core = Coral()
# Read in the unprocessed data
core.load_unproccessed_file("data/For_Curtain_Raw_PPM1H- PROTAC_TP.txt")
# Add sample column names
core.add_sample("...")
# Add condition or group names
core.add_condition("...")
# Add sample group mapping
core.add_condition_map("condition_name", "sample_name")
# Add comparison
core.add_comparison("condition_A", "condition_B", "comparison_name")
# Add index columns
core.index_columns = ["index_column_name"]
# Filter column by NA
core.filter_missing_columns(0.7)
# Create QFeatures object
core.prepare()
# Filter row by NA
core.filter_missing_rows(0.7)
# Impute missing values
core.impute("knn")
# log2 transform
core.log_transform()
# aggregate features
core.aggregate_features("new_feature_column")
# normalize
core.normalize()
# Prepare limma matrix
core.prepare_for_limma()
# Run limma
results = []
for d in core.run_limma():
results.append(d)
if len(results) > 1:
# Merge limma results
results = pd.concat(results)
else:
results = results[0]
# Write results
results.to_csv("output.txt", sep="\t", index=False)
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