Sample-specific weighted correlation network (SWEET) method is desinged to model SINs by integrating the genome-wide sample weight with the differential correlation between the perturbed and aggregate networks.
- Gene expression matrix (tab-delimited):
- Column: Samples
- Row: Genes
- Samples of interest: seperate with
\n - Genes of interest: seperate with
\n
The code is written in Python3. Additionally, the following package must also be installed:
- Numpy
The example datasets are stored inside example folder, as well the example outputs.
Step 1: calculate genome-wide sample weight:
python3 1.SWEET_sample_weight_calculating.py -f ./example/expression.txt -s ./example/weight.txt
-h: Get help with the commands
-f: A path to "gene expression matrix" file
-k: Balance parameter
-s: A path to the output "sample weight" file
Step 2: calculate confidence scores of edges between given genes for each sample of interest:
python3 2.SWEET_edge_score_calculating.py -f ./example/expression.txt -w ./example/weight.txt -p ./example/patient.txt -g ./example/gene.txt -s ./example
-h: Get help with the commands
-f: A path to "gene expression matrix" file
-w: A path to "sample weight" file (i.e., the output file from step 1)
-p: A path to "samples of interest" file
-g: A path to "genes of interest" file
-s: A path to the output "confidence scores of edges" files for each sample of interest
Step 3: calculate the significance level of the confidence score for the edge between any two genes by a z-test:
python3 3.SWEET_calculating_mean_std_zscore.py -p ./example/patient.txt -l ./example -s ./example/mean_std.txt -z False
-h: Get help with the commands
-p: A path to "samples of interest" file
-l: A path to the "confidence scores of edges" file for each sample of interest (i.e., the output files from step 2)
-s: A path to the output file(s)
-z: Indicates whether the calculation of z score (Ture) or not (False)
Note that the mean and standard deviation are calculated by the confidence scores of all edges for the samples of interest; therefore, different lists of "samples of interest" will generate distinct means and standard deviations.