Sample-specific weighted correlation network (SWEET) method is designed to model SINs by integrating the genome-wide sample weight with the differential correlation between the perturbed and aggregate networks.
SWEET_plus is an expanded version of SWEET for modeling SINs of two omics-data, eg. bacteria-virome or others.
Otherwise, I also provide R functions for SWEET and SWEET_plus.
- 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
- Pandas
- scipy
- argparse
The example datasets are stored inside example folder, as well the example outputs.
Step 1: calculate genome-wide sample weight:
raw method
python3 1.SWEET_sample_weight_calculating.py -f ./example/expression.txt -s ./example/weight.txt
use two matrix as the input and spearman correlation
python3 1.SWEET_sample_weight_calculating.py -f ./example/mf_bacterime.txt -f2 ./example/mf_vrome.txt -s ./test2/weight.spearman -c spearman
-h: --help show this help message and exit
-f: F A path to 'gene expression matrix' or 'gene/species correlation matrix' file
-f2: F2 A path to 'gene expression matrix2/species correlation matrix' file
-k: K balance parameter
-n: N network or expession matrix
-c: C correlation method [spearman/pearson]
-s: S A path to the output 'sample weight' file
Step 2: calculate confidence scores of edges between given genes for each sample of interest:
raw method
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
use two matrix as the input and spearman correlation
python3 2.SWEET_edge_score_calculating.py -f ./example/mf_bacterime.txt -f2 ./example/mf_vrome.txt -w ./test2/weight.spearman.mean.txt -p ./example/patient_mf.txt -g ./example/species.txt -c spearman -s ./test2/
-h: --help show this help message and exit
-f: F A path to 'gene expression matrix' file
-f2: F2 A path to 'gene expression matrix2/species correlation matrix' file
-w: W A path to 'sample weight' file (i.e., the output file from step1)
-p: P A path to 'samples of interest' file
-g: G A path to 'genes of interest' file
-c: C correlation method [spearman/pearson]
-s: 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.