Overview of SIDCOSiDCo (SIgned Distance COrrelation) application calculates pairwise distance correlation coefficients between all columns of a datasheet. The application is implemented in Python with RShiny frontend and includes:
The primary use for SiDCo is in metabolomics and lipidomics. However, this site provides seamless application of signed distance correlation for any data set. The main advantage of distance correlation is it's ability to detect nonlinear correlations while at the same time allowing comparison of matrices of different dimensions through the calculation of distance covariances. Due to this unique ability distance correlation can be used to calculate onetoall or onetoone correlations, which are both provided as options on this site. If a linear trend is detected by Pearson's correlation, the sign of the coefficient will reflect this trend. It is important to note that the strength of the correlation coefficient does not indicate the strength of the linear correlation, but rather a strong distance correlation, and some amount of overall linear trend. The output file contains both the correlation coefficients and the p values for each calculation.
SIDCO output includes:
In both cases distance correlation values are set to zero if their absolute value is below the threshold value or their corresponding pvalue is above user defined significance level pvalue. 
SIDCO workflow. 
Preparing your data for SIDCOSIDCO input must be a single file in .xlsx or .csv format with features (metabolites or lipids) in columns and samples in rows. The file should contain column names in the top row, and all numeric data should be below and to the right of the specified start column.Having any nonnumeric data to the right of the start column specified will cause the calculation to be aborted. Distance correlation calculations cannot work with data that have missing values. Therefore, it is recommended that the user imputes any missing data with a method that is the most appropriate for their dataset prior to using SIDCO. Any remaining missing values will be imputed with the value equal to the one fifth of the lowest measured value for the feature (assuming that the value is missing because it is below the level of detection for the feature). Sample DataProvided example datasets include allowed input formats. In the provided dataset, column A includes sample group names and row 1 includes feature names. For the calculation of distance correlation for Group 1 in this data set, the user will input: Start Column: B; First Row: 2 (or 1 indicating first data row); Last Row: 31. For analysis of Group 2, the user will input: Start Column: B; First Row: 32; Last Row: 46 or 1 (stating last row).
Sample Data 
When troubleshooting, please review this list of common reasons for SIDCO failing to run. If you are still experiencing difficulties running our tool, please contact ldomic@uottawa.ca for further assistance. Please include your input dataset and a description of the problem that you experienced. We will reproduce the problem and provide you with a solution.

My file loads but when trying to download output system reports a problem with output
SIDCO only accepts commadelimited or xlsx files as input. Tabdelimited files will be read but will not produce any results. Please convert your input data into .csv format before running SIDCO. Additionally, make sure that your column and row information leads to numeric values and data that you are trying to analyze. Data can start from any row and column however has to be numeric right and down from user defined start point.

All obtained values are zero
Check your tolerance, i.e. threshold information in the input. SIDCO sets to zero values that are below correlation value threshold as well as above p value limit.

How come there are no negative values for correlation in onetoall output
As Pearson correlation cannot be calculated for vectors of different length it is not possible to determine sign in this calculation without averaging or sampling that could bias the result.
Cite your use of SIDCO in a publication
Miroslava ČuperlovićCulf*, Ali Yilmaz, David Stewart, Anuradha Surendra, Sumeyya Akyol, Sangeetha Vishweswaraiah, Xiaojian Shao, Irina Alecu, Thao NguyenTran, Bernadette McGuinness, Peter Passmore, Patrick G. Kehoe, Michael E. Maddens, Brian D. Green, Stewart F. Graham, Steffany A.L. Bennett* Signed Distance Correlation (SiDCo): A network application that combines Pearson and distance correlation identifying metabolic networks disrupted in Dementia with Lewy Bodies
Public Server
SIDCO: https://complimet.ca/sidco/
Software License
SIDCO is free software. You can redistribute it and/or modify it under the terms of the GNU General Public License v3 (or later versions) as published by the Free Software Foundation. As per the GNU General Public License, SIDCO is distributed as a bioinformatic tool to assist users WITHOUT ANY WARRANTY and without any implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. All limitations of warranty are indicated in the GNU General Public License.
Calculating. This might take a minute...
Remember that the sign of the coefficient is coming from Pearson's correlation.
eg. a high coefficient with a negative sign does NOT mean a significant negative trend.
It only indicates a strong correlation, with some negative overall, linear trend also detected