DPoP：衍生分析组学包附matlab代码.zip

BSD-3 Copyright Copyright 2023 Harley Edwards @ University of Maryland, Baltimore County Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- DPoP: Derivative Profiling omics Package Written by Harley Edwards @ MartenLab @ UMBC MacOS NOT SUPPORTED! Linnux NOT TESTED! The zip folder found on Mathworks contains the app as a .mlapp file, the app as a .mlappinstall file, the two .gaf files, one for A. nidulans and one for S. cerevisiae, and three omics data sets, and lastly, the example data sets. One transcriptomic dataset about A. nidulans(as a .xlsx), one phosphoproteomic dataset about A. nidulans (as both .xlsx and .mat), and one phosphoproteomic dataset about S. cerevisiae(as a .xlsx). At the time of writing this, you can use the browser based version of matlab to run this app, even if you dont have matlab on your computer. The only known bug specific to the online version is that when you click load data or load database, you must double click the button to see the prompt, as opposed to the local version which only requires 1 click to see the prompt. To install to the APP Bar in Matlab, create folder in MATLAB Directory, only .mlapinstall files go and are ran there. Make different working directory. Running the .mlapp file from any directory will also work, but will not install the app in the APP Bar of Matlab. DPoP only requires a .gaf GO database file about your organism in order to run the GO enrichment analysis. DPoP works with just a .mat or .xlsx data file. Inside your .mat file should be 1 variable named the same as the file, as a string array of all data and tags. The -omics data should be arranged, in either file format, such that the first column is data labels, the first row is time(or your other dynamic variable), and the data should fill in the (2:end,2:end) rectangle matrix corresponding to the size of tags and time. The single column of datalabels should take the form of a column array of strings 'ANXXXX' for transcriptomic data, OR 'AN#### UNIPROT# PSite' for phosphoproteomic data. You can use your own identifiers from another organism, only the AN/Uniprot headers will still persist in the table, your data will have all nessesary identifiers in the exported excel sheet. The output file will record some DPoP run settings in the name, but not all. It will be named "Results...Span#to#_Interestedin#to#_Pstat##. It is highly recomended you add identifying information where the "..." is in the name or the next output will write in the same file. Depending on the system resources running the app, it can be slow. Wait for Matlab to stop saying "Busy" in the bottom of the command window, wait for the app to visually, or for some functions wait for the the command window to tell you when the app is done. Clicking things in fast succession will increase likelyhood of errors. In the "interested in Data" Fields, the first(left) field can never be a greater number than the second(right) field number. If your data is 13 timepoints long, and you select "Total span data" 3-9, and you select "interested in data" `1 to 1, that will label point 3 out of 13 as 1 in the export sheet. If your data is 13 timepoints long, and you select "Total span data" 3-9, and you select "interested in data" `2 to 2, that will label point 4 out of 13 as 2 in the export sheet. Please email [email protected] for support or questions. =)