This respository is currently for describing the OneStopWrapper.pl of maccu, which performs co-expression clustering in one command. In this document, we also present the use of a co-expression database of Arabidopsis Col-0 RNAseq samples made based on data from DEE2.
See here for updated databases for arabidopsis, worm, fly, zebrafish, bacteria, and yeast. Also see our approaches for downloadeding specified SRS metadata and classifying samples.
It should be feasible to run OneStopWrapper.pl in recent linux distributions. The only requirement is java runtime environment greater than or equal to 8. In this document, we use an Ubuntu20 VM with 16GB ram to run all commands. All steps were also tested in WSL Ubuntu20 of a 16GB RAM Windows 11 PC.
Install java runtime environment.
ubuntu@maccu:~$ sudo apt update
ubuntu@maccu:~$ sudo apt install openjdk-11-jre-headless
Downlaod the wrapper package and extract it. Must be sure to keep extracted files been placed under the same directory.
ubuntu@maccu:~$ wget https://downloads.sourceforge.net/project/maccu/0.8a/maccuWrapper.tar.gz
ubuntu@maccu:~$ tar -zxvf maccuWrapper.tar.gz
Here we provide the co-expression database made based on data from the DEE2. Please note that this database and the DEE2 database are both under GPLv3 public license and allow academic and commercial use. We thank DEE2 and ask you to consider supporting DEE2 if your organization uses DEE2 data for financial benefit.
Download the co-expression database and extract it.
ubuntu@maccu:~$ wget https://maccu.project.sinica.edu.tw/maccu/coexDB/coexDB20230714.tar.gz
ubuntu@maccu:~$ tar -zxvf coexDB20230714.tar.gz
coexDB20230714/
coexDB20230714/ath/
coexDB20230714/ath/Col0/
coexDB20230714/ath/Col0/README.txt
coexDB20230714/ath/Col0/sel20210116.col0.TMM.ALL
coexDB20230714/ath/Col0/sel20210116.col0.TMM.flower
coexDB20230714/ath/Col0/sel20210116.col0.TMM.leaf
coexDB20230714/ath/Col0/sel20210116.col0.TMM.root
coexDB20230714/ath/Col0/sel20210116.col0.TMM.rosette
coexDB20230714/ath/Col0/sel20210116.col0.TMM.seed
coexDB20230714/ath/Col0/sel20210116.col0.TMM.seedling
coexDB20230714/ath/Col0/sel20210116.col0.TMM.shoot
coexDB20230714/ath/Col0/sel20210116.col0.TMM.whole
The files named sel20210116.col0.TMM.* are our database files in tab-delimited text format. They are all read count matrix normalized by the TMM method (PMID: 20196867). Their columns are samples and rows are genes. The one suffixed by ALL are composed of 5556 Arabidopsis Col-0 RNAseq samples, which were selected from the DEE2 database following a series of considerations. All other database files are extracted portions of this ALL file, where the extracted portions were decided by parsing metadata downloaded from NCBI. For example, the root one is composed of root samples. For another example, the whole one is composed of samples using whole plants.
It is OK to execute the script by specifying a full path or a relative path, and it is also OK to put its path into the PATH environment variable. Simple description of options will be displayed if no options entered.
ubuntu@maccu:~$ maccuWrapper/OneStopWrapper.pl
Usage: OneStopWrapper.pl [<options>]+
options:
[-input <name> <fileName>]+ : input gene set names and gene set files, at least one.
-inputParam <parameterStr> : fishing/clustering option (default: -L true)
-maccu <JARpath> : path to maccu.jar (default: /home/ubuntu/maccuWrapper/maccu.jar)
-method <classStr> : maccu.CoExpressFishing or maccu.RelationComputer(default)
[-dbase <DBname> <DBloc>]+ : coex database names and database files
-probeMap <fileName> : probe-gene mapping file, in case of using array database (default: /dev/null)
-thresh <from> <to> <step> : correlation threshold range and step (default: 0.70 0.95 0.05)
-outDir <dirName> : output folder name in the final tar.gz file (default: coex)
-graphAdj <optionStr> : post computation adjustment parameters (default: "")
[-fold <name> <foldChangeFileName>]+ : fold-change set name and files (default: no setting)
[-graphAdjStr <inputPrefix> -remove <operandPrefix> <outputPrefix>]+ : graph-level remove operation on graphs
made base on different coex databases
-graphAdjShift <shift> : graph-level operand shift on computed network arrays (default: 1)
-keep : keep working directory (default: no keep)
-outputTGZ <outputFilename> : output .tgz filename (default: coexRes.tgz)
This github repository contains example files under the example directory.
ubuntu@maccu:~$ git clone https://github.com/wdlingit/maccu.git
ubuntu@maccu:~$ cd maccu/example/deg/
ubuntu@maccu:~/maccu/example/deg$ ls
foldchange.txt genelist.txt
The deg directory contains two files, they are from experiment data of PMID: 20181752. The genelist.txt file contains simply gene IDs in AGI accessions. The foldchange.txt file is in tab-delimited format, where the first column is for gene IDs and the second column is for fold-chagne values. It is strongly suggested to use fold-change in log scale as the fold-change values because that could be handled easily in Cytoscape.
Since the experiment is for studying Arabidopsis roots and the initial analysis (of arrays) discovered 187 genes showed differential expression, it is of our interests to figure out potential co-expression modules inside the 187 genes. To do that, we use the database of root samples to infer potential co-expression modules.
ubuntu@maccu:~/maccu/example/deg$ /home/ubuntu/maccuWrapper/OneStopWrapper.pl \
-input DEG genelist.txt \
-dbase root /home/ubuntu/coexDB20230714/ath/Col0/sel20210116.col0.TMM.root \
-fold foldchange foldchange.txt
Points to be noticed:
-inputis used to set the input gene list filegenelist.txtand its aliasDEG.-dbaseis uesed to set the database file for co-expression clustering, as well as its alias in this computation. Note that the database files must be assigned with absolute paths.-foldis used to associate specified fold-change values to attributefoldchangeof nodes in the final XGMML files. XGMML files can be imported into Cytoscape and it should be easy to fill node color according to node attributes.-foldis actually optional; that is, this can be omitted.
The execution should be finished with the following lines in the screen.
coex/root.DEG.0.70-all.graph
coex/root.DEG.0.75-all.graph
coex/root.DEG.0.80-all.graph
coex/root.DEG.0.85-all.graph
coex/root.DEG.0.90-all.graph
coex/root.DEG.0.95-all.graph
coex/root.DEG.0.70.xgmml
coex/root.DEG.0.75.xgmml
coex/root.DEG.0.80.xgmml
coex/root.DEG.0.85.xgmml
coex/root.DEG.0.90.xgmml
coex/root.DEG.0.95.xgmml
These are files to be stored in the final tar.gz file.
ubuntu@maccu:~/maccu/example/deg$ ls
coexRes.tgz foldchange.txt genelist.txt
ubuntu@maccu:~/maccu/example/deg$ tar -zxvf coexRes.tgz
coex/root.DEG.0.70-all.graph
coex/root.DEG.0.75-all.graph
coex/root.DEG.0.80-all.graph
coex/root.DEG.0.85-all.graph
coex/root.DEG.0.90-all.graph
coex/root.DEG.0.95-all.graph
coex/root.DEG.0.70.xgmml
coex/root.DEG.0.75.xgmml
coex/root.DEG.0.80.xgmml
coex/root.DEG.0.85.xgmml
coex/root.DEG.0.90.xgmml
coex/root.DEG.0.95.xgmml
There are two kinds of files been outputted:
.graph: tab-delimited text files, representing graphs in adjacency list format. Each line represents one node (the first token) and edges connecting to it (other token, where numbers in parentheses are correlations)..xgmml: XGMML files. These files can be imported into Cytoscape use itsFile->Import->Network from Filefunction. In so doing, correlations between edges would be saved asweightattributes of edges in Cytoscape.
Note that the files were named in the format <database>.<gene set>.<threshold>, where each file represents edges been recognized by using <database> between genes in <gene set> that are above <threshold> (in terms of correlation). In this naming scheme, we can apply options -input and -dbase multiple times with different aliases and perform all combinations of computations in one execution. By the way, the default threshold series is 0.95, 0.90, 0.85, ... 0.70 and this can be adjusted by applying the -thresh option.
The following picture was made by importing root.DEG.0.70.xgmml into Cytoscape and setting continuous mapping on node filling colors with attribute foldchange.
The timecourse directory contains four files, 797genes.txt for the gene list and the three files for log2-fold-changes in different time points. They are from the time course experiment of PMID: 21248074.
ubuntu@maccu:~/maccu/example/deg$ cd ../timecourse/
ubuntu@maccu:~/maccu/example/timecourse$ ls
797genes.txt t01.txt t06.txt t24.txt
From the last example, we learned that options -input, -dbase, and -fold can be applied multiple times.
ubuntu@maccu:~/maccu/example/timecourse$ /home/ubuntu/maccuWrapper/OneStopWrapper.pl \
-input DEG 797genes.txt \
-dbase root /home/ubuntu/coexDB20230714/ath/Col0/sel20210116.col0.TMM.root \
-dbase ALL /home/ubuntu/coexDB20230714/ath/Col0/sel20210116.col0.TMM.ALL \
-fold t01 t01.txt \
-fold t06 t06.txt \
-fold t24 t24.txt
(deleted outputs...)
coex/ALL.DEG.0.70-all.graph
coex/ALL.DEG.0.75-all.graph
coex/ALL.DEG.0.80-all.graph
coex/ALL.DEG.0.85-all.graph
coex/ALL.DEG.0.90-all.graph
coex/ALL.DEG.0.95-all.graph
coex/root.DEG.0.70-all.graph
coex/root.DEG.0.75-all.graph
coex/root.DEG.0.80-all.graph
coex/root.DEG.0.85-all.graph
coex/root.DEG.0.90-all.graph
coex/root.DEG.0.95-all.graph
coex/ALL.DEG.0.70.xgmml
coex/ALL.DEG.0.75.xgmml
coex/ALL.DEG.0.80.xgmml
coex/ALL.DEG.0.85.xgmml
coex/ALL.DEG.0.90.xgmml
coex/ALL.DEG.0.95.xgmml
coex/root.DEG.0.70.xgmml
coex/root.DEG.0.75.xgmml
coex/root.DEG.0.80.xgmml
coex/root.DEG.0.85.xgmml
coex/root.DEG.0.90.xgmml
coex/root.DEG.0.95.xgmml
In so doing, we will have networks computed based on root database and ALL database, respectively. Also, the application of -fold would set three node attributes t01, t06, and t24 in the XGMML files.
Computing different combinations of <database>.<gene set> would enable us to perform meaningful graph-level operations. For this example, we just computed root.DEG and ALL.DEG, doing root.DEG - ALL.DEG should give use gene pairs that show high enough correlations in root samples but not that high in ALL samples. More specifically, operation root.DEG.0.8 - ALL.DEG.0.7 should give us gene pairs with correlations 0.8 or above in root samples an also with correlation lower than 0.7 in ALL samples => gene paris with correlations in roots significantly higher than that in ALL samples => gene pairs with root-specific correlations.
ubuntu@maccu:~/maccu/example/timecourse$ /home/ubuntu/maccuWrapper/OneStopWrapper.pl \
-input DEG 797genes.txt \
-dbase root /home/ubuntu/coexDB20230714/ath/Col0/sel20210116.col0.TMM.root \
-dbase ALL /home/ubuntu/coexDB20230714/ath/Col0/sel20210116.col0.TMM.ALL \
-fold t01 t01.txt \
-fold t06 t06.txt \
-fold t24 t24.txt \
-graphAdjStr root.DEG -remove ALL.DEG specific \
-graphAdjShift 2 \
-thresh 0.8 0.95 0.05
(deleted outputs...)
coex/ALL.DEG.0.80-all.graph
coex/ALL.DEG.0.85-all.graph
coex/ALL.DEG.0.90-all.graph
coex/ALL.DEG.0.95-all.graph
coex/root.DEG.0.80-all.graph
coex/root.DEG.0.85-all.graph
coex/root.DEG.0.90-all.graph
coex/root.DEG.0.95-all.graph
coex/specific.0.90-all.graph
coex/specific.0.95-all.graph
coex/ALL.DEG.0.80.xgmml
coex/ALL.DEG.0.85.xgmml
coex/ALL.DEG.0.90.xgmml
coex/ALL.DEG.0.95.xgmml
coex/root.DEG.0.80.xgmml
coex/root.DEG.0.85.xgmml
coex/root.DEG.0.90.xgmml
coex/root.DEG.0.95.xgmml
coex/specific.0.90.xgmml
coex/specific.0.95.xgmml
The two options -graphAdjStr and -graphAdjShift are related with the above described graph-level operation. -graphAdjStr is used for specifying operand networks in the computation, in terms of <db1>.<set1> vs <db2>.<set2>. The <threshold> part of generated networks is the series of 0.95, 0.90, 0.85, and 0.80 (assigned by -thresh). -graphAdjShift is used for dealiing with the <threshold> part in the graph-level computation. It specifies the threshold shift between <db1>.<set1> ve <db2>.<set2>. In this example, we would like to compute root.DEG.0.95 - ALL.DEG.0.85 and root.DEG.0.9 - ALL.DEG.0.8 as the threshold step is 0.05 (specified by -thresh). The threshold difference between network operands is 0.1, which is twice of the threshold step, so we set -graphAdjShift 2. As a result, there will be a number of .graph files and XGMML files prefixed by "specific" (specified by -graphAdjStr) made by the graph-level operation.
The followig picture was made by importing root.DEG.0.90.xgmml to Cytoscape, where the coloring was based on t01.
The following picture was made by importing specific.0.90.xgmml to Cytoscape, which was computed by root.DEG.0.90 - ALL.DEG.0.80. The three subpictures were colored based on t01, t06, and t24 (from left to right), respectively.
By default, -method is set as maccu.RelationComputer, which was used for computing relationships between input genes. To discover relatioinships between input genes and all other genes, we will have to set -method to maccu.CoExpressFishing. In this example, we go to the fishing directory and use the bait.txt file as the input.
ubuntu@maccu:~/maccu/example/timecourse$ cd ../fishing/
ubuntu@maccu:~/maccu/example/fishing$ ls
bait.txt
ubuntu@maccu:~/maccu/example/fishing$ cat bait.txt
AT5G13630 1
The bait.txt file contains exactly one gene ID and is in tab-delimited format. Note that this tab-delimited format is not a requrirement. The gene list reading function reads exactly the first token of every line. Doing so is to let the -fold option can use the same file so that we can have some node attribute been set to 1 only for the initial bait genes.
ubuntu@maccu:~/maccu/example/fishing$ /home/ubuntu/maccuWrapper/OneStopWrapper.pl \
-input bait bait.txt \
-method maccu.CoExpressFishing \
-inputParam "-STEP 1" \
-dbase root /home/ubuntu/coexDB20230714/ath/Col0/sel20210116.col0.TMM.root \
-fold bait bait.txt
Points to be noticed:
-methodwas set tomaccu.CoExpressFishingso that the fishing algorithm would be invoked,-inputParam "-STEP 1"to transfer the parameter"-STEP 1"tomaccu.CoExpressFishingso that the fishing procedre would go only depth 1.-fold bait bait.txtso that node attributebaitof AT5G13630 will be 1.
The followig picture was made by importing root.bait.0.80.xgmml to Cytoscape. Note that AT5G13630 was marked red via the bait node attribute.
