2.12. Loading Coexpression analyzes

2.12.1. Load Coexpression correlated pairs of features

Coexpression analyzes usually generate correlation statistics regarding gene sets in a pairwise manner. For example, LSTrAP can generate a table with the Pearson correlation coefficient for every pair of genes in a RNA-seq experiment.

To load such a table into Machado, the file must be headless and tab separated, with the two first columns containing the correlated pair of IDs for the genes/features and the third column must contain the correlation coefficient among them. In the case of the output of the LSTrAP software, the coefficient is subtracted by 0.7 for normalization sakes.

A one line sample from such a table is showed below:

AT2G44195.1.TAIR10      AT1G30080.1.TAIR10      0.18189286870895194
AT2G44195.1.TAIR10      AT5G24750.1.TAIR10      0.1715779378273995

Note: The feature pairs from columns 1 and 2 need to be loaded previously.

To load such a table, one can run the command below:

python manage.py load_coexpression_pairs --organism 'Arabidopsis thaliana' --file pcc.mcl.txt

• Loading this file can be faster if you increase the number of threads (–cpu).

python manage.py load_coexpression_pairs --help

–file	‘pcc.mcl.txt’ File *
–organism	Species name (eg. Homo sapiens, Mus musculus) *
–soterm	‘mRNA’ sequence ontology term default
–cpu	Number of threads

* example output file from LSTrAP software.

2.12.2. Remove coexpression pairs

If, by any reason, you need to remove coexpression pair analyzes, all you need to do is pass the filename used to load the analyzes to the remove_relationships command: remove_relationship –file <coexpression_file>’ Every coexpression relations from coexpression_file (e.g. ‘pcc.mcl.txt’ from LSTrAP) will be deleted on cascade.

python manage.py remove_relationship --help

–file

‘mcl.clusters.txt’ file *

* example output file from LSTrAP software.

2.12.3. Load Coexpression clusters

Other type of coexpression analyzes involve clustering features using its correlation values. LSTrAP does that using the ‘mcl’ software. To load data from such analyzes, the input file must be headless and fields tab separated, with each line representing one cluster, and each column representing one gene/feature from that cluster. The first column should represent the cluster name and must have the format: “<cluster_name>:”. Three-cluster sample from such a file is shown below, the first line represents a cluster with three individuals, the second line a cluster with two, and the third line an orphaned cluster with only one individual (obs: orphaned clusters are discarded).:

ath_1:    AT3G18715.1.TAIR10      AT3G08790.1.TAIR10      AT5G42230.1.TAIR10
ath_2:    AT1G27040.1.TAIR10      AT1G71692.1.TAIR10
ath_3:    AT5G24750.1.TAIR10

Note: The genes/features from each column must be loaded previously.

To load such a file, one can run the command below:

python manage.py load_coexpression_clusters --file mcl.clusters.txt --organism 'Arabidopsis thaliana'

• Loading this file can be faster if you increase the number of threads (–cpu).

python manage.py load_coexpression_clusters --help

–file	‘mcl.clusters.txt’ file *
–organism	Scientific name (e.g.: ‘Arabidopsis thaliana’)
–soterm	‘mRNA’ sequence ontology term default
–cpu	Number of threads

* example output file from LSTrAP software.

2.12.4. Remove coexpression clusters

If, by any reason, you need to remove coexpression cluster analyzes, you need to pass the controlled vocabulary term ‘coexpression group’ and the organism scientific name to the command remove_feature_group: remove_feature_group –cvterm ‘coexpression group’ –organism ‘Arabidopsis thaliana’ *

*Every coexpression group relations from that organism will be deleted on cascade**.

python manage.py remove_feature_annotation --help

–cvterm TERM	mandatory: ‘coexpression group’
–organism ORGANISM	Scientific name (e.g.: ‘Oryza sativa’)