Correlation

This simple executable calculates pearson or spearman correlation from a set of expression data.

Running correlation

Correlation needs a minimum of two input files:

  • -i, --infile: An expression matrix (genes are columns, samples are rows) without headers.
  • -g, --genes: A file containing gene names that correspond to columns in the expression matrix.

Here is an example matrix containing expression data for five genes in ten samples:

0.4254475 0.0178292 0.9079888 0.4482474 0.1723238
0.4424002 0.0505248 0.8693676 0.4458513 0.1733112
1.0568470 0.2084539 0.4674478 0.5050774 0.2448833
1.1172264 0.0030010 0.3176543 0.3872039 0.2537921
0.9710677 0.0010565 0.3546514 0.4745322 0.2077183
1.1393856 0.1220468 0.4024654 0.3484362 0.1686139
1.0648694 0.1405077 0.4817628 0.4748571 0.1826433
0.8761173 0.0738140 1.0582917 0.7303661 0.0536562
1.2059661 0.1534070 0.7608608 0.6558457 0.1577311
1.0006755 0.0789863 0.8036309 0.8389751 0.0883061

In the genes files, we provide the column headers for the expression matrix in order:

G1
G2
G3
G4
G5

With that, we can run correaltion in Pearson mode:

correlation -m pearson -i expr_mat.tsv -g genes.txt

or in Spearman mode:

correlation -m spearman -i expr_mat.tsv -g genes.txt

The output is a lower triangular matrix of scores:

0.469355
-0.587163   -0.0704821
0.127765    0.16474     0.597376
0.145338    0.0138744   -0.77125    -0.758263

Optional arguments to correlation

  • -a, --absolute: By default, the executable reports signed correlation values. Using this option will turn on reporting of the absolute value of the correlation coefficient. It is generally recommended to export correlation with signs (i.e. not absolute) and instead run seidr import in absolute mode, which will rank genes by their magnitude, but won’t throw away the sign information.
  • -s, --scale: This triggers feature scaling of the expression matrix before the correlation calculation. Generally this should be on especially when calculating Pearson’s rho.

Running Correlation for a subset of genes

Often we have only a small number of genes of interest. We can instruct correlation to only calculate interactions involving those genes by providing a -t, --targets file containing these gene names:

G3
G4

And running it with the -t, --targets options:

correlation -i expr_mat.tsv -g genes.txt -t targets.txt

In this case we will receive an edge list as output:

G3  G1  -0.587163
G3  G2  -0.0704821
G3  G4  0.597376
G3  G5  -0.77125
G4  G1  0.127765
G4  G2  0.16474
G4  G3  0.597376
G4  G5  -0.758263