TOM Similarity¶
The toplogical overlap matrix (TOM) is the similarity measure implemented by WGCNA [Langfelder2008]. It calculates a correlation matrix from the expression data, calculates a soft threshold and assigns two genes a high topological overlap if they share common neighbourhoods.
Running TOM similarity¶
tomsimilarity
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:
6.107967 7.188796 7.139945 9.417835 6.195927
8.602925 9.134458 8.630118 10.695973 6.930023
6.699199 8.307864 8.174942 10.874148 7.143233
7.661777 8.891523 8.348661 10.439793 6.868748
7.031853 9.019152 8.539557 10.726523 7.461354
8.931517 9.246769 8.944240 10.774747 6.729316
6.815357 9.209684 8.607074 9.574451 7.400409
7.424712 9.603071 8.347164 10.609222 7.168921
8.465108 8.788967 8.875855 10.537852 6.628380
8.559188 8.992996 8.279209 10.640245 6.744078
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 PCor:
tomsimilarity -i expr_mat.tsv -g genes.txt -b 4
The output is a lower triangular matrix of scores:
0.44357
0.486974 0.504881
0.370446 0.408224 0.42039
0.225011 0.465292 0.396999 0.252425
Optional arguments for tomsimilarity
¶
-s, --scale
: This triggers feature scaling of the expression matrix before the correlation calculation. Generally this should be on.-m, --method
: Choose between “pearson” or “bicor” (biweight midcorrelation. The latter is typically a good choice unless you have a lot of outliers.)-b, --sft
: The soft threshold power. This is the exponent for soft thresholding the correlation matrix. Unless you know why, leave it default.-M, --max-power
: When auto-detecting the soft threshold power, this is the maximum value that will be tested. It’s usually not a good idea to go above 30. If you cannot get a good fit, decrease the cutoff instead.-S, --sft-cutoff
: When the network reaches this scale free fit R^2 value, stop testing powers. Sometimes, you cannot get a good fit (>0.8) on larger datasets. In this case, decrease this value.-T, --tom-type
: “unsigned”, “signed”, or “signed-hybrid”. This defines how to score the TOM. “unsigned” is \(\vert a_{ij} \vert\), “signed” is \(\frac{a_{ij} + 1}{2}\) and “signed-hybrid” is \(\vert a_{ij} \vert\) for positive correlation, 0 otherwise.
Running tomsimilarity
for a subset of genes¶
Often we have only a small number of genes of interest. We can instruct
tomsimilarity
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:
tomsimilarity -i expr_mat.tsv -g genes.txt -t targets.txt -b 4
In this case we will receive an edge list as output:
G3 G1 0.486974
G3 G2 0.504881
G3 G4 0.42039
G3 G5 0.396999
G4 G1 0.370446
G4 G2 0.408224
G4 G3 0.42039
G4 G5 0.252425