Output

pyTEnrich writes two tables per input bed file:
  • One table for TE subfamily enrichment

  • One table for TE family enrichment

They both have similar format :

pyTEnrich output (bedfile_name_SUBFAM.tsv)

n_i

n_T.te

n_T.peak

expected

fc.expected

comparison

padj.final

significance

SVA_D

934

1433

4395

2.74

250.2

peak_in_te

0

****

LTR83

4

724

4395

0.79

2.8

te_in_peak

0.773

n.s

Columns interpretations

First columns represents TE family/subfamily name (Note that this column is not named in output)

n_i

The overlap found between input bed file and this TE family/subfamily. The size of the overlap rely on Bedtools intersect and the options used for the intersection.

n_T.te

Total number of loci in the TE subfamily/family.

n_T.peak

Total number of intervals in input bed files. Typically, the total number of peaks in ChIP-seq.

expected

Expected overlap between TE group and input bed file, according to a probability derived from TE genome occupancy.

fc.expected

Fold change between the observed overlap n_i and the expected one expected.

comparison

Type of comparison that was done for enrichment analysis. Can be either te_in_peak or peak_in_te. For more explanation consult Details methods section.

padj.final

Adjusted p-value.

significance

Symbol representing p-value as either

n.s

No significance

*

\(0.01 < p-val \leq 0.05\)

**

\(10^{-3} < p-val \leq 10^{-2}\)

***

\(10^{-4} < p-val \leq 10^{-3}\)

****

\(p-val \leq 10^{-4}\)