Examples
JASPAR is a very highly-touted transcription factor motif database from which motif count
matrices can be downloaded for a large variety of organisms and transcription factors. There exist numerous other motif
databases as well (TRANSFAC, CIS-BP, MEME, HOMER, WORMBASE, etc), most of which use a relatively similar format
for their motifs. Typically, a motif file consists of four rows or columns with each position in a given row or column corresponding
to a base within the motif. Sometimes there is an comment line started with >. The row or column order is always A, C, G, T.
In this example, the motif consists of four rows corresponding to the 16 positions of the motif with counts for each base at each position.
>>> path = "MA0007.1.pfm"
>>> ! cat MA0007.1.pfm
- >MA0007.1 Ar
9.00 9.00 11.00 16.00 0.00 12.00 21.00 0.00 15.00 4.00 5.00 6.00 3.00 0.00 4.00 11.00 1.00 3.00 6.00 6.00 10.00 5.00 7.00 2.00 3.00 1.00 0.00 6.00 2.00 24.00 0.00 9.00 11.00 9.00 5.00 0.00 0.00 5.00 22.00 16.00 7.00 5.00 11.00 11.00 2.00 3.00 3.00 7.00 22.00 1.00 0.00 0.00 8.00 2.00 2.00 9.00 1.00 24.00 0.00 1.00 1.00 1.00 5.00 9.00 0.00 6.00 6.00 10.00 7.00 0.00 2.00 5.00 1.00 0.00 1.00 9.00 6.00 0.00 15.00 0.00 20.00 7.00 0.00 4.00 6.00 4.00 3.00 2.00
>>> import tfm_utils
>>> m = tfmp.create_matrix("MA0045.pfm")
>>> tfmp.score2pval(m, 8.7737)
9.992625564336777e-06
>>> tfmp.pval2score(m, 0.00001)
8.773708000000001
This could also be done using pandas as follows
>>> import pandas as pd
>>> import tfm_utils
>>> df = pd.read_csv("tests/M08490_1.94d.txt", sep = "\t", index_col=0)
>>> df.head()
A C G T
Pos
1 0.215492 0.220404 0.340647 0.223457
2 0.534211 0.101312 0.330926 0.033551
3 0.000000 0.000191 0.000286 0.999523
4 0.014867 0.000000 0.756531 0.228601
5 0.999333 0.000000 0.000000 0.000667
>>> matrix = tfm_utils.df_to_matrix(df)
>>> matrix
<tfm_utils.pytfmpval.Matrix; proxy of <Swig Object of type 'Matrix *' at 0x7fc2f8bfa330> >
>>> tfm_utils.score2pval(matrix, 7.14)
3.516674041748047e-06
This also works by passing the DataFrame into the functions directly and works for any orientation:
>>> df.head()
Pos 1 2 3 4 5 6 7 8 9 10 11 12
A 0.215492 0.534211 0.000000 0.014867 0.999333 0.000000 0.03061 0.0 0.226232 1.0 0.035516 0.221931
C 0.220404 0.101312 0.000191 0.000000 0.000000 0.968450 0.00000 0.0 0.758146 0.0 0.328779 0.327673
G 0.340647 0.330926 0.000286 0.756531 0.000000 0.000508 0.96939 0.0 0.000000 0.0 0.101733 0.227845
T 0.223457 0.033551 0.999523 0.228601 0.000667 0.031042 0.00000 1.0 0.015622 0.0 0.533973 0.222551
>>> tfm_utils.score2pval(df, 7.14)
5.960464477539063e-08
If you are more used to R, this could also be done by creating a string for the matrix by concatenating the rows (or columns) and using the read_matrix() function.
This method is usually easier, as it allows the user to parse the motif file as necessary to ensure a proper input. It’s also more fitting for high-throughput use.
>>> from tfm_utils import tfmp
>>> mat = (" 3 7 9 3 11 11 11 3 4 3 8 8 9 9 11 2"
... " 5 0 1 6 0 0 0 3 1 4 5 1 0 5 0 7"
... " 4 3 1 4 3 2 2 2 8 6 1 4 2 0 3 0"
... " 2 4 3 1 0 1 1 6 1 1 0 1 3 0 0 5"
... )
>>> m = tfmp.read_matrix(mat)
>>> tfmp.pval2score(m, 0.00001)
8.773708000000001
>>> tfmp.score2pval(m, 8.7737)
9.992625564336777e-06