TF-MoDISco results

Summary of motifs

TOMTOM matches to motifs

In [1]:
import os
import sys
sys.path.append(os.path.abspath("/users/amtseng/tfmodisco/src/"))
from tfmodisco.run_tfmodisco import import_shap_scores, import_tfmodisco_results
from motif.read_motifs import pfm_info_content, pfm_to_pwm, trim_motif_by_ic
from motif.match_motifs import match_motifs_to_database
from util import figure_to_vdom_image
import plot.viz_sequence as viz_sequence
import numpy as np
import h5py
import matplotlib.pyplot as plt
import vdom.helpers as vdomh
from IPython.display import display

Define constants and paths

In [2]:
# Define parameters/fetch arguments
tf_name = os.environ["TFM_TF_NAME"]
shap_scores_path = os.environ["TFM_SHAP_PATH"]
tfm_results_path = os.environ["TFM_TFM_PATH"]
hyp_score_key = os.environ["TFM_HYP_SCORE_KEY"]
if "TFM_MOTIF_CACHE" in os.environ:
    tfm_motifs_cache_dir = os.environ["TFM_MOTIF_CACHE"]
else:
    tfm_motifs_cache_dir = None

print("TF name: %s" % tf_name)
print("DeepSHAP scores path: %s" % shap_scores_path)
print("TF-MoDISco results path: %s" % tfm_results_path)
print("Importance score key: %s" % hyp_score_key)
print("Saved TF-MoDISco-derived motifs cache: %s" % tfm_motifs_cache_dir)

TF name: NR3C1-reddytime
DeepSHAP scores path: /users/amtseng/tfmodisco/results/importance_scores/multitask_profile/NR3C1-reddytime_multitask_profile_fold2/NR3C1-reddytime_multitask_profile_fold2_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/NR3C1-reddytime_multitask_profile_fold2/NR3C1-reddytime_multitask_profile_fold2_count_tfm.h5
Importance score key: count_hyp_scores
Saved TF-MoDISco-derived motifs cache: /users/amtseng/tfmodisco/results/reports/tfmodisco_results//cache/multitask_profile/NR3C1-reddytime_multitask_profile_fold2/NR3C1-reddytime_multitask_profile_fold2_count
In [3]:
# Define paths and constants
input_length = 2114
shap_score_center_size = 400
In [4]:
if tfm_motifs_cache_dir:
    os.makedirs(tfm_motifs_cache_dir, exist_ok=True)

Import SHAP scores and TF-MoDISco results

In [5]:
# Import SHAP coordinates and one-hot sequences
hyp_scores, _, one_hot_seqs, shap_coords = import_shap_scores(shap_scores_path, hyp_score_key, center_cut_size=shap_score_center_size)
# This cuts the sequences/scores off just as how TF-MoDISco saw them, but the coordinates are uncut

Importing SHAP scores: 100%|██████████| 186/186 [01:30<00:00,  2.05it/s]
In [6]:
# Import the TF-MoDISco results object
tfm_obj = import_tfmodisco_results(tfm_results_path, hyp_scores, one_hot_seqs, shap_score_center_size)

Plot some SHAP score tracks

Plot the central region of some randomly selected actual importance scores

In [7]:
plot_slice = slice(int(shap_score_center_size / 4), int(3 * shap_score_center_size / 4))
for index in np.random.choice(hyp_scores.shape[0], size=5, replace=False):
    viz_sequence.plot_weights((hyp_scores[index] * one_hot_seqs[index])[plot_slice], subticks_frequency=100)

Plot TF-MoDISco results

Plot all motifs by metacluster

In [8]:
motif_pfms, motif_hcwms, motif_cwms = [], [], []  # Save the trimmed PFMs, hCWMs, and CWMs
motif_pfms_short = []  # PFMs that are even more trimmed (for TOMTOM)
num_seqlets = []  # Number of seqlets for each motif
motif_seqlets = []  # Save seqlets of each motif
metaclusters = tfm_obj.metacluster_idx_to_submetacluster_results
num_metaclusters = len(metaclusters.keys())
if tfm_motifs_cache_dir:
    motif_hdf5 = h5py.File(os.path.join(tfm_motifs_cache_dir, "all_motifs.h5"), "w")
for metacluster_i, metacluster_key in enumerate(metaclusters.keys()):
    metacluster = metaclusters[metacluster_key]
    display(vdomh.h3("Metacluster %d/%d" % (metacluster_i + 1, num_metaclusters)))
    patterns = metacluster.seqlets_to_patterns_result.patterns
    if not patterns:
        break
    motif_pfms.append([])
    motif_hcwms.append([])
    motif_cwms.append([])
    motif_pfms_short.append([])
    num_seqlets.append([])
    motif_seqlets.append([])
    num_patterns = len(patterns)
    for pattern_i, pattern in enumerate(patterns):
        seqlets = pattern.seqlets
        display(vdomh.h4("Pattern %d/%d" % (pattern_i + 1, num_patterns)))
        display(vdomh.p("%d seqlets" % len(seqlets)))
        
        pfm = pattern["sequence"].fwd
        hcwm = pattern["task0_hypothetical_contribs"].fwd
        cwm = pattern["task0_contrib_scores"].fwd
        
        pfm_fig = viz_sequence.plot_weights(pfm, subticks_frequency=10, return_fig=True)
        hcwm_fig = viz_sequence.plot_weights(hcwm, subticks_frequency=10, return_fig=True)
        cwm_fig = viz_sequence.plot_weights(cwm, subticks_frequency=10, return_fig=True)
        pfm_fig.tight_layout()
        hcwm_fig.tight_layout()
        cwm_fig.tight_layout()
        
        motif_table = vdomh.table(
            vdomh.tr(
                vdomh.td("Sequence (PFM)"),
                vdomh.td(figure_to_vdom_image(pfm_fig))
            ),
            vdomh.tr(
                vdomh.td("Hypothetical contributions (hCWM)"),
                vdomh.td(figure_to_vdom_image(hcwm_fig))
            ),
            vdomh.tr(
                vdomh.td("Actual contributions (CWM)"),
                vdomh.td(figure_to_vdom_image(cwm_fig))
            )
        )
        display(motif_table)
        plt.close("all")  # Remove all standing figures
        
        # Trim motif based on information content
        short_trimmed_pfm = trim_motif_by_ic(pfm, pfm)
        motif_pfms_short[-1].append(short_trimmed_pfm)
        
        # Expand trimming to +/- 4bp on either side
        trimmed_pfm = trim_motif_by_ic(pfm, pfm, pad=4)
        trimmed_hcwm = trim_motif_by_ic(pfm, hcwm, pad=4)
        trimmed_cwm = trim_motif_by_ic(pfm, cwm, pad=4)
        
        motif_pfms[-1].append(trimmed_pfm)
        motif_hcwms[-1].append(trimmed_hcwm)
        motif_cwms[-1].append(trimmed_cwm)
        
        num_seqlets[-1].append(len(seqlets))
        
        if tfm_motifs_cache_dir:
            # Save results and figures
            motif_id = "%d_%d" % (metacluster_i, pattern_i)
            pfm_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_pfm_full.png"))
            hcwm_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_hcwm_full.png"))
            cwm_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_cwm_full.png"))
            motif_dset = motif_hdf5.create_group(motif_id)
            motif_dset.create_dataset("pfm_full", data=pfm, compression="gzip")
            motif_dset.create_dataset("hcwm_full", data=hcwm, compression="gzip")
            motif_dset.create_dataset("cwm_full", data=cwm, compression="gzip")
            motif_dset.create_dataset("pfm_trimmed", data=trimmed_pfm, compression="gzip")
            motif_dset.create_dataset("hcwm_trimmed", data=trimmed_hcwm, compression="gzip")
            motif_dset.create_dataset("cwm_trimmed", data=trimmed_cwm, compression="gzip")
            motif_dset.create_dataset("pfm_short_trimmed", data=short_trimmed_pfm, compression="gzip")
if tfm_motifs_cache_dir:
    motif_hdf5.close()

Metacluster 1/1

Pattern 1/13

6589 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 2/13

3229 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 3/13

970 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 4/13

927 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 5/13

430 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 6/13

361 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 7/13

281 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 8/13

230 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 9/13

180 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 10/13

50 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 11/13

34 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 12/13

32 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 13/13

30 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Summary of motifs

Motifs are trimmed based on information content, and presented in descending order by number of supporting seqlets. The motifs are separated by metacluster. The motifs are presented as hCWMs. The forward orientation is defined as the orientation that is richer in purines.

In [9]:
colgroup = vdomh.colgroup(
    vdomh.col(style={"width": "5%"}),
    vdomh.col(style={"width": "5%"}),
    vdomh.col(style={"width": "45%"}),
    vdomh.col(style={"width": "45%"})
)
header = vdomh.thead(
    vdomh.tr(
        vdomh.th("#", style={"text-align": "center"}),
        vdomh.th("Seqlets", style={"text-align": "center"}),
        vdomh.th("Forward", style={"text-align": "center"}),
        vdomh.th("Reverse", style={"text-align": "center"})
    )
)

for i in range(len(motif_hcwms)):
    display(vdomh.h3("Metacluster %d/%d" % (i + 1, num_metaclusters)))
    body = []
    for j in range(len(motif_hcwms[i])):
        motif = motif_hcwms[i][j]
        if np.sum(motif[:, [0, 2]]) > 0.5 * np.sum(motif):
            # Forward is purine-rich, reverse-complement is pyrimidine-rich
            f, rc = motif, np.flip(motif, axis=(0, 1))
        else:
            f, rc = np.flip(motif, axis=(0, 1)), motif
            
        f_fig = viz_sequence.plot_weights(f, figsize=(20, 4), return_fig=True)
        f_fig.tight_layout()
        rc_fig = viz_sequence.plot_weights(rc, figsize=(20, 4), return_fig=True)
        rc_fig.tight_layout()
        
        if tfm_motifs_cache_dir:
            # Save results and figures
            motif_id = "%d_%d" % (i, j)
            f_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_hcwm_trimmed_fwd.png"))
            rc_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_hcwm_trimmed_rev.png"))

        body.append(
            vdomh.tr(
                vdomh.td(str(j + 1)),
                vdomh.td(str(num_seqlets[i][j])),
                vdomh.td(figure_to_vdom_image(f_fig)),
                vdomh.td(figure_to_vdom_image(rc_fig))
            )
        )
    display(vdomh.table(colgroup, header, vdomh.tbody(*body)))
    plt.close("all")

Metacluster 1/1

/users/amtseng/tfmodisco/src/plot/viz_sequence.py:152: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  fig = plt.figure(figsize=figsize)
#SeqletsForwardReverse
16589
23229
3970
4927
5430
6361
7281
8230
9180
1050
1134
1232
1330

Top TOMTOM matches for each motif

Here, the TF-MoDISco motifs are plotted as hCWMs, but the TOMTOM matches are shown as PWMs.

In [10]:
num_matches_to_keep = 10
num_matches_to_show = 5

header = vdomh.thead(
    vdomh.tr(
        vdomh.th("Motif ID", style={"text-align": "center"}),
        vdomh.th("q-val", style={"text-align": "center"}),
        vdomh.th("PWM", style={"text-align": "center"})
    )
)

for i in range(len(motif_pfms)):
    display(vdomh.h3("Metacluster %d/%d" % (i + 1, num_metaclusters)))
    
    # Compute TOMTOM matches for all motifs in the metacluster at once
    out_dir = os.path.join(tfm_motifs_cache_dir, "tomtom", "metacluster_%d" % i) if tfm_motifs_cache_dir else None
    tomtom_matches = match_motifs_to_database(motif_pfms_short[i], top_k=num_matches_to_keep, temp_dir=out_dir)
    
    for j in range(len(motif_pfms[i])):
        display(vdomh.h4("Motif %d/%d" % (j + 1, len(motif_pfms[i]))))
        viz_sequence.plot_weights(motif_hcwms[i][j])
    
        body = []
        for k, (match_name, match_pfm, match_qval) in enumerate(tomtom_matches[j]):
            fig = viz_sequence.plot_weights(pfm_to_pwm(match_pfm), return_fig=True)
            fig.tight_layout()
            if k < num_matches_to_show:
                body.append(
                    vdomh.tr(
                        vdomh.td(match_name),
                        vdomh.td(str(match_qval)),
                        vdomh.td(figure_to_vdom_image(fig))
                    )
                )
                if tfm_motifs_cache_dir:
                    # Save results and figures
                    motif_id = "%d_%d" % (i, j)
                    fig.savefig(os.path.join(out_dir, motif_id + ("_hit-%d.png" % (k + 1))))
            else:
                body.append(
                    vdomh.tr(
                        vdomh.td(match_name),
                        vdomh.td(str(match_qval)),
                        vdomh.td("Not shown")
                    )
                )
        if not body:
            display(vdomh.p("No TOMTOM matches passing threshold"))
        else:
            display(vdomh.table(header, vdomh.tbody(*body)))
        plt.close("all")

Metacluster 1/1

Motif 1/13

Motif IDq-valPWM
GCR_HUMAN.H11MO.0.A2.98117e-08
PRGR_HUMAN.H11MO.0.A8.74975e-07
ANDR_HUMAN.H11MO.1.A3.85535e-06
MA0727.1_NR3C20.00014140000000000002
MA0113.3_NR3C10.00025498
PRGR_HUMAN.H11MO.1.A0.00860344Not shown
MA0007.3_Ar0.014816899999999997Not shown
GCR_HUMAN.H11MO.1.A0.0367253Not shown
MA1508.1_IKZF10.362673Not shown
MA1623.1_Stat20.47194899999999995Not shown

Motif 2/13

Motif IDq-valPWM
FOSL1_HUMAN.H11MO.0.A6.08596e-07
FOSB_HUMAN.H11MO.0.A2.06804e-06
FOSL2_HUMAN.H11MO.0.A4.78359e-06
JUND_HUMAN.H11MO.0.A4.78359e-06
JUN_HUMAN.H11MO.0.A4.78359e-06
MA0478.1_FOSL28.107080000000001e-06Not shown
MA0099.3_FOS::JUN8.107080000000001e-06Not shown
MA1130.1_FOSL2::JUN8.107080000000001e-06Not shown
FOS_HUMAN.H11MO.0.A8.107080000000001e-06Not shown
MA1622.1_Smad2::Smad31.37783e-05Not shown

Motif 3/13

Motif IDq-valPWM
MA0836.2_CEBPD5.6535400000000005e-08
CEBPB_HUMAN.H11MO.0.A4.6537e-06
MA0102.4_CEBPA1.1614800000000001e-05
CEBPA_HUMAN.H11MO.0.A1.1614800000000001e-05
CEBPD_HUMAN.H11MO.0.C0.00019758599999999997
MA0837.1_CEBPE0.00118503Not shown
MA0466.2_CEBPB0.00125282Not shown
MA0838.1_CEBPG0.00125282Not shown
MA0025.2_NFIL30.00401568Not shown
DBP_HUMAN.H11MO.0.B0.0089111Not shown

Motif 4/13

Motif IDq-valPWM
FOXA2_HUMAN.H11MO.0.A1.16171e-06
FOXA1_HUMAN.H11MO.0.A2.16255e-06
FOXM1_HUMAN.H11MO.0.A9.07802e-06
FOXF2_HUMAN.H11MO.0.D3.9458899999999996e-05
MA0846.1_FOXC25.6943199999999994e-05
FOXD3_HUMAN.H11MO.0.D5.6943199999999994e-05Not shown
MA0847.2_FOXD26.15249e-05Not shown
FOXA3_HUMAN.H11MO.0.B7.177899999999999e-05Not shown
FOXC1_HUMAN.H11MO.0.C7.177899999999999e-05Not shown
FOXJ2_HUMAN.H11MO.0.C0.00165524Not shown

Motif 5/13

Motif IDq-valPWM
MA0833.2_ATF40.00147165
ATF4_HUMAN.H11MO.0.A0.00147165
MA1636.1_CEBPG(var.2)0.00147165
BATF_HUMAN.H11MO.1.A0.00147165
DDIT3_HUMAN.H11MO.0.D0.00171967
CEBPG_HUMAN.H11MO.0.B0.00177734Not shown
MA1143.1_FOSL1::JUND(var.2)0.00712529Not shown
MA0466.2_CEBPB0.0241358Not shown
MA0837.1_CEBPE0.0241358Not shown
CEBPB_HUMAN.H11MO.0.A0.0340395Not shown

Motif 6/13

Motif IDq-valPWM
MA1145.1_FOSL2::JUND(var.2)1.17957e-05
MA1127.1_FOSB::JUN4.3207399999999996e-05
MA1129.1_FOSL1::JUN(var.2)5.0220500000000005e-05
MA1136.1_FOSB::JUNB(var.2)5.0220500000000005e-05
ATF2_HUMAN.H11MO.2.C5.0220500000000005e-05
ATF7_HUMAN.H11MO.0.D5.0220500000000005e-05Not shown
MA1131.1_FOSL2::JUN(var.2)5.0220500000000005e-05Not shown
MA1133.1_JUN::JUNB(var.2)5.0220500000000005e-05Not shown
JDP2_HUMAN.H11MO.0.D5.0220500000000005e-05Not shown
MA1126.1_FOS::JUN(var.2)7.532140000000001e-05Not shown

Motif 7/13

Motif IDq-valPWM
FOXD2_HUMAN.H11MO.0.D0.00732748
FOXB1_HUMAN.H11MO.0.D0.08941080000000001
MA0032.2_FOXC10.29429099999999997
MA0845.1_FOXB10.29429099999999997
MA0791.1_POU4F30.29429099999999997
MA0683.1_POU4F20.308324Not shown
FOXJ2_HUMAN.H11MO.0.C0.46356800000000004Not shown
PO4F3_HUMAN.H11MO.0.D0.46356800000000004Not shown
MA0847.2_FOXD20.46356800000000004Not shown
MA0846.1_FOXC20.46356800000000004Not shown

Motif 8/13

Motif IDq-valPWM
PRGR_HUMAN.H11MO.0.A0.231977
GCR_HUMAN.H11MO.0.A0.239717
MA0808.1_TEAD30.239717
HXB2_HUMAN.H11MO.0.D0.289918
HSF1_HUMAN.H11MO.0.A0.319601
HSF2_HUMAN.H11MO.0.A0.330997Not shown
HSF4_HUMAN.H11MO.0.D0.330997Not shown
NR1I3_HUMAN.H11MO.0.C0.374843Not shown
TEAD1_HUMAN.H11MO.0.A0.374843Not shown
MA0771.1_HSF40.374843Not shown

Motif 9/13

Motif IDq-valPWM
NFIC_HUMAN.H11MO.0.A7.3883e-08
NFIA_HUMAN.H11MO.0.C5.0165e-05
MA1527.1_NFIC(var.2)6.0913100000000005e-05
MA1528.1_NFIX(var.2)6.0913100000000005e-05
MA0119.1_NFIC::TLX16.0913100000000005e-05
MA1643.1_NFIB0.00031791Not shown
NFIA_HUMAN.H11MO.1.D0.00044145400000000003Not shown
NFIC_HUMAN.H11MO.1.A0.00177924Not shown
NFIB_HUMAN.H11MO.0.D0.00300681Not shown
MA0671.1_NFIX0.00529357Not shown

Motif 10/13

Motif IDq-valPWM
MA0466.2_CEBPB0.00031431
MA0837.1_CEBPE0.00031431
MA0838.1_CEBPG0.0009356480000000001
MA0043.3_HLF0.011970100000000001
DBP_HUMAN.H11MO.0.B0.011970100000000001
MA0025.2_NFIL30.011970100000000001Not shown
MA0639.1_DBP0.015553899999999999Not shown
MA1143.1_FOSL1::JUND(var.2)0.020247400000000002Not shown
MA0843.1_TEF0.0236545Not shown
CEBPB_HUMAN.H11MO.0.A0.0280836Not shown

Motif 11/13

Motif IDq-valPWM
MA0466.2_CEBPB0.00137646
MA0837.1_CEBPE0.00137646
MA0838.1_CEBPG0.00321388
MA0639.1_DBP0.0046423
HLF_HUMAN.H11MO.0.C0.00556988
MA0843.1_TEF0.0077066999999999995Not shown
CEBPB_HUMAN.H11MO.0.A0.024119400000000003Not shown
CEBPE_HUMAN.H11MO.0.A0.025817200000000002Not shown
CEBPD_HUMAN.H11MO.0.C0.0339251Not shown
CEBPA_HUMAN.H11MO.0.A0.0406843Not shown

Motif 12/13

Motif IDq-valPWM
CEBPB_HUMAN.H11MO.0.A0.000118152
MA0025.2_NFIL30.000792693
MA1636.1_CEBPG(var.2)0.000943756
CEBPE_HUMAN.H11MO.0.A0.000943756
MA0833.2_ATF40.000943756
MA0466.2_CEBPB0.000943756Not shown
MA0837.1_CEBPE0.000943756Not shown
CEBPA_HUMAN.H11MO.0.A0.00134561Not shown
CEBPD_HUMAN.H11MO.0.C0.00134561Not shown
MA0838.1_CEBPG0.00145602Not shown

Motif 13/13

Motif IDq-valPWM
MA0466.2_CEBPB0.011387999999999999
MA0837.1_CEBPE0.011387999999999999
MA0838.1_CEBPG0.0194011
DBP_HUMAN.H11MO.0.B0.0194011
DDIT3_HUMAN.H11MO.0.D0.028875
MA0639.1_DBP0.029244599999999996Not shown
MA0843.1_TEF0.03545159999999999Not shown
HLF_HUMAN.H11MO.0.C0.036110699999999996Not shown
CEBPB_HUMAN.H11MO.0.A0.0409842Not shown
SP4_HUMAN.H11MO.0.A0.0561553Not shown