Direct links to results¶

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¶

# 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_fold5/NR3C1-reddytime_multitask_profile_fold5_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/NR3C1-reddytime_multitask_profile_fold5/NR3C1-reddytime_multitask_profile_fold5_profile_tfm.h5
Importance score key: profile_hyp_scores
Saved TF-MoDISco-derived motifs cache: /users/amtseng/tfmodisco/results/reports/tfmodisco_results//cache/multitask_profile/NR3C1-reddytime_multitask_profile_fold5/NR3C1-reddytime_multitask_profile_fold5_profile

# Define paths and constants
input_length = 2114
shap_score_center_size = 400

if tfm_motifs_cache_dir:
    os.makedirs(tfm_motifs_cache_dir, exist_ok=True)

Import SHAP scores and TF-MoDISco results¶

# 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 [01:29<00:00,  2.07it/s]

# 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

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

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()

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.

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")

Top TOMTOM matches for each motif¶

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

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")

#	Seqlets	Forward	Reverse
1	5059
2	2971
3	1938
4	1435
5	748
6	576
7	271
8	192
9	30

Motif ID	q-val	PWM
GCR_HUMAN.H11MO.0.A	1.78566e-09
PRGR_HUMAN.H11MO.0.A	4.75053e-07
ANDR_HUMAN.H11MO.1.A	1.6937099999999998e-06
MA0727.1_NR3C2	0.00017166799999999998
MA0113.3_NR3C1	0.000324423
PRGR_HUMAN.H11MO.1.A	0.00891088	Not shown
MA0007.3_Ar	0.0164291	Not shown
GCR_HUMAN.H11MO.1.A	0.039453300000000004	Not shown
RARG_HUMAN.H11MO.0.B	0.458685	Not shown
MA1508.1_IKZF1	0.458685	Not shown

Motif ID	q-val	PWM
FOSL1_HUMAN.H11MO.0.A	1.13152e-06
JUN_HUMAN.H11MO.0.A	1.2647e-06
FOSB_HUMAN.H11MO.0.A	1.2647e-06
FOSL2_HUMAN.H11MO.0.A	1.2647e-06
MA1622.1_Smad2::Smad3	1.51763e-06
MA0099.3_FOS::JUN	3.2505799999999997e-06	Not shown
MA1135.1_FOSB::JUNB	5.41763e-06	Not shown
MA1137.1_FOSL1::JUNB	5.41763e-06	Not shown
MA1130.1_FOSL2::JUN	5.41763e-06	Not shown
MA1138.1_FOSL2::JUNB	8.87178e-06	Not shown

Motif ID	q-val	PWM
CEBPB_HUMAN.H11MO.0.A	9.264129999999999e-10
CEBPA_HUMAN.H11MO.0.A	8.60177e-07
CEBPD_HUMAN.H11MO.0.C	8.60177e-07
MA0836.2_CEBPD	7.6722e-06
MA0102.4_CEBPA	2.02257e-05
MA0025.2_NFIL3	0.000823523	Not shown
MA0837.1_CEBPE	0.000823523	Not shown
MA0466.2_CEBPB	0.00107478	Not shown
MA0838.1_CEBPG	0.0014047	Not shown
NFIL3_HUMAN.H11MO.0.D	0.00204691	Not shown

Motif ID	q-val	PWM
FOXA1_HUMAN.H11MO.0.A	3.98673e-07
FOXA2_HUMAN.H11MO.0.A	1.06665e-06
FOXM1_HUMAN.H11MO.0.A	1.3861199999999999e-06
FOXF2_HUMAN.H11MO.0.D	3.56843e-06
FOXA3_HUMAN.H11MO.0.B	5.70948e-06
FOXD3_HUMAN.H11MO.0.D	7.136849999999999e-06	Not shown
MA0846.1_FOXC2	1.6707e-05	Not shown
MA0847.2_FOXD2	1.94915e-05	Not shown
FOXC1_HUMAN.H11MO.0.C	2.5988699999999997e-05	Not shown
FOXJ2_HUMAN.H11MO.0.C	0.0008356460000000001	Not shown

Direct links to results¶

Define constants and paths¶

Import SHAP scores and TF-MoDISco results¶

Plot some SHAP score tracks¶

Plot TF-MoDISco results¶

Metacluster 1/1

Pattern 1/9

Pattern 2/9

Pattern 3/9

Pattern 4/9

Pattern 5/9

Pattern 6/9

Pattern 7/9

Pattern 8/9

Pattern 9/9

Summary of motifs¶

Metacluster 1/1

Top TOMTOM matches for each motif¶

Metacluster 1/1

Motif 1/9

Motif 2/9

Motif 3/9

Motif 4/9

Motif 5/9

Motif 6/9

Motif 7/9

Motif 8/9

Motif 9/9

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

Motif ID	q-val	PWM
ATF2_HUMAN.H11MO.2.C	3.83824e-05
ATF7_HUMAN.H11MO.0.D	3.83824e-05
MA1127.1_FOSB::JUN	3.83824e-05
JDP2_HUMAN.H11MO.0.D	3.83824e-05
MA0605.2_ATF3	3.83824e-05
MA0656.1_JDP2(var.2)	3.83824e-05	Not shown
MA1133.1_JUN::JUNB(var.2)	3.83824e-05	Not shown
MA1140.2_JUNB(var.2)	3.83824e-05	Not shown
MA0834.1_ATF7	4.60589e-05	Not shown
MA1145.1_FOSL2::JUND(var.2)	4.91295e-05	Not shown

Motif ID	q-val	PWM
TEAD2_HUMAN.H11MO.0.D	0.00047804300000000004
MA1121.1_TEAD2	0.000800059
TEAD4_HUMAN.H11MO.0.A	0.000800059
MA0808.1_TEAD3	0.000800059
TEAD1_HUMAN.H11MO.0.A	0.00128197
MA0809.2_TEAD4	0.0034014	Not shown
MA0090.3_TEAD1	0.00438796	Not shown

Motif ID	q-val	PWM
FOXD2_HUMAN.H11MO.0.D	0.0060917
FOXB1_HUMAN.H11MO.0.D	0.13409400000000002
MA0032.2_FOXC1	0.230567
MA0845.1_FOXB1	0.230567

Motif ID	q-val	PWM
PRGR_HUMAN.H11MO.0.A	0.00177547
GCR_HUMAN.H11MO.0.A	0.00177547
ANDR_HUMAN.H11MO.1.A	0.027656299999999998
ZN394_HUMAN.H11MO.1.D	0.0798907
MA0808.1_TEAD3	0.08076900000000001
HSF1_HUMAN.H11MO.0.A	0.102743	Not shown
MA0727.1_NR3C2	0.113337	Not shown
MA0113.3_NR3C1	0.205342	Not shown
HSF2_HUMAN.H11MO.0.A	0.21186	Not shown
HSF4_HUMAN.H11MO.0.D	0.21186	Not shown

Motif ID	q-val	PWM
MA0466.2_CEBPB	0.000256429
MA0837.1_CEBPE	0.000256429
MA0838.1_CEBPG	0.00030665900000000003
MA0639.1_DBP	0.04141
CEBPD_HUMAN.H11MO.0.C	0.04141
MA0843.1_TEF	0.04141	Not shown
DBP_HUMAN.H11MO.0.B	0.04141	Not shown
CEBPB_HUMAN.H11MO.0.A	0.0586789	Not shown
HLF_HUMAN.H11MO.0.C	0.0627587	Not shown
MA0862.1_GMEB2	0.0631972	Not shown