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: GABPA
DeepSHAP scores path: /users/amtseng/tfmodisco/results/importance_scores/multitask_profile/GABPA_multitask_profile_fold7/GABPA_multitask_profile_fold7_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/GABPA_multitask_profile_fold7/GABPA_multitask_profile_fold7_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/GABPA_multitask_profile_fold7/GABPA_multitask_profile_fold7_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%|██████████| 104/104 [02:04<00:00,  1.20s/it]
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 1/10

7553 seqlets

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

Pattern 2/10

953 seqlets

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

Pattern 3/10

594 seqlets

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

Pattern 4/10

555 seqlets

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

Pattern 5/10

187 seqlets

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

Pattern 6/10

156 seqlets

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

Pattern 7/10

151 seqlets

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

Pattern 8/10

103 seqlets

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

Pattern 9/10

93 seqlets

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

Pattern 10/10

58 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

#SeqletsForwardReverse
17553
2953
3594
4555
5187
6156
7151
8103
993
1058

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 1/10

Motif IDq-valPWM
ETV1_HUMAN.H11MO.0.A4.75454e-08
MA0076.2_ELK48.66848e-07
MA0750.2_ZBTB7A8.66848e-07
ELK4_HUMAN.H11MO.0.A2.50614e-05
ELF2_HUMAN.H11MO.0.C2.50614e-05
ELF1_HUMAN.H11MO.0.A2.50614e-05Not shown
GABPA_HUMAN.H11MO.0.A2.50614e-05Not shown
MA0759.1_ELK33.44588e-05Not shown
ELK1_HUMAN.H11MO.0.B3.6756e-05Not shown
MA0765.2_ETV53.6756e-05Not shown

Motif 2/10

Motif IDq-valPWM
GABPA_HUMAN.H11MO.0.A0.0774551
MA0076.2_ELK40.0774551
MA0772.1_IRF70.0774551
MA0640.2_ELF30.14181400000000002
MA0765.2_ETV50.14181400000000002
ELK4_HUMAN.H11MO.0.A0.14181400000000002Not shown
ELK1_HUMAN.H11MO.0.B0.14181400000000002Not shown
ELF1_HUMAN.H11MO.0.A0.157671Not shown
MA0652.1_IRF80.157671Not shown
MA0098.3_ETS10.157671Not shown

Motif 3/10

Motif IDq-valPWM
ZN143_HUMAN.H11MO.0.A1.97294e-21
ZNF76_HUMAN.H11MO.0.C4.24696e-21
THA11_HUMAN.H11MO.0.B6.480719999999999e-18
MA1573.1_THAP116.40642e-09
MA0088.2_ZNF1430.00791759
STAT3_HUMAN.H11MO.0.A0.056260000000000004Not shown
P63_HUMAN.H11MO.0.A0.0646082Not shown
MA1625.1_Stat5b0.09575349999999999Not shown
MA0519.1_Stat5a::Stat5b0.138794Not shown
MA0525.2_TP630.168686Not shown

Motif 4/10

Motif IDq-valPWM
MA0765.2_ETV50.11088800000000001
MA0645.1_ETV60.11088800000000001
ETV1_HUMAN.H11MO.0.A0.11088800000000001
MA0076.2_ELK40.11088800000000001
GABPA_HUMAN.H11MO.0.A0.11088800000000001
ELK4_HUMAN.H11MO.0.A0.131046Not shown
ELF2_HUMAN.H11MO.0.C0.131046Not shown
ZN770_HUMAN.H11MO.0.C0.308155Not shown
EGR1_HUMAN.H11MO.0.A0.308155Not shown
MA0763.1_ETV30.308155Not shown

Motif 5/10

Motif IDq-valPWM
MA0076.2_ELK40.00314081
MA0765.2_ETV50.00314081
MA0750.2_ZBTB7A0.00314081
ELK4_HUMAN.H11MO.0.A0.00404581
ETV1_HUMAN.H11MO.0.A0.007056499999999999
FEV_HUMAN.H11MO.0.B0.010522799999999999Not shown
ELK1_HUMAN.H11MO.0.B0.0232718Not shown
SP2_HUMAN.H11MO.0.A0.0316257Not shown
MA0474.2_ERG0.0643858Not shown
GABPA_HUMAN.H11MO.0.A0.0731909Not shown

Motif 6/10

No TOMTOM matches passing threshold

Motif 7/10

Motif IDq-valPWM
SP2_HUMAN.H11MO.0.A0.027338599999999998
MA1475.1_CREB3L4(var.2)0.0283263
MA0605.2_ATF30.0283263
MA0656.1_JDP2(var.2)0.0283263
MA0840.1_Creb50.0283263
MA1140.2_JUNB(var.2)0.030071399999999998Not shown
CREB5_HUMAN.H11MO.0.D0.0339101Not shown
ATF2_HUMAN.H11MO.2.C0.0355455Not shown
MA1131.1_FOSL2::JUN(var.2)0.0355455Not shown
MA1139.1_FOSL2::JUNB(var.2)0.0355455Not shown

Motif 8/10

Motif IDq-valPWM
SP2_HUMAN.H11MO.0.A0.15215599999999999
ETV1_HUMAN.H11MO.0.A0.15215599999999999
ZFX_HUMAN.H11MO.1.A0.15215599999999999
KLF3_HUMAN.H11MO.0.B0.15215599999999999
MA1122.1_TFDP10.15215599999999999
SP3_HUMAN.H11MO.0.B0.20588499999999998Not shown
MA1596.1_ZNF4600.269286Not shown
MA0645.1_ETV60.28992199999999996Not shown
THAP1_HUMAN.H11MO.0.C0.28992199999999996Not shown
PTF1A_HUMAN.H11MO.0.B0.28992199999999996Not shown

Motif 9/10

Motif IDq-valPWM
SP1_HUMAN.H11MO.0.A0.0010438
SP2_HUMAN.H11MO.0.A0.00125311
MA1102.2_CTCFL0.00651576
ELK4_HUMAN.H11MO.0.A0.00651576
MA0076.2_ELK40.00651576
ETV1_HUMAN.H11MO.0.A0.00651576Not shown
SP3_HUMAN.H11MO.0.B0.00651576Not shown
PATZ1_HUMAN.H11MO.0.C0.00987332Not shown
MA0765.2_ETV50.0106577Not shown
PURA_HUMAN.H11MO.0.D0.0182296Not shown

Motif 10/10

Motif IDq-valPWM
SP2_HUMAN.H11MO.0.A0.0759445
THAP1_HUMAN.H11MO.0.C0.0759445
KLF9_HUMAN.H11MO.0.C0.0845178
KLF3_HUMAN.H11MO.0.B0.0963113
KLF6_HUMAN.H11MO.0.A0.144523
MA0750.2_ZBTB7A0.170452Not shown
MA1522.1_MAZ0.175Not shown
SP3_HUMAN.H11MO.0.B0.175Not shown
MA0765.2_ETV50.182619Not shown
MAZ_HUMAN.H11MO.0.A0.182619Not shown