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: SPI1
DeepSHAP scores path: /users/amtseng/tfmodisco/results/importance_scores/multitask_profile/SPI1_multitask_profile_fold4/SPI1_multitask_profile_fold4_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/SPI1_multitask_profile_fold4/SPI1_multitask_profile_fold4_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/SPI1_multitask_profile_fold4/SPI1_multitask_profile_fold4_profile
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%|██████████| 194/194 [04:45<00:00,  1.47s/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 1/2

Pattern 1/8

12289 seqlets

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

Pattern 2/8

791 seqlets

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

Pattern 3/8

53 seqlets

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

Pattern 4/8

41 seqlets

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

Pattern 5/8

37 seqlets

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

Pattern 6/8

31 seqlets

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

Pattern 7/8

31 seqlets

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

Pattern 8/8

30 seqlets

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

Metacluster 2/2

Pattern 1/9

72 seqlets

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

Pattern 2/9

68 seqlets

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

Pattern 3/9

67 seqlets

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

Pattern 4/9

64 seqlets

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

Pattern 5/9

62 seqlets

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

Pattern 6/9

60 seqlets

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

Pattern 7/9

58 seqlets

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

Pattern 8/9

49 seqlets

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

Pattern 9/9

44 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 1/2

#SeqletsForwardReverse
112289
2791
353
441
537
631
731
830

Metacluster 2/2

#SeqletsForwardReverse
172
268
367
464
562
660
758
849
944

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 1/2

Motif 1/8

Motif IDq-valPWM
SPIB_HUMAN.H11MO.0.A6.702380000000001e-16
MA0081.2_SPIB5.40573e-13
SPI1_HUMAN.H11MO.0.A7.011910000000001e-12
BC11A_HUMAN.H11MO.0.A1.5342e-11
IRF4_HUMAN.H11MO.0.A2.17519e-10
MA0080.5_SPI11.8029800000000001e-09Not shown
IRF8_HUMAN.H11MO.0.B2.58805e-09Not shown
MA0761.2_ETV17.15991e-05Not shown
MA0062.3_GABPA0.00011778899999999999Not shown
MA0050.2_IRF10.000581259Not shown

Motif 2/8

No TOMTOM matches passing threshold

Motif 3/8

Motif IDq-valPWM
ETS2_HUMAN.H11MO.0.B0.376319
ZN713_HUMAN.H11MO.0.D0.376319
ZN467_HUMAN.H11MO.0.C0.376319
RUNX1_HUMAN.H11MO.0.A0.376319
CPEB1_HUMAN.H11MO.0.D0.376319
SPIB_HUMAN.H11MO.0.A0.376319Not shown
BC11A_HUMAN.H11MO.0.A0.376319Not shown
RARA_HUMAN.H11MO.0.A0.376319Not shown
SPI1_HUMAN.H11MO.0.A0.376319Not shown
ZBT17_HUMAN.H11MO.0.A0.376319Not shown

Motif 4/8

Motif IDq-valPWM
PRDM6_HUMAN.H11MO.0.C0.260929
SPI1_HUMAN.H11MO.0.A0.28068200000000004
BC11A_HUMAN.H11MO.0.A0.28068200000000004
MA0081.2_SPIB0.28068200000000004
BATF_HUMAN.H11MO.1.A0.33305300000000004
SPIB_HUMAN.H11MO.0.A0.33305300000000004Not shown
BACH2_HUMAN.H11MO.0.A0.33305300000000004Not shown
MA1622.1_Smad2::Smad30.33305300000000004Not shown
ZN418_HUMAN.H11MO.0.C0.33305300000000004Not shown
NFAT5_HUMAN.H11MO.0.D0.34108099999999997Not shown

Motif 5/8

Motif IDq-valPWM
CPEB1_HUMAN.H11MO.0.D1.7326e-05
MA1125.1_ZNF3840.0218118
PRDM6_HUMAN.H11MO.0.C0.0218118
FOXL1_HUMAN.H11MO.0.D0.0262735
FOXG1_HUMAN.H11MO.0.D0.0584198
MA0679.2_ONECUT10.10625599999999999Not shown
ANDR_HUMAN.H11MO.0.A0.10625599999999999Not shown
FOXJ3_HUMAN.H11MO.0.A0.10625599999999999Not shown
ONEC2_HUMAN.H11MO.0.D0.155658Not shown
FUBP1_HUMAN.H11MO.0.D0.19304000000000002Not shown

Motif 6/8

Motif IDq-valPWM
IRF8_HUMAN.H11MO.0.B0.000226774
MA0081.2_SPIB0.000440403
SPIB_HUMAN.H11MO.0.A0.000440403
SPI1_HUMAN.H11MO.0.A0.000440403
IRF4_HUMAN.H11MO.0.A0.000450386
MA0080.5_SPI10.000566832Not shown
BC11A_HUMAN.H11MO.0.A0.00132206Not shown
PRDM1_HUMAN.H11MO.0.A0.00881999Not shown
MA0640.2_ELF30.010327600000000001Not shown
IRF3_HUMAN.H11MO.0.B0.010657600000000001Not shown

Motif 7/8

Motif IDq-valPWM
CPEB1_HUMAN.H11MO.0.D0.0431445
ETS2_HUMAN.H11MO.0.B0.0431445
IRF3_HUMAN.H11MO.0.B0.0474973
PRDM6_HUMAN.H11MO.0.C0.0474973
ZFP28_HUMAN.H11MO.0.C0.0474973
SPI1_HUMAN.H11MO.0.A0.08431430000000001Not shown
SPIB_HUMAN.H11MO.0.A0.0904648Not shown
BC11A_HUMAN.H11MO.0.A0.124547Not shown
MA0080.5_SPI10.124547Not shown
ZN586_HUMAN.H11MO.0.C0.164176Not shown

Motif 8/8

Motif IDq-valPWM
SPIB_HUMAN.H11MO.0.A0.000334307
SPI1_HUMAN.H11MO.0.A0.000334307
BC11A_HUMAN.H11MO.0.A0.0006185419999999999
MA0081.2_SPIB0.0024056
MA0080.5_SPI10.00460029
FLI1_HUMAN.H11MO.0.A0.00924917Not shown
ERG_HUMAN.H11MO.0.A0.022910800000000002Not shown
ETS2_HUMAN.H11MO.0.B0.022910800000000002Not shown
MZF1_HUMAN.H11MO.0.B0.022910800000000002Not shown
FLI1_HUMAN.H11MO.1.A0.022910800000000002Not shown

Metacluster 2/2

Motif 1/9

Motif IDq-valPWM
SPIB_HUMAN.H11MO.0.A8.4783e-12
MA0081.2_SPIB1.0289000000000002e-11
SPI1_HUMAN.H11MO.0.A1.0289000000000002e-11
BC11A_HUMAN.H11MO.0.A2.33179e-11
MA0080.5_SPI13.4410400000000004e-09
IRF4_HUMAN.H11MO.0.A8.91905e-09Not shown
IRF8_HUMAN.H11MO.0.B3.98185e-08Not shown
MA0761.2_ETV16.71439e-05Not shown
MA0062.3_GABPA8.31543e-05Not shown
ETV5_HUMAN.H11MO.0.C0.00023541599999999998Not shown

Motif 2/9

No TOMTOM matches passing threshold

Motif 3/9

Motif IDq-valPWM
MA1650.1_ZBTB140.33482399999999995

Motif 4/9

No TOMTOM matches passing threshold

Motif 5/9

No TOMTOM matches passing threshold

Motif 6/9

No TOMTOM matches passing threshold

Motif 7/9

Motif IDq-valPWM
MA0483.1_Gfi1b0.114604
SMAD3_HUMAN.H11MO.0.B0.114604

Motif 8/9

No TOMTOM matches passing threshold

Motif 9/9

No TOMTOM matches passing threshold