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_fold10/GABPA_multitask_profile_fold10_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/GABPA_multitask_profile_fold10/GABPA_multitask_profile_fold10_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_fold10/GABPA_multitask_profile_fold10_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 [01:51<00:00,  1.07s/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 1/12

6612 seqlets

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

Pattern 2/12

848 seqlets

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

Pattern 3/12

633 seqlets

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

Pattern 4/12

229 seqlets

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

Pattern 5/12

164 seqlets

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

Pattern 6/12

83 seqlets

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

Pattern 7/12

55 seqlets

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

Pattern 8/12

52 seqlets

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

Pattern 9/12

45 seqlets

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

Pattern 10/12

33 seqlets

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

Pattern 11/12

32 seqlets

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

Pattern 12/12

32 seqlets

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

Metacluster 2/2

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

/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
16612
2848
3633
4229
5164
683
755
852
945
1033
1132
1232

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 1/12

Motif IDq-valPWM
MA0750.2_ZBTB7A2.37766e-06
GABPA_HUMAN.H11MO.0.A2.37766e-06
MA0076.2_ELK47.9081e-06
ELK1_HUMAN.H11MO.0.B7.9081e-06
ELK4_HUMAN.H11MO.0.A7.9081e-06
MA0759.1_ELK32.44223e-05Not shown
MA0765.2_ETV52.44223e-05Not shown
ETV1_HUMAN.H11MO.0.A2.44223e-05Not shown
MA1483.1_ELF22.7135900000000002e-05Not shown
ELF2_HUMAN.H11MO.0.C2.93068e-05Not shown

Motif 2/12

Motif IDq-valPWM
GABPA_HUMAN.H11MO.0.A0.0819534
MA0076.2_ELK40.0819534
MA0772.1_IRF70.0819534
MA0640.2_ELF30.143507
ELK1_HUMAN.H11MO.0.B0.143507
ELK4_HUMAN.H11MO.0.A0.143507Not shown
MA0765.2_ETV50.143507Not shown
BC11A_HUMAN.H11MO.0.A0.156914Not shown
MA0760.1_ERF0.156914Not shown
ELF1_HUMAN.H11MO.0.A0.156914Not shown

Motif 3/12

Motif IDq-valPWM
ZNF76_HUMAN.H11MO.0.C7.999600000000001e-22
ZN143_HUMAN.H11MO.0.A1.81179e-21
THA11_HUMAN.H11MO.0.B2.7160100000000004e-17
MA1573.1_THAP112.55285e-09
MA0088.2_ZNF1430.00917808
STAT3_HUMAN.H11MO.0.A0.0476211Not shown
P63_HUMAN.H11MO.0.A0.0672958Not shown
MA0519.1_Stat5a::Stat5b0.0816971Not shown
MA1625.1_Stat5b0.0816971Not shown
MA0525.2_TP630.169876Not shown

Motif 4/12

No TOMTOM matches passing threshold

Motif 5/12

Motif IDq-valPWM
MA0765.2_ETV50.08067200000000001
MA0645.1_ETV60.08067200000000001
ETV1_HUMAN.H11MO.0.A0.08067200000000001
MA0076.2_ELK40.08067200000000001
GABPA_HUMAN.H11MO.0.A0.08067200000000001
ELF2_HUMAN.H11MO.0.C0.08067200000000001Not shown
ELK4_HUMAN.H11MO.0.A0.08067200000000001Not shown
ELF5_HUMAN.H11MO.0.A0.16103800000000001Not shown
FEV_HUMAN.H11MO.0.B0.16103800000000001Not shown
MA0750.2_ZBTB7A0.16103800000000001Not shown

Motif 6/12

Motif IDq-valPWM
ETV2_HUMAN.H11MO.0.B9.07874e-05
MA0598.3_EHF0.000149963
GABPA_HUMAN.H11MO.0.A0.00018967900000000002
ELF2_HUMAN.H11MO.0.C0.000236748
MA0473.3_ELF10.000246445
ELF1_HUMAN.H11MO.0.A0.000312452Not shown
MA0076.2_ELK40.00047696699999999996Not shown
ELF5_HUMAN.H11MO.0.A0.000494799Not shown
EHF_HUMAN.H11MO.0.B0.000528179Not shown
MA1508.1_IKZF10.000582763Not shown

Motif 7/12

Motif IDq-valPWM
SP2_HUMAN.H11MO.0.A0.00115267
THAP1_HUMAN.H11MO.0.C0.00932821
SP1_HUMAN.H11MO.0.A0.00932821
SP3_HUMAN.H11MO.0.B0.00932821
RFX1_HUMAN.H11MO.0.B0.0146341
MBD2_HUMAN.H11MO.0.B0.0171117Not shown
CTCFL_HUMAN.H11MO.0.A0.021839599999999997Not shown
MA1513.1_KLF150.0228796Not shown
ZFX_HUMAN.H11MO.1.A0.0255124Not shown
WT1_HUMAN.H11MO.0.C0.027500599999999997Not shown

Motif 8/12

Motif IDq-valPWM
SP2_HUMAN.H11MO.0.A0.00397211
USF2_HUMAN.H11MO.0.A0.00824028
SP3_HUMAN.H11MO.0.B0.0167314
CTCFL_HUMAN.H11MO.0.A0.0167314
SP1_HUMAN.H11MO.1.A0.0167314
SP1_HUMAN.H11MO.0.A0.0167314Not shown
KLF16_HUMAN.H11MO.0.D0.0338022Not shown
KLF15_HUMAN.H11MO.0.A0.053449699999999996Not shown
MA0506.1_NRF10.053449699999999996Not shown
MA1653.1_ZNF1480.053449699999999996Not shown

Motif 9/12

Motif IDq-valPWM
GABPA_HUMAN.H11MO.0.A0.000504446
SP2_HUMAN.H11MO.0.A0.00150378
ERG_HUMAN.H11MO.0.A0.00309527
SP3_HUMAN.H11MO.0.B0.00309527
FLI1_HUMAN.H11MO.1.A0.00309527
ETS1_HUMAN.H11MO.0.A0.00309527Not shown
MA0076.2_ELK40.00309527Not shown
ELF2_HUMAN.H11MO.0.C0.00309527Not shown
ELK4_HUMAN.H11MO.0.A0.00324571Not shown
MA0645.1_ETV60.00324571Not shown

Motif 10/12

Motif IDq-valPWM
SP2_HUMAN.H11MO.0.A0.0904592
SP1_HUMAN.H11MO.0.A0.0904592
SP3_HUMAN.H11MO.0.B0.142232
RFX1_HUMAN.H11MO.0.B0.221793
MA1650.1_ZBTB140.221793
MA0765.2_ETV50.221793Not shown
AP2B_HUMAN.H11MO.0.B0.221793Not shown
ZN589_HUMAN.H11MO.0.D0.221793Not shown
MA0076.2_ELK40.240292Not shown
USF2_HUMAN.H11MO.0.A0.240292Not shown

Motif 11/12

Motif IDq-valPWM
ETV1_HUMAN.H11MO.0.A0.0101479
MA0765.2_ETV50.0101479
GABPA_HUMAN.H11MO.0.A0.0101479
USF2_HUMAN.H11MO.0.A0.0111265
ELK4_HUMAN.H11MO.0.A0.0200878
MA0750.2_ZBTB7A0.0200878Not shown
MA0076.2_ELK40.021638Not shown
ELK1_HUMAN.H11MO.0.B0.0270997Not shown
MA0645.1_ETV60.0391949Not shown
FEV_HUMAN.H11MO.0.B0.045872300000000005Not shown

Motif 12/12

Motif IDq-valPWM
SP1_HUMAN.H11MO.0.A0.000784903
KLF15_HUMAN.H11MO.0.A0.00446832
SP2_HUMAN.H11MO.0.A0.013853
KLF16_HUMAN.H11MO.0.D0.013853
WT1_HUMAN.H11MO.0.C0.0148109
MA1513.1_KLF150.0161684Not shown
ZN219_HUMAN.H11MO.0.D0.0161684Not shown
MAZ_HUMAN.H11MO.0.A0.0161684Not shown
ZN263_HUMAN.H11MO.0.A0.017833Not shown
ZN467_HUMAN.H11MO.0.C0.0190906Not shown