Link to results

DiChIPMunk: on peaks and on multi-task seqlets and on single-task seqlets

HOMER: on peaks and on multi-task seqlets and on single-task seqlets

MEME: on peaks and on multitask seqlets and on single-task seqlets

import sys
import os
sys.path.append(os.path.abspath("/users/amtseng/tfmodisco/src/"))
from util import figure_to_vdom_image
import motif.read_motifs as read_motifs
from motif.read_motifs import pfm_to_pwm
import plot.viz_sequence as viz_sequence
import numpy as np
import matplotlib.pyplot as plt
import vdom.helpers as vdomh
from IPython.display import display

# Define parameters/fetch arguments
tf_name = os.environ["TFM_TF_NAME"]
multitask_fold = int(os.environ["TFM_MULTITASK_FOLD"])

if "TFM_TASK_INDEX" in os.environ:
    task_index = int(os.environ["TFM_TASK_INDEX"])
    singletask_fold = int(os.environ["TFM_SINGLETASK_FOLD"])
else:
    task_index = None
    singletask_fold = None
    
print("TF name: %s" % tf_name)
print("Multi-task fold: %s" % multitask_fold)
print("Task index: %s" % task_index)
print("Single-task fold: %s" % singletask_fold)

TF name: NR3C1-reddytime
Multi-task fold: 5
Task index: 12
Single-task fold: 5

# Define paths and constants
base_path = "/users/amtseng/tfmodisco/results/classic_motifs/"

multitask_seqlets_dir = os.path.join(
    base_path, "seqlets", "multitask_profile_finetune",
    "%s_multitask_profile_finetune_fold%s" % (tf_name, multitask_fold)
)

if task_index is None:
    peaks_path = os.path.join(base_path, "peaks", tf_name, "%s_peaks_taskall" % tf_name)
    multitask_profile_seqlets_path = os.path.join(
        multitask_seqlets_dir,
        "%s_seqlets_profile_taskall" % tf_name
    )
    multitask_count_seqlets_path = os.path.join(
        multitask_seqlets_dir,
        "%s_seqlets_count_taskall" % tf_name
    )
else:
    peaks_path = os.path.join(base_path, "peaks", tf_name, "%s_peaks_task%d" % (tf_name, task_index))
    multitask_profile_seqlets_path = os.path.join(
        multitask_seqlets_dir,
        "%s_seqlets_profile_task%d" % (tf_name, task_index)
    )
    multitask_count_seqlets_path = os.path.join(
        multitask_seqlets_dir,
        "%s_seqlets_count_task%d" % (tf_name, task_index)
    )
    
    singletask_seqlets_dir = os.path.join(
        base_path, "seqlets", "singletask_profile_finetune",
        "%s_singletask_profile_finetune_fold%s" % (tf_name, singletask_fold),
        "task_%d" % task_index
    )
    singletask_profile_seqlets_path = os.path.join(
        singletask_seqlets_dir,
        "%s_seqlets_profile_task%d" % (tf_name, task_index)
    )
    singletask_count_seqlets_path = os.path.join(
        singletask_seqlets_dir,
        "%s_seqlets_count_task%d" % (tf_name, task_index)
    )

Helper functions

def show_peaks_motif_table(results_path, mode):
    """
    Shows a table of motifs from the given results path.
    `mode` is either `dichipmunk`, `homer`, `meme`, or `memechip`.
    """
    assert mode in ("dichipmunk", "homer", "meme", "memechip")
    if mode == "dichipmunk":
        score_name = "Supporting sequences"
        pfms, score_vals = read_motifs.import_dichipmunk_pfms(results_path)
    elif mode == "homer":
        score_name = "Log enrichment"
        pfms, score_vals = read_motifs.import_homer_pfms(results_path)
    elif mode == "meme":
        score_name = "E-value"
        pfms, score_vals = read_motifs.import_meme_pfms(results_path)
    else:
        score_name = "E-value"
        pfms, score_vals = read_motifs.import_meme_pfms(
            os.path.join(results_path, "meme_out")
        )
        
    colgroup = vdomh.colgroup(
        vdomh.col(style={"width": "5%"}),
        vdomh.col(style={"width": "5%"}),
        vdomh.col(style={"width": "40%"})
    )
    header = vdomh.thead(
        vdomh.tr(
            vdomh.th("Motif", style={"text-align": "center"}),
            vdomh.th(score_name, style={"text-align": "center"}),
            vdomh.th("PWM", style={"text-align": "center"})
        )
    )

    body = []
    for i, pfm in enumerate(pfms):
        pwm = pfm_to_pwm(pfm)
        if np.sum(pwm[:, [0, 2]]) < 0.5 * np.sum(pwm):
            # Flip to purine-rich version
            pwm = np.flip(pwm, axis=(0, 1))
        fig = viz_sequence.plot_weights(pwm, figsize=(20, 4), return_fig=True)
        fig.tight_layout()

        body.append(
            vdomh.tr(
                vdomh.td(str(i + 1)),
                vdomh.td(str(score_vals[i])),
                vdomh.td(figure_to_vdom_image(fig))
            )
        )

    display(vdomh.table(colgroup, header, vdomh.tbody(*body)))
    plt.close("all")

def show_seqlets_motif_table(profile_results_path, count_results_path, mode):
    """
    Shows a table of motifs from the given results path.
    `mode` is either `dichipmunk`, `homer`, `meme`, or `memechip`
    """
    assert mode in ("dichipmunk", "homer", "meme", "memechip")
    if mode == "dichipmunk":
        score_name = "Supporting sequences"
        p_pfms, p_score_vals = read_motifs.import_dichipmunk_pfms(profile_results_path)
        c_pfms, c_score_vals = read_motifs.import_dichipmunk_pfms(count_results_path)
    elif mode == "homer":
        score_name = "Log enrichment"
        p_pfms, p_score_vals = read_motifs.import_homer_pfms(profile_results_path)
        c_pfms, c_score_vals = read_motifs.import_homer_pfms(count_results_path)
    elif mode == "meme":
        score_name = "E-value"
        p_pfms, p_score_vals = read_motifs.import_meme_pfms(profile_results_path)
        c_pfms, c_score_vals = read_motifs.import_meme_pfms(count_results_path)
    else:
        score_name = "E-value"
        p_pfms, p_score_vals = read_motifs.import_meme_pfms(
            os.path.join(profile_results_path, "meme_out")
        )
        c_pfms, c_score_vals = read_motifs.import_meme_pfms(
            os.path.join(count_results_path, "meme_out")
        )
        
    colgroup = vdomh.colgroup(
        vdomh.col(style={"width": "5%"}),
        vdomh.col(style={"width": "5%"}),
        vdomh.col(style={"width": "40%"}),
        vdomh.col(style={"width": "5%"}),
        vdomh.col(style={"width": "40%"})
    )
    header = vdomh.thead(
        vdomh.tr(
            vdomh.th("Motif", style={"text-align": "center"}),
            vdomh.th(score_name + " (profile)", style={"text-align": "center"}),
            vdomh.th("PWM (profile)", style={"text-align": "center"}),
            vdomh.th(score_name + " (count)", style={"text-align": "center"}),
            vdomh.th("PWM (count)", style={"text-align": "center"})
        )
    )

    body = []
    for i in range(max(len(p_pfms), len(c_pfms))):
        rows = [vdomh.td(str(i + 1))]
        if i < len(p_pfms):
            pwm = pfm_to_pwm(p_pfms[i])
            if np.sum(pwm[:, [0, 2]]) < 0.5 * np.sum(pwm):
                # Flip to purine-rich version
                pwm = np.flip(pwm, axis=(0, 1))
            fig = viz_sequence.plot_weights(pwm, figsize=(20, 4), return_fig=True)
            fig.tight_layout()
            rows.extend([
                vdomh.td(str(p_score_vals[i])),
                vdomh.td(figure_to_vdom_image(fig))
            ])
        else:
            rows.extend([vdomh.td(), vdomh.td()])
            
        if i < len(c_pfms):
            pwm = pfm_to_pwm(c_pfms[i])
            if np.sum(pwm[:, [0, 2]]) < 0.5 * np.sum(pwm):
                # Flip to purine-rich version
                pwm = np.flip(pwm, axis=(0, 1))
            fig = viz_sequence.plot_weights(pwm, figsize=(20, 4), return_fig=True)
            fig.tight_layout()
            rows.extend([
                vdomh.td(str(c_score_vals[i])),
                vdomh.td(figure_to_vdom_image(fig))
            ])
        else:
            rows.extend([vdomh.td(), vdomh.td()])
            

        body.append(vdomh.tr(*rows))

    display(vdomh.table(colgroup, header, vdomh.tbody(*body)))
    plt.close("all")

Show benchmark motifs

DiChIPMunk on peaks

show_peaks_motif_table(os.path.join(peaks_path, "dichipmunk"), "dichipmunk")

Motif	Supporting sequences	PWM
1	1635
2	1296
3	1440
4	952
5	1270
6	1350
7	1240
8	1995
9	2000
10	1939

DiChIPMunk on multi-task seqlets

show_seqlets_motif_table(
    os.path.join(multitask_profile_seqlets_path, "dichipmunk"),
    os.path.join(multitask_count_seqlets_path, "dichipmunk"),
    "dichipmunk"
)

Motif	Supporting sequences (profile)	PWM (profile)	Supporting sequences (count)	PWM (count)
1	11150		7026
2	650		1344
3	153		1248
4	34		154
5			17
6			5

DiChIPMunk on single-task seqlets

if task_index is not None:
    show_seqlets_motif_table(
        os.path.join(singletask_profile_seqlets_path, "dichipmunk"),
        os.path.join(singletask_count_seqlets_path, "dichipmunk"),
        "dichipmunk"
    )

Motif	Supporting sequences (profile)	PWM (profile)	Supporting sequences (count)	PWM (count)
1	12777		8865
2	891		1288
3	304		240
4	39		50
5	21		60
6			17
7			1

HOMER on peaks

show_peaks_motif_table(os.path.join(peaks_path, "homer"), "homer")

Motif	Log enrichment	PWM
1	-6685.751381
2	-5514.447682
3	-3682.904347
4	-2527.549718
5	-2229.133146
6	-1728.747876
7	-1669.660741
8	-1071.611408
9	-849.717191
10	-845.278202
11	-737.884612
12	-586.972716
13	-535.718235
14	-465.211263
15	-318.141141
16	-246.061079
17	-225.317651
18	-129.36832

HOMER on multi-task seqlets

show_seqlets_motif_table(
    os.path.join(multitask_profile_seqlets_path, "homer"),
    os.path.join(multitask_count_seqlets_path, "homer"),
    "homer"
)

/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)

Motif	Log enrichment (profile)	PWM (profile)	Log enrichment (count)	PWM (count)
1	-8333.554621		-13128.93511
2	-8113.611007		-9150.289947
3	-4317.501956		-3462.150709
4	-3245.019871		-2216.572462
5	-562.774974		-268.48051
6	-384.359604		-185.089245
7	-242.648196		-123.884567
8	-238.337972		-101.967112
9	-216.381391		-98.025662
10	-144.444746		-95.820967
11	-135.607306		-84.03157
12	-113.87141
13	-107.106604
14	-13.001758

HOMER on single-task seqlets

if task_index is not None:
    show_seqlets_motif_table(
        os.path.join(singletask_profile_seqlets_path, "homer"),
        os.path.join(singletask_count_seqlets_path, "homer"),
        "homer"
    )

Motif	Log enrichment (profile)	PWM (profile)	Log enrichment (count)	PWM (count)
1	-10924.812681		-15202.831453
2	-6166.374155		-5135.480156
3	-2917.608237		-3212.107745
4	-2856.119402		-1053.631864
5	-874.226576		-519.483458
6	-427.616062		-317.236352
7	-149.834043		-219.739496
8	-139.263196		-158.163208
9	-139.263196		-155.955109
10	-115.084313		-112.58903
11	-90.494822		-112.357112
12	-77.651211		-106.077239
13	-54.473005		-101.957247
14	-34.733508		-66.401765

MEMEChIP on peaks

show_peaks_motif_table(os.path.join(peaks_path, "memechip"), "memechip")

Motif	E-value	PWM
1	1.6e-131
2	1.1e-61
3	1.4e-49
4	2.8e-21
5	1.2e-15
6	2.1e-13
7	5.7e-11
8	2.5e-11
9	0.00034
10	0.011

MEME on multi-task seqlets

show_seqlets_motif_table(
    os.path.join(multitask_profile_seqlets_path, "meme"),
    os.path.join(multitask_count_seqlets_path, "meme"),
    "meme"
)

Motif	E-value (profile)	PWM (profile)	E-value (count)	PWM (count)
1	0.0		0.0
2	2.3e-178		2e-257
3	3.1e-57		1.4e-64
4	1.1e-19		2.5e-18
5	0.69		460.0
6	93.0		260000.0
7	660.0		270000.0
8	6500.0		680000.0
9	150000.0		480000.0
10	110000.0		790000.0

MEME on single-task seqlets

if task_index is not None:
    show_seqlets_motif_table(
        os.path.join(singletask_profile_seqlets_path, "meme"),
        os.path.join(singletask_count_seqlets_path, "meme"),
        "meme"
    )

Motif	E-value (profile)	PWM (profile)	E-value (count)	PWM (count)
1	0.0		0.0
2	4.6e-147		8.4e-139
3	1.2e-67		7.4e-54
4	1.4e-25		6.5e-19
5	8.3e-13		0.72
6	2100.0		3900.0
7	34000.0		270000.0
8	940000.0		920000.0
9	1300000.0		2600000.0
10	2000000.0		4000000.0