def df_to_faerun(df, x, y, s, t):
print('Making Faerun plot')
f = Faerun(view="front", coords=False)
f.add_scatter(
# No space in the string allowed for the name, use underscore!!
# Cannot start with a number, it has to be a letter!!
# the string is to be converted to a variable name,
# therefore it has to be compatible with python variable naming scheme
"SampleDock",
{
"x": x,
"y": y,
"c":
[df['Score'], df['MolWeight'], df['LogP'], df['QED'], df['SAS']],
"labels": df['SMILES'],
},
shader="smoothCircle",
point_scale=2.0,
max_point_size=20,
categorical=[False, False, False, False, False],
colormap=["rainbow_r", "rainbow", "rainbow", "rainbow", "Blues"],
series_title=[
"Docking Score",
"Molecular Weight",
"Lipophilicity",
"Quantitative Estimate of Druglikeness",
"Synthetic Accessibility Score",
],
has_legend=True,
)
# The first character of the name has to be a letter!
f.add_tree("SnD_Tree", {"from": s, "to": t}, point_helper="SampleDock")
print('Plotting finished')
return f
def main():
""" Main function """
# Initialize and configure tmap
dims = 2048
enc = tm.Minhash(16384, 42, dims)
lf = tm.LSHForest(dims * 2, 128, weighted=True)
images = []
labels = []
image_labels = []
for file in os.listdir("coil_20"):
labels.append(int(file.split("__")[0].replace("obj", "")) - 1)
images.append(list(Image.open("coil_20/" + file).getdata()))
for image in images:
img = Image.fromarray(np.uint8(np.split(np.array(image), 128)))
buffered = BytesIO()
img.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue())
image_labels.append("data:image/bmp;base64," +
str(img_str).replace("b'", "").replace("'", ""))
tmp = []
for _, image in enumerate(images):
avg = sum(image) / sum([1 if x > 0 else 0 for x in image])
tmp.append([i / 255 for i in image])
lf.batch_add(enc.batch_from_weight_array(tmp))
lf.index()
x, y, s, t, _ = tm.layout_from_lsh_forest(lf)
faerun = Faerun(clear_color="#111111", view="front", coords=False)
faerun.add_scatter(
"COIL20",
{
"x": x,
"y": y,
"c": labels,
"labels": image_labels
},
colormap="tab20",
shader="smoothCircle",
point_scale=2.5,
max_point_size=10,
has_legend=True,
categorical=True,
)
faerun.add_tree("COIL20_tree", {
"from": s,
"to": t
},
point_helper="COIL20",
color="#666666")
faerun.plot("coil", template="url_image")
def main():
""" Main function """
# Initialize and configure tmap
dims = 1024
enc = tm.Minhash(dims)
lf = tm.LSHForest(dims, 128)
print("Converting images ...")
for image in tqdm(IMAGES):
img = Image.fromarray(np.uint8(np.split(np.array(image), 28)))
buffered = BytesIO()
img.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue())
IMAGE_LABELS.append("data:image/bmp;base64," +
str(img_str).replace("b'", "").replace("'", ""))
tmp = []
for _, image in enumerate(IMAGES):
avg = sum(image) / sum([1 if x > 0 else 0 for x in image])
tmp.append(tm.VectorUchar([1 if x >= avg else 0 for x in image]))
# tmp.append(tm.VectorUint(image))
print("Running tmap ...")
lf.batch_add(enc.batch_from_binary_array(tmp))
# LF.batch_add(ENC.batch_from_int_weight_array(tmp))
lf.index()
x, y, s, t, _ = tm.layout_from_lsh_forest(lf, CFG)
faerun = Faerun(clear_color="#111111", view="front", coords=False)
faerun.add_scatter(
"MNIST",
{
"x": x,
"y": y,
"c": LABELS,
"labels": IMAGE_LABELS
},
colormap="tab10",
shader="smoothCircle",
point_scale=2.5,
max_point_size=10,
has_legend=True,
categorical=True,
)
faerun.add_tree("MNIST_tree", {
"from": s,
"to": t
},
point_helper="MNIST",
color="#666666")
faerun.plot("i3d-tmap-mnist", path="outputs", template="url_image")
def main():
""" Main function """
# Initialize and configure tmap
dims = 256
enc = tm.Minhash(len(DATA.columns), 42, dims)
lf = tm.LSHForest(dims * 2, 32, weighted=True)
fps = []
for _, row in DATA.iterrows():
fps.append(tm.VectorFloat(list(row)))
lf.batch_add(enc.batch_from_weight_array(fps))
lf.index()
x, y, s, t, _ = tm.layout_from_lsh_forest(lf, CFG_TMAP)
lf.clear()
legend_labels = {(1, "PRAD"), (2, "LUAD"), (3, "BRCA"), (4, "KIRC"),
(5, "COAD")}
# Create the plot
faerun = Faerun(view="front", coords=False, legend_title="")
faerun.add_scatter(
"RNASEQ",
{
"x": x,
"y": y,
"c": LABELS,
"labels": LABELS
},
colormap="tab10",
point_scale=5.0,
max_point_size=10,
shader="smoothCircle",
has_legend=True,
categorical=True,
legend_labels=legend_labels,
legend_title="Tumor Types",
)
faerun.add_tree("RNASEQ_tree", {
"from": s,
"to": t
},
point_helper="RNASEQ",
color="#666666")
faerun.plot("rnaseq")
def main():
""" Main function """
# Building a k-nearest neighbor graph using annoy and cosine distance
annoy = AnnoyIndex(len(DATA.columns), metric="angular")
annoy_graph = []
for i, v in enumerate(DATA.values):
annoy.add_item(i, v)
annoy.build(10)
for i in range(len(DATA)):
for j in annoy.get_nns_by_item(i, 10):
annoy_graph.append(
(i, j, cosine_distance(DATA.values[i], DATA.values[j])))
# Creating the tmap layout
x, y, s, t, _ = tm.layout_from_edge_list(len(DATA), annoy_graph)
faerun = Faerun(view="front", coords=False)
faerun.add_scatter(
"MINIBOONE",
{
"x": x,
"y": y,
"c": LABELS,
"labels": LABELS
},
shader="smoothCircle",
colormap="Set1",
point_scale=2.0,
max_point_size=20,
has_legend=True,
categorical=True,
legend_labels={(0, "Noise"), (1, "Signal")},
)
faerun.add_tree(
"MINIBOONE_tree",
{
"from": s,
"to": t
},
point_helper="MINIBOONE",
color="#666666",
)
faerun.plot("miniboone", template="default")
def main():
f = Faerun(clear_color="#222222", coords=True, view="front")
x = np.linspace(0, 12.0, 326)
y = np.sin(np.pi * x)
z = np.cos(np.pi * x)
c = np.random.randint(0, 2, len(x))
labels = [str(l) + "__Test" for l in c]
data = {"x": x, "y": y, "z": z, "c": c, "labels": labels}
f.add_scatter(
"helix",
data,
shader="smoothCircle",
colormap="Dark2",
point_scale=5.0,
categorical=True,
has_legend=True,
legend_labels=[(0, "Zero"), (1, "One")],
selected_labels=["None", "Just a Small Test"],
)
f.add_tree("helixtree", {
"from": [1, 5, 6, 7],
"to": [2, 7, 8, 9]
},
point_helper="helix")
f.plot("helix")
with open("helix.faerun", "wb+") as handle:
pickle.dump(f.create_python_data(),
handle,
protocol=pickle.HIGHEST_PROTOCOL)
def main():
""" Main function """
data = []
time = []
for path in PATHS:
sample = fk.Sample(path)
data.append(load_data(sample))
time.append(load_time(sample))
sources = []
for i, e in enumerate(data):
sources.extend([i] * len(e))
data = np.concatenate(data, axis=0)
time = np.concatenate(time, axis=0)
d = len(data[0])
# Initialize a new Annoy object and index it using 10 trees
annoy = AnnoyIndex(d, metric="angular")
for i, v in enumerate(data):
annoy.add_item(i, v)
annoy.build(10)
# Create the k-nearest neighbor graph (k = 10)
edge_list = []
for i in range(len(data)):
for j in annoy.get_nns_by_item(i, 10):
edge_list.append((i, j, cosine_distance(data[i], data[j])))
# Compute the layout from the edge list
x, y, s, t, _ = tm.layout_from_edge_list(len(data), edge_list)
legend_labels = [(0, "No Target Probe Negative Control"),
(1, "Stained Sample")]
# Create the plot
faerun = Faerun(
view="front",
coords=False,
legend_title=
"RNA Flow Cytometry: evaluation of detection sensitivity in low abundant intracellular RNA ",
)
faerun.add_scatter(
"CYTO",
{
"x": x,
"y": y,
"c": sources,
"labels": sources
},
point_scale=1.0,
max_point_size=10,
shader="smoothCircle",
colormap="Set1",
has_legend=True,
categorical=True,
legend_labels=legend_labels,
legend_title="Cell Types",
)
faerun.add_tree("CYTO_tree", {
"from": s,
"to": t
},
point_helper="CYTO",
color="#222222")
faerun.plot("cyto")
def main():
""" The main function """
df = pd.read_csv("papers.tar.xz")
df.drop(df.tail(1).index, inplace=True)
df["title"] = df["title"].apply(lambda t: t.replace("'", '"'))
enc = tm.Minhash()
lf = tm.LSHForest()
ctr = Counter()
texts = []
for _, row in df.iterrows():
text = re.sub(r"[^a-zA-Z-]+", " ", row["paper_text"])
text = [t.lower() for t in text.split(" ") if len(t) > 2]
ctr.update(text)
texts.append(text)
# Remove the top n words
n = 6000
ctr = ctr.most_common()[: -(len(ctr) - n) - 1 : -1]
# Make it fast using a lookup map
all_words = {}
for i, (key, _) in enumerate(ctr):
all_words[key] = i
# Create the fingerprints and also check whether the word
# "deep" is found in the document
fingerprints = []
has_word = []
for text in texts:
if "deep" in text:
has_word.append(1)
else:
has_word.append(0)
fingerprint = []
for t in text:
if t in all_words:
fingerprint.append(all_words[t])
fingerprints.append(tm.VectorUint(fingerprint))
# Index the article fingerprints
lf.batch_add(enc.batch_from_sparse_binary_array(fingerprints))
lf.index()
# Create the tmap
config = tm.LayoutConfiguration()
config.k = 100
x, y, s, t, _ = tm.layout_from_lsh_forest(lf, config=config)
faerun = Faerun(
view="front", coords=False, legend_title="", legend_number_format="{:.0f}"
)
# Add a scatter that is bigger than the one above, to add colored
# circles.
faerun.add_scatter(
"NIPS_word",
{"x": x, "y": y, "c": has_word, "labels": df["title"]},
colormap="Set1",
point_scale=7.5,
max_point_size=25,
shader="smoothCircle",
has_legend=True,
categorical=True,
legend_title="Contains word<br/>'deep'",
legend_labels=[(0, "No"), (1, "Yes")],
interactive=False,
)
# Add a scatter that is colored by year on top
faerun.add_scatter(
"NIPS",
{"x": x, "y": y, "c": df["year"], "labels": df["title"]},
colormap="gray",
point_scale=5.0,
max_point_size=20,
shader="smoothCircle",
has_legend=True,
legend_title="Year of<br/>Publication",
)
faerun.add_tree(
"NIPS_tree", {"from": s, "to": t}, point_helper="NIPS", color="#666666"
)
faerun.plot("nips_papers")
def main():
""" Main function """
dims = 512
lf = tm.LSHForest(dims, 128, store=True)
# Due to the large data size (> 1GB) the following files are not provided directly
smiles, target_class, activity, chembl_id = pickle.load(
open("chembl.pickle", "rb"))
labels = []
for i, s in enumerate(smiles):
labels.append(
s + "__" + chembl_id[i] + "__" +
f'<a target="_blank" href="https://www.ebi.ac.uk/chembl/compound_report_card/{chembl_id[i]}">{chembl_id[i]}</a>'
)
lf.restore("chembl.dat")
target_class_map = dict([(y, x + 1)
for x, y in enumerate(sorted(set(target_class)))])
classes = [
"enzyme",
"kinase",
"protease",
"cytochrome p450",
"ion channel",
"transporter",
"transcription factor",
"membrane receptor",
"epigenetic regulator",
]
i = 0
for key, value in target_class_map.items():
if key not in classes:
target_class_map[key] = 7
else:
target_class_map[key] = i
i += 1
if i == 7:
i = 8
cfg = tm.LayoutConfiguration()
cfg.node_size = 1 / 70
cfg.mmm_repeats = 2
cfg.sl_repeats = 2
start = timer()
x, y, s, t, _ = tm.layout_from_lsh_forest(lf, cfg)
end = timer()
print(end - start)
activity = np.array(activity)
activity = np.maximum(0.0, activity)
activity = np.minimum(100.0, activity)
activity = 10.0 - activity
legend_labels = [
(0, "Cytochrome p450"),
(1, "Other Enzyme"),
(2, "Epigenetic Regulator"),
(3, "Ion Channel"),
(4, "Kinase"),
(5, "Membrane Receptor"),
(6, "Protease"),
(8, "Transcription Factor"),
(9, "Transporter"),
(7, "Other"),
]
vals = [int(target_class_map[x]) for x in target_class]
faerun = Faerun(view="front", coords=False)
faerun.add_scatter(
"chembl",
{
"x": x,
"y": y,
"c": vals,
"labels": labels
},
colormap="tab10",
point_scale=1.0,
max_point_size=10,
has_legend=True,
categorical=True,
shader="smoothCircle",
legend_labels=legend_labels,
title_index=1,
)
faerun.add_tree("chembl_tree", {
"from": s,
"to": t
},
point_helper="chembl",
color="#222222")
faerun.plot("chembl", template="smiles")
def main():
""" The main function """
df = pd.read_csv("drugbank.csv").dropna(subset=["SMILES"]).reset_index(
drop=True)
enc = MHFPEncoder()
lf = tm.LSHForest(2048, 128)
fps = []
labels = []
groups = []
tpsa = []
logp = []
mw = []
h_acceptors = []
h_donors = []
ring_count = []
is_lipinski = []
has_coc = []
has_sa = []
has_tz = []
substruct_coc = AllChem.MolFromSmiles("COC")
substruct_sa = AllChem.MolFromSmiles("NS(=O)=O")
substruct_tz = AllChem.MolFromSmiles("N1N=NN=C1")
total = len(df)
for i, row in df.iterrows():
if i % 1000 == 0 and i > 0:
print(f"{round(100 * (i / total))}% done ...")
smiles = row[6]
mol = AllChem.MolFromSmiles(smiles)
if mol and mol.GetNumAtoms() > 5 and smiles.count(".") < 2:
fps.append(tm.VectorUint(enc.encode_mol(mol, min_radius=0)))
labels.append(
f'{smiles}__<a href="https://www.drugbank.ca/drugs/{row[0]}" target="_blank">{row[0]}</a>__{row[1]}'
.replace("'", ""))
groups.append(row[3].split(";")[0])
tpsa.append(Descriptors.TPSA(mol))
logp.append(Descriptors.MolLogP(mol))
mw.append(Descriptors.MolWt(mol))
h_acceptors.append(Descriptors.NumHAcceptors(mol))
h_donors.append(Descriptors.NumHDonors(mol))
ring_count.append(Descriptors.RingCount(mol))
is_lipinski.append(lipinski_pass(mol))
has_coc.append(mol.HasSubstructMatch(substruct_coc))
has_sa.append(mol.HasSubstructMatch(substruct_sa))
has_tz.append(mol.HasSubstructMatch(substruct_tz))
# Create the labels and the integer encoded array for the groups,
# as they're categorical
labels_groups, groups = Faerun.create_categories(groups)
tpsa_ranked = ss.rankdata(np.array(tpsa) / max(tpsa)) / len(tpsa)
logp_ranked = ss.rankdata(np.array(logp) / max(logp)) / len(logp)
mw_ranked = ss.rankdata(np.array(mw) / max(mw)) / len(mw)
h_acceptors_ranked = ss.rankdata(
np.array(h_acceptors) / max(h_acceptors)) / len(h_acceptors)
h_donors_ranked = ss.rankdata(
np.array(h_donors) / max(h_donors)) / len(h_donors)
ring_count_ranked = ss.rankdata(
np.array(ring_count) / max(ring_count)) / len(ring_count)
lf.batch_add(fps)
lf.index()
cfg = tm.LayoutConfiguration()
cfg.k = 100
# cfg.sl_extra_scaling_steps = 1
cfg.sl_repeats = 2
cfg.mmm_repeats = 2
cfg.node_size = 2
x, y, s, t, _ = tm.layout_from_lsh_forest(lf, config=cfg)
# Define a colormap highlighting approved vs non-approved
custom_cmap = ListedColormap(
[
"#2ecc71", "#9b59b6", "#ecf0f1", "#e74c3c", "#e67e22", "#f1c40f",
"#95a5a6"
],
name="custom",
)
bin_cmap = ListedColormap(["#e74c3c", "#2ecc71"], name="bin_cmap")
f = Faerun(
clear_color="#222222",
coords=False,
view="front",
impress=
'made with <a href="http://tmap.gdb.tools" target="_blank">tmap</a><br />and <a href="https://github.com/reymond-group/faerun-python" target="_blank">faerun</a><br /><a href="https://gist.github.com/daenuprobst/5cddd0159c0cf4758fb16b4b4acbef89">source</a>',
)
f.add_scatter(
"Drugbank",
{
"x":
x,
"y":
y,
"c": [
groups,
is_lipinski,
has_coc,
has_sa,
has_tz,
tpsa_ranked,
logp_ranked,
mw_ranked,
h_acceptors_ranked,
h_donors_ranked,
ring_count_ranked,
],
"labels":
labels,
},
shader="smoothCircle",
colormap=[
custom_cmap,
bin_cmap,
bin_cmap,
bin_cmap,
bin_cmap,
"viridis",
"viridis",
"viridis",
"viridis",
"viridis",
"viridis",
],
point_scale=2.5,
categorical=[
True, True, True, True, True, False, False, False, False, False
],
has_legend=True,
legend_labels=[
labels_groups,
[(0, "No"), (1, "Yes")],
[(0, "No"), (1, "Yes")],
[(0, "No"), (1, "Yes")],
[(0, "No"), (1, "Yes")],
],
selected_labels=["SMILES", "Drugbank ID", "Name"],
series_title=[
"Group",
"Lipinski",
"Ethers",
"Sulfonamides",
"Tetrazoles",
"TPSA",
"logP",
"Mol Weight",
"H Acceptors",
"H Donors",
"Ring Count",
],
max_legend_label=[
None,
None,
None,
None,
None,
str(round(max(tpsa))),
str(round(max(logp))),
str(round(max(mw))),
str(round(max(h_acceptors))),
str(round(max(h_donors))),
str(round(max(ring_count))),
],
min_legend_label=[
None,
None,
None,
None,
None,
str(round(min(tpsa))),
str(round(min(logp))),
str(round(min(mw))),
str(round(min(h_acceptors))),
str(round(min(h_donors))),
str(round(min(ring_count))),
],
title_index=2,
legend_title="",
)
f.add_tree("drugbanktree", {"from": s, "to": t}, point_helper="Drugbank")
f.plot("drugbank", template="smiles")
def main():
""" Main function """
# Initialize and configure tmap
dims = 2048
enc = tm.Minhash(dims)
lf = tm.LSHForest(dims, 128, store=True)
fps = []
# fps_umap = []
for row in DATA:
fps.append(tm.VectorUint(list(row)))
lf.batch_add(enc.batch_from_sparse_binary_array(fps))
lf.index()
x_tmap, y_tmap, s, t, _ = tm.layout_from_lsh_forest(lf, CFG_TMAP)
lf.clear()
# Prepare custom color map
tab10 = plt.get_cmap("tab10").colors
colors_gray = [(0.2, 0.2, 0.2), tab10[0], tab10[1], tab10[2], tab10[3],
tab10[4]]
custom_cm_gray = LinearSegmentedColormap.from_list("custom_cm_gray",
colors_gray,
N=len(colors_gray))
legend_labels = [
(1, "Rudyard Kipling"),
(2, "Herbert George Wells"),
(3, "Charles Darwin"),
(4, "George Bernard Shaw"),
(5, "William Wymark Jacobs"),
(0, "Other"),
]
faerun = Faerun(
clear_color="#111111",
view="front",
coords=False,
alpha_blending=True,
legend_title="",
)
faerun.add_scatter(
"gutenberg",
{
"x": x_tmap,
"y": y_tmap,
"c": LABELS,
"labels": FAERUN_LABELS
},
colormap=custom_cm_gray,
point_scale=4.2,
max_point_size=10,
has_legend=True,
categorical=True,
legend_title="Authors",
legend_labels=legend_labels,
shader="smoothCircle",
selected_labels=["Author", "Title"],
)
faerun.add_tree(
"gutenberg_tree",
{
"from": s,
"to": t
},
point_helper="gutenberg",
color="#222222",
)
faerun.plot("gutenberg", template="default")
def main():
""" Main function """
# Initialize and configure tmap
dims = 1024
enc = tm.Minhash(28 * 28, 42, dims)
lf = tm.LSHForest(dims * 2, 128)
print("Converting images ...")
for image in IMAGES:
img = Image.fromarray(np.uint8(np.split(np.array(image), 28)))
buffered = BytesIO()
img.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue())
IMAGE_LABELS.append("data:image/bmp;base64," +
str(img_str).replace("b'", "").replace("'", ""))
tmp = []
for _, image in enumerate(IMAGES):
tmp.append(tm.VectorFloat(image / 255))
print("Running tmap ...")
start = timer()
lf.batch_add(enc.batch_from_weight_array(tmp))
lf.index()
x, y, s, t, _ = tm.layout_from_lsh_forest(lf, CFG)
print("tmap: " + str(timer() - start))
legend_labels = [
(0, "T-shirt/top"),
(1, "Trouser"),
(2, "Pullover"),
(3, "Dress"),
(4, "Coat"),
(5, "Sandal"),
(6, "Shirt"),
(7, "Sneaker"),
(8, "Bag"),
(9, "Ankle boot"),
]
faerun = Faerun(clear_color="#111111", view="front", coords=False)
faerun.add_scatter(
"FMNIST",
{
"x": x,
"y": y,
"c": LABELS,
"labels": IMAGE_LABELS
},
colormap="tab10",
shader="smoothCircle",
point_scale=2.5,
max_point_size=10,
has_legend=True,
categorical=True,
legend_labels=legend_labels,
)
faerun.add_tree("FMNIST_tree", {
"from": s,
"to": t
},
point_helper="FMNIST",
color="#666666")
faerun.plot("fmnist", template="url_image")
