def hc_plot_dendrogram(data,
row_colours,
mad=None,
n_ftr=3000,
metric='correlation',
**kwargs):
"""
For each value in n_gene_arr, plot a dendrogram showing the result of hierarchical clustering of the data using
that many genes (selected in descending MAD order)
:param data: Cols are samples, rows are genes (or similar)
:param row_colours: As passed to dendrogram routine
:param n_gene_arr: The values to test
:return:
"""
if 'fig_kws' not in kwargs:
kwargs['fig_kws'] = {'figsize': (5.5, 10)}
if mad is None:
mad = transformations.median_absolute_deviation(data).sort_values(
ascending=False)
the_dat = data.loc[mad.index[:n_ftr]]
fig_dict = clustering.dendrogram_with_colours(the_dat,
row_colours,
vertical=False,
metric=metric,
**kwargs)
return fig_dict
def hc_plot_dendrogram_vary_n_gene(data,
row_colours,
mad=None,
n_ftr=(1000, 2000, 3000, 5000, 10000),
metric='correlation'):
"""
For each value in n_gene_arr, plot a dendrogram showing the result of hierarchical clustering of the data using
that many genes (selected in descending MAD order)
:param data: Cols are samples, rows are genes (or similar)
:param row_colours: As passed to dendrogram routine
:param n_gene_arr: The values to test
:return:
"""
if mad is None:
mad = transformations.median_absolute_deviation(data).sort_values(
ascending=False)
fig_dict = {}
for ng in n_ftr:
the_dat = data.loc[mad.index[:ng]]
d = clustering.dendrogram_with_colours(the_dat,
row_colours,
fig_kws={'figsize': (5.5, 10)},
vertical=False,
metric=metric)
fig_dict[ng] = d
return fig_dict
def plot_dendrogram(obj_arr,
n_by_mad=None,
qn_method=None,
eps=0.01,
min_val=1,
n_above_min=3,
vertical=False,
figsize=(7, 8),
**kwargs):
if len(obj_arr) > 1:
the_obj = loader.MultipleBatchLoader(obj_arr)
else:
the_obj = obj_arr[0]
the_obj = filter_loader(the_obj, min_val=min_val, n_above_min=n_above_min)
dat = np.log2(the_obj.data + eps)
if qn_method is not None:
dat = transformations.quantile_normalisation(dat, method=qn_method)
if n_by_mad is not None:
mad = transformations.median_absolute_deviation(dat).sort_values(
ascending=False)
dat = dat.loc[mad.index[:n_by_mad]]
cc, st, leg_dict = construct_colour_array_legend_studies(the_obj.meta)
dend = clustering.dendrogram_with_colours(dat,
cc,
vertical=vertical,
legend_labels=leg_dict,
fig_kws={'figsize': figsize},
**kwargs)
return dend
ax.figure.savefig(os.path.join(
outdir, "pca_top%d_by_mad_with_names.png" % n_t),
dpi=200)
row_colours = pd.DataFrame('gray', index=our_dat.columns, columns=[''])
row_colours.loc[row_colours.index.str.contains(
r'eNSC[0-9]med')] = '#66c2a5'
row_colours.loc[row_colours.index.str.contains(
r'eNSC[0-9]mouse')] = '#fc8d62'
row_colours.loc[row_colours.index.str.contains(
r'mDura.[AN0-9]*mouse')] = '#8da0cb'
row_colours.loc[row_colours.index.str.contains(
r'mDura.[AN0-9]*human')] = '#e78ac3'
for n_t in n_gene_try:
fname = "clustering_by_gene_corr_log_top%d_by_mad.{ext}" % n_t
d = clustering.dendrogram_with_colours(our_dat.loc[mad.index[:n_t]],
row_colours,
fig_kws={'figsize': (10, 5.5)})
d['fig'].savefig(os.path.join(outdir, fname.format(ext='png')),
dpi=200)
cm = clustering.plot_clustermap(our_dat.loc[mad.index[:n_t]],
cmap='RdBu_r',
metric='correlation',
col_colors=row_colours)
cm.gs.update(bottom=0.2)
cm.savefig(os.path.join(
outdir, "clustermap_by_gene_corr_log_top%d_by_mad.png" % n_t),
dpi=200)
'Cell type'] = '#f6ffaa' # pale yellow
row_colours_all.loc[row_colours_all.index.str.contains(r'ESC'),
'Cell type'] = '#8b33dd' # pale purple
row_colours_all.loc[row_colours_all.index.str.contains(r'[iI]PSC'),
'Cell type'] = '#8b33dd' # pale purple
row_colours_all.loc[row_colours_all.index.str.contains(r'[Mm]icroglia'),
'Cell type'] = '#ffd8af' # pale orange
row_colours_all.loc[row_colours_all.index.str.contains(r'Yanez'),
'Cell type'] = '#ffa03d' # orange
# these override previously-defined colours
row_colours_all.loc[row_colours_all.index.str.contains(r'eNSC[0-9]med'),
'Cell type'] = '#96ff9d' # pale green
row_colours_all.loc[row_colours_all.index.str.contains(r'eNSC[0-9]mouse'),
'Cell type'] = '#008408' # dark green
row_colours_all.loc[
row_colours_all.index.str.contains(r'mDura.[AN0-9]*mouse'),
'Cell type'] = '#3543ff' # dark blue
row_colours_all.loc[
row_colours_all.index.str.contains(r'mDura.[AN0-9]*human'),
'Cell type'] = '#c4c8ff' # pale blue
d = clustering.dendrogram_with_colours(dat.loc[mad.index[:3000]],
row_colours_all,
fig_kws={'figsize': (5.5, 10)},
vertical=False)
# cm = clustering.plot_correlation_clustermap(
# dat.loc[mad.index[:3000]],
# row_colors=row_colours_all,
# )
fig.savefig(os.path.join(outdir, "filtering_effect.pdf"))
idx = gene_idx & ((obj.data > 10).sum(axis=1) > 10)
data = obj.data.loc[idx]
mad = transformations.median_absolute_deviation(data).sort_values(
ascending=False)
logdata = np.log(data + 1)
# start with a dendrogram
col_colours = clustering.generate_colour_map_dict(obj.meta,
'sample',
matches,
label='Patient',
non_matching='gray')
out = clustering.dendrogram_with_colours(logdata,
col_colours=col_colours,
vertical=False)
dist = clustering.dendrogram_threshold_by_nclust(out['linkage'], 3)
out['dendrogram_ax'].axvline(dist, ls='--', c='gray')
out['fig'].savefig(os.path.join(outdir, "dendrogram_all_genes.png"),
dpi=200)
out['fig'].savefig(os.path.join(outdir, "dendrogram_all_genes.pdf"))
# repeat but now only use N genes (by MAD)
# tested and the result is unchanged for most values in the region [500, 5000]
n_gene = 1500
out = clustering.dendrogram_with_colours(logdata.loc[mad.index[:n_gene]],
col_colours=col_colours,
vertical=False)
dist = clustering.dendrogram_threshold_by_nclust(out['linkage'], 3)
out['dendrogram_ax'].axvline(dist, ls='--', c='gray')
r'Fibroblast')] = '#fff89e'
row_colours_all.loc[row_colours_all.index.str.contains(
r'Fetal')] = 'yellow'
row_colours_all.loc[row_colours_all.index.str.contains('ES1')] = '#ff7777'
row_colours_all.loc[row_colours_all.index.str.contains('PSC')] = '#ff7777'
row_colours_all.loc[row_colours_all.index.str.contains(
r'DURA[0-9]*_NSC')] = '#7fc97f' # green
row_colours_all.loc[row_colours_all.index.str.contains(
r'DURA[0-9]*_IPSC')] = '#fdc086' # orange
n_gene = 3000
fname = "hier_clust_by_gene_log_corr_top%d_by_mad.{ext}" % n_gene
cm, mad_all = cluster_logdata_with_threshold(abg,
n=n_gene,
eps=eps,
col_colors=row_colours_all)
cm.gs.update(bottom=0.3)
cm.savefig(os.path.join(outdir, fname.format(ext='png')), dpi=300)
cm.savefig(os.path.join(outdir, fname.format(ext='tiff')), dpi=200)
fname = "hier_clust_dendrogram_log_corr_top%d_by_mad.{ext}" % n_gene
d = clustering.dendrogram_with_colours(
abg_log.loc[amad_log.index[:n_gene]],
row_colours_all,
fig_kws={'figsize': (5.5, 10)},
vertical=False)
d['fig'].savefig(os.path.join(outdir, fname.format(ext='png')), dpi=300)
d['fig'].savefig(os.path.join(outdir, fname.format(ext='tiff')), dpi=200)
vmin=-10,
vmax=10)
clustering.add_legend(leg_dict, cm.ax_heatmap, loc='right')
cm.gs.update(bottom=0.25, right=0.85, left=0.03)
cm.savefig(os.path.join(
outdir, "nogbm_clustermap_M_corr_linkage%d_heatmap%d.png" %
(clust_n_ftr, n_probe_to_show)),
dpi=200)
cm.savefig(os.path.join(
outdir, "nogbm_clustermap_M_corr_linkage%d_heatmap%d.tiff" %
(clust_n_ftr, n_probe_to_show)),
dpi=200)
## our samples only, all probes, beta values
idx = ((bdat.columns.str.contains('GBM')) |
(bdat.columns.str.contains('DURA')))
bdat_ours = bdat.loc[:, idx]
bmad_ours = transformations.median_absolute_deviation(bdat_ours)
row_colours_ours = row_colours_all.loc[bdat_ours.columns]
x = clustering.dendrogram_with_colours(bdat_ours,
row_colours_ours,
fig_kws={'figsize': (5.5, 10)},
vertical=False,
metric='correlation')
fname = "ours_dendrogram_B_corr_all.{ext}"
x['fig'].savefig(os.path.join(outdir, fname.format(ext='png')), dpi=200)
x['fig'].savefig(os.path.join(outdir, fname.format(ext='png')), dpi=200)
# normalised version with counts / sum(counts)
datan = data.divide(data.sum(axis=0), axis=1)
contains_arr = ['MES', re.compile(r'RTK_I$'), 'RTK_II']
col_colours, legend_labels = clustering.generate_colour_map_dict(
meta,
'subgroup',
contains_arr,
label='group',
sample_names=data.columns,
non_matching='gray',
group_names=['Mesenchymal', 'RTK I', 'RTK II'])
clustering.dendrogram_with_colours(data,
col_colours,
legend_labels=legend_labels,
metric='euclidean',
method='average')
clustering.dendrogram_with_colours(data,
col_colours,
legend_labels=legend_labels,
metric='euclidean',
method='single')
clustering.dendrogram_with_colours(data,
col_colours,
legend_labels=legend_labels,
metric='correlation',
method='average')
clustering.dendrogram_with_colours(data,
col_colours,
legend_labels=legend_labels,
row_colours = pd.DataFrame('gray', index=our_dat.columns, columns=[''])
row_colours.loc[row_colours.index.str.contains(
r'eNSC[0-9]med')] = '#66c2a5'
row_colours.loc[row_colours.index.str.contains(
r'eNSC[0-9]mouse')] = '#fc8d62'
row_colours.loc[row_colours.index.str.contains(
r'mDura.[AN0-9]*mouse')] = '#8da0cb'
row_colours.loc[row_colours.index.str.contains(
r'mDura.[AN0-9]*human')] = '#e78ac3'
for n_t in n_gene_try:
fname = "clustering_by_gene_corr_log_top%d_by_mad.{ext}" % n_t
d = clustering.dendrogram_with_colours(our_dat.loc[mad.index[:n_t]],
row_colours,
fig_kws={'figsize': (10, 5.5)})
d['fig'].savefig(os.path.join(outdir, fname.format(ext='png')),
dpi=200)
cm, _ = cluster_data_with_threshold(our_dat,
n=n_t,
mad=mad,
col_colors=row_colours)
cm.savefig(os.path.join(
outdir, "clustermap_by_gene_corr_log_top%d_by_mad.png" % n_t),
dpi=200)
raise Exception("TODO: complete the script refactor")
# bring in reference data
# normalise by read count
cpm = (data + 1).divide((data + 1).sum(axis=0), axis=1) * 1e6
# transform
log_data = np.log2(cpm)
vst_data = variance_stabilizing_transform(cpm)
mad_log_srt = median_absolute_deviation(log_data).sort_values(
ascending=False)
mad_vst_srt = median_absolute_deviation(vst_data).sort_values(
ascending=False)
for NGENE in [500, 1000, 1500, 2000, 2500]:
out = clustering.dendrogram_with_colours(
log_data.loc[mad_log_srt.index[:NGENE]],
col_colours=col_colours,
vertical=False,
metric='correlation',
method='average',
)
out['fig'].savefig(os.path.join(
outdir,
"gbm_nsc_correlation_dendrogram_logtransform_top%d.png" % NGENE),
dpi=200)
out['fig'].savefig(
os.path.join(
outdir,
"gbm_nsc_correlation_dendrogram_logtransform_top%d.pdf" %
NGENE))
cg = clustering.plot_correlation_clustermap(
log_data.loc[mad_log_srt.index[:NGENE]],
ax.figure.savefig(os.path.join(outdir, "pca_ribozero_polya.pdf"))
# by cell line
subgroups = data_all_n.columns.str.replace(r'[^0-9]*', '')
ax = pca_plot_by_group_2d(y, subgroups=subgroups, ellipses=False, auto_scale=False)
ax.legend(loc='upper left', frameon=True, facecolor='w', edgecolor='b')
plt.tight_layout()
ax.figure.savefig(os.path.join(outdir, "pca_ribozero_polya_byline.png"), dpi=200)
ax.figure.savefig(os.path.join(outdir, "pca_ribozero_polya_byline.pdf"))
# hierarchical clustering
subgroups = pd.DataFrame(
['b'] * 3 + ['r'] * 2 + ['g'] * 5,
index=data_all_n.columns,
columns=['Prep type']
)
legend_lbl = {'FFPE': 'b', 'frozen': 'r', 'Poly(A)': 'g'}
res = clustering.dendrogram_with_colours(
comp_data,
subgroups,
legend_labels=legend_lbl,
metric='correlation',
method='average'
)
res['fig'].savefig(os.path.join(outdir, 'clustering_dendrogram.png'), dpi=200)
res['fig'].savefig(os.path.join(outdir, 'clustering_dendrogram.pdf'))
# can add n_gene kwarg here to pick top N genes by MAD:
cg = clustering.plot_correlation_clustermap(comp_data)
cg.savefig(os.path.join(outdir, 'clustering_corr_map.png'), dpi=200)
cg.savefig(os.path.join(outdir, 'clustering_corr_map.pdf'))
# Spearman rank correlation distance
# pdist = spearman_rank_corr(dat)
# dist = hc.distance.squareform(1 - pdist.values)
# lnk = hc.linkage(dist)
# dend = clustering.dendrogram_with_colours(
# dat,
# cc,
# linkage=lnk,
# vertical=True,
# legend_labels=leg_dict,
# fig_kws={'figsize': [14, 6]}
# )
# Pearson correlation distance
dend = clustering.dendrogram_with_colours(dat, cc, vertical=True, legend_labels=leg_dict, fig_kws={'figsize': [14, 6]})
# Pearson with a limited number of probes
# dend = clustering.dendrogram_with_colours(dat.loc[mad.index[:5000]], cc, vertical=True, legend_labels=leg_dict, fig_kws={'figsize': [14, 6]})
dend['fig'].savefig(os.path.join(outdir, "cluster_ipsc_esc_fb_all_probes.png"), dpi=200)
# similar, but clustermap (dendrogram + heatmap)
gc = clustering.plot_clustermap(
dat.loc[mad.index[:5000]],
cmap='RdBu_r',
col_linkage=dend['linkage'],
col_colors=cc
)
clustering.add_legend(leg_dict, gc.ax_heatmap, loc='right')
gc.gs.update(bottom=0.2, right=0.82)
obj_salmon = loader.load_by_patient(['ICb1299', '3021'], source='salmon', type='cell_culture', include_control=False)
# cluster plot
tpm = filter.filter_by_cpm(obj_salmon.data, min_cpm=1, min_n_samples=4)
batch_colours = common.COLOUR_BREWERS[len(obj_salmon.meta.batch.unique())]
line_colours = common.COLOUR_BREWERS[2]
cc = pd.DataFrame(line_colours[0], index=tpm.columns, columns=['Batch', 'Cell line'])
aa, bb = obj_salmon.meta.batch.factorize()
for i in range(aa.max()):
cc.loc[aa == i, 'Batch'] = batch_colours[i]
cc.loc[cc.index.str.contains('3021'), 'Cell line'] = line_colours[1]
cg = clustering.dendrogram_with_colours(
np.log2(tpm + eps),
cc,
)
cg['fig'].savefig(os.path.join(outdir, "dendrogram_pearson_log_tpm_all_genes.png"), dpi=200)
# pca plot
p = PCA()
y = p.fit_transform(np.log2(tpm + eps).transpose())
fig = plt.figure()
ax = fig.add_subplot(111)
for a, b in enumerate(bb):
ax.scatter(
y[aa == a, 0],
y[aa == a, 1],
facecolor=batch_colours[a],
edgecolor='k',
s=30,
pdbg_log = np.log2(pdbg + eps)
mad_log = transformations.median_absolute_deviation(pdbg_log).sort_values(
ascending=False)
row_colours = pd.DataFrame('gray', index=pdbg_log.columns, columns=[''])
row_colours.loc[row_colours.index.str.contains('IPSC')] = '#fdc086'
row_colours.loc[row_colours.index.str.contains(
r'DURA[0-9]*_NSC')] = '#7fc97f'
row_colours.loc[row_colours.index.str.contains('GIBCO')] = '#96daff'
for n_t in n_gene_try:
fname = "clustering_by_gene_corr_top%d_by_mad.{ext}" % n_t
fname_log = "clustering_by_gene_corr_log_top%d_by_mad.{ext}" % n_t
d = clustering.dendrogram_with_colours(
pdbg_log.loc[mad_log.index[:n_t]],
row_colours,
fig_kws={'figsize': (5.5, 10)},
vertical=False)
d['fig'].savefig(os.path.join(outdir, fname_log.format(ext='png')),
dpi=200)
plt.draw()
plt.close('all')
# bring in reference data
# IDs (if req), lab (appears in label), loader
ref_dats = [
(
None,
'Barres et al.',
