Cluster annotation¶

In a typical single cell RNAseq experiment, the process of annotating cell types manually can be laborious and as such, a number of automated methods have emerged.

In a typical in situ transcriptomics experiment, the annotation of cell types is usually much easier as these assays are usually profile established cell type markers. Cluster can be annotated easily based on marker gene expression.

The diagnostic plots can be used to compare existing signatures against those identified de novo

from scipy.stats import pearsonr, spearmanr

for idx in range(len(ds.centroids)):
    plt.figure(figsize=[50, 15])
    ds.plot_diagnostic_plot(idx, known_signatures=[
        ("scRNA-seq", scrna_uniq_labels, scrna_centroids, scrna_colors),
    ], correlation_methods=[
        ("r", pearsonr),
        ("rho", spearmanr)
    ])
    plt.tight_layout()
    plt.savefig('diagplots_multiplexed_smFISH/diagplot_centroid_%d.png'%idx)
    plt.close()

This will generate a diagnostic plot for each cluster, which can be used to assign cluster labels. E.g. the following cluster matches known gene expression patterns of Vip Arhgap36 Hmcn1 cell types from scRNAseq experiments with high correlation (panel 3):

While this is a good example of cluster that can be easily annotated, some clusters may prepresent noise and would need to be removed, and when over clustering occurs then clusters may have to be merged. The diagnostic plots documentation assist the decision making process.

Once each cluster is reviewed, a cell-type be assigned, or removed, or merged. In the following code snippet, we show an elegent way to annotate, remove, and merge clusters.

Determine that (i) clusters with a name will be annotated, (ii) clusters with a “N/A” will be removed, (iii) clusters with the same name will be merged

denovo_labels = [
    "N/A",
    "VLMC",
    "Vip Arhgap36 Hmcn1 / Vip Igfbp4 Map21l1",
    "L2/3 IT Rrad",
    "N/A",
    "L2/3 IT Adamts2",
    "Sst Nts / Sst Rxfp1 Eya1",
    "Lamp5 Lsp1",
    "N/A",
    "Sst Crhr2 Efemp1 / Sst Esm1",

    "Pvalb Calb1 Sst / Pvalb Reln Tac1",
    "Astro Aqp4",
    "L6 IT Penk Fst",
    "L4 IT Superficial",
    "L5 IT Col27a1",
    "L2/3 IT Adamts2",
    "OPC",
    "Oligo",
    "L4 IT Rspo1",
    "L5 NP Trhr Met",

    "L5 IT Hsd11b1 Endou",
    "Pvalb Th Sst / Pvalb Reln Tac1",
    "L6 CT Ctxn3 Brinp3 / L6 CT Gpr139",
    "L5 PT Chrna6",
    "L5 IT Batf3",
    "L5 PT C1ql2 Cdh13",
    "L5 PT Krt80",
    "L6 IT Penk Col27a1",
    "L6 IT Penk Col27a1",
    "L6b Crh",

    "Sst Chodl",
]

make objects for storing the index of clusters to be annotated, removed and merged

denovo_labels_final = []
exclude_indices = []
merge_indices = []

iterate over the denovo_labels object and populate the denovo_labels_final, exclude_indices, merge_indices objects

for idx, cl in enumerate(denovo_labels):
    if cl == 'N/A':
        exclude_indices.append(idx)
        continue
    if cl in denovo_labels_final:
        continue
    denovo_labels_final.append(cl)

for cl in np.unique(denovo_labels):
    if cl == 'N/A':
        continue
    mask = [cl == e for e in denovo_labels]
    if np.sum(mask) > 1:
        merge_indices.append(np.where(mask)[0])

plot the removed clusters in t-SNE embedding

cmap = plt.get_cmap('jet')
jet_colors = cmap(np.array(list(range(len(ds.centroids)))) / (len(ds.centroids) - 1))
tsne_colors = np.zeros_like(jet_colors)
tsne_colors[..., :] = [0.8, 0.8, 0.8, 1]
tsne_colors[exclude_indices] = [0, 0, 0, 1] #jet_colors[exclude_indices]
import matplotlib.patheffects as PathEffects
plt.figure(figsize=[5, 5])
ds.plot_tsne(pca_dims=33, metric="correlation", s=5, run_tsne=False, colors=tsne_colors)
plt.axis('off')

plot the merged clusters in t-SNE embedding

cmap = plt.get_cmap('rainbow')
jet_colors = cmap(np.array(list(range(len(merge_indices)))) / (len(merge_indices) - 1))
plt.figure(figsize=[5, 5])
tsne_colors = np.zeros([len(ds.centroids), 4])
tsne_colors[..., :] = [0.8, 0.8, 0.8, 1]
for idx, mi in enumerate(merge_indices):
    tsne_colors[mi] = jet_colors[idx]
    ds.plot_tsne(pca_dims=33, metric="correlation", s=5, run_tsne=False, colors=tsne_colors)
plt.axis('off')

update the analysis object with the clusters to remove and merge

analysis.exclude_and_merge_clusters(exclude_indices, merge_indices, centroid_correction_threshold=0.6)