def plot_by_genes(rdata, tenx_analysis, genes, prefix, rep, pcs):
tenx = TenxAnalysis(tenx_analysis)
tenx.load()
sce = SingleCellExperiment.fromRData(rdata)
tsne_dims = sce.getReducedDims(rep)
barcodes = sce.colData["Barcode"]
transcripts = sce.rowData["Symbol"]
adata = tenx.create_scanpy_adata(barcodes=barcodes,
transcripts=transcripts)
x_coded = dict(zip(barcodes, tsne_dims[0]))
y_coded = dict(zip(barcodes, tsne_dims[1]))
if not os.path.exists("figures/expression"):
os.makedirs("figures/expression")
x = []
y = []
for barcode in barcodes:
x.append(x_coded[barcode])
y.append(y_coded[barcode])
for gene in genes:
expression = []
for barcode in barcodes:
val = adata[barcode, gene].X
expression.append(float(val))
f, ax = plt.subplots(figsize=(10, 8))
sns.scatterplot(x=x, y=y, hue=expression, alpha=0.85)
ax.set_title("{} Counts".format(gene))
ax.legend()
plt.tight_layout()
plt.savefig("figures/expression/expression_{}.png".format(gene))
def umap_by_gene(rdata, gene, prefix, pcs):
tenx = TenxAnalysis(tenx_analysis)
tenx.load()
sce = SingleCellExperiment.fromRData(rdata)
tsne_dims = sce.reducedDims["UMAP"]
barcodes = sce.colData["Barcode"]
transcripts = sce.rowData["Symbol"]
adata = tenx.create_scanpy_adata(barcodes=barcodes, transcripts=symbols)
assert len(barcodes) == len(adata[:, gene])
expression = dict(zip(barcodes, adata[:, gene]))
tsne_dims = numpy.array(tsne_dims).reshape(2, len(barcodes))
x_coded = dict(zip(barcodes, tsne_dims[0]))
y_coded = dict(zip(barcodes, tsne_dims[1]))
x = []
y = []
clusters = []
for barcode in barcodes:
clusters.append(float(expression[barcode]))
x.append(x_coded[barcode])
y.append(y_coded[barcode])
f, ax = plt.subplots(figsize=(10, 8))
sns.scatterplot(x=x, y=y, hue=clusters, alpha=0.85)
ax.set_title("PCA - Clusters - {}".format(prefix))
ax.legend()
plt.tight_layout()
plt.savefig("figures/umap_by_{}.png".format(gene))
