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Update README.md #1

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75 changes: 19 additions & 56 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -83,14 +83,18 @@ unary_scale_factor=100
label_cost=10
algorithm='expansion'
ff = 'README_file/Rep'+str(j)+'_MOB_count_matrix-1.tsv'
locs,data=read_spatial_expression(ff,sep='\t',num_exp_genes=0.01, num_exp_spots=0.05, min_expression=1)
locs,data,_=read_spatial_expression(ff,sep='\t',num_exp_genes=0.01, num_exp_spots=0.05, min_expression=1)

data_norm = normalize_count_cellranger(data)
print('Rep{}_processing: {}'.format(j,data_norm.shape))
```

raw data dim: (262, 16218)
Rep11_processing: (259, 12522)
raw data dim: (262, 16218)
Number of expressed genes a spot must have to be kept (0.01% of total expressed genes) 3375
Marked 3 spots
Removing genes that are expressed in less than 3 spots with a count of at least 1
Dropped 1559 genes
Rep11_processing: (262, 14659)


## **Step 2:**
Expand Down Expand Up @@ -129,46 +133,6 @@ plt.title('CellGraph')

![png](README_file/output_9_1.png)

<br><font size=3> Both Mouse olfactor bulb and Breast cancer data sets used in the analysis have missing spots, creating holes in the grid. We recomment the users to fill the missing spot, and impute the missing values using <mark><font color=red> **AddPoints_XY_and_update_data**</font></mark> function, for more accurate analysis results. <font>


```python
locs_new,data_norm_new,newPoints=AddPoints_XY_and_update_data(locs,data_norm,cellGraph,axis=1)
print('Filled {} points'.format(len(newPoints)))
print('After filled points: ', data_norm_new.shape)
```

Filled 6 points
After filled points: (265, 12522)



```python
fig, ax= plt.subplots(1,1,figsize=(5,5)) #, dpi=300)
ax.set_aspect('equal')

exp_new = data_norm_new.iloc[:,0].values
cellGraph_new = create_graph_with_weight(locs_new, exp_new)
ax.scatter(locs_new[:,0], locs_new[:,1], s=1, color='black')
for i in np.arange(cellGraph_new.shape[0]):
x = (locs_new[int(cellGraph_new[i,0]), 0], locs_new[int(cellGraph_new[i,1]), 0])
y = (locs_new[int(cellGraph_new[i,0]), 1], locs_new[int(cellGraph_new[i,1]), 1])
ax.plot(x, y, color='black', linewidth=0.5)

plt.title('New CellGraph')
```




Text(0.5, 1.0, 'New CellGraph')




![png](README_file/output_12_1.png)


## **Step3:**
### Gene expression classification via Gaussian mixture modeling

Expand Down Expand Up @@ -355,7 +319,7 @@ smooth_factor=20

ff = '../../data/Raw_data/MOB-breast_cancer/Rep11_MOB_count_matrix-1.tsv'
# read in spatial gene expression data
locs, data = read_spatial_expression(ff,sep='\t')
locs, data, _ = read_spatial_expression(ff,sep='\t')

# normalize gene expression
data_norm = normalize_count_cellranger(data)
Expand All @@ -366,27 +330,26 @@ exp = data_norm.iloc[:,0]
# create graph representation of spatial coordinates of cells
cellGraph = create_graph_with_weight(locs, exp)

## Fill spots
locs_new,data_norm_new,newPoints=AddPoints_XY_and_update_data(locs,data_norm,cellGraph,axis=1)

# recreate new cellGraph after filled sopts.
exp_new = data_norm_new.iloc[:,0]
cellGraph_new = create_graph_with_weight(locs_new, exp_new)


# GMM
count = data_norm_new.loc[:,geneID].values
count = data_norm.loc[:,geneID].values
gmm=perform_gmm(count)

# do graph cut
temp_factor=smooth_factor
newLabels = cut_graph_general(cellGraph_new, count, gmm, unary_scale_factor,
temp_factor, label_cost, algorithm)
newLabels, label_pred = cut_graph_general(cellGraph, count, gmm, unary_scale_factor,
temp_factor, label_cost, algorithm)

# calculate p values
p, node, com = compute_p_CSR(locs_new, newLabels, gmm, count, cellGraph_new)
p, node, com = compute_p_CSR(locs, newLabels, gmm, count, cellGraph)

# Visualize graph cut results
plot_voronoi_boundary(geneID, locs_new, count, newLabels, min(p))
plot_voronoi_boundary(geneID, locs, count, newLabels, min(p))

# save the graph cut results to pdf
# pdf_voronoi_boundary(geneID, locs, count, newLabels, min(p),
# fileName=None, # '../../results//{}.pdf'.format(geneID),
# point_size=0)

```

Expand Down