# You can also analyze one gene of interest

geneID='Apod' # Lets use Nrgn as an example
unary_scale_factor = 100 # scale factor for unary energy, default value works well
label_cost=10
algorithm='expansion'
# set smooth factor to 20 for example; 
# use bigger smooth_factor to get less segments
# use small smooth_factor to get more segments
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')

# normalize gene expression
data_norm = normalize_count_cellranger(data)

# select anyone gene's expression
exp =  data_norm.iloc[:,0]

# create graph representation of spatial coordinates of cells
cellGraph = create_graph_with_weight(locs, exp)

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

# do graph cut
temp_factor=smooth_factor
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, newLabels, gmm, count, cellGraph)

# Visualize graph cut results
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)

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

Reproducibility

The container file system

/ [root]
├── code
│   ├── scGCO_code
|         ├── scGCO # the source code folder for running scGCO
|   ├── analysis
|   |        ├── FIG2_a_b_c_d.ipynb:this notebook will reproduce main Figure2a_2b_2c_2d
|   |        ├── FIG2_e_f.ipynb:this notebook will reproduce main Figure2e_2f
|   |        ├── Simulation
|   |               ├── notebooks : this folder contains simulation scripts and runing scripts
|   |               ├── processed_data : this folder contains sim_mob sample info, tissue mat and simulation counts
|   |               ├── compare : this notebook will reproduce Suppl Figure6
|   |                    └── gen_Supple_Fig1.ipynb : this notebook will reproduce Supple Figure1
|   |        ├── MouseOB
|   |               ├── Supple_Fig3_tsne_Genes_cluster_MOB.ipynb : this notebook will reproduce Supple Figure3
|   |               ├── Supple_Fig4-markGenes-Rep9.ipynb : this notebook will reprodece Supple Figure4
|   |               ├── Supple_Fig8-Tissue_structure.ipynb : this notebook will reproduce Supple Figure8
|   |                    └── ...
|   |        ├── Breast_Cancer
|   |               ├── Supple_Fig12-Breast cancer.ipynb : this notebook will reproduce Figure2e_2f and Suppl Figure12
|   |        ├── MERFISH
|   |               ├── Supple_Fig15-MERFISH.ipynb : this notebook will reprodece Supple Figure15
│   ├── Computation-Performance
|         ├── Fig2g_Compare_memory_simulation_data.ipynb # this notebook is for comparing occpuied memory of three three methods and shown in main Fig2g
|         ├── Fig2h_Compare_time_simulation_data.ipynb # this notebook is for comparing running speed of three three methods and shown in main Fig2h
|   |     ├── Simulate_script
|                   ├── create_performance_bigdata_R.ipynb # the code is for millions simulate data
|                   ├── scGCO_performance_run.ipynb # the code is for testing scGCO running speed and occupied CPU memory with simulate data

|                   ├── SPARK_performance_run.R # the code is for testing SPARK running speed and occupied CPU memory with simulate data
|                   ├── spatialDE_simulate_script.ipynb # the code is for testing spatialDE running speed and occupied CPU memory with simulate data
│   ├── README.md
|
├── data
|   ├── HighVariableGenes
|           ├── [Rep11_MOB seruat results  Tepe_MOB_hvg]
|   ├── MOB_DESeq2_results
|           ├── [Rep11 clustering 5 Layers and DESeq2 results]
|   ├── Performance_BigData
|           ├── [1M_cells_100genes_counts]
|   ├── HighVariableGenes
|           ├── [All datasets seruat results]
|   ├── Raw_data
|           ├── [All datasets counts data and HE image]
└── results
|    ├── Figure 
|    |        ├── Figure2.pdf # Figures that were output by execution of the notebook
|    |        ├── Suppl_Fig2.pdf
|    |        └── ...
|    ├── MouseOB # Notebooks that have been pre-compiled with nbconvert
|    ├── BreastCancer
|    └── ...
└── Temp_files
|    |        ├── Rep11_scGCO_tsne_proj_df # DataFrame that were used in execution of the notebook
|    |        ├── data_norm_new 
|    |               └── ...
|    |        └── tissue_mat
|    |               └── ...

Several Jupyter Notebooks are provided in the analysis directory to reproduce figures of the paper.

Computation Performance with small and large data sets

Several Jupyter Notebooks are provided in the Computation-Performance directory to reproduce the running time simulation results reported in the main text.

Generate memroy profiling plot (Fig. 2g)

• Compare_memory_simulation_data.ipynb: This notebook generates Fig. 2g using precomputed data.

Generate running time profiling plot (Fig. 2h)

• Compare_time_simulation_data.ipynb: This notebook generates Fig. 2h using precomputed data.