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34 changes: 19 additions & 15 deletions docs/PBMC.md
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Expand Up @@ -92,7 +92,7 @@ adata_ATAC = adata_ATAC[barcode_idx.loc[selected_barcode][0]]
```
#### Generate the pseudo-bulk/metacell:
```python
from pseudo_bulk import *
from LingerGRN.pseudo_bulk import *
adata_RNA,adata_ATAC=find_neighbors(adata_RNA,adata_ATAC)
samplelist=list(set(adata_ATAC.obs['sample'].values)) # sample is generated from cell barcode
tempsample=samplelist[0]
Expand All @@ -110,37 +110,41 @@ adata_ATAC.raw.var['gene_ids'].to_csv('data/Peaks.txt',header=None,index=None)
TG_pseudobulk.to_csv('data/TG_pseudobulk.tsv')
RE_pseudobulk.to_csv('data/RE_pseudobulk.tsv')
```

### Training model
We first map the PBMCs to the provided general GRN
```python
from LingerGRN.preprocess import *
Datadir='/path/to/LINGER/'# This directory should be the same as Datadir defined in the above 'Download the general gene regulatory network' section
GRNdir=Datadir+'data_bulk/'
GRNdir=Datadir+'data_bulk/' # for example GRNdir='/data2/duren_lab/Kaya/TF_TG_software/code/data_bulk/'
genome='hg38'
outdir='/path/to/output/' #output dir

outdir='/path/to/output/' #output dir, for example outdir='/data2/duren_lab/Kaya/TF_TG_software/code/output/'
method='LINGER'
preprocess(TG_pseudobulk,RE_pseudobulk,GRNdir,genome,method,outdir)
```

### Training model
Then we can train the sc-data
```python
import LingerGRN.LINGER_tr as LINGER_tr
activef='ReLU' # active function chose from 'ReLU','sigmoid','tanh'
LINGER_tr.training(GRNdir,Input_dir,method,outdir,activef)
LINGER_tr.training(GRNdir,method,outdir,activef)
```

### Cell population gene regulatory network
#### TF binding potential
The output is 'cell_population_TF_RE_binding.txt', a matrix of the TF-RE binding score.
```python
import LingerGRN.LL_net as LL_net
LL_net.TF_RE_binding(Input_dir,GRNdir,RNA_file,ATAC_file,genome,method,outdir)
LL_net.TF_RE_binding(GRNdir,adata_RNA,adata_ATAC,genome,method,outdir)
```

#### *cis*-regulatory network
The output is 'cell_population_cis_regulatory.txt' with 3 columns: region, target gene, cis-regulatory score.
```python
LL_net.cis_reg(Input_dir,GRNdir,RNA_file,ATAC_file,genome,method,outdir)
LL_net.cis_reg(GRNdir,adata_RNA,adata_ATAC,genome,method,outdir)
```
#### *trans*-regulatory network
The output is 'cell_population_trans_regulatory.txt', a matrix of the trans-regulatory score.
```python
LL_net.trans_reg(Input_dir,GRNdir,RNA_file,ATAC_file,method,outdir)
LL_net.trans_reg(GRNdir,method,outdir)
```

### Cell type sepecific gene regulaory network
Expand All @@ -153,24 +157,24 @@ celltype='0'#use a string to assign your cell type
```python
celltype='all'
```
Please make sure that 'all' is not a cell type in your data.
Please ensure that 'all' is not a cell type in your data.

#### TF binding potential
The output is 'cell_population_TF_RE_binding_*celltype*.txt', a matrix of the TF-RE binding potential.
```python
LL_net.cell_type_specific_TF_RE_binding(Input_dir,GRNdir,RNA_file,ATAC_file,label_file,genome,celltype,outdir)
LL_net.cell_type_specific_TF_RE_binding(GRNdir,adata_RNA,adata_ATAC,genome,celltype,outdir)
```

#### *cis*-regulatory network
The output is 'cell_type_specific_cis_regulatory_{*celltype*}.txt' with 3 columns: region, target gene, cis-regulatory score.
```python
LL_net.cell_type_specific_cis_reg(Input_dir,GRNdir,RNA_file,ATAC_file,label_file,genome,celltype,outdir)
LL_net.cell_type_specific_cis_reg(GRNdir,adata_RNA,adata_ATAC,genome,celltype,outdir)
```

#### *trans*-regulatory network
The output is 'cell_type_specific_trans_regulatory_{*celltype*}.txt', a matrix of the trans-regulatory score.
```python
LL_net.cell_type_specific_trans_reg(Input_dir,GRNdir,RNA_file,label_file,ATAC_file,celltype,outdir)
LL_net.cell_type_specific_trans_reg(GRNdir,adata_RNA,ATAC_file,celltype,outdir)
```

## Note
Expand Down