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Made function running step appear #36

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1a3945d
Made function running step appear
spirosmaggioros Nov 27, 2024
ed4a366
Fixed readme installation instructions
spirosmaggioros Nov 27, 2024
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21 changes: 4 additions & 17 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -17,23 +17,10 @@ NiChart is a comprehensive framework designed to revolutionize neuroimaging rese
For recommended system configuration, please refer to: [nnUNet hardware requirements](https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/installation_instructions.md#hardware-requirements).

## Installation Instructions

1. Mamba installation
[Mamba Installation Guide (Official)](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html)

Example (Linux x86):
```bash
wget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh

bash Mambaforge-Linux-x86_64.sh
mamba create -c conda-forge -c bioconda -n NCP_env python=3.12 snakemake
mamba activate NCP_env
```
2. Manual installation
```bash
git clone https://github.com/CBICA/NiChart_Project.git
pip install -r requirements.txt
```
```bash
git clone https://github.com/CBICA/NiChart_Project.git
pip install -r requirements.txt
```

## Run NiChart Locally (GUI)
```bash
Expand Down
76 changes: 39 additions & 37 deletions src/workflows/w_sMRI/w_mlscores.py
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Expand Up @@ -56,17 +56,17 @@ def calc_subject_centiles(df_in: str, df_cent: str, df_dict: str):
# Rename centiles roi names
rdict = dict(zip(df_dict['Name'], df_dict['Code']))
df_cent['VarName'] = df_cent['VarName'].replace(rdict)

cent = df_cent.columns[2:].str.replace("centile_", "").astype(int).values

# Get age bin
df_in['Age'] = df_in.Age.round(0)
df_in.loc[df_in['Age']>df_cent.Age.max(), 'Age'] = df_cent.Age.max()
df_in.loc[df_in['Age']<df_cent.Age.min(), 'Age'] = df_cent.Age.min()

# Find ROIs
sel_vars = df_in.columns[df_in.columns.isin(df_cent.VarName.unique())].tolist()

# For each subject find the centile value of each roi
cent_subj_all = np.zeros([df_in.shape[0], len(sel_vars)])
for i, tmp_ind in enumerate(df_in.index):
Expand All @@ -92,7 +92,7 @@ def calc_subject_centiles(df_in: str, df_cent: str, df_dict: str):

# Estimate subj centile
cent_subj_all[i,j] = cent[ind1] + slope * (sval - vals_cent[ind1])

# Create and save output data
df_out = pd.DataFrame(columns=sel_vars, data=cent_subj_all)
df_out = pd.concat([df_in[['MRID']], df_out], axis=1)
Expand All @@ -112,7 +112,7 @@ def run_workflow(
max_age=95
suff_combat='_HARM'
spare_types=['AD', 'Age']
icv_ref_val=1430000
icv_ref_val=1430000
cent_csv=os.path.join(
bdir, 'resources', 'centiles', 'istag_centiles_CN_ICV_Corrected.csv'
)
Expand Down Expand Up @@ -182,7 +182,7 @@ def step_centiles():
# Read input
f_in=os.path.join(out_wdir, f"{dset_name}_combat.csv")
df_in=pd.read_csv(f_in, dtype={"MRID": str})

# Normalize ROIs
df_icvcorr=df_in.copy()
var_muse=df_icvcorr.columns[df_icvcorr.columns.str.contains('MUSE')]
Expand All @@ -208,7 +208,7 @@ def step_spare():

# Apply spare
df_spare=df_in[['MRID']]
for spare_type in spare_types:
for spare_type in spare_types:
spare_mdl=os.path.join(spare_dir, f'{spare_pref}{spare_type}{suff}')
f_spare_out=os.path.join(out_wdir, f'{dset_name}_combat_spare_{spare_type}.csv')
os.system(f"spare_score -a test -i {f_in} -m {spare_mdl} -o {f_spare_out}")
Expand All @@ -227,20 +227,20 @@ def step_sgan():
# Read input
f_in=os.path.join(out_wdir, f"{dset_name}_rois_init.csv")
df_in=pd.read_csv(f_in, dtype={"MRID": str})

# Select input
sel_covars=['MRID', 'Age', 'Sex', 'DLICV']
sel_vars=sel_covars + list_muse_single
df_sel=df_in[sel_vars]
f_sgan_in=os.path.join(out_wdir, f"{dset_name}_sgan_in.csv")
df_sel.to_csv(f_sgan_in, index=False)

# Run prediction
f_sgan_out=os.path.join(out_wdir, f"{dset_name}_sgan_init.csv")
cmd = f'PredCRD -i {f_sgan_in} -o {f_sgan_out}'
cmd = f'PredCRD -i {f_sgan_in} -o {f_sgan_out}'
print(f'About to run {cmd}')
os.system(cmd)

# Edit columns
df_sgan=pd.read_csv(f_sgan_out)
df_sgan.columns=['MRID'] + df_sgan.add_prefix('SurrealGAN_').columns[1:].tolist()
Expand All @@ -257,26 +257,26 @@ def step_sgan():
# Read roi lists
df_tmp=pd.read_csv(csv_muse_single)
list_muse_single=df_tmp.Code.tolist()

# Read data
df = pd.read_csv(in_csv, dtype={"MRID": str})
df.columns = df.columns.astype(str)

# Rename ROIs
df_roidict = pd.read_csv(csv_muse_all)
df_roidict.Index = df_roidict.Index.astype(str)
vdict = df_roidict.set_index('Index')['Code'].to_dict()
df=df.rename(columns=vdict)

# Keep DLICV in a separate df
df_icv=df[['MRID', 'MUSE_702']]
df_icv.columns=['MRID', 'DLICV']
df=df.drop(columns=['MUSE_702'])

# Add DLICV to demog
df_demog = pd.read_csv(in_demog, dtype={"MRID": str})
df_demog = df_demog.merge(df_icv, on='MRID')

# Add covars
df_raw=df.merge(df_demog, on=key_var)
f_raw=os.path.join(out_wdir, f"{dset_name}_rois_init.csv")
Expand All @@ -289,13 +289,15 @@ def step_sgan():
'SPARE index calculation',
'SurrealGAN index calculation'
]

for i, sel_func in stqdm(
enumerate(list_func),
desc=f"Running step ...",

idx = 0
for sel_func in stqdm(
list_func,
desc=f"Running {list_fnames[idx]}",
total=len(list_func),
):
sel_func()
idx += 1

# Combine results

Expand All @@ -313,15 +315,15 @@ def step_sgan():
f_spare=os.path.join(out_wdir, f"{dset_name}_combat_spare-all.csv")
df_spare=pd.read_csv(f_spare, dtype={"MRID": str})
df_out=df_out.merge(df_spare, on='MRID')

f_sgan=os.path.join(out_wdir, f"{dset_name}_sgan.csv")
df_sgan=pd.read_csv(f_sgan, dtype={"MRID": str})
df_out=df_out.merge(df_sgan, on='MRID')

# Write out file
f_results = os.path.join(out_dir, f"{dset_name}_DLMUSE+MLScores.csv")
df_out.to_csv(f_results, index=False)


def run_workflow_noharmonization(
dset_name,
Expand Down Expand Up @@ -359,7 +361,7 @@ def step_centiles():
# Read input
f_in=os.path.join(out_wdir, f"{dset_name}_rois_init.csv")
df_in=pd.read_csv(f_in, dtype={"MRID": str})

# Normalize ROIs
df_icvcorr=df_in.copy()
var_muse=df_icvcorr.columns[df_icvcorr.columns.str.contains('MUSE')]
Expand All @@ -385,7 +387,7 @@ def step_spare():

# Apply spare
df_spare=df_in[['MRID']]
for spare_type in spare_types:
for spare_type in spare_types:
spare_mdl=os.path.join(spare_dir, f'{spare_pref}{spare_type}{spare_suff}')
f_spare_out=os.path.join(out_wdir, f'{dset_name}_spare_{spare_type}.csv')
os.system(f"spare_score -a test -i {f_in} -m {spare_mdl} -o {f_spare_out}")
Expand All @@ -404,20 +406,20 @@ def step_sgan():
# Read input
f_in=os.path.join(out_wdir, f"{dset_name}_rois_init.csv")
df_in=pd.read_csv(f_in, dtype={"MRID": str})

# Select input
sel_covars=['MRID', 'Age', 'Sex', 'DLICV']
sel_vars=sel_covars + list_muse_single
df_sel=df_in[sel_vars]
f_sgan_in=os.path.join(out_wdir, f"{dset_name}_sgan_in.csv")
df_sel.to_csv(f_sgan_in, index=False)

# Run prediction
f_sgan_out=os.path.join(out_wdir, f"{dset_name}_sgan_init.csv")
cmd = f'PredCRD -i {f_sgan_in} -o {f_sgan_out}'
cmd = f'PredCRD -i {f_sgan_in} -o {f_sgan_out}'
print(f'About to run {cmd}')
os.system(cmd)

# Edit columns
df_sgan=pd.read_csv(f_sgan_out)
df_sgan.columns=['MRID'] + df_sgan.add_prefix('SurrealGAN_').columns[1:].tolist()
Expand All @@ -434,26 +436,26 @@ def step_sgan():
# Read roi lists
df_tmp=pd.read_csv(csv_muse_single)
list_muse_single=df_tmp.Code.tolist()

# Read data
df = pd.read_csv(in_csv, dtype={"MRID": str})
df.columns = df.columns.astype(str)

# Rename ROIs
df_roidict = pd.read_csv(csv_muse_all)
df_roidict.Index = df_roidict.Index.astype(str)
vdict = df_roidict.set_index('Index')['Code'].to_dict()
df=df.rename(columns=vdict)

# Keep DLICV in a separate df
df_icv=df[['MRID', 'MUSE_702']]
df_icv.columns=['MRID', 'DLICV']
df=df.drop(columns=['MUSE_702'])

# Add DLICV to demog
df_demog = pd.read_csv(in_demog, dtype={"MRID": str})
df_demog = df_demog.merge(df_icv, on='MRID')

# Add covars
df_raw=df.merge(df_demog, on=key_var)
f_raw=os.path.join(out_wdir, f"{dset_name}_rois_init.csv")
Expand All @@ -465,7 +467,7 @@ def step_sgan():
'SPARE index calculation',
'SurrealGAN index calculation'
]

for i, sel_func in stqdm(
enumerate(list_func),
desc=f"Running step ...",
Expand All @@ -489,11 +491,11 @@ def step_sgan():
f_spare=os.path.join(out_wdir, f"{dset_name}_spare-all.csv")
df_spare=pd.read_csv(f_spare, dtype={"MRID": str})
df_out=df_out.merge(df_spare, on='MRID')

f_sgan=os.path.join(out_wdir, f"{dset_name}_sgan.csv")
df_sgan=pd.read_csv(f_sgan, dtype={"MRID": str})
df_out=df_out.merge(df_sgan, on='MRID')

# Write out file
f_results = os.path.join(out_dir, f"{dset_name}_DLMUSE+MLScores.csv")
df_out.to_csv(f_results, index=False)
Expand Down