def process_batch1_batchtoind(data_dir,
result_dir,
batch="A",
ind="1085",
sample=True,
sample_seed=0,
celltype_gran=1):
'''Process batch1 A -> S5, as opposed to S1 -> S5
@batch: batch A as training datasets
@ind: ind S5 as predictor
@sample: whether to downsample or not
@sample_seed: random seed for sampling
'''
## get training dataset
train_adata = load_PBMC_data.load_PBMC_batch1_data(data_dir, batch=batch)
train_adata.obs.index.name = None
## get test dataset
test_adata = load_PBMC_data.load_PBMC_batch1_data(data_dir, ind=ind)
test_adata.obs.index.name = None
## downsample training dataset to S1 = 1,551 cells
if sample:
random.seed(sample_seed)
sampled_cells = random.sample(list(train_adata.obs_names), k=1551)
train_adata = train_adata[sampled_cells]
## curate given sub-cell types to major cell types
train_adata = load_PBMC_data.curate_PBMC_demulx_celltypes(
train_adata, celltype_gran)
test_adata = load_PBMC_data.curate_PBMC_demulx_celltypes(
test_adata, celltype_gran)
return train_adata, test_adata
def process_batch1_multiinds(data_dir,
result_dir,
sample_ind,
pred_ind,
celltype_gran=1,
sample=False,
sample_seed=0):
'''Process multi individuals to predict one individual
@sample_ind: downsample to a certain individual number
@pred_ind: the predicted individual
'''
batch1_inds = [1043, 1079, 1154, 1249, 1493, 1511, 1598, 1085]
exclude_list = batch1_inds
exclude_list.remove(int(pred_ind))
train_adata = load_PBMC_data.load_PBMC_batch1_data(data_dir,
ind=exclude_list)
if sample: ## sample to average number
avg_number = train_adata.shape[0] // len(exclude_list)
print("=== Downsample to number:", avg_number)
random.seed(sample_seed)
sampled_cells = random.sample(list(train_adata.obs_names),
k=avg_number)
train_adata = train_adata[sampled_cells]
test_adata = load_PBMC_data.load_PBMC_batch1_data(data_dir, ind=pred_ind)
train_adata.obs.index.name = None
test_adata.obs.index.name = None
return train_adata, test_adata
def process_batch1_batch2_ind(data_dir,
result_dir,
input1,
input2,
celltype_gran=1):
'''Use individuals from one batch to predict another batch
@input1/input2: can be batch1_indID/batc2_indID
@celltype_gran: 0 major; 1 sub
'''
## split input1
input1_list = input1.split('_')
input1_batch = input1_list[0]
if len(input1_list) > 1:
input1_inds = '_'.join(input1_list[1:])
else:
input1_inds = None
input2_list = input2.split('_')
input2_batch = input2_list[0]
if len(input2_list) > 1:
input2_inds = '_'.join(input2_list[1:])
else:
input2_inds = None
## extract train and test adata according to batch information
if input1_batch == "batch1":
train_adata = load_PBMC_data.load_PBMC_batch1_data(data_dir,
ind=input1_inds)
elif "batch2" in input1_batch:
cond = input1_batch.replace("batch2", "")
if cond == "":
train_adata = load_PBMC_data.load_PBMC_batch2_data(data_dir,
ind=input1_inds)
else:
train_adata = load_PBMC_data.load_PBMC_batch2_data(data_dir,
ind=input1_inds,
condition=cond)
if input2_batch == "batch1":
test_adata = load_PBMC_data.load_PBMC_batch1_data(data_dir,
ind=input2_inds)
elif "batch2" in input2_batch:
cond = input2_batch.replace("batch2", "")
if cond == "":
test_adata = load_PBMC_data.load_PBMC_batch2_data(data_dir,
ind=input2_inds)
else:
test_adata = load_PBMC_data.load_PBMC_batch2_data(data_dir,
ind=input2_inds,
condition=cond)
## curate given sub-cell types to major cell types
train_adata = load_PBMC_data.curate_PBMC_demulx_celltypes(
train_adata, celltype_gran)
test_adata = load_PBMC_data.curate_PBMC_demulx_celltypes(
test_adata, celltype_gran)
return train_adata, test_adata
def process_PBMC_comparison(data_dir,
result_dir,
train="Kang",
test="Zheng",
sample_seed=0):
'''Use curated PBMC Zheng dataset as target (B cells, CD14+ Monocytes, NK cells, CD4 T cells, CD8 T cells)
@train:
- Kang: select one individual from healthy as reference, 10X
- Ding: select pbmc fresh sample (pbmc2) sequenced by 10X as reference
'''
input = train.split('_')
dataset = input[0]
infos = None
if len(input) > 1:
infos = input[1:]
## build train dataset
if "Kang" == dataset:
if infos is None:
train_adata = load_PBMC_data.load_PBMC_batch1_data(data_dir,
ind="1154")
elif infos[0] == "batch1":
train_adata = load_PBMC_data.load_PBMC_batch1_data(data_dir)
if "Ding" == dataset:
if infos is None:
train_adata = load_PBMCprotocol_data.load_PBMC_protocols_data(
data_dir, exp="pbmc2", protocol="10x-v2", curate=True)
elif infos[0] == "droplet":
train_adata = load_PBMCprotocol_data.load_PBMC_protocols_data(
data_dir, exp="pbmc2", protocol_type="droplet", curate=True)
## build test dataset
adata = load_PBMCZheng_data.load_PBMCZheng_data(data_dir, curate=True)
random.seed(sample_seed)
train_cells = random.sample(adata.obs_names.tolist(),
round(0.8 * adata.shape[0]))
test_cells = list(set(adata.obs_names) - set(train_cells))
test_adata = adata[test_cells]
return train_adata, test_adata
def process_batch1_ind(data_dir,
result_dir,
ind1="1154",
ind2="1085",
celltype_gran=1):
''' Process individual data of PBMC batch1
@data_dir: where PBMC batch1 data stroes
@result_dir: where to store PCA/tSNE/UMAP result
@celltype_gran: granularity of cell types, 0: major cell types, 1:sub-celltypes
According to the dataset, the give cell types are sub-cell types
'''
## add batch info
train_adata = load_PBMC_data.load_PBMC_batch1_data(data_dir, ind=ind1)
test_adata = load_PBMC_data.load_PBMC_batch1_data(data_dir, ind=ind2)
## curate given sub-cell types to major cell types
train_adata = load_PBMC_data.curate_PBMC_demulx_celltypes(
train_adata, celltype_gran)
test_adata = load_PBMC_data.curate_PBMC_demulx_celltypes(
test_adata, celltype_gran)
return train_adata, test_adata
def process_batch1_ABC(data_dir,
result_dir,
batch1="A",
batch2="B",
celltype_gran=1):
''' Process two batches for GEDFN
@data_dir: where PBMC batch1/batch2 data stroes
@result_dir: where to store PCA/tSNE/UMAP result
@celltype_gran: default 1, given as sub-cell types
'''
## add batch info
train_adata = load_PBMC_data.load_PBMC_batch1_data(data_dir, batch=batch1)
test_adata = load_PBMC_data.load_PBMC_batch1_data(data_dir, batch=batch2)
train_adata.obs.index.name = None
test_adata.obs.index.name = None
## curate given sub-cell types to major cell types
train_adata = load_PBMC_data.curate_PBMC_demulx_celltypes(
train_adata, celltype_gran)
test_adata = load_PBMC_data.curate_PBMC_demulx_celltypes(
test_adata, celltype_gran)
return train_adata, test_adata