> dataset_simple_1:

A simple dataset with no sample_sample adjacency.

Distribution: NB
Batches: 2
batch_balance = 50%

N (genes): 10000
M (samples): 10000

> dataset_single_1:

Dataset with a sample/sample edges matrix to indicate when samples associate with each other.

Distribution: NB
Batches: 2
batch_balance = 50%

N (genes): 10000
M (samples): 10000

> dataset_full_1:

Multiple dataset with a sample/sample edges matrix to indicate when samples associate with each other.

# of Tissues: 3

Distribution: NB
Batches: 2
batch_balance = 50%

N (genes): 10000
M (samples): 10000

> dataset_omega_subtype_1

Uses computes PAM50 NB paramaters (from paper) to assign expression of various genes based on their given subtype.

N=50 (just the PAM50 genes)
M=1000

> dataset_omega_subtype_2 + 3

Here, samples are assigned a PAM50 subtype and a "stroma" subtype (A-D) where basal is A or B, her2 is always C, and LumA or B are D.
Thus, 50 genes are PAM50 (using our precomputed NB params) and 50 genes created from 4 distinct omega stickbreaks (to simulate 4 stromal subtypes).

N=100 (the PAM50 genes + "stromal" genes)
M=1000

> dataset_omega_batch_1
# assigns genes a generative omega based on a stick break algorithm

N=900
M=1000
B (batch effect strength): 100
p (fraction of genes receiving a batch effect): 1 (or 100%)
L (library size): 100,000
Batch Balance = 0.5 (samples assigned a batch effect are given by a Bernoulli)

Within this folder there are 2 other datasets:
L (library size) = 1000000 (marked LMillion)
B (batch effect strength): 1000 (marked ‘LMillion_B1000’)

> dataset_omega_batch_2
- Same as dataset_omega_batch_1 except p=0.5 (50% of genes are adjusted)

dataset_omega_batch_2_60, dataset_omega_batch_2_70, dataset_omega_batch_2_80, dataset_omega_batch_2_90, dataset_omega_batch_2_100
- Same as dataset_omega_batch_2 except p=0.6, p=0.7, p=0.8, p=0.9 and p=1.0 (respectively)

> dataset_omega_batch_3
- Same as dataset_omega_batch_2, except:

- here, two omega vectors (of length N) are created from two independent separate stick break algorithm runs
- then 50% of samples are assigned one of these two omega
- this is to simulate a difference between the two patient sets, like a disease state

dataset_omega_batch_3_60, dataset_omega_batch_3_70, dataset_omega_batch_3_80, dataset_omega_batch_3_90, dataset_omega_batch_3_100
- Same as dataset_omega_batch_3 except p=0.6, p=0.7, p=0.8, p=0.9 and p=1.0 (respectively)