scTriangulate_v0.5.0

In this new version:

1. completely parallelizable, no matter how many cells you have and how many set of annotations, it is readily scalable (no need to tune the cores, the program figure out by itself)
2. accurately garbage collection and memory management, try best to avoid the subprocess consume too much memory
3. throwing intermediate file to re-run and debug
4. in case memory overhead in a parallel version, I add scTriangulate sequential version

scTriangulate_0.2.4

In this new version:

1. tested it is able to apply to human datasets
2. make sure your adata.raw is a sparse matrix (which usually is)
3. fix the bug that in the reassign score step, if n_sample/centroid is less than n_neighbors, set neighbors = n_centroid
4. fix the bug that when filter any cluster whose cell number = 1 at the first step, change 'gs' to key

scTriangulate_0.2.3

In this small update version:

1. precomputing the cluster size, reducing repetitive computing in tie-breaking and pruning
2. additional pruning mechanisms, proportion_to_self must be greater than 0.1 as well even if neraly=True
3. add listening log file while computing Shapley, flush to a log file

scTriangulate_0.2

In this new version:

1. add parallelization
2. improved visualization (automatic display maximum number of genes, add cluster location, make 0 expression grey)
3. prefer granular cluster when tie happen and reference is not the one that involves
4. support multi-omics (RNA and cite-seq)

scTriangulate_0.1

In this new version,

1. two viewers: individual viewer and inspection viewer
2. updated shaley computing, consider tie situation
3. reference-based pruning
4. remove gsea, only have enrichr, example run finish in 17 mins

scTriangulate_0.0.1

This is the first working version:

1. provide an input adata, with adata.raw available.
2. metric -> decision -> pruning
3. no tie, no inclusive and reference pruning