EBI Single Cell Expression Atlas Scanpy Prod 1.3

Annotation: No TPM filtering

StepAnnotation
Step 1: Scanpy ParameterIterator
Perplexity
List of all parameter values to be iterated
1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50
Step 2: Input dataset
select at runtime
Step 3: Input dataset
select at runtime
Step 4: Input dataset
select at runtime
Step 5: Input dataset
select at runtime
Step 6: Scanpy ParameterIterator
Resolution
List of all parameter values to be iterated
0.1, 0.3, 0.5, 0.7, 1.0, 2.0, 3.0, 4.0, 5.0
Step 7: Scanpy Read10x
Output dataset 'output' from step 2
Output dataset 'output' from step 3
Output dataset 'output' from step 4
AnnData format hdf5
Gene ID
Step 8: GTF2GeneList
Output dataset 'output' from step 5
Step 9: Scanpy FilterCells
Output dataset 'output_h5' from step 7
AnnData format hdf5
AnnData format hdf5
Parameters used to filter cells
Parameters used to filter cells 1
n_genes
400.0
1000000000.0
Parameters used to filter cells 2
n_counts
0.0
1000000000.0
select at runtime
Step 10: Scanpy FilterGenes
Output dataset 'output_h5' from step 9
AnnData format hdf5
AnnData format hdf5
Parameters used to filter genes
Parameters used to filter genes 1
n_cells
3.0
1000000000.0
Output dataset 'gene_list' from step 8
True
Step 11: Scanpy NormaliseData
Output dataset 'output_h5' from step 10
AnnData format hdf5
AnnData format hdf5
1000000.0
True
True
Step 12: Scanpy FindVariableGenes
Output dataset 'output_h5' from step 11
AnnData format hdf5
AnnData format hdf5
Cell-ranger
Parameters used to find variable genes
Parameters used to find variable genes 1
Mean of expression
0.0125
1000000000.0
Parameters used to find variable genes 2
Dispersion of expression
3.0
1000000000.0
20
Not available.
Step 13: Scanpy RunPCA
Output dataset 'output_h5' from step 12
AnnData format hdf5
AnnData format hdf5
50
False
False
ARPACK
1234
Nothing selected.
True
n_genes
Empty.
2D
1,2
Empty.
False
False
False
True
True
Step 14: Scanpy ComputeGraph
Output dataset 'output_h5' from step 13
AnnData format hdf5
AnnData format hdf5
False
15
X_pca, use PCs
50
False
UMAP
euclidean
0
Step 15: Scanpy RunTSNE
Output dataset 'output_h5' from step 14
AnnData format hdf5
AnnData format hdf5
True
False
Automatically chosen based on problem size
30.0
Output dataset 'parameter_iteration' from step 1
12.0
400.0
False
Not available.
Not available.
1234
False
Step 16: Scanpy RunUMAP
Output dataset 'output_h5' from step 14
AnnData format hdf5
AnnData format hdf5
True
False
2
0.5
1.0
1.0
1.0
5
spectral
Not available.
Not available.
Not available.
0
True
Empty.
Empty.
2D
1,2
Empty.
False
False
False
True
False
Step 17: Scanpy FindCluster
Output dataset 'output_h5' from step 14
AnnData format hdf5
AnnData format hdf5
True
False
vtraag
1.0
Output dataset 'parameter_iteration' from step 6
Empty.
louvain
False
1234
Step 18: Scanpy FindMarkers
Output dataset 'output_h5' from step 17
AnnData format hdf5
AnnData format hdf5
100
True
False
louvain
True
rest
t-test with over-estimated variance
False
Empty.