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EpiScanpy
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scEpiEnsemble
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Run analysis with scEpiEnsemble
H5ad file
*
:
Browse
Example
Specify input file type
*
:
H5ad file with suffix .h5ad or .h5ad.gz
10x Genomics formatted file with suffix .h5 or .h5.gz
Seurat formatted file with suffix .h5Seurat or .h5Seurat.gz
SingleCellExperiment object stored in RDS with suffix .rds or .rds.gz
Specify parameters for ensembling
Use EpiScanpy
*
:
False
True
Use Signac
*
:
False
True
Use SnapATAC
*
:
False
True
Ensemble method
*
:
raw
minmax
z-score
Specify parameters in preprocessing step
Cell type key
*
:
TF-IDF
*
:
False
True
Binarization
*
:
False
True
Normalization
*
:
False
True
Minimum peaks
*
:
Minimum cells
*
:
Specify parameters in dimension reduction step
Number of PCs
*
:
Number of neighbors
*
:
Method for connectivity
*
:
umap
gauss
Distance metric
*
:
euclidean
cityblock
cosine
l1
l2
manhattan
braycurtis
canberra
chebyshev
correlation
dice
hamming
jaccard
kulsinski
mahalanobis
minkowski
rogerstanimoto
russellrao
seuclidean
sokalmichener
sokalsneath
sqeuclidean
yule
Perplexity
*
:
Minimum distance
*
:
Reference genome
*
:
None
hg19
hg38
mm10
mm9
Specify parameters in clustering and visualization step
Clustering algorithm
*
:
louvain
leiden
Visualization method
*
:
umap
tsne
Number of clusters
*
:
Cluster resolution
*
:
Random seed
*
:
Email address:
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Retrieve previously submitted task
TaskID:
Example
Retrieve
Individual steps of the analysis on example dataset