CytoSEE:
automatic computation and evaluation of cytometry data
View tutorial
Single-file analysis
Note:
This module is an entrance for single file analysis and display.
Only .fcs is permitted, Demo: 001.fcs
Rdata display
Note:
This module is an entrance for dataset which is already analyzed by CytoSEE.
Only .Rdata is permitted, Demo: 001.RData
Contact: yczhou@zju.edu.cn; mchen@zju.edu.cn
VERSION: 1.2.1
Preview
Markers for clustering
Markers for data transformation
Cell clustering
Setting
FlowSOM
Description:
Method to run general FlowSOM workflow. Will scale the data and uses consensus meta-clustering by default.
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DensityCut
Description:
densityCut first roughly estimates the densities of data points from a K-nearest neighbour graph and then refines the densities via a random walk. A cluster consists of points falling into the basin of attraction of an estimated mode of the underlining density function. A post-processing step merges clusters and generates a hierarchical cluster tree. The number of clusters is selected from the most stable clustering in the hierarchical cluster tree. Experimental results on ten synthetic benchmark datasets and two microarray gene expression datasets demonstrate that densityCut performs better than state-of-the-art algorithms for clustering biological datasets.
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Consboost
Description:
adaboost; consensus clustering; down-sampling
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Phenograph
Description:
R implementation of the PhenoGraph algorithm A simple R implementation of the [PhenoGraph](http://www.cell.com/cell/abstract/S0092-8674(15)00637-6) algorithm, which is a clustering method designed for high-dimensional single-cell data analysis. It works by creating a graph ('network') representing phenotypic similarities between cells by calclating the Jaccard coefficient between nearest-neighbor sets, and then identifying communities using the well known [Louvain method](https://sites.google.com/site/findcommunities/) in this graph.
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FlowMeans
Description:
Finds a good fit to the data using k-means clustering algorithm. Then merges the adjacent dense spherical clusters to find non-spherical clusters.
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SamSPECTRAL
Description:
Given an FCS file as input, SamSPECTRAL first builds the communities to sample the data points. Then, it builds a graph and after weighting the edges of the graph by conductance computation, it is passed to a classic spectral clustering algorithm to find the spectral clusters. The last stage of SamSPECTRAL is to combine the spectral clusters. The resulting 'connected components' estimate biological cell populations in the data sample.
speed:
Arguments:
Maximum number of clusters to try out
nClus:
Exact number of clusters to use. If not NULL, max will be ignored.
method:
Clustering method to use, given as a string.
A integer to specify the number of neighbours in building the Knn graph.
maxit:
The maximum number of iteration allowed in density refinement, default to 50.
eps:
The threshold in density refinement, default to 1e-5.
alpha:
The damping factor between 0 and 1, default to 0.90.
Number of final clusters.
sample_n:
sampling the data for consensus clustering.
n_Core:
number of CPU threads be used for clustering
Number of clusters. If set to '0' (default) the value will be estimated automatically.
max K:
Maximum number of clusters. If set to '0' (default) the value will be estimated automatically.
Mahalanobis:
Boolean value. If TRUE (default) mahalanobis distance will be used. Otherwised, euclidean distance will be used.
Standardize:
Boolean value. If TRUE (default) the data will be transformed to the [0,1] interval.
addNoise:
Boolean value. Determines if uniform noise must be added to the data to prevent singularity issues or not.
iter.max:
The maximum number of iterations allowed.
Update:
String value. If set to 'Mahalanobis' the distance function will be updated at each merging iteration with recalculating mahalanobis distances.
OrthagonalResiduals:
Boolean value, indicates if the residuals must be transformed to orthagonal distance or not.
nstart:
The number of random sets used for initialization.
integer; number of nearest neighbours (default:30).
Determines the precision of computations. Setting it to 6 will work and increasing it does not improve results.
stabilizer:
The larger this integer is, the final results will be more stable because the underlying kmeans will restart many more times.
normal.sigma:
A scaling parameter that determines the 'resolution' in the spectral clustering stage. By increasing it, more spectral clusters are identified. This can be useful when 'small' population are aimed. See the user manual for a suggestion on how to set this parameter using the eigenvalue curve.
k.for_kmeans:
The number of clusters for running kmeans algorithm in spectral clustering. The default value of 'NA' leads to automatic estimation based on eigen values curve.
separation.factor:
This threshold controls to what extend clusters should be combined or kept separate.Normally, an appropriate value will fall in range 0.3-2.
number.of.clusters:
The default value is '0' which leads to computing the number of spectral clusters automatically, otherwise it can be a vector of integers each of which determines the number of spectral clusters. The output will contain a clustering resulting from each value..
Cluster Label Generation
Note:
It is an Cell type identification method for labeling the clusters divided by our clustering methods.
This approach labeling the clusters based on the relative abundence of cell surface markers e.g.CD38, CCR7.
Users can change the marker name in the following textbox:
Clusters Table
Clusters information:
Revise the label for each cell population- Scatter Plot
- MST
- HeatMap
- Population Marker
- Final report
- Visual Display