Flow Cytometry

diffcyt operator

Description

diffcyt operator a differential analysis of flow cyto data and indicates which marker and cluster combinations are relevant.

Usage
Input projection .
col group_id, patient_id
row marker_class, marker_name
y-axis values representing measurement
Input parameters .
analysis_type can be either DA (Differential Abundance) or DS (Differential State)
Output relations .
cluster_id character, cluster name, per cluster, DA and DS output
LogFC numeric, log fold change, per cluster, DA
LR numeric, lr, per cluster, DA output
p_val numeric, p value, per cluster, DA output
p_adj numeric, adjusted p value, per cluster, DA output
LogFC numeric, log fold change, per cluster-marker, DS output
p_val numeric, p value, per cluster-marker, DS output
p_adj numeric, adjusted p value, per cluster-marker, DS output
t numeric, t, per cluster-marker, DS output
B numeric, B, per cluster-marker, DS output
AvgExp numeric, adjusted p value, per cluster-marker, DS output
Details

Performs differential analysis (abundance or state). See the diffcyt::diffcyt function in the Bioconductor R pacakge.

References

see the github for documentation, https://github.com/lmweber/diffcyt

See Also

t-test, anova, rfImp

Examples

flowai operator

Description

flowai operator performs quality control on flowcytometry data.

Usage
Input projection .
row represents the variables (e.g. channels, markers)
col represents the observations (e.g. cells, samples, individuals)
y-axis measurement value
Output relations .
QCvector numeric, values above 10000 are considered FAIL, under 10000 PASS
Details

This operator is able to perform an automatic quality control on FCS data acquired using flow cytometry instruments. By evaluating three different properties:

  • flow rate
  • signal acquisition
  • dynamic range

The quality control enables the detection and removal of anomalies. The operator returns a QCvector value for each cell, values below 10000 are given to cells who have passed the QC, above 10000 for those who did not pass the QC criteria.

This operator wraps the flowAI::flow_auto_qc function from the flowAI R package

Reference

flowAI R package

See Also
Examples

FlowSOM tuning operator

Description

flowsomtuning operator performs flowSOM clustering for different numbers of clusters.

Usage
Input projection .
row represents the variables (e.g. channels, markers)
col represents the clusters (e.g. cells)
y-axis is the value of measurement signal of the channel/marker
Input parameters .
min_cluster_number Minimum number of clusters to make
max_cluster_number Maximal number of clusters to make
transform Transform data?
seed Random seed
Output relations .
cluster character, cluster label
Details

The operator is a wrapper for the FlowSOM function of the FlowSOM R/Bioconductor package.

FlowSOM operator

Description

FlowSOM operator for flow cytometry data.

Usage
Input projection .
row represents the variables (e.g. channels, markers)
col represents the clusters (e.g. cells)
y-axis is the value of measurement signal of the channel/marker
Input parameters .
nclust Number of clusters to make (default = NULL)
maxMeta Maximal number of cluster (ignored if nclust is not NULL)
seed Random seed
xdim Width of the grid
ydim Hight of the grid
rlen Number of times to loop over the training data for each MST
mst Number of times to build an MST
alpha_start Start learning rate
alpha_end End learning rate
dstf Distance function (1=manhattan, 2=euclidean, 3=chebyshev, 4=cosine)
Output relations .
cluster character, cluster label
Details

The operator is a wrapper for the FlowSOM function of the FlowSOM R/Bioconductor package.

MEM operator

Description

Marker Enrichment Modeling operator for flow cytometry data.

Usage
Input projection .
row represents the variables (e.g. channels, markers)
col represents the clusters (e.g. cells)
colors represents the groups (e.g. flowSOM clusters)
y-axis is the value of measurement signal of the channel/marker
Output relations .
mem numeric, mem scores per row and per color (e.g. per channel/marker and per flowSOM clusters)
cluster character, cluster value
Details

The operator is a wrapper for the MEM function of the MEM R package.

neighbourhood operator

Description

neighbourhood operator returns a neighbourhood enrichment of clusters of tissue cells.

Usage
Input projection .
y-axis numeric, input data, object numbers (from cell profiler for example)
x-axis numeric, input data, object numbers of neighbours (from cell profiler for example)
color character, input data, color by cluster label
Input Parameter .
permutation numeric, number of permutations
num of cores numeric, number of cores
seed numeric, seed setting, -1 indicates random
Output relations .
p numeric, p-value of enrichment for each cluster pair
Details

The operator takes as input the neighboorhood annotation of cells (i.e. object) aswell as the cluster label of the cells. The ouput is a p-value indicating how enriched (or depleted), in terms of neighbours, for each cluster to cluster relationship. It uses the neighbouRhood code on the bodenmiller github location (https://github.com/BodenmillerGroup/neighbouRhood).

The neighbour relationship of the cells typically comes from Cell Profiler tool but any tool which generates cell neighbourhood information can be used.

References
Examples

flowsom operator

Description

flowsom operator performs the SOM (self organizing maps) in the flowSOM R package.

Usage
Input projection .
row represents the variables (e.g. channels, markers)
col represents the observations (e.g. cells)
y-axis is the value of measurement signal of the channel/marker
Input parameters .
xdim Width of the grid
ydim Hight of the grid
rlen Number of times to loop over the training data for each MST
mst Number of times to build an MST
alpha_start Start learning rate
alpha_end End learning rate
dstf Distance function (1=manhattan, 2=euclidean, 3=chebyshev, 4=cosine)
Output relations .
mapping_node_num numeric, per column (e.g. per cell)
mapping_node_label character, per column (e.g. per cell)
Details

The operator is the SOM function of the flowSOM R package.

References

see the flowSOM::SOM function of the R package for the documentation,

See Also

clusterx

Examples

rphenograph operator

Description

rephenograph operator performs a phenotype clustering in the Rphenograph R package.

Usage
Input projection .
row represents the variables (e.g. channels, markers)
col represents the observations (e.g. cells)
y-axis is the value of measurement signal of the channel/marker
Input parameters .
xdim Width of the grid
ydim Hight of the grid
rlen Number of times to loop over the training data for each MST
mst Number of times to build an MST
alpha_start Start learning rate
alpha_end End learning rate
dstf Distance function (1=manhattan, 2=euclidean, 3=chebyshev, 4=cosine)
Output relations .
mapping_node_num numeric, per column (e.g. per cell)
mapping_node_label character, per column (e.g. per cell)
Details

The operator is the rphenograph function of the Rphenograh R package.

References

see the rphenograph::SOM function of the R package for the documentation,

See Also
Examples

somflow operator

Description

somflow operator performs the SOM (self organizing maps) in the FlowSOM R package.

Usage
Input projection .
row represents the variables (e.g. channels, markers)
col represents the observations (e.g. cells)
y-axis is the value of measurement signal of the channel/marker
Input parameters .
xdim Width of the grid
ydim Hight of the grid
rlen Number of times to loop over the training data for each MST
mst Number of times to build an MST
alpha_start Start learning rate
alpha_end End learning rate
dstf Distance function (1=manhattan, 2=euclidean, 3=chebyshev, 4=cosine)
Output relations .
mapping_node_label character, per column (e.g. per cell)
Details

The operator is the SOM function of the flowSOM R package.

References

see the FlowSOM::SOM function of the R package for the documentation,

See Also

clusterx

Examples

umap operator

Description

umap operator performs umap analysis.

Usage
Input projection .
row represents the variables (e.g. genes, channels, markers)
col represents the observations (e.g. cells, samples, individuals)
y-axis measurement value
Input parameters .
init character, type of initialization for the coordinates, see details
scale numeric, type of scaling to apply to data
spread numeric, the effective scale of embedded points. In combination with min_dist, this determines how clustered/clumped the embedded points are
min_dist numeric, the effective minimum distance between embedded point
pca numeric, If set to a positive integer value, reduce data to this number of columns using PCA
Output relations .
umap01, umap02 first two components containing the new projected values
Details

The operator performs umap analysis. It reduces the amount of variables (i.e. indicated by rows) to a lower number (default 2). This operators wraps the uwot::umap(). See (https://github.com/jlmelville/uwot) for more details, especially settings and examples.

Reference
See Also

pca, tsne

Examples