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
Examples
GitHub link
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
See Also
Examples
GitHub link
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.
References
GitHub link
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.
References
GitHub link
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.
References
GitHub link
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
See Also
Examples
GitHub link
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
Examples
GitHub link
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
GitHub link
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
Examples
GitHub link
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.