RNA sequencing

calcnormfactors operator

Description

calcnormfactors operator calculates a normalization factor per column (i.e sample).

Usage
Input projection .
row represents the genes
col represents the samples
y-axis is the input data for which to compute the normalization factor
Output relations .
normfactor numeric, per column (i.e. per sample)
Details

The operator is the calcnormfactor in the edgeR bioconductor.

References

see the edgeR R package for the documentation.

See Also
Examples

DESeq2_two_conditions operator

Description

DESeq2_two_conditions tests for differential gene expression in samples from two conditions using the DESeq2 package from BioConductor (Love, et al, Genome Biology, 2014).

Usage
Input projection Description
row Gene name/identifier
column Sample name/identifier
color Represents the groups to compare
y-axis Sequence counts
Input parameters Description
alpha Numeric, adjusted p value cutoff for independent filtering (default = 0.1)
LFC_shrinkage Logical, whether the returned log fold-change values should be shrinked (default = TRUE)
shrinkage_type “normal”, “apeglm” or “ashr”, Type of shrinkage estimator to use (default = “normal”)
Output relations Description
pvalue numeric, p-value calculated per gene
padj numeric, p-value calculated per gene after adjusting for multiple testing
baseMean numeric, mean of normalized counts for all the samples
log2FoldChange numeric, shrunken log2 fold-change between the two groups to compare
minus_log10_padj numeric, negative log10 transformation of padj for more intuitive plotting
Details

The operator uses the DESeq2 package from BioConductor.

References

Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15, 550. doi: 10.1186/s13059-014-0550-8.

See “Analyzing RNA-seq data with DESeq2” for further information on DESeq2 by Love, et al.

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

fgsea operator

Description

fgsea operator performs a fast gene set enrichment analysis.

Usage
Input projection .
y-axis numeric, input data usually a ranking statistics, per cell
Output relations .
NES numeric, fgsea of the input data
padj numeric, adjusted p-value
Details

The operator takes all the values of a cell and returns the value which is the fgsea. The computation is done per cell. There is one value returned for each of the input cell.

References
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.

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

vsn operator

Description

vsn operator performs a normalization factor per column (i.e. sample).

Usage
Input projection .
row represents the genes
col represents the samples
y-axis is the input data for which to normalization
Output relations .
norm numeric, the normalized values of the measurements (i.e. of each sample)
Details

The operator is the justvsn function from the VSN bioconductor package.

References

see the vsn R package for the documentation.

See Also
Examples