Calculate secreted protein signaling activity of spots from spatial transcriptomocs data.
Usage
SecAct.activity.inference.ST(
inputProfile,
inputProfile_control = NULL,
scale.factor = 1e+05,
sigMatrix = "SecAct",
is.filter.sig = FALSE,
is.group.sig = TRUE,
is.group.cor = 0.9,
lambda = 5e+05,
nrand = 1000
)Arguments
- inputProfile
A SpaCET object.
- inputProfile_control
A SpaCET object.
- scale.factor
Sets the scale factor for spot-level normalization.
- sigMatrix
Secreted protein signature matrix.
- is.filter.sig
A logical flag indicating whether to filter the secreted protein signatures based on the genes from inputProfile (Default: FALSE). Because some sequencing platforms (e.g., CosMx) cover only a subset of secreted proteins, setting this option to TRUE restricts the activity inference on those proteins.
- is.group.sig
A logical flag indicating whether to group similar signatures (Default: TRUE). Many secreted proteins, such as cytokines with similar cell surface receptors and downstream pathways, have cellular effects that appear redundant within a cellular context. When enabled, this option clusters secreted proteins based on Pearson correlations among their composite signatures. The output still reports activity estimates for all secreted proteins prior to clustering. Secreted proteins assigned to the same non-redundant cluster share the same inferred activity.
- is.group.cor
A numeric value specifying the correlation cutoff used to define similar signatures (Default: 0.90). When r > 0.90, 1,170 secreted protein signatures are grouped into 657 non-redundant signature groups.
- lambda
Penalty factor in the ridge regression.
- nrand
Number of randomization in the permutation test, with a default value 1000.