Package 'SecAct'

Secreted protein activity inference

Description

Infer the signaling activity of 1248 secreted proteins from gene expression profiles.

Usage

SecAct.activity.inference(
  inputProfile,
  inputProfile_control = NULL,
  is.differential = FALSE,
  is.paired = FALSE,
  is.singleSampleLevel = FALSE,
  sigMatrix = "SecAct",
  is.group.sig = TRUE,
  is.group.cor = 0.9,
  lambda = 5e+05,
  nrand = 1000,
  sigFilter = FALSE
)

Arguments

inputProfile	Gene expression matrix with gene symbol (row) x sample (column).
inputProfile_control	Gene expression matrix with gene symbol (row) x sample (column).
is.differential	A logical indicating whether inputProfile has been differential profiles against to control.
is.paired	A logical indicating whether you want a paired operation of differential profiles between inputProfile and inputProfile_control if samples in inputProfile and inputProfile_control are paired.
is.singleSampleLevel	A logical indicating whether to calculate activity change for each single sample between inputProfile and inputProfile_control. If FALSE, calculate the overall activity change between two phenotypes.
sigMatrix	Secreted protein signature matrix.
is.group.sig	A logical indicating whether group similar signatures.
is.group.cor	Correlation cutoff of similar signatures.
lambda	Penalty factor in the ridge regression.
nrand	Number of randomization in the permutation test, with a default value 1000.
sigFilter	A logical indicating whether filter the secreted protein signatures with the genes from inputProfile.

Value

A list with four items, each is a matrix. beta: regression coefficients se: standard errors of coefficients zscore: beta/se pvalue: statistical significance

---

Cell state activity inference from single cell data

Description

Calculate secreted protein signaling activity of cell states from single cell RNA-Sequencing data.

Usage

SecAct.activity.inference.scRNAseq(
  inputProfile,
  cellType_meta,
  sigMatrix = "SecAct",
  is.singleCellLevel = FALSE,
  is.group.sig = TRUE,
  is.group.cor = 0.9,
  lambda = 5e+05,
  nrand = 1000,
  sigFilter = FALSE
)

Arguments

inputProfile	A Seurat object.
cellType_meta	Column name in meta data that includes cell-type annotations.
sigMatrix	Secreted protein signature matrix.
is.singleCellLevel	A logical indicating whether to calculate for each single cell.
is.group.sig	A logical indicating whether to group similar signatures.
is.group.cor	Correlation cutoff of similar signatures.
lambda	Penalty factor in the ridge regression.
nrand	Number of randomization in the permutation test, with a default value 1000.
sigFilter	A logical indicating whether filter the secreted protein signatures with the genes from inputProfile.

Value

A Seurat object.

---

Spot activity inference from spatial data

Description

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.group.sig = TRUE,
  is.group.cor = 0.9,
  lambda = 5e+05,
  nrand = 1000,
  sigFilter = FALSE
)

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.group.sig	A logical indicating whether to group similar signatures.
is.group.cor	Correlation cutoff of similar signatures.
lambda	Penalty factor in the ridge regression.
nrand	Number of randomization in the permutation test, with a default value 1000.
sigFilter	A logical indicating whether filter the secreted protein signatures with the genes from inputProfile.

Value

A SpaCET object.

---

Draw a bar plot

Description

Draw a bar plot of secreted proteins.

Usage

SecAct.bar.plot(fg.vec, title = NULL, colors = c("#91bfdb", "#fc8d59"))

Arguments

fg.vec	A vector of values.
title	The title for plot.
colors	Colors.

Value

A ggplot2 object.

---

Cell-cell communication circle plot

Description

Draw a circle plot of cell-cell communication mediated by secreted proteins.

Usage

SecAct.CCC.circle(data, colors_cellType, sender = NULL, receiver = NULL)

Arguments

data	A SpaCET object or a Seurat object.
colors_cellType	Colors for cell types.
sender	Sender cell types to highlight.
receiver	Receiver cell types to highlight.

Value

A circlize object.

---

Cell-cell communication dot plot

Description

Draw a dot plot of cell-cell communication mediated by secreted proteins.

Usage

SecAct.CCC.dot(data, sender = NULL, secretedProtein = NULL, receiver = NULL)

Arguments

data	A SpaCET object or a Seurat object.
sender	Sender cell types.
secretedProtein	Secreted proteins.
receiver	Receiver cell types.

Value

A ggplot2 object.

---

Cell-cell communication heatmap

Description

Draw a heatmap of cell-cell communication mediated by secreted proteins.

Usage

SecAct.CCC.heatmap(
  data,
  row.sorted = FALSE,
  column.sorted = FALSE,
  colors_cellType
)

Arguments

data	A SpaCET object or a Seurat object.
row.sorted	Whether to sort rows.
column.sorted	Whether to sort columns.
colors_cellType	Colors for cell types.

Value

A Heatmap-class object.

---

Cell-cell communication sankey plot

Description

Draw a sankey plot of cell-cell communication mediated by secreted proteins.

Usage

SecAct.CCC.sankey(
  data,
  colors_cellType,
  sender = NULL,
  secretedProtein = NULL,
  receiver = NULL
)

Arguments

data	A SpaCET object or a Seurat object.
colors_cellType	Colors for cell types.
sender	Sender cell types.
secretedProtein	Secreted proteins.
receiver	Receiver cell types.

Value

A ggplot2 object.

---

Cell-cell communication from single cell data

Description

Calculate condition-specific cell-cell communication mediated by secreted proteins from scRNA-Seq data.

Usage

SecAct.CCC.scRNAseq(
  Seurat_obj,
  cellType_meta,
  condition_meta,
  conditionCase,
  conditionControl,
  scale.factor = 1e+05,
  act_diff_cutoff = 2,
  exp_logFC_cutoff = 0.2,
  exp_mean_all_cutoff = 2,
  exp_fraction_case_cutoff = 0.1,
  padj_cutoff = 0.01,
  sigMatrix = "SecAct",
  is.group.sig = TRUE,
  is.group.cor = 0.9,
  lambda = 5e+05,
  nrand = 1000
)

Arguments

Seurat_obj	A Seurat object.
cellType_meta	Column name in meta data that includes cell-type annotations.
condition_meta	Column name in meta data that includes condition information.
conditionCase	Case condition.
conditionControl	Control condition.
scale.factor	Sets the scale factor for cell-level normalization in step2.
act_diff_cutoff	Cut off for activity change (i.e., z score) in step 1.
exp_logFC_cutoff	Cut off for log fold change in step 2.
exp_mean_all_cutoff	Cut off for mean expression across all cells.
exp_fraction_case_cutoff	Cut off for the fraction of cells expressing secreted protein-coding genes in step 2.
padj_cutoff	Adjusted p value cut off.
sigMatrix	Secreted protein signature matrix.
is.group.sig	A logical indicating whether to group similar signatures.
is.group.cor	Correlation cutoff of similar signatures.
lambda	Penalty factor in the ridge regression.
nrand	Number of randomization in the permutation test, with a default value 1000.

Value

A Seurat object.

---

Cell-cell communication from spatial data

Description

Calculate cell-cell communication mediated by secreted proteins from spatial transcriptomics data.

Usage

SecAct.CCC.scST(
  SpaCET_obj,
  cellType_meta,
  scale.factor = 1000,
  radius = 20,
  ratio_cutoff = 0.2,
  padj_cutoff = 0.01,
  coreNo = 6
)

Arguments

SpaCET_obj	A SpaCET object.
cellType_meta	Column name in meta data that includes cell-type annotations.
scale.factor	Sets the scale factor for spot-level normalization.
radius	Radius cut off (unit: um).
ratio_cutoff	Ratio cut off.
padj_cutoff	Adjusted p value cut off.
coreNo	Core number in parallel computation.

Value

A Seurat object.

---

Check SecAct data status

Description

Check if SecAct data is available and show its location.

Usage

SecAct.check.data()

Value

Invisible logical indicating whether data is available.

Examples

SecAct.check.data()

---

Survival regression

Description

Calculate the risk score of each secreted protein.

Usage

SecAct.coxph.regression(mat, surv)

Arguments

mat	Activity matrix.
surv	Survival matrix.

Value

A matrix.

---

Download SecAct signature database

Description

Downloads the SecAct signature matrix from GitHub Release.

Usage

SecAct.download.data(force = FALSE, timeout = 600)

Arguments

force	Logical, whether to force re-download even if file exists. Default FALSE.
timeout	Download timeout in seconds. Default 600.

Value

Invisible path to the downloaded file.

Examples

## Not run: 
SecAct.download.data()

## End(Not run)

---

Draw a heatmap plot

Description

Draw a heatmap plot of secreted proteins.

Usage

SecAct.heatmap.plot(
  fg.mat,
  title = NULL,
  colors = c("#03c383", "#aad962", "#fbbf45", "#ef6a32")
)

Arguments

fg.mat	A matrix of values.
title	The title for plot.
colors	Colors.

Value

A ggplot2 object.

---

Secreted protein activity inference

Description

Infer the activity of over 1000 secreted proteins from tumor gene expression profiles.

Usage

SecAct.inference.gsl(Y, SigMat = "SecAct", lambda = 5e+05, nrand = 1000)

Arguments

Y	Gene expression matrix with gene symbol (row) x sample (column).
SigMat	Secreted protein signature matrix.
lambda	Penalty factor in the ridge regression.
nrand	Number of randomization in the permutation test, with a default value 1000.

Value

A list with four items, each is a matrix. beta: regression coefficients se: standard errors of coefficients zscore: beta/se pvalue: statistical significance

---

Secreted protein activity inference

Description

Infer the activity of over 1000 secreted proteins from tumor gene expression profiles.

Usage

SecAct.inference.r(Y, SigMat = "SecAct", lambda = 5e+05, nrand = 1000)

Arguments

Y	Gene expression matrix with gene symbol (row) x sample (column).
SigMat	Secreted protein signature matrix.
lambda	Penalty factor in the ridge regression.
nrand	Number of randomizations in the permutation test, with a default value 1000.

Value

A list with four items, each is a matrix. beta: regression coefficients se: standard errors of coefficients zscore: beta/se pvalue: statistical significance

---

Draw a lollipop plot

Description

Draw a lollipop plot of secreted proteins.

Usage

SecAct.lollipop.plot(fg.vec, title = NULL)

Arguments

fg.vec	A vector of values.
title	The title for plot.

Value

A ggplot2 object.

---

Secreted protein signaling pattern

Description

Calculate the signaling pattern of secreted proteins based on their activities.

Usage

SecAct.signaling.pattern(SpaCET_obj, scale.factor = 1e+05, radius = 200, k)

Arguments

SpaCET_obj	A SpaCET object.
scale.factor	Sets the scale factor for spot-level normalization.
radius	Radius cut off.
k	Number of patterns for NMF.

Value

A SpaCET object with pattern results.

Examples

## Not run: 
SpaCET_obj <- SecAct.signaling.pattern(SpaCET_obj, k=3)

## End(Not run)

---

Pattern-associated secreted proteins

Description

Enumerate secreted proteins associated with each signaling pattern.

Usage

SecAct.signaling.pattern.gene(SpaCET_obj, n)

Arguments

SpaCET_obj	A SpaCET object.
n	Pattern order.

Value

A matrix.

Examples

## Not run: 
res <- SecAct.signaling.pattern.gene(SpaCET_obj, n=3)

## End(Not run)

---

Secreted protein signaling velocity for single-cell ST

Description

Calculate the signaling velocity of secreted proteins based on their activities.

Usage

SecAct.signaling.velocity.scST(
  SpaCET_obj,
  sender,
  secretedProtein,
  receiver,
  cellType_meta,
  scale.factor = 1e+05,
  CustomizedAreaCoordinates = NULL,
  radius = 20,
  colors_cellType = NULL
)

Arguments

SpaCET_obj	A SpaCET object.
sender	Sender cell types.
secretedProtein	Secreted proteins.
receiver	Receiver cell types.
cellType_meta	Column name in meta data that includes cell-type annotations.
scale.factor	Sets the scale factor for spot-level normalization.
CustomizedAreaCoordinates	Optional coordinates for customized area c(x.left, x.right, y.bottom, y.top).
radius	Radius cut off (unit: um).
colors_cellType	Named vector of colors for cell types.

Details

The velocity direction starts from the source cell producing a secreted protein and moves to sink cells receiving the secreted protein signal. The velocity magnitude represents the product between the secreted protein-coding gene expression at source cells and signaling activities at sink cells.

Value

A ggplot2 object.

Examples

## Not run: 
SecAct.signaling.velocity.scST(SpaCET_obj, sender="Fibroblast", 
  secretedProtein="THBS2", receiver="Tumor_boundary", cellType_meta="cellType")

## End(Not run)

---

Secreted protein signaling velocity

Description

Calculate the signaling velocity of secreted proteins based on their activities.

Usage

SecAct.signaling.velocity.spotST(
  SpaCET_obj,
  scale.factor = 1e+05,
  gene,
  signalMode = "receiving",
  radius = 200,
  contourMap = FALSE,
  coutourBins = 11,
  animated = FALSE
)

Arguments

SpaCET_obj	A SpaCET object.
scale.factor	Sets the scale factor for spot-level normalization.
gene	Gene symbol coding a secreted protein.
signalMode	Mode of signaling velocity, i.e., "receiving", "sending", and "both".
radius	Radius cut off.
contourMap	A logical indicating whether transform as contour map.
coutourBins	Number of bins for contour map.
animated	A logical indicating whether generate animated figure.

Details

The velocity direction starts from the source cell producing a secreted protein and moves to sink cells receiving the secreted protein signal. The velocity magnitude represents the product between the secreted protein-coding gene expression at source cells and signaling activities at sink cells.

Value

A ggplot2 object.

Examples

## Not run: 
SecAct.signaling.velocity.spotST(SpaCET_obj, gene="TGFB1", signalMode="receiving")
SecAct.signaling.velocity.spotST(SpaCET_obj, gene="TGFB1", signalMode="sending")

## End(Not run)

---

Draw a survival plot

Description

Draw a survival plot of secreted proteins.

Usage

SecAct.survival.plot(mat, surv, gene, x.title = "Time")

Arguments

mat	Activity matrix.
surv	Survival matrix.
gene	Gene symbol.
x.title	Title for x axis.

Value

A ggplot2 object.

---