Package 'SurvSPro'

Simulated spatial proteomics dataset.

Description

A simulated data containing patient ID, spatial coordinates (u, v), and protein intensity values for a given protein.

Usage

cells_example_df

Format

An object of class data.frame with 50000 rows and 4 columns.

Details

The simulated spatial proteomics dataset includes 100 patients with their spatial coordinates and protein intensity

Source

Simulated using code in 'inst/scripts/cells_example_df.R'

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fit_spatial_cox

Description

Fits a Cox proportional hazards model for time-to-event outcomes using regresses clinical covariates and spatially adjusted protein summaries generated by gam_features().

Usage

fit_spatial_cox(
  surv_df,
  features_df,
  pid = "patient_id",
  time = "time",
  status = "status",
  clin_cols = c("z1", "z2", "z3"),
  sp_cols = c("mu_sp", "tau_sp")
)

Arguments

surv_df	survival data frame
features_df	output from gam_features() function
pid	variable name of patient ID
time	variable name of the survival time
status	variable name of the event indicator
clin_cols	vector of clinical covariate names
sp_cols	spatial feature names from gam_features() function

Value

A fitted coxph object. The model includes standardized clinical covariates and spatially adjusted protein summaries as predictors of the survival outcome.

Examples

# cells_example_df: contains pid, coordinates (u, v), and intensity of a given protein
data(cells_example_df)
data(surv_example_df)

features_df = gam_features(cells_df   = cells_example_df,
                           pid        = "patient_id",
                           coord_u    = "u",
                           coord_v    = "v",
                           intensity  = "intensity",
                           grid_side  = 60,
                           k          = 20)

fit = fit_spatial_cox(surv_df     = surv_example_df,
                      features_df = features_df,
                      pid         = "patient_id",
                      time        = "time",
                      status      = "status",
                      clin_cols   = c("z1", "z2", "z3"))
summary(fit) ## To obtain coefficients, hazard ratios, and p-values

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gam_features

Description

Captures spatial trends in cell-level protein expression and extracts spatially adjusted protein summaries, including spatially adjusted mean expression and residual variance reflecting cell-to-cell variability unexplained by spatial effects.

Usage

gam_features(
  cells_df,
  pid = "patient_id",
  coord_u = "u",
  coord_v = "v",
  intensity = "intensity",
  grid_side = 60,
  k = 20
)

Arguments

cells_df	data frame containing cell-level data
pid	variable name of the patient ID
coord_u	variable name of the u-axis coordinate
coord_v	variable name of the v-axis coordinate
intensity	variable name of the intensity for a given protein
grid_side	number of grid points along each of the u and v axes
k	basis dimension for the GAM smooth term

Value

A data frame with one row per patient and columns: patient_id, mu_sp, and tau_sp. Here, mu_sp is the spatially adjusted mean expression and tau_sp is the residual variance from the fitted spatial model.

Examples

#  cells_example_df: contains pid, coordinates (u, v), and intensity of a given protein
data(cells_example_df)

features_df = gam_features(cells_df   = cells_example_df,
                           pid        = "patient_id",
                           coord_u    = "u",
                           coord_v    = "v",
                           intensity  = "intensity",
                           grid_side  = 60,
                           k          = 20)

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Simulated patient-level survival data

Description

A simulated dataset containing patient ID, three clinical covariates, survival time, and an event indicator (i.e, censoring variable).

Usage

surv_example_df

Format

An object of class data.frame with 100 rows and 6 columns.

Details

This simulated dataset includes 100 patients and is used with spatial proteomics features generated from cells_example_df.

Source

Simulated using code in 'inst/scripts/surv_example_df.R'

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