Package {SurvSPro}

Type: Package
Title: Survival Prediction with Spatially Adjusted Protein Summaries
Version: 0.1.0
Maintainer: Seungjun Ahn <seungjun.ahn@mountsinai.org>
Description: A survival prediction framework using spatially adjusted protein summaries from spatial proteomics data, including imaging mass cytometry data. Cell-level protein intensities are modeled with spatial spline regression to estimate spatially adjusted mean expression and residual variance. Methodological details are described in Ahn et al. (2026) <doi:10.64898/2026.06.08.730964>.
License: GPL-3
Encoding: UTF-8
Depends: R (≥ 3.5.0)
Imports: dplyr, mgcv, survival, sp
Suggests: testthat (≥ 3.0.0)
RoxygenNote: 7.3.2
LazyData: true
NeedsCompilation: no
Packaged: 2026-06-11 23:54:25 UTC; seungjunahn
Author: Seungjun Ahn ORCID iD [cre, aut], Eun Jeong Oh ORCID iD [aut], Diddier Prada [ctb], Ali Shojaie [ctb]
Repository: CRAN
Date/Publication: 2026-06-19 11:30:07 UTC

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'

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

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)

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'