1. Introduction

Spatial Trajectory Screening (STS) pursues the hypothesis that specific genes - or other numeric features for that matter - display non-random expression patterns in relation to spatial reference features, such as spatial trajectories. STS utilizes these reference features to incorporate the integration of potential biological forces in the identification of spatially variable genes, such as the direction of tumorous infiltration. This allows for a supervised, hypothesis-driven screening for spatial patterns, which, unlike differential expression analysis (DEA), acknowledges the continuous nature of gene expression and avoids the limitations of group-based testing. The algorithm is wrapped up in the function spatialTrajectoryScreening(). See the tutorial we are using the same example sample that we used in the tutorial on creating spatial trajectories - the glioblastoma sample UKF269T.

# load required packages
library(SPATA2)
library(tidyverse)

# load SPATA2 inbuilt data
object_t269 <- object_t269 <- loadExampleObject("UKF269T", process = TRUE, meta = TRUE)

# show image 
plotImage(object_t269)

# created with code 
plotSpatialTrajectories(
  object = object_t269, 
  ids = "horizontal_mid", 
  color_by = "histology"
)

2. Run the algorithm

The function to use is called spatialTrajectoryScreening(). The parameter variables takes the numeric variables that are supposed to be included in the screening process. Since all sorts of numeric variables can be included in the screening, the argument for the input is simply called variables. Here, we are using the genes that were already identified as spatially variable by SPARKX. The goal is to further analyze which of the genes are expressed in a non-random and biologically meaningful way along the trajectory.

# this is a wrapper around SPARK::sparkx()
object_t269 <- runSPARKX(object = object_t269)
## ## ===== SPARK-X INPUT INFORMATION ==== 
## ## number of total samples: 3213 
## ## number of total genes: 15000 
## ## Running with single core, may take some time 
## ## Testing With Projection Kernel
## ## Testing With Gaussian Kernel 1
## ## Testing With Gaussian Kernel 2
## ## Testing With Gaussian Kernel 3
## ## Testing With Gaussian Kernel 4
## ## Testing With Gaussian Kernel 5
## ## Testing With Cosine Kernel 1
## ## Testing With Cosine Kernel 2
## ## Testing With Cosine Kernel 3
## ## Testing With Cosine Kernel 4
## ## Testing With Cosine Kernel 5
# keep genes with a sparkx pvalue of 0.01 or lower
spark_df <- getSparkxGeneDf(object = object_t269, threshold_pval = 0.05)

# show results
spark_df
## # A tibble: 8,734 × 3
##    genes    combinedPval adjustedPval
##    <chr>           <dbl>        <dbl>
##  1 RPL22               0            0
##  2 ID3                 0            0
##  3 MARCKSL1            0            0
##  4 PHC2                0            0
##  5 RPS8                0            0
##  6 WLS                 0            0
##  7 GNG5                0            0
##  8 RPL5                0            0
##  9 CNN3                0            0
## 10 RHOC                0            0
## # ℹ 8,724 more rows
# name of the trajectory
id <- "horizontal_mid"

# `getSparkxGenes()` would work, too
input_genes <- spark_df[["genes"]]

# define start and end positions of the trajectory directly
object_t269 <- 
  addSpatialTrajectory(
    object = object_t269,
    id = id,
    start = c("1.5mm", "4mm"),
    end = c("6.5mm", "4mm"),
    overwrite = TRUE
  )

# note: the results are NOT stored in the SPATA2 object but in a separate object
sts_out <- 
  spatialTrajectoryScreening(
    object = object_t269, 
    id = id, # ID of the spatial trajectory
    variables = input_genes # the variables/genes to include in the screening 
  )

class(sts_out)

Note: The output of spatialTrajectoryScreening() is not saved in the SPATA2 object but returned in a separate S4 object of class SpatialTrajectoryScreening. Do not overwrite the SPATA2 object by writing object_t269 <- spatialTrajectoryScreening(object = object_t269, id = id, ...).

3. Results

The first step of the screening identifies pattern that are unlikely due to random gene expression. The second step fits the non-random gene expression pattern to predefined models which guides in interpretation and screening for specific gene expression pattern.

Slot @results$significance contains a data.frame with one row for each screened variable which provides information regarding the degree of randomness the inferred pattern contains as quantified by the total variation (tot_var). The p-value gives the probability to obtain such a total variation under complete randomness and indicates the degree of significance. Column fdr contains the adjusted p-value according to the False Discovery Rate.

sign_df <- 
  sts_out@results$significance %>% 
  filter(fdr < 0.05)

# show significance data.frame
sign_df
## # A tibble: 1,492 × 6
##    variables  rel_var tot_var p_value norm_var     fdr
##    <chr>        <dbl>   <dbl>   <dbl>    <dbl>   <dbl>
##  1 A1BG        -0.597    7.77  0.0007    0.155 0.00143
##  2 A2M         -0.348    6.19  0         0.124 0      
##  3 ABAT        -0.372    8.68  0.0063    0.174 0.00988
##  4 ABCD3       -0.282    9.26  0.0215    0.185 0.0292 
##  5 ABHD17C     -0.471    9.32  0.0233    0.186 0.0310 
##  6 ABHD2       -0.552    6.60  0         0.132 0      
##  7 ABL1        -0.671    9.59  0.0354    0.192 0.0444 
##  8 AC104986.2  -0.458    8.25  0.0021    0.165 0.00369
##  9 ACAD8       -0.631    8.29  0.0024    0.166 0.00418
## 10 ACBD6       -0.761    8.87  0.0105    0.177 0.0156 
## # ℹ 1,482 more rows

3.1 Non-random gene expression gradients

The figures below show examples for gene expression gradient identified as non-random.

# extract variables names
non_random <- getSgsResultsVec(sts_out) %>% head(4)

trajectory_add_on <- 
  ggpLayerSpatialTrajectories(object = object_t269, ids = "horizontal_mid")

plotSurfaceComparison(
  object = object_t269,
  color_by = non_random,
  pt_clrsp = "Reds 3",
  outline = T,
  nrow = 1
  ) + 
  trajectory_add_on

plotStsLineplot(object_t269, variables = non_random, id = "horizontal_mid", line_color = "red", nrow = 1) 

3.2 Random gene expression gradients

The figures below show examples for gene expression gradient identified as random.

# extract random variable names 
random <- 
  sts_out@results$significance %>% 
  filter(fdr > 0.05) %>% 
  slice_max(tot_var, n = 4) %>% 
  pull(variables) %>% 
  head(4)

plotSurfaceComparison(
  object = object_t269,
  color_by = random,
  pt_clrsp = "BuPu",
  outline = T, 
  nrow = 1
  ) + 
  trajectory_add_on

plotStsLineplot(object_t269, variables = random, id = "horizontal_mid", line_color = "blue", nrow = 1) 

Random vs. non-random

Looking at the gradient of variable LEPROT (random, blue) and SHISA5 (non-random, red) both feature a descending pattern along the trajectory. How come that one is identified as most likely random while the other is not? Spatial gradient screening decides which variables are most likely random or not random by computing the variance along the gradient, which is stored in the variable tot_var in sts_out@results$significance. Consider both gradient plots with the expression estimates plotted as black points, too. While SHISA5 features a comparatively smooth decline, LEPROT does not. The variance along its gradient is too high to be deemed significant.

# left plot
plotStsLineplot(object_t269, variables = "LEPROT", line_color = "blue", id = "horizontal_mid") + 
  ggplot2::geom_point()

# right plot
plotStsLineplot(object_t269, variables = "SHISA5", line_color = "red", id = "horizontal_mid") + 
  ggplot2::geom_point()

3.2 Model fits

The second step uses predefined models and fits them to the inferred gradients of the pattern identified as non-random in the previous step. The figure below shows the default models used by SPATA2. They can be extended by the user for specific querries with the argument model_add.

showModels(nrow = 4) + 
  labs(x = "Distance along Trajectory [%]")

Slot @results$model_fits contains the model fitting results. It is a data.frame where each row corresponds to a variable ~ model pair. The columns mae (mean absolute error) and rmse (root mean squared error) indicate the quality of the fit. The lower the value the better.

best_fits <- 
  sts_out@results$model_fits %>% 
  filter(variables %in% sign_df[["variables"]]) %>% 
  group_by(variables) %>% 
  slice_min(mae, n = 1)

best_fits
## # A tibble: 1,492 × 4
## # Groups:   variables [1,492]
##    variables  models              mae  rmse
##    <chr>      <chr>             <dbl> <dbl>
##  1 A1BG       descending_linear 0.172 0.216
##  2 A2M        descending_linear 0.172 0.218
##  3 ABAT       descending_linear 0.193 0.246
##  4 ABCD3      descending_linear 0.215 0.253
##  5 ABHD17C    descending_linear 0.201 0.260
##  6 ABHD2      descending_linear 0.181 0.217
##  7 ABL1       descending_linear 0.179 0.225
##  8 AC104986.2 descending_linear 0.196 0.235
##  9 ACAD8      descending_linear 0.171 0.212
## 10 ACBD6      descending_linear 0.240 0.281
## # ℹ 1,482 more rows

The following code chunk extracts the genes that followed each model best.

best_fits_by_model <- 
  group_by(best_fits, models) %>% 
  slice_min(mae, n = 1) %>% 
  filter(rmse < 0.2) # threshold suggestions for root mean squared error

best_fits_by_model
## # A tibble: 6 × 4
## # Groups:   models [6]
##   variables models               mae  rmse
##   <chr>     <chr>              <dbl> <dbl>
## 1 VGF       ascending_gradual  0.110 0.171
## 2 ARC       ascending_linear   0.132 0.184
## 3 RDH10     descending_gradual 0.133 0.163
## 4 SIPA1L2   descending_linear  0.112 0.141
## 5 SELENOP   peak_moderate      0.121 0.145
## 6 MOBP      peak_sharp         0.124 0.155
plotSurfaceComparison(
  object = object_t269, 
  color_by = best_fits_by_model[["variables"]], 
  outline = TRUE, 
  display_image = FALSE, 
  pt_clrsp = "Reds 3", 
  nrow = 2
  ) + 
  trajectory_add_on

plotStsLineplot(
  object = object_t269, 
  variables = best_fits_by_model[["variables"]], 
  id = "horizontal_mid", 
  line_color = "red", 
  nrow = 2
)