2. Plot Pathway Significance Levels
The first element of this results data object is a data frame of pathways and their significance levels (pVals_df).
getPathpVals(colonSurv_aespcOut)
#> # A tibble: 15 × 4
#> terms rawp FDR_BH FWER_Bonferroni
#> <chr> <dbl> <dbl> <dbl>
#> 1 PID_EPHB_FWD_PATHWAY 6.53e-6 9.80e-5 0.0000980
#> 2 REACTOME_PHOSPHOLIPID_METABOLISM 1.96e-4 1.47e-3 0.00295
#> 3 REACTOME_INSULIN_RECEPTOR_SIGNALLING_CASCADE 4.90e-4 2.45e-3 0.00735
#> 4 KEGG_ASTHMA 8.21e-4 3.08e-3 0.0123
#> 5 KEGG_ERBB_SIGNALING_PATHWAY 1.47e-3 4.42e-3 0.0221
#> 6 PID_TNF_PATHWAY 2.60e-3 6.51e-3 0.0390
#> 7 REACTOME_SIGNALING_BY_INSULIN_RECEPTOR 4.42e-3 9.08e-3 0.0662
#> 8 KEGG_PENTOSE_PHOSPHATE_PATHWAY 4.84e-3 9.08e-3 0.0726
#> 9 ST_GA12_PATHWAY 1.48e-2 2.46e-2 0.222
#> 10 KEGG_RETINOL_METABOLISM 2.55e-2 3.82e-2 0.382
#> 11 KEGG_NON_SMALL_CELL_LUNG_CANCER 4.69e-2 6.39e-2 0.703
#> 12 KEGG_ANTIGEN_PROCESSING_AND_PRESENTATION 7.37e-2 9.21e-2 1
#> 13 BIOCARTA_RELA_PATHWAY 7.09e-1 8.01e-1 1
#> 14 BIOCARTA_TNFR1_PATHWAY 7.76e-1 8.01e-1 1
#> 15 BIOCARTA_SET_PATHWAY 8.01e-1 8.01e-1 1Given the \(p\)-values from these pathways, we will now graph their score (negative log of the \(p\)-value) and description for the top 15 most-significant pathways.
2.1 Trim Pathway Names
Because pathway names are quite long, we truncate names longer than 35 characters for better display in the graphs.
pVals_df <-
getPathpVals(colonSurv_aespcOut) %>%
mutate(
terms = ifelse(
str_length(terms) > 35,
paste0(str_sub(terms, 1, 33), "..."),
terms
)
)
pVals_df
#> # A tibble: 15 × 4
#> terms rawp FDR_BH FWER_Bonferroni
#> <chr> <dbl> <dbl> <dbl>
#> 1 PID_EPHB_FWD_PATHWAY 0.00000653 0.0000980 0.0000980
#> 2 REACTOME_PHOSPHOLIPID_METABOLISM 0.000196 0.00147 0.00295
#> 3 REACTOME_INSULIN_RECEPTOR_SIGNALL... 0.000490 0.00245 0.00735
#> 4 KEGG_ASTHMA 0.000821 0.00308 0.0123
#> 5 KEGG_ERBB_SIGNALING_PATHWAY 0.00147 0.00442 0.0221
#> 6 PID_TNF_PATHWAY 0.00260 0.00651 0.0390
#> 7 REACTOME_SIGNALING_BY_INSULIN_REC... 0.00442 0.00908 0.0662
#> 8 KEGG_PENTOSE_PHOSPHATE_PATHWAY 0.00484 0.00908 0.0726
#> 9 ST_GA12_PATHWAY 0.0148 0.0246 0.222
#> 10 KEGG_RETINOL_METABOLISM 0.0255 0.0382 0.382
#> 11 KEGG_NON_SMALL_CELL_LUNG_CANCER 0.0469 0.0639 0.703
#> 12 KEGG_ANTIGEN_PROCESSING_AND_PRESE... 0.0737 0.0921 1
#> 13 BIOCARTA_RELA_PATHWAY 0.709 0.801 1
#> 14 BIOCARTA_TNFR1_PATHWAY 0.776 0.801 1
#> 15 BIOCARTA_SET_PATHWAY 0.801 0.801 12.2 Tidy the Pathway Results
For the data frame containing the survival data pathway \(p\)-values, we will transform the data for better graphics. This code takes in the pathway \(p\)-values data frame from the AESPCA method output and gathers it into a “tidy” data frame (compatible with ggplot). We also add on a column for the negative natural logarithm of the pathway \(p\)-values (called score) and recode the label of the \(p\)-value adjustment method.
colonOutGather_df <-
# Take in the results data frame,
pVals_df %>%
# "tidy" the data,
gather(variable, value, -terms) %>%
# add the score variable, and
mutate(score = -log(value)) %>%
# store the adjustment methods as a factor
mutate(variable = factor(variable)) %>%
mutate(
variable = recode_factor(
variable,
rawp = "None",
FDR_BH = "Hochberg",
FWER_Bonferroni = "Bonferroni"
)
)2.3 Plot Significant Survival Pathways for One Adjustment
Now we will plot the pathway \(p\)-values for the most significant pathways as a horizontal bar chart. For more information on how to modify ggplot graphs, or to learn how to create your own, please see Chang’s R Graphics Cookbook or Wickham’s ggplot2: Elegant Graphics for Data Analysis.
First, we select the rows of the \(p\)-values data frame which correspond to the adjustment method we are interested in. We will select the Benjamini and Hochberg FDR-adjustment method.
Now we plot the pathway significance level for the pathways based on this FDR-adjustment method.
ggplot(BHpVals_df) +
# set overall appearance of the plot
theme_bw() +
# Define the dependent and independent variables
aes(x = reorder(terms, score), y = score) +
# From the defined variables, create a vertical bar chart
geom_col(position = "dodge", fill = "#F47321") +
# Set main and axis titles
ggtitle("AES-PCA Significant Pathways: Colon Cancer") +
xlab("Pathways") +
ylab("Negative Log BH-FDR") +
# Add a line showing the alpha = 0.01 level
geom_hline(yintercept = -log(0.01), size = 2, color = "#005030") +
# Flip the x and y axes
coord_flip()
2.4 Plot Significant Survival Pathways for All Adjustments
If we were interested in comparing adjustment methods, we can. This figure shows that a few of the simulated pathways are significant at the \(\alpha = 0.01\) for either the Benjamini and Hochberg or Bonferroni FWER approaches. The vertical black line is at \(-\log(p = 0.01)\). This figure is slightly different from the figures shown in the Graph Top Pathways subsection of the Quickstart Guide.
ggplot(colonOutGather_df) +
# set overall appearance of the plot
theme_bw() +
# Define the dependent and independent variables
aes(x = reorder(terms, score), y = score) +
# From the defined variables, create a vertical bar chart
geom_col(position = "dodge", aes(fill = variable)) +
# Set the legend, main titles, and axis titles
scale_fill_discrete(guide = FALSE) +
ggtitle("AES-PCA Significant Colon Pathways by FWER Adjustment") +
xlab("Pathways") +
ylab("Negative Log p-Value") +
# Add a line showing the alpha = 0.01 level
geom_hline(yintercept = -log(0.01), size = 1) +
# Flip the x and y axes
coord_flip() +
# Create a subplot for each p-value adjustment method
facet_grid(. ~ variable)
