SCPA analysis with all qvals = 0

Question

[bio_info]

Hello all! I am running some SCPA analysis on my scRNA-seq data. I have made a pseudo-bulk, carried out Differential GE analysis and would now like to see which pathways these genes are enriched in. I am following the standard SCPA tutorial here https://jackbibby1.github.io/SCPA/articles/quick_start.html.

This is the code I am running:

library(SCPA)
library(msigdbr)

# Add time-point in metadata
[e-mail] <- [e-mail] %>%
  mutate(time_point = substr(rownames([e-mail]), 1, 4))

# Remove genes from the pseudo-bulk not present in the output of DEG
deg <- DietSeurat(pseudo, features = pseudo_markers$features)

# Extract expression matrix
TP1 <- seurat_extract(deg, meta1 = "time_point", value_meta1 = "tp1")
TP2 <- seurat_extract(deg, meta1 = "time_point", value_meta1 = "tp2")

pathways <- msigdbr("Homo sapiens", "H") %>%
  format_pathways()

scpa_out <- compare_pathways(samples = list(TP1, TP2), 
                             pathways = pathways)

scpa_out <- scpa_out %>%
  mutate(Pathway = stringr::str_replace(Pathway, "^HALLMARK_", ""))

# Plot output of GO analysis -----
plot_rank(scpa_out = scpa_out, 
          pathway = scpa_out$Pathway[1:10], 
          base_point_size = 2, 
          highlight_point_size = 5,
          label_size = 3)

However, the output of SCPA just looks like this (below) and all qvals are 0. Could anyone please help me figure out why this is happening?

> scpa_out
                             Pathway      Pval adjPval qval           FC
1                       ADIPOGENESIS 0.2071081       1    0 -0.002538098
2                ALLOGRAFT_REJECTION 0.2071081       1    0 -4.088070315
3                  ANDROGEN_RESPONSE 0.2071081       1    0  0.642358935
4                          APOPTOSIS 0.2071081       1    0  0.391498457
5                         COMPLEMENT 0.2071081       1    0  0.369646836
6                         DNA_REPAIR 0.2071081       1    0  0.944504743
7                        E2F_TARGETS 0.2071081       1    0 -1.628894520
8  EPITHELIAL_MESENCHYMAL_TRANSITION 0.2071081       1    0 -1.042956905
9            ESTROGEN_RESPONSE_EARLY 0.2071081       1    0  0.791213920
10            ESTROGEN_RESPONSE_LATE 0.2071081       1    0 -0.064148849
11             FATTY_ACID_METABOLISM 0.2071081       1    0 -2.189316369
12                    G2M_CHECKPOINT 0.2071081       1    0 -0.365309603
13                        GLYCOLYSIS 0.2071081       1    0 -1.154006020
14                   HEME_METABOLISM 0.2071081       1    0  2.057080022
15                           HYPOXIA 0.2071081       1    0  0.597916263
16               IL2_STAT5_SIGNALING 0.2071081       1    0 -3.290367123
17             INFLAMMATORY_RESPONSE 0.2071081       1    0  0.795874680
18         INTERFERON_ALPHA_RESPONSE 0.2071081       1    0 -2.630867870
19         INTERFERON_GAMMA_RESPONSE 0.2071081       1    0 -4.400775248
20                 KRAS_SIGNALING_UP 0.2071081       1    0  1.571413723

[bk11]

What cell types are TP1 and TP2? The qval = 0 in your result is telling you that there is no changes in the pathway activity between TP1 and TP2. The larger the qval is, the larger will be the change in pathway ‘activity’. You can check the SCPA output interpretation here.

[bio_info]

TP1 and TP2 are actually time-points before and after therapy. I don't think the analysis worked the way it was supposed to as all the pathways are ranked in alphabetical order and the qvals are 0. But I am not sure what went wrong here.... Would you have any advice?