Analysis of the cytokine data of the keto diet cohort and aKG treated samples
Junyan Lu
Last updated: 2024-05-24
Checks: 5 1
Knit directory: RA_Tcell_omics/analysis/
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Load libraries
Process cytokine data
cytoTab <- read_delim("../data/Cytokines_Keto_and_invitro_normalized_to_cellcount.csv", col_types = "cccccccccccccc") %>%
mutate(across(-well, as.numeric)) %>%
pivot_longer(-well, names_to = "cytokine", values_to = "value") %>%
mutate(patID = str_extract(well, "(K|RA)[0-9]*"),
time = str_extract(well, "d[0-9]*"),
group = str_extract(well, "st|ns|aKG")) %>%
mutate(diseaseGroup = ifelse(patID %in% c("K11","K13","K16"),"dCNT","RA")) %>% #11,13,16 are disease controls
mutate(log2Val = glog2(value))QA
Boxplot showing the value distribution
Original scale
ggplot(cytoTab, aes(x=cytokine, y = value)) +
geom_boxplot() +
geom_point() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
log2 scale
ggplot(cytoTab, aes(x=cytokine, y = log2Val)) +
geom_boxplot() +
geom_point() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Tests need to be performed on log2 scale, which is more normally
distributed
Identify cytokines associated with keto diet
ketoTab <- filter(cytoTab, !is.na(time))In RA patient samples
Unstimutated samples
testTab <- filter(ketoTab, group == "ns", diseaseGroup == "RA")
resTab <- group_by(testTab, cytokine) %>% nest() %>%
mutate(m = map(data, ~summary(lm(log2Val ~ patID + time,.)))) %>%
mutate(res = map(m, broom::tidy)) %>%
unnest(res) %>%
filter(term == "timed12") %>%
select(cytokine, p.value, statistic, estimate) %>%
arrange(p.value)
head(resTab)# A tibble: 6 × 4
# Groups: cytokine [6]
cytokine p.value statistic estimate
<chr> <dbl> <dbl> <dbl>
1 Granzyme A (B5) 0.430 -0.846 -0.580
2 Granzyme B (B6) 0.430 -0.846 -0.852
3 Granulysin (B9) 0.466 -0.778 -0.680
4 IL-10 (A6) 0.645 -0.485 -0.331
5 IL-6 (A7) 0.647 -0.482 -0.523
6 IFN-? (B4) 0.659 0.464 0.229No associations passed p<0.05
Stimulated samples
testTab <- filter(ketoTab, group == "st", diseaseGroup == "RA")
resTab <- group_by(testTab, cytokine) %>% nest() %>%
mutate(m = map(data, ~summary(lm(log2Val ~ patID + time,.)))) %>%
mutate(res = map(m, broom::tidy)) %>%
unnest(res) %>%
filter(term == "timed12") %>%
select(cytokine, p.value, statistic, estimate) %>%
arrange(p.value)
head(resTab)# A tibble: 6 × 4
# Groups: cytokine [6]
cytokine p.value statistic estimate
<chr> <dbl> <dbl> <dbl>
1 Granzyme B (B6) 0.140 1.70 0.803
2 Granulysin (B9) 0.171 -1.55 -0.961
3 Granzyme A (B5) 0.217 -1.38 -0.676
4 IL-2 (A4) 0.292 1.16 0.955
5 IL-6 (A7) 0.319 -1.09 -1.08
6 sFas (B2) 0.331 -1.06 -0.578In disease control samples
Unstimutated samples
testTab <- filter(ketoTab, group == "ns", diseaseGroup == "dCNT")
resTab <- group_by(testTab, cytokine) %>% nest() %>%
mutate(m = map(data, ~summary(lm(log2Val ~ patID + time,.)))) %>%
mutate(res = map(m, broom::tidy)) %>%
unnest(res) %>%
filter(term == "timed12") %>%
select(cytokine, p.value, statistic, estimate) %>%
arrange(p.value)
head(resTab)# A tibble: 6 × 4
# Groups: cytokine [6]
cytokine p.value statistic estimate
<chr> <dbl> <dbl> <dbl>
1 sFasL (B3) 0.152 -2.26 -2.23e- 1
2 IL-6 (A7) 0.266 -1.53 -1.07e+ 0
3 Granulysin (B9) 0.310 -1.35 -5.59e- 1
4 TNF-? (A10) 0.399 1.06 3.29e- 1
5 IL-2 (A4) 0.423 1 1.81e-16
6 IL-10 (A6) 0.423 -0.998 -4.38e- 1No associations passed p<0.05
Stimulated samples
testTab <- filter(ketoTab, group == "st", diseaseGroup == "dCNT")
resTab <- group_by(testTab, cytokine) %>% nest() %>%
mutate(m = map(data, ~summary(lm(log2Val ~ patID + time,.)))) %>%
mutate(res = map(m, broom::tidy)) %>%
unnest(res) %>%
filter(term == "timed12") %>%
select(cytokine, p.value, statistic, estimate) %>%
arrange(p.value)
head(resTab)# A tibble: 6 × 4
# Groups: cytokine [6]
cytokine p.value statistic estimate
<chr> <dbl> <dbl> <dbl>
1 IL-4 (A5) 0.0202 -6.93 -1.44
2 IL-6 (A7) 0.0484 -4.38 -4.60
3 Granzyme A (B5) 0.0759 -3.42 -1.49
4 IL-10 (A6) 0.0779 -3.37 -1.61
5 IL-17A (A3) 0.0867 -3.17 -2.06
6 TNF-? (A10) 0.141 -2.38 -1.01Two passed P < 0.05
resTab.sig <- filter(resTab, p.value < 0.05)
pList <- lapply(seq(nrow(resTab.sig)), function(i) {
rec <- resTab.sig[i,]
plotTab <- filter(testTab, cytokine == rec$cytokine)
ggplot(plotTab, aes(x=time, y=log2Val)) +
geom_line(aes(group = patID)) +
geom_point() +
ggtitle(sprintf("%s (P=%s)",rec$cytokine, formatC(rec$p.value, digits = 2))) +
ylab("Log2 expression") + xlab("")
})
cowplot::plot_grid(plotlist = pList, ncol=2)
Identify cytokines associated aKG supplement in RA samples
aKG group versus st group
testTab <- filter(cytoTab, is.na(time), group %in% c("st","aKG")) %>%
mutate(group = factor(group, levels = c("st","aKG")))resTab <- group_by(testTab, cytokine) %>% nest() %>%
mutate(m = map(data, ~summary(lm(log2Val ~ patID + group,.)))) %>%
mutate(res = map(m, broom::tidy)) %>%
unnest(res) %>%
filter(term == "groupaKG") %>%
select(cytokine, p.value, statistic, estimate) %>%
arrange(p.value)Associations passed P< 0.05
resTab.sig <- filter(resTab, p.value < 0.05)
resTab.sig %>% mutate_if(is.numeric, formatC, digits=2) %>% DT::datatable()Plot changes
pList <- lapply(seq(nrow(resTab.sig)), function(i) {
rec <- resTab.sig[i,]
plotTab <- filter(testTab, cytokine == rec$cytokine)
ggplot(plotTab, aes(x=group, y=log2Val)) +
geom_line(aes(group = patID)) +
geom_point() +
ggtitle(sprintf("%s (P=%s)",rec$cytokine, formatC(rec$p.value, digits = 2))) +
ylab("Log2 expression") + xlab("")
})
cowplot::plot_grid(plotlist = pList, ncol=3)
Also add ns group to the visualization
visTab <- filter(cytoTab, is.na(time), group %in% c("ns","st","aKG")) %>%
mutate(group = factor(group, levels = c("ns","st","aKG")))
pList <- lapply(seq(nrow(resTab.sig)), function(i) {
rec <- resTab.sig[i,]
plotTab <- filter(visTab, cytokine == rec$cytokine)
ggplot(plotTab, aes(x=group, y=log2Val)) +
geom_line(aes(group = patID)) +
geom_point() +
ggtitle(sprintf("%s (P=%s)",rec$cytokine, formatC(rec$p.value, digits = 2))) +
ylab("Log2 expression") + xlab("")
})
cowplot::plot_grid(plotlist = pList, ncol=3)
It seems aKG can reverse many of the stimulation
effect.
st group versus ns group
testTab <- filter(cytoTab, is.na(time), group %in% c("st","ns")) %>%
mutate(group = factor(group, levels = c("ns","st")))resTab <- group_by(testTab, cytokine) %>% nest() %>%
mutate(m = map(data, ~summary(lm(log2Val ~ patID + group,.)))) %>%
mutate(res = map(m, broom::tidy)) %>%
unnest(res) %>%
filter(term == "groupst") %>%
select(cytokine, p.value, statistic, estimate) %>%
arrange(p.value)Associations passed P<0.05
resTab.sig <- filter(resTab, p.value < 0.05)
resTab.sig %>% mutate_if(is.numeric, formatC, digits=2) %>% DT::datatable()Plot changes
pList <- lapply(seq(nrow(resTab.sig)), function(i) {
rec <- resTab.sig[i,]
plotTab <- filter(testTab, cytokine == rec$cytokine)
ggplot(plotTab, aes(x=group, y=log2Val)) +
geom_line(aes(group = patID)) +
geom_point() +
ggtitle(sprintf("%s (P=%s)",rec$cytokine, formatC(rec$p.value, digits = 2))) +
ylab("Log2 expression") + xlab("")
})
cowplot::plot_grid(plotlist = pList, ncol=3)
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur/Monterey 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] forcats_0.5.1 stringr_1.4.1
[3] dplyr_1.1.4.9000 purrr_0.3.4
[5] readr_2.1.2 tidyr_1.2.0
[7] tibble_3.2.1 ggplot2_3.4.1
[9] tidyverse_1.3.2 MultiAssayExperiment_1.22.0
[11] SummarizedExperiment_1.26.1 Biobase_2.56.0
[13] GenomicRanges_1.48.0 GenomeInfoDb_1.32.2
[15] IRanges_2.30.0 S4Vectors_0.34.0
[17] BiocGenerics_0.42.0 MatrixGenerics_1.8.1
[19] matrixStats_0.62.0 jyluMisc_0.1.5
loaded via a namespace (and not attached):
[1] readxl_1.4.0 backports_1.4.1 fastmatch_1.1-3
[4] drc_3.0-1 workflowr_1.7.0 igraph_1.3.4
[7] shinydashboard_0.7.2 splines_4.2.0 crosstalk_1.2.0
[10] BiocParallel_1.30.3 TH.data_1.1-1 digest_0.6.30
[13] htmltools_0.5.4 fansi_1.0.6 magrittr_2.0.3
[16] googlesheets4_1.0.0 cluster_2.1.3 tzdb_0.3.0
[19] limma_3.52.2 modelr_0.1.8 vroom_1.5.7
[22] sandwich_3.0-2 piano_2.12.0 colorspace_2.0-3
[25] rvest_1.0.2 haven_2.5.0 xfun_0.31
[28] crayon_1.5.2 RCurl_1.98-1.7 jsonlite_1.8.3
[31] survival_3.4-0 zoo_1.8-10 glue_1.7.0
[34] survminer_0.4.9 gtable_0.3.0 gargle_1.2.0
[37] zlibbioc_1.42.0 XVector_0.36.0 DelayedArray_0.22.0
[40] car_3.1-0 abind_1.4-5 scales_1.2.0
[43] mvtnorm_1.1-3 DBI_1.1.3 relations_0.6-12
[46] rstatix_0.7.0 Rcpp_1.0.9 plotrix_3.8-2
[49] xtable_1.8-4 bit_4.0.4 km.ci_0.5-6
[52] DT_0.23 htmlwidgets_1.5.4 httr_1.4.3
[55] fgsea_1.22.0 gplots_3.1.3 ellipsis_0.3.2
[58] farver_2.1.1 pkgconfig_2.0.3 sass_0.4.2
[61] dbplyr_2.2.1 utf8_1.2.4 labeling_0.4.2
[64] tidyselect_1.2.1 rlang_1.1.3 later_1.3.0
[67] munsell_0.5.0 cellranger_1.1.0 tools_4.2.0
[70] visNetwork_2.1.0 cachem_1.0.6 cli_3.6.2
[73] generics_0.1.3 broom_1.0.0 evaluate_0.15
[76] fastmap_1.1.0 yaml_2.3.5 bit64_4.0.5
[79] knitr_1.39 fs_1.5.2 survMisc_0.5.6
[82] caTools_1.18.2 mime_0.12 slam_0.1-50
[85] xml2_1.3.3 compiler_4.2.0 rstudioapi_0.13
[88] ggsignif_0.6.3 marray_1.74.0 reprex_2.0.1
[91] bslib_0.4.1 stringi_1.7.8 highr_0.9
[94] lattice_0.20-45 Matrix_1.5-4 shinyjs_2.1.0
[97] KMsurv_0.1-5 vctrs_0.6.5 pillar_1.9.0
[100] lifecycle_1.0.4 jquerylib_0.1.4 data.table_1.14.8
[103] cowplot_1.1.1 bitops_1.0-7 httpuv_1.6.6
[106] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20
[109] gridExtra_2.3 codetools_0.2-18 MASS_7.3-58
[112] gtools_3.9.3 exactRankTests_0.8-35 assertthat_0.2.1
[115] rprojroot_2.0.3 withr_3.0.0 multcomp_1.4-19
[118] GenomeInfoDbData_1.2.8 parallel_4.2.0 hms_1.1.1
[121] grid_4.2.0 rmarkdown_2.14 carData_3.0-5
[124] googledrive_2.0.0 git2r_0.30.1 maxstat_0.7-25
[127] ggpubr_0.4.0 sets_1.0-21 shiny_1.7.4
[130] lubridate_1.8.0