Script 25: Cell-type proportion changes in adipose
Compiled: June 26, 2024
This script loads in predicted adipose cell-type proportions and investigates how they changed during the intervention.
Setup
Load required packages
library(tidyverse)
library(SummarizedExperiment)
library(lmerTest)
library(lme4)Add to data
Format cell type data
load("../GOTO_Data/Cell_Counts/Fat/DIMENSION-GOTO_fat-counts-targets.RData")
deconv <- targets %>%
dplyr::select(IOP2_ID, timepoint, adipocytes,
cd4, MVEC, m1_macro, m2_macro) %>%
mutate(timepoint = ifelse(timepoint == "after", 1, 0))Make factors
deconv$timepoint <- as.factor(deconv$timepoint)
deconv$IOP2_ID <- as.factor(deconv$IOP2_ID)Load data
load("../GOTO_Data/GOTO_targets-filtered.Rdata")
load("../GOTO_Data/GOTO_methData-filtered.Rdata")Merge
targets <- left_join(targets, deconv, by=c("IOP2_ID", "timepoint"))Save
colData(methData) <- DataFrame(targets)
save(targets, file='../GOTO_Data/GOTO_targets-filtered.Rdata')
save(methData, file="../GOTO_Data/GOTO_methData-filtered.Rdata")Cell-type changes
Save cell names
cell_names <- c("adipocytes", "cd4",
"MVEC", "m1_macro",
"m2_macro")Keep only adipose samples
targets <- targets %>% filter(tissue == 'fat')
summary(targets[,cell_names])## adipocytes cd4 MVEC m1_macro
## Min. :53.46 Min. :0.00000 Min. : 5.267 Min. :0.00000
## 1st Qu.:66.21 1st Qu.:0.00000 1st Qu.:18.109 1st Qu.:0.00000
## Median :72.24 Median :0.00000 Median :24.885 Median :0.00000
## Mean :72.27 Mean :0.10949 Mean :24.209 Mean :0.04909
## 3rd Qu.:79.06 3rd Qu.:0.07108 3rd Qu.:29.431 3rd Qu.:0.01140
## Max. :92.63 Max. :3.83051 Max. :41.492 Max. :2.65574
## NA's :28 NA's :28 NA's :28 NA's :28
## m2_macro
## Min. : 0.000
## 1st Qu.: 1.870
## Median : 2.903
## Mean : 3.358
## 3rd Qu.: 4.485
## Max. :12.565
## NA's :28Run models
for(i in cell_names){
base_mean <- mean((targets %>%
filter(timepoint == 0) %>%
dplyr::select(all_of(i)))[,1], na.rm=TRUE)
base_sd <- sd((targets %>%
filter(timepoint == 0) %>%
dplyr::select(all_of(i)))[,1], na.rm=TRUE)
fit <- lmer(get(i) ~ timepoint + age + sex + smoke + (1|IOP2_ID),
data = targets)
df <- data.frame(
cell = i,
mean = base_mean,
sd = base_sd,
es = summary(fit)$coefficients[2,1],
se = summary(fit)$coefficients[2,2],
p = summary(fit)$coefficients[2,5]
)
if(i == cell_names[1]){
out <- df
} else {
out <- rbind(out, df)
}
}Adjust p-values
out$padj <- p.adjust(out$p, method = 'fdr')Inspect
out## cell mean sd es se p padj
## 1 adipocytes 72.4490739 7.8261128 -0.40172956 0.99316779 0.6870124 0.7278022
## 2 cd4 0.1497317 0.5014391 -0.07716510 0.05971979 0.2003299 0.7278022
## 3 MVEC 24.0569915 6.5300137 0.32221087 0.92226831 0.7278022 0.7278022
## 4 m1_macro 0.0335735 0.1559652 0.03353736 0.03741345 0.3729408 0.7278022
## 5 m2_macro 3.3106294 2.2574873 0.12313306 0.19067726 0.5204109 0.7278022Forest plot
Print plot
plot <- out %>%
mutate(lower = es - (1.96 * se),
upper = es + (1.96 * se)) %>%
filter(mean > 0.05) %>%
ggplot(aes(x = es,
y = reorder(cell, mean),
xmin = lower,
xmax = upper)) +
geom_vline(xintercept=0, linewidth=1,
color='grey60', linetype='dashed') +
geom_errorbar(width=0.5,
linewidth=1,
position=position_dodge(width=0.9)) +
geom_point(size=5,
shape=21,
stroke=1.2,
position=position_dodge(width=0.9),
fill = "#D62839") +
xlab('Intervention effect') + ylab('') + xlim(c(-7,7)) +
theme(axis.text = element_text(size=14, color = '#373334'),
axis.title = element_text(size=16, hjust=0.5,
color = '#373334'),
text=element_text(size=14),
panel.background = element_rect(fill = 'white',
color='#373334'),
panel.grid.major = element_line(color = 'grey95'),
panel.grid.minor = element_line(color = 'grey95'),
plot.background = element_rect(fill = 'white'),
axis.ticks.x = element_line(size=1))
print(plot)
Save
Save plot
png(file="../GOTO_Data/Figures/Figure_3A.png")
print(plot)
dev.off()## png
## 2Save data
write_csv(out, file = '../GOTO_Data/Tables/ST10.csv')Session Info
sessionInfo()## R version 4.2.2 (2022-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Rocky Linux 8.10 (Green Obsidian)
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.15.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] circlize_0.4.15
## [2] ComplexHeatmap_2.14.0
## [3] RColorBrewer_1.1-3
## [4] pheatmap_1.0.12
## [5] clusterProfiler_4.2.2
## [6] AnnotationHub_3.2.2
## [7] BiocFileCache_2.2.1
## [8] dbplyr_2.2.1
## [9] cinaR_0.2.3
## [10] edgeR_3.40.2
## [11] ggpubr_0.4.0
## [12] GEOquery_2.62.2
## [13] MuSiC_0.2.0
## [14] nnls_1.4
## [15] gplots_3.1.3
## [16] plotly_4.10.1
## [17] SeuratObject_4.1.3
## [18] Seurat_4.3.0
## [19] gridExtra_2.3
## [20] lattice_0.21-8
## [21] bacon_1.22.0
## [22] ellipse_0.4.5
## [23] methylGSA_1.12.0
## [24] sva_3.42.0
## [25] genefilter_1.76.0
## [26] mgcv_1.8-42
## [27] nlme_3.1-162
## [28] limma_3.54.2
## [29] lmerTest_3.1-3
## [30] lme4_1.1-30
## [31] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
## [32] snpStats_1.44.0
## [33] survival_3.5-5
## [34] ggrepel_0.9.1
## [35] ggfortify_0.4.14
## [36] irlba_2.3.5.1
## [37] Matrix_1.5-4.1
## [38] omicsPrint_1.14.0
## [39] MASS_7.3-60
## [40] DNAmArray_2.0.0
## [41] pls_2.8-2
## [42] FDb.InfiniumMethylation.hg19_2.2.0
## [43] org.Hs.eg.db_3.14.0
## [44] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [45] GenomicFeatures_1.46.5
## [46] AnnotationDbi_1.56.2
## [47] IlluminaHumanMethylationEPICmanifest_0.3.0
## [48] minfi_1.40.0
## [49] bumphunter_1.36.0
## [50] locfit_1.5-9.8
## [51] iterators_1.0.14
## [52] foreach_1.5.2
## [53] Biostrings_2.62.0
## [54] XVector_0.34.0
## [55] SummarizedExperiment_1.24.0
## [56] Biobase_2.58.0
## [57] MatrixGenerics_1.10.0
## [58] matrixStats_1.0.0
## [59] GenomicRanges_1.46.1
## [60] GenomeInfoDb_1.34.9
## [61] IRanges_2.32.0
## [62] S4Vectors_0.36.2
## [63] BiocGenerics_0.44.0
## [64] BiocParallel_1.32.6
## [65] MethylAid_1.28.0
## [66] forcats_0.5.2
## [67] stringr_1.5.0
## [68] dplyr_1.1.3
## [69] purrr_0.3.4
## [70] readr_2.1.2
## [71] tidyr_1.2.1
## [72] tibble_3.2.1
## [73] ggplot2_3.4.3
## [74] tidyverse_1.3.2
## [75] rmarkdown_2.16
##
## loaded via a namespace (and not attached):
## [1] graphlayouts_0.8.1
## [2] pbapply_1.7-0
## [3] haven_2.5.1
## [4] vctrs_0.6.3
## [5] beanplot_1.3.1
## [6] blob_1.2.4
## [7] spatstat.data_3.0-1
## [8] later_1.3.1
## [9] nloptr_2.0.3
## [10] DBI_1.1.3
## [11] rappdirs_0.3.3
## [12] uwot_0.1.14
## [13] zlibbioc_1.44.0
## [14] MatrixModels_0.5-1
## [15] GlobalOptions_0.1.2
## [16] htmlwidgets_1.5.4
## [17] future_1.32.0
## [18] leiden_0.4.3
## [19] illuminaio_0.40.0
## [20] tidygraph_1.2.2
## [21] Rcpp_1.0.10
## [22] KernSmooth_2.23-21
## [23] promises_1.2.0.1
## [24] DelayedArray_0.24.0
## [25] magick_2.7.4
## [26] fs_1.6.2
## [27] fastmatch_1.1-3
## [28] digest_0.6.31
## [29] png_0.1-8
## [30] nor1mix_1.3-0
## [31] sctransform_0.3.5
## [32] scatterpie_0.1.8
## [33] cowplot_1.1.1
## [34] DOSE_3.20.1
## [35] ggraph_2.0.6
## [36] pkgconfig_2.0.3
## [37] GO.db_3.14.0
## [38] gridBase_0.4-7
## [39] spatstat.random_3.1-5
## [40] DelayedMatrixStats_1.16.0
## [41] minqa_1.2.5
## [42] reticulate_1.30
## [43] GetoptLong_1.0.5
## [44] xfun_0.39
## [45] bslib_0.5.0
## [46] zoo_1.8-12
## [47] tidyselect_1.2.0
## [48] reshape2_1.4.4
## [49] ica_1.0-3
## [50] viridisLite_0.4.2
## [51] rtracklayer_1.54.0
## [52] rlang_1.1.1
## [53] hexbin_1.28.3
## [54] jquerylib_0.1.4
## [55] glue_1.6.2
## [56] modelr_0.1.9
## [57] ggsignif_0.6.3
## [58] labeling_0.4.2
## [59] SparseM_1.81
## [60] httpuv_1.6.11
## [61] preprocessCore_1.60.2
## [62] reactome.db_1.77.0
## [63] DO.db_2.9
## [64] annotate_1.72.0
## [65] jsonlite_1.8.5
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## [67] mime_0.12
## [68] Rsamtools_2.10.0
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## [70] spatstat.sparse_3.0-1
## [71] scattermore_0.8
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## [74] quadprog_1.5-8
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## [76] cli_3.6.1
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## [78] RSQLite_2.2.17
## [79] data.table_1.14.8
## [80] timechange_0.2.0
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## [84] listenv_0.9.0
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## [88] lifecycle_1.0.3
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## [90] munsell_0.5.0
## [91] cellranger_1.1.0
## [92] caTools_1.18.2
## [93] codetools_0.2-19
## [94] coda_0.19-4
## [95] MultiAssayExperiment_1.20.0
## [96] lmtest_0.9-40
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## [122] mclust_6.0.0
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## [128] htmltools_0.5.5
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## [199] scales_1.2.1
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## [201] backports_1.4.1
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## [203] base64_2.0.1
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## [205] enrichplot_1.14.2
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## [220] viridis_0.6.2
## [221] reshape_0.8.9
## [222] compiler_4.2.2
## [223] spatstat.geom_3.2-1Clear
rm(list=ls())