tdata.FPKM.sample.info <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_phenotypes.RData"))

tdata.FPKM <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_numeric.RData"))

log.tdata.FPKM <- log(tdata.FPKM + 1)
log.tdata.FPKM <- as.data.frame(log.tdata.FPKM)

log.tdata.FPKM.sample.info <- cbind(log.tdata.FPKM, tdata.FPKM.sample.info[,27238:27240])

log.tdata.FPKM.sample.info <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(rowname != "A113") %>% column_to_rownames()

log.tdata.FPKM.subset <- log.tdata.FPKM[,colMeans(log.tdata.FPKM != 0) > 0.5] 

log.tdata.FPKM.sample.info.subset <- cbind(log.tdata.FPKM.subset,tdata.FPKM.sample.info[,27238:27240])
log.tdata.FPKM.sample.info.subset <- log.tdata.FPKM.sample.info.subset %>% rownames_to_column() %>% filter(rowname != "A113") %>% column_to_rownames()

log.tdata.FPKM.sample.info.subset.spleen <- log.tdata.FPKM.sample.info.subset %>% rownames_to_column() %>% filter(Tissue == "Spleen") %>% column_to_rownames()

Wrangle Data

I will use Mouse IDs, Treatment, and Time to keep track of the values in the matrices. All other covariates will be discarded.

# Set rownames by mouse ID and tissue
rownames(log.tdata.FPKM.sample.info.subset.spleen) <- paste0(rownames(log.tdata.FPKM.sample.info.subset.spleen),":", log.tdata.FPKM.sample.info.subset.spleen$Time, ":", log.tdata.FPKM.sample.info.subset.spleen$Treatment)

# Discard covariates from columns 17333-17336
log.tdata.FPKM.sample.info.subset.spleen <- log.tdata.FPKM.sample.info.subset.spleen[,-(17333:17335)]

head(log.tdata.FPKM.sample.info.subset.spleen[,1:5])
##                 ENSMUSG00000000001 ENSMUSG00000000028 ENSMUSG00000000031
## A001:4 wks:None           3.819688           2.177022         0.34358970
## A010:96 hrs:2DG           3.756071           1.560248         0.41871033
## A019:4 wks:2DG            3.758639           3.150597         0.04879016
## A028:4 wks:2DG            3.834278           3.017494         0.10436002
## A037:4 wks:None           3.359333           3.205182         0.54812141
## A046:4 wks:None           3.857778           2.494032         0.34358970
##                 ENSMUSG00000000037 ENSMUSG00000000049
## A001:4 wks:None         0.07696104          0.7701082
## A010:96 hrs:2DG         0.08617770          0.6365768
## A019:4 wks:2DG          0.45107562          1.2527630
## A028:4 wks:2DG          0.85866162          0.2546422
## A037:4 wks:None         0.30010459          0.1823216
## A046:4 wks:None         0.12221763          1.4085450

Check Data for Missing Values

WGCNA will have poor results if the data have too many missing values. I checked if any metabolites fall into this category.

log.tdata.FPKM.sample.info.subset.spleen.missing <- missing(log.tdata.FPKM.sample.info.subset.spleen)
cat("logFPKM: ", goodSamplesGenes(log.tdata.FPKM.sample.info.subset.spleen.missing, verbose=0)$allOK, "\n")
## logFPKM:  TRUE

WGCNA reports that all data are good! I now use hierarchical clustering to detect any obvious outliers. I did not see any particularly egregious outliers.

sampleclustering(log.tdata.FPKM.sample.info.subset.spleen.missing)

saveRDS(log.tdata.FPKM.sample.info.subset.spleen.missing, here("Data","Spleen","log.tdata.FPKM.sample.info.subset.spleen.missing.WGCNA.RData"))

Analysis performed by Ann Wells

The Carter Lab The Jackson Laboratory 2023

ann.wells@jax.org