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.liver <- log.tdata.FPKM.sample.info.subset %>% rownames_to_column() %>% filter(Tissue == "Liver") %>% 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.liver) <- paste0(rownames(log.tdata.FPKM.sample.info.subset.liver),":", log.tdata.FPKM.sample.info.subset.liver$Time, ":", log.tdata.FPKM.sample.info.subset.liver$Treatment)

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

head(log.tdata.FPKM.sample.info.subset.liver[,1:5])
##                 ENSMUSG00000000001 ENSMUSG00000000028 ENSMUSG00000000031
## A003:4 wks:None           3.076390          0.2468601          0.0000000
## A012:96 hrs:2DG           3.438493          0.2151114          0.1222176
## A021:4 wks:2DG            3.344274          0.5007753          0.0000000
## A030:4 wks:2DG            3.364879          0.3576744          0.3074847
## A039:4 wks:None           3.264996          0.6097656          0.0000000
## A048:4 wks:None           3.117507          0.6780335          0.0861777
##                 ENSMUSG00000000037 ENSMUSG00000000049
## A003:4 wks:None         0.00000000           7.162832
## A012:96 hrs:2DG         0.00000000           7.396998
## A021:4 wks:2DG          0.00000000           7.198714
## A030:4 wks:2DG          0.00000000           7.169211
## A039:4 wks:None         0.03922071           7.104045
## A048:4 wks:None         0.01980263           7.074328

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.liver.missing <- missing(log.tdata.FPKM.sample.info.subset.liver)
cat("logFPKM: ", goodSamplesGenes(log.tdata.FPKM.sample.info.subset.liver.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.liver.missing)

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

Analysis performed by Ann Wells

The Carter Lab The Jackson Laboratory 2023

ann.wells@jax.org