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.hip.hyp.cortex <- log.tdata.FPKM.sample.info.subset %>% rownames_to_column() %>% filter(Tissue %in% c("Hippocampus", "Hypothanamus","Pre-frontal Cortex")) %>% column_to_rownames()

Wrangle Data

I will use Mouse IDs, Tissue, 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.hip.hyp.cortex) <- paste0(rownames(log.tdata.FPKM.sample.info.subset.hip.hyp.cortex),":", log.tdata.FPKM.sample.info.subset.hip.hyp.cortex$Time, ":", log.tdata.FPKM.sample.info.subset.hip.hyp.cortex$Treatment, ":", log.tdata.FPKM.sample.info.subset.hip.hyp.cortex$Tissue)

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

head(log.tdata.FPKM.sample.info.subset.hip.hyp.cortex[,1:5])
##                                    ENSMUSG00000000001 ENSMUSG00000000028
## A007:4 wks:None:Hypothanamus                 2.474856          0.7608058
## A008:4 wks:None:Hippocampus                  2.889816          0.7129498
## A009:4 wks:None:Pre-frontal Cortex           2.636912          0.6259384
## A016:96 hrs:2DG:Hypothanamus                 2.819592          0.4762342
## A017:96 hrs:2DG:Hippocampus                  2.731115          1.1847900
## A018:96 hrs:2DG:Pre-frontal Cortex           3.065258          0.7129498
##                                    ENSMUSG00000000031 ENSMUSG00000000037
## A007:4 wks:None:Hypothanamus                0.1222176          0.3435897
## A008:4 wks:None:Hippocampus                 0.2776317          0.3148107
## A009:4 wks:None:Pre-frontal Cortex          0.1655144          0.2700271
## A016:96 hrs:2DG:Hypothanamus                0.6881346          0.9360934
## A017:96 hrs:2DG:Hippocampus                 0.1133287          0.2468601
## A018:96 hrs:2DG:Pre-frontal Cortex          0.1310283          0.4121097
##                                    ENSMUSG00000000049
## A007:4 wks:None:Hypothanamus                0.5128236
## A008:4 wks:None:Hippocampus                 0.6471032
## A009:4 wks:None:Pre-frontal Cortex          0.6097656
## A016:96 hrs:2DG:Hypothanamus                0.2700271
## A017:96 hrs:2DG:Hippocampus                 0.3364722
## A018:96 hrs:2DG:Pre-frontal Cortex          0.8372475

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

saveRDS(log.tdata.FPKM.sample.info.subset.hip.hyp.cortex.missing, here("Data","Brain","log.tdata.FPKM.sample.info.subset.hip.hyp.cortex.missing.WGCNA.RData"))

Analysis performed by Ann Wells

The Carter Lab The Jackson Laboratory 2023

ann.wells@jax.org