Gender balance in sample

library(SummarizedExperiment)

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# Load the covid19 dds object that was already filtered out genes with less information
covid19_dds_2 <- readRDS("F:/BAI HC/Research 2/Covid19/covid19_dds_2.RDS")

patient_data <-as.data.frame(colData(covid19_dds_2))

## Loading required package: DESeq2

library(ggplot2)

# The count data from patient_data
bar_df <- data.frame(name= c("fe_covid_case", "ma_covid_case", 
                           "fe_colon_case", "ma_colon_case", "no_info_control"),
                     count= c(4, 5, 4, 5, 20))
head(bar_df)

##              name count
## 1   fe_covid_case     4
## 2   ma_covid_case     5
## 3   fe_colon_case     4
## 4   ma_colon_case     5
## 5 no_info_control    20

# The bar plot comparing sex of the samples
ggplot(bar_df, aes(x=name, y=count)) + 
  geom_col()+
ggtitle("Gender balance in case and control samples")

There are no record for gender of all control samples.

library(DESeq2)

library(gridExtra)

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# Load the vst_ df already filtered
vsd <-readRDS("F:/BAI HC/Research 2/Covid19/vsd_2_already_filtered.RDS")

# PCA analysis with sex and condition
p1 <- plotPCA(vsd, intgroup = c("condition")) + theme(legend.position = "top")
p2 <-plotPCA(vsd, intgroup= c("sex")) + theme(legend.position = "top")

grid.arrange(p1, p2, ncol=2)

From the PCA plot, male lung samples in the covid19 case group are clustering together, but colon samples from the same male patient in covid19 cases are scattering.