Correlations between hemagglutination inhibition (HI) and viral neutralization (VN) titers and plasmablast and plasma B cells among trivalent inactivated influenza vaccine (TIV) vaccinees.
This reports reproduces Figure 2 of Cao RG et al(2014) published as part of the original study.
First, we initialize the connection to SDY144 using CreateConnection
.
We grab the datasets of interests with the getDataset
method.
flow <- con$getDataset("fcs_analyzed_result")
hai <- con$getDataset("hai")
vn <- con$getDataset("neut_ab_titer")
Then, we select the cell populations and time points of intereset.
pb <- flow[population_name_reported %in% c("Plasma cells,Freq. of,B lym CD27+",
"Plasmablast,Freq. of,Q3: CD19+, CD20-")]
pb <- pb[, population_cell_number := as.numeric(population_cell_number)]
pb <- pb[study_time_collected == 7 & study_time_collected_unit == "Days"] # 13 subjects
pb <- pb[, list(participant_id, population_cell_number, population_name_reported)]
We compute the HI and VN titer as the fold-increase between baseline and day 30.
# HAI
hai <- hai[, response := value_preferred / value_preferred[study_time_collected == 0],
by = "virus,cohort,participant_id"][study_time_collected == 30]
hai <- hai[, list(participant_id, virus, response)]
dat_hai <- merge(hai, pb, by = "participant_id", allow.cartesian = TRUE)
# VN
vn <- vn[, response:= value_preferred/value_preferred[study_time_collected == 0],
by = "virus,cohort,participant_id"][study_time_collected == 30]
vn <- vn[, list(participant_id, virus, response)]
dat_vn <- merge(vn, pb, by = "participant_id", allow.cartesian = TRUE)
ggplot2
Correlation between the absolute number of plasmablasts and plasma B cells 7 days after vaccination with and fold-increase of HI titers from baseline to day 30 after vaccination.
Correlation between the absolute number of plasmablasts and plasma B cells 7 days after vaccination with and fold-increase of VN titers from baseline to day 30 after vaccination.