# SDY144: Correlation of HAI/Virus Neutralizition Titer and Cell Counts

#### Renan Sauteraud

#### 2018-08-30

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.

# 1 Create a connection to SDY144

First, we initialize the connection to SDY144 using `CreateConnection`.

    library(ImmuneSpaceR)
    library(data.table)
    library(ggplot2)

    con <- CreateConnection("SDY144")

# 2 Retrieve and manipulate data

We grab the datasets of interests with the `getDataset` method.

    flow <- con$getDataset("fcs_analyzed_result", maxRows = 10000) # maxRows for Rlabkey 2.1.136 / LK 17.2
    hai <- con$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)

# 3 Visualize using `ggplot2`

## 3.1 Figure 2A

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.

    ggplot(dat_hai, aes(x = population_cell_number, y = response)) +
      geom_point() +
      geom_smooth(method = "lm") +
      facet_grid(virus ~ population_name_reported, scale = "free") +
      xlab("Number of cells") +
      ylab("HI fold-increase Day 30 vs. baseline") +
      theme_IS()

## 3.2 Figure 2B

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.

    ggplot(dat_vn, aes(x = population_cell_number, y = response)) +
      geom_point() +
      geom_smooth(method = "lm") +
      facet_grid(virus ~ population_name_reported, scale = "free") +
      xlab("Number of cells") +
      ylab("VN fold-increase Day 30 vs. baseline") +
      theme_IS()