---
title: "Correlation of HAI/virus neutralizition titer and cell counts in SDY144"
author: "Renan Sauteraud"
date: "`r Sys.Date()`"
output: ImmuneSpaceR::template_IS
vignette: >
  %\VignetteIndexEntry{Reproducing an online report using ImmuneSpaceR: Correlation of HAI/virus neutralizition titer and cell counts in SDY144}
  %\VignetteEngine{knitr::rmarkdown}
--- 

### 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)](http://www.jid.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=24495909) published as part of the original study.

```{r knitr, echo = FALSE}
library(knitr)
opts_chunk$set(message = FALSE, fig.align = "center", fig.width = 10, fig.height = 8)
```
```{r netrc_req, echo = FALSE}
# This chunk is only useful for BioConductor checks and shouldn't affect any other setup
ISR_login <- Sys.getenv("ISR_login")
ISR_pwd <- Sys.getenv("ISR_pwd")
if(ISR_login != ""  &  ISR_pwd != ""){
  netrc_file <- tempfile("ImmuneSpaceR_tmp_netrc")
  netrc_string <- paste("machine www.immunespace.org login", ISR_login, "password", ISR_pwd)
  write(x = netrc_string, file = netrc_file)
  labkey.netrc.file <- netrc_file
}
```

#### Load ImmuneSpaceR
```{r}
library(ImmuneSpaceR)
library(data.table)
library(ggplot2)
```

#### Initialize the connection to SDY144 and get data
First we initialize the connection to the selected study using `CreateConnection`.
Then we grab the datasets of interests with the `getDataset` method.
```{r}
con <- CreateConnection("SDY144")
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.
```{r subset}
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.
```{r FC}
# HAI
hai <- hai[,response:=value_reported/value_reported[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_reported/value_reported[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)
```

#### Plot using `ggplot2`
Figure 2 A: Correlation between the absolute number of plasmablasts and plasma
Bcells 7 days after vaccination with and fold-increase of HI titers from baseline
to day 30 after vaccination.
```{r HAI, dev='png'}
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()
```

Figure 2 B: Correlation between the absolute number of plasmablasts and plasma
Bcells 7 days after vaccination with and fold-increase of VN titers from baseline
to day 30 after vaccination.
```{r VN, dev='png'}
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()
```