---
title: "Correlating HAI with flow cytometry and ELISPOT results in SDY269"
author: "Renan Sauteraud"
date: "`r Sys.Date()`"
output: rmarkdown::html_document
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{Reproducing an online report using ImmuneSpaceR: Correlation between HAI and flow cytometry in SDY269}
---


ImmuneSpaceR code produces consistent results, regardless of whether it is being
executed from a module or UI based report on the server or on a local machine. 
This vignette reproduces a report available on <a href="https://www.immunespace.org/reports/Studies/SDY269/runReport.view?reportId=module%3ASDY269%2Freports%2Fschemas%2Fhai_flow_elispot.Rmd">www.immunespace.org</a> using the same code.

### Summary
This report investigate the association between the number influenza-specific 
cells measured by ELISPOT measured at day 7 with the number of plasmablast 
measured by flow cytometry and day 7 and the HAI response measured at day 28 
(log-fold day28/day0). It basically reproduces Figure 1 d-e) of <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3140559/" target="_BLANK"> Nakaya et al. (2011)</a> published as part of the original study.


```{r knitr-opts, echo = FALSE, message = FALSE, cache = FALSE}
library(knitr)
opts_chunk$set(cache=FALSE, echo=TRUE, message=FALSE, warning=FALSE,
               fig.width=8, fig.height=4, dpi=100, fig.align="center")
```
```{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 and other libraries
```{r libraries, cache=FALSE}
library(ImmuneSpaceR)
library(ggplot2)
library(data.table)
```


#### Connect to the study and get datasets
```{r connection}
study <- CreateConnection(c("SDY269"))
dt_hai <- study$getDataset("hai", reload=TRUE)
dt_fcs <- study$getDataset("fcs_analyzed_result", reload=TRUE)
dt_elispot <- study$getDataset("elispot", reload=TRUE)
```
#### Transform data
```{r data-subset}
# Compute max fold change for HAI, and remove time zero
dt_hai <- dt_hai[,hai_response:=value_reported/value_reported[study_time_collected==0],
                 by="virus,cohort,participant_id"][study_time_collected==28]
dt_hai <- dt_hai[,list(hai_response=max(hai_response)),by="cohort,participant_id"]

# Define variable for ELISPOT, keep only the IgG class
dt_elispot <- dt_elispot[,elispot_response:=spot_number_reported+1][study_time_collected==7 & analyte=="IgG"]
# Compute % plasmablasts
dt_fcs <- dt_fcs[,fcs_response:=(as.double(population_cell_number)+1) /
                   as.double(base_parent_population)][study_time_collected==7]
```

#### Merge data and phenodata
```{r merging}
# Let's key the different datasets
setkeyv(dt_hai, c("participant_id"))
setkeyv(dt_fcs, c("participant_id"))
setkeyv(dt_elispot, c("participant_id"))
dt_all <- dt_hai[dt_fcs, nomatch=0][dt_elispot, nomatch=0]
```


The figure below shows the absolute number of plasmablast cells measured by flow cytometry vs. the number of frequency of influenza-specific cells measured by ELISPOT. 
```{r plot1, dev='png'}
ggplot(dt_all, aes(x=as.double(fcs_response), y=elispot_response, color=cohort)) +
  geom_point() + scale_y_log10() + scale_x_log10() + geom_smooth(method="lm") +
  xlab("Total plasmablasts (%)") + ylab("Influenza specific cells\n (per 10^6 PBMCs)") +
  theme_IS()
```

The figure below shows the HAI fold increase over baseline vs. the number of frequency of influenza-specific cells measured by ELISPOT. 
```{r plot2, dev='png'}
ggplot(dt_all, aes(x=as.double(hai_response), y=elispot_response, color=cohort)) +
  geom_point() + scale_x_continuous(trans="log2") + scale_y_log10() +
  geom_smooth(method="lm") + xlab("HAI fold") +
  ylab("Influenza specific cells\n (per 10^6 PBMCs)") + theme_IS()
```


In each case, we observe good correlations between the different responses, at least for the TIV cohort.
