Carbetocin-Report.Rmd

---
title: "Fluoxetine and IL6"
author: "Dr. Ali Sajid Imami"
date: "31/03/2020"
output: pdf_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
suppressPackageStartupMessages(library(tidyverse))
all_results <- read_csv("results/all_results.csv")
all_averaged <- read_csv("results/all_averaged.csv")
tnfcrosstab <- read_csv("results/tnfcrosstab.csv")
```

## Background

To assess the potential of Fluoxetine in preventing and/or mitigating cytokine storm, we performed an in-silico analysis of the transcriptional profile of the drugs. The drugs chosen were:

1. Carbetocin
2. Desmopressin
3. Chloroquine
4. Hydroxychloroquine

The identified IL-6 related target genes were:

1. IL6
2. IL6R (Receptor)
3. IL6ST (Signal Transducer)

After reviewing iLINCS data, 4 cell lines that had signatures for all the drugs were chosen. The cell lines are: 

1. A375
1. HA1E
1. MCF7
1. PC3

A simple summary of the data is given below along with interpretation.

## Averaged Similarity

To assess the similarity across cell lines, we downloaded the signatures of the individual drugs in the selected cell lines, obtained connected knock-down signatures and then looked at their similarity or "concordance".

The table is below:

```{r, echo = FALSE}
all_averaged %>% arrange(treatment, desc(mean_similarity)) %>%  knitr::kable()
```

We can see from the table that mean similarity across the cell lines is variable. In addition, we only have Chloroquine and Desmopressin as having a significant concrdance value. And within that, only IL6R has common expression between the two drugs. 

## Cell-Specific Similarity

We also checked the similarity in cell-line specific manner. 

The table is below:

```{r, echo = FALSE}
all_results %>% arrange(cellline, treatment, desc(similarity)) %>%  knitr::kable()
```

We only have hits from all 4 cell lines. Those are heterogeneuos but we can focus on just TNF here.

## TNF Specific similarity

```{r, echo=FALSE}

tnfcrosstab %>% knitr::kable()

```