1 A version of Kajal’s R markdown document with some more minor changes

1.1 Libraries

In this version of Kajal’s document, I will repeat each step with some changes to see if I can make things a little easier for future modification.

I will therefore leave Kajal’s work unchanged and follow each block with an alternative.

1.4 Create counts table

## Note: importing `abundance.h5` is typically faster than `abundance.tsv`
## reading in files with read_tsv
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 
## transcripts missing from tx2gene: 14613
## summarizing abundance
## summarizing counts
## summarizing length
## [1] 34431

This is a potentially important difference in how I and Kajal treated the data. This is because I created the expressionset without using ‘lengthScaledTPM’, but let tximport use the raw counts so that the various normalizations are not affected. E.g. using these scaled numbers results in less variance in the data and may lead to some confusion for the downstream tools (edger/limma/deseq/etc). On the other hand, it may lead to cleaner plots, I am not sure.

1.5 Create DESeqDataSet

Not sure what this is actually doing

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
## using just counts from tximport
## [1] 34431

1.6 My version of the above

## The biomart annotations file already exists, loading from it.
## Reading the sample metadata.
## The sample definitions comprises: 39 rows(samples) and 8 columns(metadata fields).
## Reading count tables.
## Using the transcript to gene mapping.
## Reading kallisto data with tximport.
## Finished reading count data.
## Matched 34431 annotations and counts.
## Bringing together the count matrix and gene information.
## The mapped IDs are not the rownames of your gene information, changing them now.
## Some annotations were lost in merging, setting them to 'undefined'.
## Saving the expressionset to 'expt.rda'.
## The final expressionset has 34431 rows and 39 columns.
## Using a subset expression.
## There were 39, now there are 30 samples.

1.9 My Barplot of counts

##    condition      min      1st   median     mean      3rd      max
## 1:        NS 14327794 16797487 17086171 18433743 17427077 25536756
## 2:       LPS 14788518 17005543 18507594 19201561 20567000 26005826
## 3:        LA 16014957 18662352 19800400 21804785 23207183 35215275
## 4:        LP 15864738 17917203 19813259 20710341 22113833 28681140

1.11 Median Pairwise Correlation

## Performing correlation.

## Performing correlation.

1.13 PCA

##   propVar cumPropVar cond.R2 batch.R2
## 1   28.82      28.82   69.66    25.21
## 2   13.93      42.75   41.92    44.39
## 3   10.96      53.71   73.10    18.68
## 4    7.39      61.10    9.06    75.08
## 5    5.80      66.90    5.43    78.47
##   propVar cumPropVar cond.R2 batch.R2
## 1   28.82      28.82   69.66    30.95
## 2   13.93      42.75   41.92    46.07
## 3   10.96      53.71   73.10    20.98
## 4    7.39      61.10    9.06    80.68
## 5    5.80      66.90    5.43    91.16
##   propVar cumPropVar cond.R2 batch.R2
## 1   19.48      19.48   63.12    32.88
## 2   11.01      30.49   26.93    54.19
## 3    6.91      37.40    1.42    70.89
## 4    5.79      43.19    2.32    70.10
## 5    5.08      48.27    4.32    81.90
##   propVar cumPropVar cond.R2 batch.R2
## 1   19.48      19.48   63.12    35.52
## 2   11.01      30.49   26.93    53.86
## 3    6.91      37.40    1.42    88.21
## 4    5.79      43.19    2.32    94.87
## 5    5.08      48.27    4.32    82.44

1.14 Plot PC1 v PC2

## This function will replace the expt$expressionset slot with:
## log2(quant(cbcb(data)))
## It will save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep libsizes in mind
##  when invoking limma.  The appropriate libsize is non-log(cpm(normalized)).
##  This is most likely kept at:
##  'new_expt$normalized$intermediate_counts$normalization$libsizes'
##  A copy of this may also be found at:
##  new_expt$best_libsize
## Leaving the data unconverted.  It is often advisable to cpm/rpkm
##  the data to normalize for sampling differences, keep in mind though that rpkm
##  has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
##  will try to detect this).
## Not correcting the count-data for batch effects.  If batch is
##  included in EdgerR/limma's model, then this is probably wise; but in extreme
##  batch effects this is a good parameter to play with.
## Step 1: performing count filter with option: cbcb
## Removing 21885 low-count genes (12546 remaining).
## Step 2: normalizing the data with quant.
## Step 3: not converting the data.
## Step 4: transforming the data with log2.
## transform_counts: Found 841 values equal to 0, adding 1 to the matrix.
## Step 5: not doing batch correction.
## Not putting labels on the plot.

## This function will replace the expt$expressionset slot with:
## log2(quant(cbcb(data)))
## It will save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep libsizes in mind
##  when invoking limma.  The appropriate libsize is non-log(cpm(normalized)).
##  This is most likely kept at:
##  'new_expt$normalized$intermediate_counts$normalization$libsizes'
##  A copy of this may also be found at:
##  new_expt$best_libsize
## Leaving the data unconverted.  It is often advisable to cpm/rpkm
##  the data to normalize for sampling differences, keep in mind though that rpkm
##  has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
##  will try to detect this).
## Not correcting the count-data for batch effects.  If batch is
##  included in EdgerR/limma's model, then this is probably wise; but in extreme
##  batch effects this is a good parameter to play with.
## Step 1: performing count filter with option: cbcb
## Removing 22066 low-count genes (12365 remaining).
## Step 2: normalizing the data with quant.
## Step 3: not converting the data.
## Step 4: transforming the data with log2.
## transform_counts: Found 644 values equal to 0, adding 1 to the matrix.
## Step 5: not doing batch correction.
## Not putting labels on the plot.

1.15 Euclidian Distance Heat Map

1.16 Correct for Patient in Limma model

##   propVar cumPropVar cond.R2 batch.R2
## 1   36.04      36.04   95.47     5.38
## 2   17.81      53.85   91.87     6.83
## 3    8.05      61.90   29.00    25.52
## 4    5.39      67.29   78.12    21.01
## 5    3.06      70.35    9.63     8.75
##   propVar cumPropVar cond.R2 batch.R2
## 1   36.04      36.04   95.47        0
## 2   17.81      53.85   91.87        0
## 3    8.05      61.90   29.00        0
## 4    5.39      67.29   78.12        0
## 5    3.06      70.35    9.63        0
## [1] 34431    30
##   propVar cumPropVar cond.R2 batch.R2
## 1   23.26      23.26   95.02    13.72
## 2    8.03      31.29   18.28    26.78
## 3    6.01      37.30   81.15    22.10
## 4    4.60      41.90   13.36     4.61
## 5    4.35      46.25    6.15    28.65
##   propVar cumPropVar cond.R2 batch.R2
## 1   23.26      23.26   95.02        0
## 2    8.03      31.29   18.28        0
## 3    6.01      37.30   81.15        0
## 4    4.60      41.90   13.36        0
## 5    4.35      46.25    6.15        0

1.17 Plot PC1 and PC2 with patient correction

1.21 M_LPS v M_NS

eBayes finds an F-statistic from the set of t-statistics for that gene

Limit list to genes with an adjusted p value < 0.05

## [1] 4840

Filter out rows with less than 2-fold change (log2 fold change of > 1)

## [1] 1350

Filter out rows with less than 4-fold change (log2 fold change of > 2)

## [1] 431

Make an MA plot

Annotate sigGenes list using Biomart

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1.22 M_LPS v M_LA

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1.23 M_LPS v M_LP

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1.24 M_LP v M_NS

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1.25 M_LA v M_NS

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##GM_LPS v. GM_NS

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1.26 GM_LPS v GM_LP

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1.27 GM_LPS v GM_LA

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1.29 Volcano Plots

M_LPS v M_LA

##                ID  logFC AveExpr      t   P.Value adj.P.Val     B
## 1 ENSG00000139112  1.346   6.583 11.281 1.346e-12 1.730e-08 18.74
## 2 ENSG00000095794  2.802   6.009 10.697 5.130e-12 3.298e-08 17.42
## 3 ENSG00000132906  2.466   4.467  9.520 8.693e-11 2.235e-07 14.62
## 4 ENSG00000124466  5.662   1.526  9.775 4.642e-11 1.492e-07 14.59
## 5 ENSG00000163235  2.390   4.942  9.241 1.745e-10 3.739e-07 13.98
## 6 ENSG00000068305 -1.155   6.819 -9.117 2.391e-10 4.392e-07 13.67

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M_LPS v M_LP

##                ID logFC AveExpr     t   P.Value adj.P.Val     B
## 1 ENSG00000121966 4.167   5.148 13.64 9.189e-15 1.182e-10 23.50
## 2 ENSG00000182568 2.467   3.922 13.00 3.316e-14 2.132e-10 22.23
## 3 ENSG00000176595 4.467   1.729 11.43 9.698e-13 4.157e-09 18.60
## 4 ENSG00000198814 2.443   6.435 11.18 1.695e-12 4.986e-09 18.53
## 5 ENSG00000112394 2.502   5.836 10.96 2.791e-12 5.982e-09 18.04
## 6 ENSG00000109321 4.500   1.516 11.12 1.939e-12 4.986e-09 17.86

## png 
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1.30 Rich Factor Graphs

1.31 GO terms shared LA LP

2 Volcano plot M_LPS_4 v M_LP_4

##                ID logFC AveExpr     t   P.Value adj.P.Val     B
## 1 ENSG00000121966 4.167   5.148 13.64 9.189e-15 1.182e-10 23.50
## 2 ENSG00000182568 2.467   3.922 13.00 3.316e-14 2.132e-10 22.23
## 3 ENSG00000176595 4.467   1.729 11.43 9.698e-13 4.157e-09 18.60
## 4 ENSG00000198814 2.443   6.435 11.18 1.695e-12 4.986e-09 18.53
## 5 ENSG00000112394 2.502   5.836 10.96 2.791e-12 5.982e-09 18.04
## 6 ENSG00000109321 4.500   1.516 11.12 1.939e-12 4.986e-09 17.86

## png 
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## png 
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3 Volcano plot M_LPS_4 v M_LA_4

##                ID  logFC AveExpr      t   P.Value adj.P.Val     B
## 1 ENSG00000139112  1.346   6.583 11.281 1.346e-12 1.730e-08 18.74
## 2 ENSG00000095794  2.802   6.009 10.697 5.130e-12 3.298e-08 17.42
## 3 ENSG00000132906  2.466   4.467  9.520 8.693e-11 2.235e-07 14.62
## 4 ENSG00000124466  5.662   1.526  9.775 4.642e-11 1.492e-07 14.59
## 5 ENSG00000163235  2.390   4.942  9.241 1.745e-10 3.739e-07 13.98
## 6 ENSG00000068305 -1.155   6.819 -9.117 2.391e-10 4.392e-07 13.67

## png 
##   3
## png 
##   2

4 Volcano plot GM_LPS_4 v GM_LA_4

##                ID  logFC AveExpr      t   P.Value adj.P.Val     B
## 1 ENSG00000139112  1.346   6.583 11.281 1.346e-12 1.730e-08 18.74
## 2 ENSG00000095794  2.802   6.009 10.697 5.130e-12 3.298e-08 17.42
## 3 ENSG00000132906  2.466   4.467  9.520 8.693e-11 2.235e-07 14.62
## 4 ENSG00000124466  5.662   1.526  9.775 4.642e-11 1.492e-07 14.59
## 5 ENSG00000163235  2.390   4.942  9.241 1.745e-10 3.739e-07 13.98
## 6 ENSG00000068305 -1.155   6.819 -9.117 2.391e-10 4.392e-07 13.67

## png 
##   3
## png 
##   2

5 Volcano plot GM_LPS_4 v GM_LP_4

##                ID  logFC AveExpr      t   P.Value adj.P.Val      B
## 1 ENSG00000163235 -2.244  4.9419 -8.405 1.501e-09 1.609e-05 11.866
## 2 ENSG00000095794 -2.099  6.0086 -8.059 3.753e-09 1.609e-05 10.995
## 3 ENSG00000099625 -2.359  0.7463 -8.098 3.377e-09 1.609e-05  9.933
## 4 ENSG00000120875 -3.357  4.0494 -7.671 1.065e-08 3.425e-05  9.931
## 5 ENSG00000188042 -2.337  5.0272 -6.987 7.011e-08 1.663e-04  8.154
## 6 ENSG00000197872 -1.919  5.9633 -6.909 8.739e-08 1.663e-04  7.939

## png 
##   3
## png 
##   2
---
title: "Human Macrophages: M-CSF v GM-CSF: LPS, LPS+Adenosine, LPS+PGE2"
author: "Kajal Hamidzadeh and atb"
date: "`r Sys.Date()`"
output:
  html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    keep_md: false
    mode: selfcontained
    number_sections: true
    self_contained: true
    theme: readable
    toc: true
    toc_float:
      collapsed: false
      smooth_scroll: false
  rmdformats::readthedown:
    code_download: true
    code_folding: show
    df_print: paged
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    width: 300
    keep_md: false
    mode: selfcontained
    toc_float: true
  BiocStyle::html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    keep_md: false
    mode: selfcontained
    toc_float: true
---

<style type="text/css">
body, td {
  font-size: 16px;
}
code.r{
  font-size: 16px;
}
pre {
 font-size: 16px
}
</style>

```{r options, include=FALSE}
library("hpgltools")
tt <- devtools::load_all("/data/hpgltools")
knitr::opts_knit$set(width=120,
                     progress=TRUE,
                     verbose=TRUE,
                     echo=TRUE)
knitr::opts_chunk$set(error=TRUE,
                      dpi=96)
old_options <- options(digits=4,
                       stringsAsFactors=FALSE,
                       knitr.duplicate.label="allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size=10))
rundate <- format(Sys.Date(), format="%Y%m%d")
previous_file <- ""
ver <- "20200330"

##tmp <- sm(loadme(filename=paste0(gsub(pattern="\\.Rmd", replace="", x=previous_file), "-v", ver, ".rda.xz")))
##rmd_file <- "03_expression_infection_20180822.Rmd"
```

# A version of Kajal's R markdown document with some more minor changes

## Libraries

```{r warning=FALSE, message=FALSE, include=FALSE}
library(devtools)
library(tximport)
library(biomaRt)
library(hpgltools)
library(DESeq2)
library(gplots)
library(cbcbSEQ)
library(RColorBrewer)
library(Vennerable)
library(edgeR)
library(calibrate)
library(scales)
```

In this version of Kajal's document, I will repeat each step with some changes
to see if I can make things a little easier for future modification.

I will therefore leave Kajal's work unchanged and follow each block with an alternative.

## Set working directory, import metadata and abundance files

```{r read_data}
design <- read.table('MetaData only 4 hour.txt', header=T, sep='\t')
design[["Patient"]] <- as.factor(design[["Patient"]])
design[["Stimulation"]] <- as.factor(design[["Stimulation"]])
design[["Batch"]] <- as.factor(design[["Batch"]])
design[["Growth"]] <- as.factor(design[["Growth"]])
files <- file.path("kallisto abundance files/", design$HPGL.Identifier, "abundance.tsv")
names(files) <- paste0("HPGL09", c(12:31, 42:60))
rownames(design) <- design[[1]]

stim_design_idx <- design[["Stimulation"]] != "NS"
stim_design <- design[stim_design_idx, ]
```

## Convert transcript ID to gene ID

```{r ensembl}
ensembl <- useMart("ENSEMBL_MART_ENSEMBL", dataset="hsapiens_gene_ensembl", host="useast.ensembl.org")
tx2gene <- getBM(attributes=c("ensembl_transcript_id", "ensembl_gene_id", "chromosome_name"), mart=ensembl)
good_idx <- grepl(x=tx2gene[["chromosome_name"]], pattern="^[[:alnum:]]{1,2}$")
good_ones <- tx2gene[good_idx, -3]
```

## Create counts table

```{r}
txi.kallisto.tsv <- tximport(files, type="kallisto", tx2gene=good_ones, countsFromAbundance="lengthScaledTPM")
nrow(txi.kallisto.tsv$counts)

write.table(txi.kallisto.tsv$counts, "TPM_MvGM_CDS_20180711.txt", col.names=T, row.names=F, quote=F)
write.csv(txi.kallisto.tsv$counts, "TPM_MvGM_20180711.csv", row.names=T, quote=F)

stim_counts <- txi.kallisto.tsv$counts
stim_counts_idx <- colnames(stim_counts) %in% rownames(stim_design)
stim_counts <- stim_counts[, stim_counts_idx]
```

This is a potentially important difference in how I and Kajal treated the data.
This is because I created the expressionset without using 'lengthScaledTPM',
but let tximport use the raw counts so that the various normalizations are not
affected.  E.g. using these scaled numbers results in less variance in the data
and may lead to some confusion for the downstream tools
(edger/limma/deseq/etc).  On the other hand, it may lead to cleaner plots, I am
not sure.

## Create DESeqDataSet

Not sure what this is actually doing

```{r deseq_dds}
df <- data.frame(condition=paste(design$HPGL.Identifier, design$Growth,
                                 design$Stimulation, design$Patient, sep="_"))
rownames(df)=colnames(txi.kallisto.tsv$counts)
dds <- DESeqDataSetFromTximport(txi.kallisto.tsv, df, ~condition)
nrow(dds)

dds_stim <- dds[, rownames(stim_design)]
```

## My version of the above

```{r atb_read_data}
my_design <- design
rownames(my_design) <- design[[1]]
my_design[["condition"]] <- my_design[["Stimulation"]]
my_design[["file"]] <- glue::glue("preprocessing/{rownames(my_design)}/abundance.tsv")
colnames(my_design) <- tolower(colnames(my_design))
gene_info <- load_biomart_annotations(host="useast.ensembl.org")$annotation
rownames(gene_info) <- make.names(gene_info[["ensembl_gene_id"]], unique=TRUE)
tx_gene_map <- gene_info[, c("ensembl_transcript_id", "ensembl_gene_id")]
hs_expt <- create_expt(metadata=my_design, gene_info=gene_info, tx_gene_map=tx_gene_map)
hs_expt <- set_expt_batches(hs_expt, fact="growth")

stim_expt <- subset_expt(hs_expt, subset="stimulation!='NS'")
```

## Bar Plot of Counts

```{r barplot}
par(mar=c(10, 4.5, 3.5, 1))
par(oma=c(0, 0, 0, 0))
barplot(colSums(txi.kallisto.tsv$counts), las=3, main='Raw Counts By Sample')

barplot(colSums(stim_counts), las=3, main='Raw Counts By Sample')
```

## Box Plot of Counts

```{r count_barplot}
dds <- estimateSizeFactors(dds)
ncts <- counts(dds, normalized=TRUE)
y <- log(ncts + 1)

par(mar=c(5, 5, 2, 1))
par(oma=c(0, 0, 0, 0))
boxplot(y, names=colnames(txi.kallisto.tsv$counts), las=3, main='Per Sample Log of Size-factor Counts')

dds_stim <- estimateSizeFactors(dds_stim)
stim_ncts <- counts(dds_stim, normalized=TRUE)
stim_y <- log(stim_ncts + 1)

par(mar=c(5, 5, 2, 1))
par(oma=c(0, 0, 0, 0))
boxplot(stim_y, names=colnames(stim_counts), las=3,
        main='Per Sample Log of Size-factor Counts')
```

```{r my_boxplot}
boxplot <- plot_boxplot(hs_expt)
boxplot
```

## My Barplot of counts

```{r my_barplot}
libsize <- plot_libsize(hs_expt)
libsize$plot
libsize$summary

plot_libsize(stim_expt)$plot
```

## Heatmap of Pearson Correlation

```{r corheat}
datCor <- cor(txi.kallisto.tsv$counts)
heatmap.2(datCor, Rowv=NA, Colv=NA,
          margins=c(10, 10),
          labRow=df$condition,
          labCol=df$condition,
          dendrogram="none",
          scale="none",
          trace="none",
          srtCol=45, main='Pearson Correlation')


stim_datCor <- cor(stim_counts)
heatmap.2(stim_datCor, Rowv=NA, Colv=NA,
          margins=c(10, 10),
          labRow=df$condition,
          labCol=df$condition,
          dendrogram="none",
          scale="none",
          trace="none",
          srtCol=45, main='Pearson Correlation')
```

```{r my_corheat}
plot_corheat(hs_expt)$plot

plot_corheat(stim_expt)$plot
```

## Median Pairwise Correlation

```{r smc}
corM <- matrixStats::rowMedians(cor(txi.kallisto.tsv$counts))
qs <- quantile(corM, p=c(1, 3)/4)
iqr <- diff(qs)
outLimit <- qs[1] - 1.5 * iqr
ylim <- c(pmin(min(corM), outLimit), max(corM))
cond <- paste(design$Growth, design$Stimulation, sep="_")
col <- ifelse(cond=="M_NS", "gray60",
              ifelse(cond=="M_LPS", "deeppink3",
                     ifelse(cond=="M_LA", "darkseagreen2",
                            ifelse(cond=="M_LP", "lavender",
                                   ifelse(cond=="GM_NS", "lightpink1",
                                          ifelse(cond=="GM_LPS", "moccasin",
                                                 ifelse(cond=="GM_LA", "black",
                                                        ifelse(cond=="GM_LP", "springgreen4",
                                                               "blue2"))))))))
par(mar=c(5, 4.5, 2, 1))
plot(corM, xaxt="n", ylim=ylim, ylab="Median Pairwise Correlation", xlab="", main="", col=col, pch=16, cex=1.5)
axis(side=1, at=seq(along=corM), labels=colnames(txi.kallisto.tsv$counts), las=2)
abline(h=outLimit, lty=2)
abline(v=1:ncol(txi.kallisto.tsv$counts), lty=3, col="black")

stim_corM <- matrixStats::rowMedians(cor(stim_counts))
stim_qs <- quantile(stim_corM, p=c(1, 3)/4)
stim_iqr <- diff(stim_qs)
stim_outLimit <- stim_qs[1] - (1.5 * iqr)
stim_ylim <- c(pmin(min(stim_corM), stim_outLimit), max(stim_corM))
stim_cond <- paste(stim_design$Growth, stim_design$Stimulation, sep="_")
col <- ifelse(cond=="M_NS", "gray60",
              ifelse(cond=="M_LPS", "deeppink3",
                     ifelse(cond=="M_LA", "darkseagreen2",
                            ifelse(cond=="M_LP", "lavender",
                                   ifelse(cond=="GM_NS", "lightpink1",
                                          ifelse(cond=="GM_LPS", "moccasin",
                                                 ifelse(cond=="GM_LA", "black",
                                                        ifelse(cond=="GM_LP", "springgreen4",
                                                               "blue2"))))))))
par(mar=c(5, 4.5, 2, 1))
plot(stim_corM, xaxt="n", ylim=ylim, ylab="Median Pairwise Correlation", xlab="", main="", col=col, pch=16, cex=1.5)
axis(side=1, at=seq(along=stim_corM), labels=colnames(stim_counts), las=2)
abline(h=stim_outLimit, lty=2)
abline(v=1:ncol(stim_counts), lty=3, col="black")
```

```{r my_smc}
plot_sm(hs_expt)$plot

plot_sm(stim_expt)$plot
```

## Filter and Normalize Counts

```{r}
filterCounts = function (counts, lib.size = NULL, thresh = 1, minSamples = 2) {
  cpms <- 2^log2CPM(counts, lib.size = lib.size)$y
  keep <- rowSums(cpms > thresh) >= minSamples
  counts <- counts[keep, ]
  counts
}
x_table <- table(design$Stimulation)
dim(txi.kallisto.tsv$counts)

counts <- filterCounts(txi.kallisto.tsv$counts, thresh=1, minSamples=min(x_table))
dim(counts)
countsSubQ <- qNorm(counts)
x <- log2CPM(countsSubQ)
s <- makeSVD(x$y)

stim_table <- table(stim_design$Stimulation)
stim_counts <- filterCounts(stim_counts, thresh=1, minSamples=min(stim_table))
dim(stim_counts)
stim_countsSubQ <- qNorm(stim_counts)
stim_x <- log2CPM(stim_countsSubQ)
stim_s <- makeSVD(stim_x$y)
```

## PCA

```{r}
cbcbSEQ::pcRes(s$v, s$d, cond, design$Batch)[1:5, ]
cbcbSEQ::pcRes(s$v, s$d, cond, design$Patient)[1:5, ]

cbcbSEQ::pcRes(stim_s$v, stim_s$d, stim_cond, stim_design$Batch)[1:5, ]
cbcbSEQ::pcRes(stim_s$v, stim_s$d, stim_cond, stim_design$Patient)[1:5, ]
```

## Plot PC1 v PC2

```{r original_pca}
condnum <- as.numeric(as.factor(design$Stimulation))
condnum <- ifelse(condnum==4, "green",
           ifelse(condnum==3, "lightblue",
           ifelse(condnum==2, "pink",
           ifelse(condnum==1, "purple", "black"))))
patnum <- as.numeric(as.factor(design$Patient))
patnum <- ifelse(patnum==4, 21,
          ifelse(patnum==3, 22,
          ifelse(patnum==2, 23,
          ifelse(patnum==1, 24,
          ifelse(patnum==5, 25, 1)))))
cbcbSEQ::plotPC(s$v, s$d, col="black", pch=patnum, bg=condnum)
legend(x=0.2, y=0.2, legend=unique(design$Stimulation), pch=22, col=0,
       pt.bg=c("green","lightblue","pink","purple"), pt.cex=1.5, cex=0.5, bty="n")
legend(x=0.2, y=-0.1, legend=unique(design$Patient), pch=unique(patnum), col=0,
       pt.bg="gray90", pt.cex=1.5, cex=0.5, bty="n")

stim_condnum <- as.numeric(as.factor(stim_design$Stimulation))
stim_condnum <- ifelse(stim_condnum==4, "green",
           ifelse(stim_condnum==3, "lightblue",
           ifelse(stim_condnum==2, "pink",
           ifelse(stim_condnum==1, "purple", "black"))))
stim_patnum <- as.numeric(as.factor(stim_design$Patient))
stim_patnum <- ifelse(stim_patnum==4, 21,
          ifelse(stim_patnum==3, 22,
          ifelse(stim_patnum==2, 23,
          ifelse(stim_patnum==1, 24,
          ifelse(stim_patnum==5, 25, 1)))))
cbcbSEQ::plotPC(stim_s$v, stim_s$d, col="black", pch=stim_patnum, bg=stim_condnum)
legend(x=0.2, y=0.2, legend=unique(stim_design$Stimulation), pch=22, col=0,
       pt.bg=c("green","lightblue","pink","purple"), pt.cex=1.5, cex=0.5, bty="n")
legend(x=0.2, y=-0.1, legend=unique(stim_design$Patient), pch=unique(stim_patnum), col=0,
       pt.bg="gray90", pt.cex=1.5, cex=0.5, bty="n")
```

```{r my_pca}
hs_norm <- normalize_expt(hs_expt, norm="quant", filter="cbcb", transform="log2")
my_pca <- plot_pca(hs_norm, plot_labels=FALSE, cis=NULL)
my_pca$plot

hsstim_norm <- normalize_expt(stim_expt, norm="quant", filter="cbcb", transform="log2")
stim_pca <- plot_pca(hsstim_norm, plot_labels=FALSE, cis=NULL)
stim_pca$plot
```

## Euclidian Distance Heat Map

```{r disheat}
dists <- dist(t(counts))
mat <- as.matrix(dists)
rownames(mat) <- colnames(mat) <- cond
hmcol <- colorRampPalette(brewer.pal(9, "GnBu"))(100)
vec.patient <- rainbow(nlevels(design$Patient), start=0, end=.8)
patient.color <- rep(0, length(design$Patient))
for (i in 1:length(design$Patient))
    patient.color[i] <- vec.patient[design$Patient[i]==levels(design$Patient)]
vec.condition <- c("green", "lightblue", "pink", "purple")
condition.color <- rep(0, length(design$Stimulation))
for (i in 1:length(design$Stimulation))
    condition.color[i] <- vec.condition[design$Stimulation[i]==levels(design$Stimulation)]

heatmap <- heatmap.2(mat, trace="none", col = rev(hmcol), margin=c(11, 11), ColSideColors=condition.color,
                     RowSideColors=patient.color, key="FALSE", srtCol=45)

stim_dists <- dist(t(stim_counts))
stim_mat <- as.matrix(stim_dists)
rownames(stim_mat) <- colnames(stim_mat) <- stim_cond
stim_hmcol <- colorRampPalette(brewer.pal(9, "GnBu"))(100)
stim_vec.patient <- rainbow(nlevels(stim_design$Patient), start=0, end=.8)
stim_patient.color <- rep(0, length(stim_design$Patient))
for (i in 1:length(stim_design$Patient))
    stim_patient.color[i] <- stim_vec.patient[stim_design$Patient[i]==levels(stim_design$Patient)]
stim_vec.condition <- c("green", "lightblue", "pink", "purple")
stim_condition.color <- rep(0, length(stim_design$Stimulation))
for (i in 1:length(stim_design$Stimulation))
    stim_condition.color[i] <- stim_vec.condition[stim_design$Stimulation[i]==levels(stim_design$Stimulation)]
heatmap <- heatmap.2(stim_mat, trace="none", col = rev(hmcol), margin=c(11, 11),
                     ColSideColors=stim_condition.color,
                     RowSideColors=stim_patient.color, key="FALSE", srtCol=45)
```

```{r my_disheat}
my_disheat <- plot_disheat(hs_norm)

stim_disheat <- plot_disheat(hsstim_norm)
```

## Correct for Patient in Limma model

```{r}
mod <- model.matrix(~design$Patient)
v <- voom(countsSubQ, mod)
fit <- lmFit(v)
newData <- residuals(fit, v)
s <- makeSVD(newData)
pcRes(s$v, s$d, cond, design$Batch)[1:5, ]
pcRes(s$v, s$d, cond, design$Patient)[1:5, ]

stim_table <- table(stim_design$Stimulation)
stim_counts <- filterCounts(stim_counts, thresh=2, minSamples=min(stim_table))
dim(stim_counts)
stim_countsSubQ <- qNorm(stim_counts)
stim_mod <- model.matrix(~stim_design$Patient)
stim_v <- voom(stim_countsSubQ, stim_mod)
stim_fit <- lmFit(stim_v)
stim_newData <- residuals(stim_fit, stim_v)
stim_s <- makeSVD(stim_newData)
pcRes(stim_s$v, stim_s$d, stim_cond, stim_design$Batch)[1:5, ]
pcRes(stim_s$v, stim_s$d, stim_cond, stim_design$Patient)[1:5, ]
```

## Plot PC1 and PC2 with patient correction

```{r}
gronum <- as.numeric(as.factor(design$Growth))
gronum <- ifelse(gronum==2, 19,
                 ifelse(gronum==1, 15, 1))
patnum <- as.numeric(as.factor(design$Patient))
patnum <- ifelse(patnum==1, "pink",
                ifelse(patnum==2, "green",
                        ifelse(patnum==3, "blue",
                              ifelse(patnum==4, "yellow",
                                      ifelse(patnum==5, "black", "orange")))))
samplenum <- as.numeric(as.factor(design$Stimulation))
samplenum <- ifelse(samplenum==4, "green",
                ifelse(samplenum==3, "blue",
                        ifelse(samplenum==1, "black",
                              ifelse(samplenum==2, "yellow", "grey"))))
plotPC(s$v, s$d, pch=gronum, col=samplenum, cex=2)
legend(x=0.05, y=0.3, legend=c("NS", "LPS", "LP", "LA"), pch=22, col=0,
       pt.bg=c("green", "blue", "black", "yellow"), pt.cex=1.5, cex=1, bty="n")
legend(x=0.05, y=0.1, legend=unique(design$Growth), pch=unique(gronum), col="black",
       pt.bg="black", pt.cex=1.0, cex=1, bty="n")


stim_gronum <- as.numeric(as.factor(stim_design$Growth))
stim_gronum <- ifelse(stim_gronum==2, 19,
               ifelse(stim_gronum==1, 15, 1))
stim_patnum <- as.numeric(as.factor(stim_design$Patient))
stim_patnum <- ifelse(stim_patnum==1, "pink",
                ifelse(stim_patnum==2, "green",
                        ifelse(stim_patnum==3, "blue",
                              ifelse(stim_patnum==4, "yellow",
                                      ifelse(stim_patnum==5, "black", "orange")))))
stim_samplenum <- as.numeric(as.factor(stim_design$Stimulation))
stim_samplenum <- ifelse(stim_samplenum==3, "green",
                ifelse(stim_samplenum==2, "blue",
                        ifelse(stim_samplenum==1, "black", "grey")))
plotPC(stim_s$v, stim_s$d, pch=stim_gronum, col=stim_samplenum, cex=2)
legend(x=0.05, y=0.3, legend=c("LPS", "LP", "LA"), pch=22, col=0,
       pt.bg=c("green", "blue", "black", "yellow"), pt.cex=1.5, cex=1, bty="n")
legend(x=0.05, y=0.1, legend=unique(stim_design$Growth), pch=unique(stim_gronum), col="black",
       pt.bg="black", pt.cex=1.0, cex=1, bty="n")
```

## My Figure 3A

```{r my_pca2}
##stim_counts <- filterCounts(stim_counts, thresh=1, minSamples=min(x))
##dim(stim_counts)
##stim_countsSubQ <- qNorm(stim_counts)
##stim_x <- log2CPM(stim_countsSubQ)
##stim_s <- makeSVD(stim_x$y)
hss <- set_expt_batches(stim_expt, fact="patient")
hss <- sm(normalize_expt(hss, filter=TRUE))
hss <- sm(normalize_expt(hss, norm="quant"))
hss <- sm(normalize_expt(hss, convert="cpm"))
hss <- sm(normalize_expt(hss, transform="log2"))
hss <- sm(normalize_expt(hss, batch="limma"))
hss <- sm(set_expt_batches(hss, fact="growth"))
plot_pca(hss, plot_labels=FALSE)$plot
```

## Heatmap with patient correction??

```{r}
dists <- dist(t(newData))
mat <- as.matrix(dists)
rownames(mat) <- colnames(mat) <- cond
heatmap <- heatmap.2(mat, trace="none", col = rev(hmcol), margin=c(11, 11),
                     ColSideColors=condition.color,
                     RowSideColors=patient.color, key="FALSE", srtCol=45)
```

## DE analysis

```{r}
countsSubQ <- qNorm(counts)
patient <- design$Patient
mod <- model.matrix(~0+cond+patient, data=design)
v <- voom(countsSubQ, mod, plot=TRUE)
fit <- lmFit(v)
```

## M_LPS v M_NS

eBayes finds an F-statistic from the set of t-statistics for that gene

```{r}
M_LPS.M_NS.contr.mat <- makeContrasts(M_LPSvM_NS=(condM_LPS-condM_NS), levels=v$design)
M_LPS.M_NS.fit <- contrasts.fit(fit, M_LPS.M_NS.contr.mat)
M_LPS.M_NS.eb <- eBayes(M_LPS.M_NS.fit)
M_LPS.M_NS.topTab <- topTable(M_LPS.M_NS.eb, coef="M_LPSvM_NS", number=nrow(v$E))
M_LPS.M_NS.topTab <- cbind(rownames(M_LPS.M_NS.topTab), M_LPS.M_NS.topTab)
colnames(M_LPS.M_NS.topTab) <- c("ID", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "B")
rownames(M_LPS.M_NS.topTab) <- c(1:nrow(M_LPS.M_NS.topTab))

lps_ns_table <- "topTab_M_LPSvM_NS_CDS_limmabatchcorrection_20171120.csv"
write.csv(M_LPS.M_NS.topTab, file=lps_ns_table,
          row.names=FALSE, quote=FALSE)
```

Limit list to genes with an adjusted p value < 0.05

```{r}
M_LPS.M_NS.sigGenes <- M_LPS.M_NS.topTab[M_LPS.M_NS.topTab$adj.P.Val <0.05, ]
length(M_LPS.M_NS.sigGenes$ID)
```

Filter out rows with less than 2-fold change (log2 fold change of > 1)

```{r}
M_LPS.M_NS.sigGenesFold1 <- subset(M_LPS.M_NS.sigGenes, abs(logFC) > 1)
length(M_LPS.M_NS.sigGenesFold1$ID)
```

Filter out rows with less than 4-fold change (log2 fold change of > 2)

```{r}
M_LPS.M_NS.sigGenesFold2 <- subset(M_LPS.M_NS.sigGenes, abs(logFC) > 2)
length(M_LPS.M_NS.sigGenesFold2$ID)
```

Make an MA plot

```{r}
sel <- M_LPS.M_NS.topTab$adj.P.Val < 0.05
top <- M_LPS.M_NS.topTab
sub <- paste("No. of sig. genes: ", sum(sel), "/", length(sel))
cpm <- v$E

plot(rowMeans(cpm[top$ID,]), top$logFC, pch=16, cex=0.5, col="darkgrey",
     main="M_LPSvM_NS adjusted",
     ylab="log FC", xlab="Average Expression",
     sub=sub)
points(rowMeans(cpm[top$ID, ])[sel], top$logFC[sel], col="red", cex=0.5)
abline(h=c(-1, 0, 1), col="red")
```

Annotate sigGenes list using Biomart

```{r}
M_LPS.M_NS.sigGenes <- M_LPS.M_NS.sigGenes[order(-M_LPS.M_NS.sigGenes$logFC), ]

sigGenes <- M_LPS.M_NS.sigGenes
ids <- sigGenes$ID

desc <- getBM(attributes=c("ensembl_gene_id", "hgnc_symbol", "description", "gene_biotype"),
              filters="ensembl_gene_id", values=ids, mart=ensembl)
colnames(desc)=c("ID", "Symbol", "Description", "Type")

desc$Description <- gsub(",", "", desc$Description)

DEG_LPS <- merge(sigGenes, desc, by="ID", all=TRUE)
DEG_LPS <- subset(DEG_LPS, select=c(ID, Symbol, Description, logFC, adj.P.Val, AveExpr, Type), FS="/t")
DEG_LPS <- DEG_LPS[order(-DEG_LPS$logFC), ]

##Filter out Genes -1<FC<1
DEG_LPS <- subset(DEG_LPS, abs(DEG_LPS$logFC)>1)

##Save DE genes
write.table(DEG_LPS, "DEG_M_LPSvM_NS_CDS_limmabatchcorrection_20171120.txt",
            col.names=TRUE, row.names=FALSE, quote=FALSE)
write.csv(DEG_LPS, "DEG_M_LPSvM_NS_CDS_limmabatchcorrection_20171120.csv",
          row.names=FALSE, quote=FALSE)
```

## M_LPS v M_LA

```{r}
#eBayes finds an F-statistic from the set of t-statistics for that gene
M_LPS.M_LA.contr.mat <- makeContrasts(M_LPSvM_LA=((condM_LA-condM_NS)-(condM_LPS-condM_NS)),
                                      levels=v$design)
M_LPS.M_LA.fit <- contrasts.fit(fit, M_LPS.M_LA.contr.mat)
M_LPS.M_LA.eb <- eBayes(M_LPS.M_LA.fit)
M_LPS.M_LA.topTab <- topTable(M_LPS.M_LA.eb, coef="M_LPSvM_LA", number=nrow(v$E))
M_LPS.M_LA.topTab <- cbind(rownames(M_LPS.M_LA.topTab), M_LPS.M_LA.topTab)
colnames(M_LPS.M_LA.topTab) <- c("ID", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "B")
rownames(M_LPS.M_LA.topTab) <- c(1:nrow(M_LPS.M_LA.topTab))

lps_la_table <- "topTab_M_LPSvM_LA_CDS_limmabatchcorrection_20171120rev.csv"
write.csv(M_LPS.M_LA.topTab, file=lps_la_table, row.names=FALSE, quote=FALSE)

##Limit list to genes with an adjusted p value < 0.05
M_LPS.M_LA.sigGenes <- M_LPS.M_LA.topTab[M_LPS.M_LA.topTab$adj.P.Val <0.05, ]
length(M_LPS.M_LA.sigGenes$ID)
##[1] 617

##Filter out rows with less than 2-fold change (log2 fold change of > 1)
M_LPS.M_LA.sigGenesFold1 <- subset(M_LPS.M_LA.sigGenes, abs(logFC) > 1)
length(M_LPS.M_LA.sigGenesFold1$ID)
##[1] 240

##Filter out rows with less than 4-fold change (log2 fold change of > 2)
M_LPS.M_LA.sigGenesFold2 <- subset(M_LPS.M_LA.sigGenes, abs(logFC) > 2)
length(M_LPS.M_LA.sigGenesFold2$ID)
##[1] 69

##Make an MA plot
sel <- M_LPS.M_LA.topTab$adj.P.Val < 0.05
top <- M_LPS.M_LA.topTab
sub <- paste("No. of sig. genes: ", sum(sel), "/", length(sel))
cpm <- v$E

plot(rowMeans(cpm[top$ID,]), top$logFC, pch=16, cex=0.5, col="darkgrey",
     main="M_LPSvM_LA adjusted",
     ylab="log FC", xlab="Average Expression",
     sub=sub)
points(rowMeans(cpm[top$ID, ])[sel], top$logFC[sel], col="red", cex=0.5)
abline(h=c(-1, 0, 1), col="red")

dev.copy(pdf, "MAplot_M_LPSvM_LA_CDS_limmabatchcorrection_20171120rev.pdf", width=8, height=8)
dev.off()

dev.copy(png, "MAplot_M_LPSvM_LA_CDS_limmabatchcorrection_20171120rev.png", width=700,
         height=700)
dev.off()

M_LPS.M_LA.sigGenes <- M_LPS.M_LA.sigGenes[order(-M_LPS.M_LA.sigGenes$logFC), ]

##Annotate sigGenes list using Biomart
sigGenes <- M_LPS.M_LA.sigGenes
ids <- sigGenes$ID

desc <- getBM(attributes=c("ensembl_gene_id", "hgnc_symbol", "description", "gene_biotype"),
              filters="ensembl_gene_id",
              values=ids, mart=ensembl)
colnames(desc)=c("ID", "Symbol", "Description", "Type")

##Remove commas from description
desc$Description <- gsub(",", "", desc$Description)

DEG <- merge(sigGenes, desc, by="ID", all=TRUE)
DEG <- subset(DEG, select=c(ID, Symbol, Description, logFC, adj.P.Val, AveExpr, Type), FS="/t")
DEG <- DEG[order(-DEG$logFC), ]

##Filter out Genes -1<FC<1
DEG <- subset(DEG, abs(DEG$logFC)>1)

##Save DE genes
write.table(DEG, "DEG_M_LPSvM_LA_CDS_limmabatchcorrection_20171120rev.txt",
            col.names=T, row.names=F, quote=F)
write.csv(DEG, "DEG_M_LPSvM_LA_CDS_limmabatchcorrection_20171120rev.csv", row.names=F, quote=F)
```

## M_LPS v M_LP

```{r}
#eBayes finds an F-statistic from the set of t-statistics for that gene
M_LPS.M_LP.contr.mat <- makeContrasts(M_LPSvM_LP=((condM_LP-condM_NS)-(condM_LPS-condM_NS)),
                                      levels=v$design)
M_LPS.M_LP.fit <- contrasts.fit(fit, M_LPS.M_LP.contr.mat)
M_LPS.M_LP.eb <- eBayes(M_LPS.M_LP.fit)
M_LPS.M_LP.topTab <- topTable(M_LPS.M_LP.eb, coef="M_LPSvM_LP", number=nrow(v$E))
M_LPS.M_LP.topTab <- cbind(rownames(M_LPS.M_LP.topTab), M_LPS.M_LP.topTab)
colnames(M_LPS.M_LP.topTab) <- c("ID", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "B")
rownames(M_LPS.M_LP.topTab) <- c(1:nrow(M_LPS.M_LP.topTab))

lps_lp_table <- "topTab_M_LPSvM_LP_CDS_limmabatchcorrection_20171120rev.csv"
write.csv(M_LPS.M_LP.topTab, file=lps_lp_table, row.names=FALSE, quote=FALSE)

##Limit list to genes with an adjusted p value < 0.05
M_LPS.M_LP.sigGenes <- M_LPS.M_LP.topTab[M_LPS.M_LP.topTab$adj.P.Val <0.05, ]
length(M_LPS.M_LP.sigGenes$ID)
##[1] 1473

##Filter out rows with less than 2-fold change (log2 fold change of > 1)
M_LPS.M_LP.sigGenesFold1 <- subset(M_LPS.M_LP.sigGenes, abs(logFC) > 1)
length(M_LPS.M_LP.sigGenesFold1$ID)
##[1] 467

##Filter out rows with less than 4-fold change (log2 fold change of > 2)
M_LPS.M_LP.sigGenesFold2 <- subset(M_LPS.M_LP.sigGenes, abs(logFC) > 2)
length(M_LPS.M_LP.sigGenesFold2$ID)
##[1] 125

##Make an MA plot
sel <- M_LPS.M_LP.topTab$adj.P.Val < 0.05
top <- M_LPS.M_LP.topTab
sub <- paste("No. of sig. genes: ", sum(sel),"/",length(sel))
cpm <- v$E

plot(rowMeans(cpm[top$ID,]), top$logFC, pch=16, cex=0.5, col="darkgrey",
     main="M_LPSvM_LP adjusted",
     ylab="log FC", xlab="Average Expression",
     sub=sub)
points(rowMeans(cpm[top$ID, ])[sel], top$logFC[sel], col="red", cex=0.5)
abline(h=c(-1, 0, 1), col="red")

dev.copy(pdf, "MAplot_M_LPSvM_LP_CDS_limmabatchcorrection_20171120rev.pdf", width=8, height=8)
dev.off()

dev.copy(png, "MAplot_M_LPSvM_LP_CDS_limmabatchcorrection_20171120rev.png", width=700,
         height=700)
dev.off()

M_LPS.M_LP.sigGenes <- M_LPS.M_LP.sigGenes[order(-M_LPS.M_LP.sigGenes$logFC), ]

##Annotate sigGenes list using Biomart
sigGenes <- M_LPS.M_LP.sigGenes
ids <- sigGenes$ID

##To see possibilities for attributes, use head(listAttributes(ensembl), n=20L)
desc <- getBM(attributes=c("ensembl_gene_id", "hgnc_symbol", "description", "gene_biotype"),
              filters="ensembl_gene_id",
              values=ids, mart=ensembl)
colnames(desc)=c("ID", "Symbol", "Description", "Type")

##Remove commas from description
desc$Description <- gsub(",", "", desc$Description)

DEG <- merge(sigGenes, desc, by="ID", all=TRUE)
DEG <- subset(DEG, select=c(ID, Symbol, Description, logFC, adj.P.Val, AveExpr, Type), FS="/t")
DEG <- DEG[order(-DEG$logFC), ]

##Filter out Genes -1<FC<1
DEG <- subset(DEG, abs(DEG$logFC)>1)

##Save DE genes
write.table(DEG, "DEG_M_LPSvM_LP_CDS_limmabatchcorrection_20171120rev.txt",
            col.names=T, row.names=F, quote=F)
write.csv(DEG, "DEG_M_LPSvM_LP_CDS_limmabatchcorrection_20171120rev.csv", row.names=F, quote=F)
```

## M_LP v M_NS

```{r}
##eBayes finds an F-statistic from the set of t-statistics for that gene
M_LP.M_NS.contr.mat <- makeContrasts(M_LPvM_NS=(condM_LP-condM_NS), levels=v$design)
M_LP.M_NS.fit <- contrasts.fit(fit, M_LP.M_NS.contr.mat)
M_LP.M_NS.eb <- eBayes(M_LP.M_NS.fit)
M_LP.M_NS.topTab <- topTable(M_LP.M_NS.eb, coef="M_LPvM_NS", number=nrow(v$E))
M_LP.M_NS.topTab <- cbind(rownames(M_LP.M_NS.topTab), M_LP.M_NS.topTab)
colnames(M_LP.M_NS.topTab) <- c("ID", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "B")
rownames(M_LP.M_NS.topTab) <- c(1:nrow(M_LP.M_NS.topTab))

lp_ns_table <- "topTab_M_LPvM_NS_CDS_limmabatchcorrection_20171018.csv"
write.csv(M_LP.M_NS.topTab, file=lp_ns_table, row.names=FALSE, quote=FALSE)

##Limit list to genes with an adjusted p value < 0.05
M_LP.M_NS.sigGenes <- M_LP.M_NS.topTab[M_LP.M_NS.topTab$adj.P.Val <0.05, ]
length(M_LP.M_NS.sigGenes$ID)
##[1] 4386

##Filter out rows with less than 2-fold change (log2 fold change of > 1)
M_LP.M_NS.sigGenesFold1 <- subset(M_LP.M_NS.sigGenes, abs(logFC) > 1)
length(M_LP.M_NS.sigGenesFold1$ID)
##[1] 1455

##Filter out rows with less than 4-fold change (log2 fold change of > 2)
M_LP.M_NS.sigGenesFold2 <- subset(M_LP.M_NS.sigGenes, abs(logFC) > 2)
length(M_LP.M_NS.sigGenesFold2$ID)
##[1] 519

##Make an MA plot
sel <- M_LP.M_NS.topTab$adj.P.Val < 0.05
top <- M_LP.M_NS.topTab
sub <- paste("No. of sig. genes: ", sum(sel), "/", length(sel))
cpm <- v$E

plot(rowMeans(cpm[top$ID,]), top$logFC, pch=16, cex=0.5, col="darkgrey",
     main="M_LPvM_NS adjusted",
     ylab="log FC", xlab="Average Expression",
     sub=sub)
points(rowMeans(cpm[top$ID,])[sel], top$logFC[sel], col="red", cex=0.5)
abline(h=c(-1, 0, 1), col="red")

dev.copy(pdf, "MAplot_M_LPvM_NS_CDS_limmabatchcorrection_20171018.pdf", width=8, height=8)
dev.off()

dev.copy(png, "MAplot_M_LPvM_NS_CDS_limmabatchcorrection_20171018.png", width=700, height=700)
dev.off()

M_LP.M_NS.sigGenes <- M_LP.M_NS.sigGenes[order(-M_LP.M_NS.sigGenes$logFC), ]

##Annotate sigGenes list using Biomart
sigGenes <- M_LP.M_NS.sigGenes
ids <- sigGenes$ID

##To see possibilities for attributes, use head(listAttributes(ensembl), n=20L)
desc <- getBM(attributes=c("ensembl_gene_id", "hgnc_symbol", "description", "gene_biotype"),
              filters="ensembl_gene_id",
              values=ids, mart=ensembl)
colnames(desc)=c("ID", "Symbol", "Description", "Type")

##Remove commas from description
desc$Description <- gsub(",", "", desc$Description)

DEG_LP4 <- merge(sigGenes, desc, by="ID", all=TRUE)
DEG_LP4 <- subset(DEG_LP4, select=c(ID, Symbol, Description, logFC, adj.P.Val, AveExpr, Type),
                  FS="/t")
DEG_LP4 <- DEG_LP4[order(-DEG_LP4$logFC), ]

##Filter out Genes -1<FC<1
DEG_LP4 <- subset(DEG_LP4, abs(DEG_LP4$logFC)>1)

##Save DE genes
write.table(DEG_LP4, "DEG_M_LPvM_NS_CDS_limmabatchcorrection_20171018.txt",
            col.names=T, row.names=F, quote=F)
write.csv(DEG_LP4, "DEG_M_LPvM_NS_CDS_limmabatchcorrection_20171018.csv", row.names=F, quote=F)
```

## M_LA v M_NS

```{r}
#eBayes finds an F-statistic from the set of t-statistics for that gene
M_LA.M_NS.contr.mat <- makeContrasts(M_LAvM_NS=(condM_LA-condM_NS), levels=v$design)
M_LA.M_NS.fit <- contrasts.fit(fit, M_LA.M_NS.contr.mat)
M_LA.M_NS.eb <- eBayes(M_LA.M_NS.fit)
M_LA.M_NS.topTab <- topTable(M_LA.M_NS.eb, coef="M_LAvM_NS", number=nrow(v$E))
M_LA.M_NS.topTab <- cbind(rownames(M_LA.M_NS.topTab), M_LA.M_NS.topTab)
colnames(M_LA.M_NS.topTab) <- c("ID", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "B")
rownames(M_LA.M_NS.topTab) <- c(1:nrow(M_LA.M_NS.topTab))

la_ns_table <- "topTab_M_LAvM_NS_CDS_limmabatchcorrection_20171120.csv"
write.csv(M_LA.M_NS.topTab, file=la_ns_table, row.names=FALSE, quote=FALSE)

##Limit list to genes with an adjusted p value < 0.05
M_LA.M_NS.sigGenes <- M_LA.M_NS.topTab[M_LA.M_NS.topTab$adj.P.Val <0.05, ]
length(M_LA.M_NS.sigGenes$ID)
##[1] 617

##Filter out rows with less than 2-fold change (log2 fold change of > 1)
M_LA.M_NS.sigGenesFold1 <- subset(M_LA.M_NS.sigGenes, abs(logFC) > 1)
length(M_LA.M_NS.sigGenesFold1$ID)
##[1] 240

##Filter out rows with less than 4-fold change (log2 fold change of > 2)
M_LA.M_NS.sigGenesFold2 <- subset(M_LA.M_NS.sigGenes, abs(logFC) > 2)
length(M_LA.M_NS.sigGenesFold2$ID)
##[1] 69

##Make an MA plot
sel <- M_LA.M_NS.topTab$adj.P.Val < 0.05
top <- M_LA.M_NS.topTab
sub <- paste("No. of sig. genes: ", sum(sel), "/", length(sel))
cpm <- v$E

plot(rowMeans(cpm[top$ID,]), top$logFC, pch=16, cex=0.5, col="darkgrey",
     main="M_LAvM_NS adjusted",
     ylab="log FC", xlab="Average Expression",
     sub=sub)
points(rowMeans(cpm[top$ID, ])[sel], top$logFC[sel], col="red", cex=0.5)
abline(h=c(-1, 0, 1), col="red")

dev.copy(pdf, "MAplot_M_LAvM_NS_CDS_limmabatchcorrection_20171120.pdf", width=8, height=8)
dev.off()

dev.copy(png, "MAplot_M_LAvM_NS_CDS_limmabatchcorrection_20171120.png", width=700, height=700)
dev.off()

M_LA.M_NS.sigGenes <- M_LA.M_NS.sigGenes[order(-M_LA.M_NS.sigGenes$logFC), ]

##Annotate sigGenes list using Biomart
sigGenes <- M_LA.M_NS.sigGenes
ids <- sigGenes$ID

##To see possibilities for attributes, use head(listAttributes(ensembl), n=20L)
desc <- getBM(attributes=c("ensembl_gene_id", "hgnc_symbol", "description", "gene_biotype"),
              filters="ensembl_gene_id",
              values=ids, mart=ensembl)
colnames(desc)=c("ID", "Symbol", "Description", "Type")

##Remove commas from description
desc$Description <- gsub(",", "", desc$Description)

DEG_LA4 <- merge(sigGenes, desc, by="ID", all=TRUE)
DEG_LA4 <- subset(DEG_LA4, select=c(ID, Symbol, Description, logFC, adj.P.Val, AveExpr, Type),
                  FS="/t")
DEG_LA4 <- DEG_LA4[order(-DEG_LA4$logFC), ]

##Filter out Genes -1<FC<1
DEG_LA4 <- subset(DEG_LA4, abs(DEG_LA4$logFC)>1)

##Save DE genes
write.table(DEG_LA4, "DEG_M_LAvM_NS_CDS_limmabatchcorrection_20171120.txt",
            col.names=T, row.names=F, quote=F)
write.csv(DEG_LA4, "DEG_M_LAvM_NS_CDS_limmabatchcorrection_20171120.csv", row.names=F, quote=F)
```

##GM_LPS v. GM_NS

```{r}
#eBayes finds an F-statistic from the set of t-statistics for that gene
GM_LPS.GM_NS.contr.mat <- makeContrasts(GM_LPSvGM_NS=(condGM_LPS-condGM_NS), levels=v$design)
GM_LPS.GM_NS.fit <- contrasts.fit(fit, GM_LPS.GM_NS.contr.mat)
GM_LPS.GM_NS.eb <- eBayes(GM_LPS.GM_NS.fit)
GM_LPS.GM_NS.topTab <- topTable(GM_LPS.GM_NS.eb, coef="GM_LPSvGM_NS", number=nrow(v$E))
GM_LPS.GM_NS.topTab <- cbind(rownames(GM_LPS.GM_NS.topTab), GM_LPS.GM_NS.topTab)
colnames(GM_LPS.GM_NS.topTab) <- c("ID", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "B")
rownames(GM_LPS.GM_NS.topTab) <- c(1:nrow(GM_LPS.GM_NS.topTab))

gm_lps_ns_table <- "topTab_GM_LPSvGM_NS_CDS_limmabatchcorrection_20171120.csv"
write.csv(GM_LPS.GM_NS.topTab, file= gm_lps_ns_table, row.names=FALSE, quote=FALSE)

##Limit list to genes with an adjusted p value < 0.05
GM_LPS.GM_NS.sigGenes <- GM_LPS.GM_NS.topTab[GM_LPS.GM_NS.topTab$adj.P.Val <0.05, ]
length(GM_LPS.GM_NS.sigGenes$ID)
##[1] 302

##Filter out rows with less than 2-fold change (log2 fold change of > 1)
GM_LPS.GM_NS.sigGenesFold1 <- subset(GM_LPS.GM_NS.sigGenes, abs(logFC) > 1)
length(GM_LPS.GM_NS.sigGenesFold1$ID)
##[1] 197

##Filter out rows with less than 4-fold change (log2 fold change of > 2)
GM_LPS.GM_NS.sigGenesFold2 <- subset(GM_LPS.GM_NS.sigGenes, abs(logFC) > 2)
length(GM_LPS.GM_NS.sigGenesFold2$ID)
##[1] 101

##Make an MA plot
sel <- GM_LPS.GM_NS.topTab$adj.P.Val < 0.05
top <- GM_LPS.GM_NS.topTab
sub <- paste("No. of sig. genes: ", sum(sel), "/", length(sel))
cpm <- v$E

plot(rowMeans(cpm[top$ID,]), top$logFC, pch=16, cex=0.5, col="darkgrey",
     main="GM_LPSvGM_NS adjusted",
     ylab="log FC", xlab="Average Expression",
     sub=sub)
points(rowMeans(cpm[top$ID, ])[sel], top$logFC[sel], col="red", cex=0.5)
abline(h=c(-1, 0, 1), col="red")

dev.copy(pdf, "MAplot_GM_LPSvGM_NS_CDS_limmabatchcorrection_20171120.pdf", width=8, height=8)
dev.off()

dev.copy(png, "MAplot_GM_LPSvGM_NS_CDS_limmabatchcorrection_20171120.png", width=700, height=700)
dev.off()

GM_LPS.GM_NS.sigGenes <- GM_LPS.GM_NS.sigGenes[order(-GM_LPS.GM_NS.sigGenes$logFC), ]

##Annotate sigGenes list using Biomart
sigGenes <- GM_LPS.GM_NS.sigGenes
ids <- sigGenes$ID

##To see possibilities for attributes, use head(listAttributes(ensembl), n=20L)
desc <- getBM(attributes=c("ensembl_gene_id", "hgnc_symbol", "description", "gene_biotype"),
              filters="ensembl_gene_id",
              values=ids, mart=ensembl)
colnames(desc)=c("ID", "Symbol", "Description", "Type")

##Remove commas from description
desc$Description <- gsub(",", "", desc$Description)

DEG <- merge(sigGenes, desc, by="ID", all=TRUE)
DEG <- subset(DEG, select=c(ID, Symbol, Description, logFC, adj.P.Val, AveExpr, Type), FS="/t")
DEG <- DEG[order(-DEG$logFC), ]

##Filter out Genes -1<FC<1
DEG <- subset(DEG, abs(DEG$logFC)>1)

##Save DE genes
write.table(DEG, "DEG_GM_LPSvGM_NS_CDS_limmabatchcorrection_20171120.txt",
            col.names=T, row.names=F, quote=F)
write.csv(DEG, "DEG_GM_LPSvGM_NS_CDS_limmabatchcorrection_20171120.csv",
          row.names=F, quote=F)
```

## GM_LPS v GM_LP

```{r}
#eBayes finds an F-statistic from the set of t-statistics for that gene
GM_LPS.GM_LP.contr.mat <- makeContrasts(GM_LPSvGM_LP=((condGM_LPS-condGM_NS)-(condGM_LP-condGM_NS)),
                                        levels=v$design)
GM_LPS.GM_LP.fit <- contrasts.fit(fit, GM_LPS.GM_LP.contr.mat)
GM_LPS.GM_LP.eb <- eBayes(GM_LPS.GM_LP.fit)
GM_LPS.GM_LP.topTab <- topTable(GM_LPS.GM_LP.eb, coef="GM_LPSvGM_LP", number=nrow(v$E))
GM_LPS.GM_LP.topTab <- cbind(rownames(GM_LPS.GM_LP.topTab), GM_LPS.GM_LP.topTab)
colnames(GM_LPS.GM_LP.topTab) <- c("ID", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "B")
rownames(GM_LPS.GM_LP.topTab) <- c(1:nrow(GM_LPS.GM_LP.topTab))

gm_lps_lp_table <- "topTab_GM_LPSvGM_LP_CDS_limmabatchcorrection_20171023.csv"
write.csv(GM_LPS.GM_LP.topTab, file=gm_lps_lp_table, row.names=FALSE, quote=FALSE)

##Limit list to genes with an adjusted p value < 0.05
GM_LPS.GM_LP.sigGenes <- GM_LPS.GM_LP.topTab[GM_LPS.GM_LP.topTab$adj.P.Val <0.05, ]
length(GM_LPS.GM_LP.sigGenes$ID)
##[1] 194

##Filter out rows with less than 2-fold change (log2 fold change of > 1)
GM_LPS.GM_LP.sigGenesFold1 <- subset(GM_LPS.GM_LP.sigGenes, abs(logFC) > 1)
length(GM_LPS.GM_LP.sigGenesFold1$ID)
##[1] 114

##Filter out rows with less than 4-fold change (log2 fold change of > 2)
GM_LPS.GM_LP.sigGenesFold2 <- subset(GM_LPS.GM_LP.sigGenes, abs(logFC) > 2)
length(GM_LPS.GM_LP.sigGenesFold2$ID)
##[1] 27

##Make an MA plot
sel <- GM_LPS.GM_LP.topTab$adj.P.Val < 0.05
top <- GM_LPS.GM_LP.topTab
sub <- paste("No. of sig. genes: ", sum(sel), "/", length(sel))
cpm <- v$E

plot(rowMeans(cpm[top$ID,]), top$logFC, pch=16, cex=0.5, col="darkgrey",
     main="GM_LPSvGM_LP adjusted",
     ylab="log FC", xlab="Average Expression",
     sub=sub)
points(rowMeans(cpm[top$ID, ])[sel], top$logFC[sel], col="red", cex=0.5)
abline(h=c(-1, 0, 1), col="red")

dev.copy(pdf, "MAplot_GM_LPSvGM_LP_CDS_limmabatchcorrection_20171120.pdf", width=8, height=8)
dev.off()

dev.copy(png, "MAplot_GM_LPSvGM_LP_CDS_limmabatchcorrection_20171120.png", width=700,
         height=700)
dev.off()

GM_LPS.GM_LP.sigGenes <- GM_LPS.GM_LP.sigGenes[order(-GM_LPS.GM_LP.sigGenes$logFC), ]

##Annotate sigGenes list using Biomart
sigGenes <- GM_LPS.GM_LP.sigGenes
ids <- sigGenes$ID

##To see possibilities for attributes, use head(listAttributes(ensembl), n=20L)
desc <- getBM(attributes=c("ensembl_gene_id", "hgnc_symbol", "description", "gene_biotype"),
              filters="ensembl_gene_id",
              values=ids, mart=ensembl)
colnames(desc)=c("ID", "Symbol", "Description", "Type")

##Remove commas from description
desc$Description <- gsub(",", "", desc$Description)

DEG <- merge(sigGenes, desc, by="ID", all=TRUE)
DEG <- subset(DEG, select=c(ID, Symbol, Description, logFC, adj.P.Val, AveExpr, Type), FS="/t")
DEG <- DEG[order(-DEG$logFC), ]

##Filter out Genes -1<FC<1
DEG <- subset(DEG, abs(DEG$logFC)>1)

##Save DE genes
write.table(DEG, "DEG_GM_LPSvGM_LP_CDS_limmabatchcorrection_20171120.txt",
            col.names=T, row.names=F, quote=F)
write.csv(DEG, "DEG_GM_LPSvGM_LP_CDS_limmabatchcorrection_20171120.csv",
          row.names=F, quote=F)
```

## GM_LPS v GM_LA

```{r}
#eBayes finds an F-statistic from the set of t-statistics for that gene
GM_LPS.GM_LA.contr.mat <- makeContrasts(GM_LPSvGM_LA=((condGM_LPS-condGM_NS)-(condGM_LA-condGM_NS)),
                                        levels=v$design)
GM_LPS.GM_LA.fit <- contrasts.fit(fit, GM_LPS.GM_LA.contr.mat)
GM_LPS.GM_LA.eb <- eBayes(GM_LPS.GM_LA.fit)
GM_LPS.GM_LA.topTab <- topTable(GM_LPS.GM_LA.eb, coef="GM_LPSvGM_LA", number=nrow(v$E))
GM_LPS.GM_LA.topTab <- cbind(rownames(GM_LPS.GM_LA.topTab), GM_LPS.GM_LA.topTab)
colnames(GM_LPS.GM_LA.topTab) <- c("ID", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "B")
rownames(GM_LPS.GM_LA.topTab) <- c(1:nrow(GM_LPS.GM_LA.topTab))

gm_lps_la_table <- "topTab_GM_LPSvGM_LA_CDS_limmabatchcorrection_20171023.csv"
write.csv(GM_LPS.GM_LA.topTab, file=gm_lps_la_table, row.names=FALSE, quote=FALSE)

##Limit list to genes with an adjusted p value < 0.05
GM_LPS.GM_LA.sigGenes <- GM_LPS.GM_LA.topTab[GM_LPS.GM_LA.topTab$adj.P.Val <0.05, ]
length(GM_LPS.GM_LA.sigGenes$ID)
##[1] 17

##Filter out rows with less than 2-fold change (log2 fold change of > 1)
GM_LPS.GM_LA.sigGenesFold1 <- subset(GM_LPS.GM_LA.sigGenes, abs(logFC) > 1)
length(GM_LPS.GM_LA.sigGenesFold1$ID)
##[1] 15

##Filter out rows with less than 4-fold change (log2 fold change of > 2)
GM_LPS.GM_LA.sigGenesFold2 <- subset(GM_LPS.GM_LA.sigGenes, abs(logFC) > 2)
length(GM_LPS.GM_LA.sigGenesFold2$ID)
##[1] 7

##Make an MA plot
sel <- GM_LPS.GM_LA.topTab$adj.P.Val < 0.05
top <- GM_LPS.GM_LA.topTab
sub <- paste("No. of sig. genes: ", sum(sel),"/", length(sel))
cpm <- v$E

plot(rowMeans(cpm[top$ID,]), top$logFC, pch=16, cex=0.5, col="darkgrey",
     main="GM_LPSvGM_LA adjusted",
     ylab="log FC", xlab="Average Expression",
     sub=sub)
points(rowMeans(cpm[top$ID,])[sel], top$logFC[sel], col="red", cex=0.5)
abline(h=c(-1, 0, 1), col="red")

dev.copy(pdf, "MAplot_GM_LPSvGM_LA_CDS_limmabatchcorrection_20171120.pdf", width=8, height=8)
dev.off()

dev.copy(png, "MAplot_GM_LPSvGM_LA_CDS_limmabatchcorrection_20171120.png", width=700, height=700)
dev.off()

GM_LPS.GM_LA.sigGenes <- GM_LPS.GM_LA.sigGenes[order(-GM_LPS.GM_LA.sigGenes$logFC), ]

##Annotate sigGenes list using Biomart
sigGenes <- GM_LPS.GM_LA.sigGenes
ids <- sigGenes$ID

##To see possibilities for attributes, use head(listAttributes(ensembl), n=20L)
desc <- getBM(attributes=c("ensembl_gene_id", "hgnc_symbol", "description", "gene_biotype"),
              filters="ensembl_gene_id",
              values=ids, mart=ensembl)
colnames(desc)=c("ID", "Symbol", "Description", "Type")

##Remove commas from description
desc$Description <- gsub(",", "", desc$Description)

DEG <- merge(sigGenes, desc, by="ID", all=TRUE)
DEG <- subset(DEG, select=c(ID, Symbol, Description, logFC, adj.P.Val, AveExpr, Type), FS="/t")
DEG <- DEG[order(-DEG$logFC), ]

##Filter out Genes -1<FC<1
DEG <- subset(DEG, abs(DEG$logFC)>1)

##Save DE genes
write.table(DEG, "DEG_GM_LPSvGM_LA_CDS_limmabatchcorrection_20171120.txt",
            col.names=T, row.names=F, quote=F)
write.csv(DEG, "DEG_GM_LPSvGM_LA_CDS_limmabatchcorrection_20171120.csv",
          row.names=F, quote=F)
```

## Venn Diagram of shared DEG between M-CSF LPS v LA v LP

Upregulated genes compared to NS

```{r}
v_data <- list(DEG_LA4[DEG_LA4$logFC >0, ]$ID,
               DEG_LP4[DEG_LP4$logFC >0, ]$ID,
               DEG_LPS[DEG_LPS$logFC >0, ]$ID)
v_data <- Venn(v_data, SetNames=c("LPS+Ado", "LPS+PGE2", "LPS"), numberOfSets=3)
plot(v_data, doWeights=F)
##dev.copy(png, "VennDiagram Upregulated LPS LA LP", width=700, height=700)
##dev.off();
```

Downregulated genes compared to NS

```{r}
v_data <- list(DEG_LA4[DEG_LA4$logFC <0, ]$ID,
               DEG_LP4[DEG_LP4$logFC <0, ]$ID,
               DEG_LPS[DEG_LPS$logFC <0, ]$ID)
v_data <- Venn(v_data, SetNames=c("LPS+Ado", "LPS+PGE2", "LPS"), numberOfSets=3)
plot(v_data, doWeights=F)
##dev.copy(png, "VennDiagram Downregulated LPS LA LP", width=700, height=700)
##dev.off();
```

## Volcano Plots

M_LPS v M_LA

```{r previous_volcano}
res <- read.csv(lps_la_table)
##res <- read.table("topTab_M_LPSvM_LA_CDS_limmabatchcorrection_20171120.txt", header=TRUE)
head(res)
with(res, plot(logFC, -log10(P.Value), cex=0.8, pch=20,
               main="Volcano plot M_LPS v M_LA", xlim=c(-7, 7)))

## Add colored points: red if padj<0.05, orange of log2FC>1, green if both)
with(subset(res, adj.P.Val<.05 ),
     points(logFC, -log10(P.Value), cex=0.8, pch=20, col="violetred2"))
with(subset(res, abs(logFC)>1),
     points(logFC, -log10(P.Value), cex=0.8, pch=20, col="orange"))
with(subset(res, adj.P.Val<.05 & abs(logFC)>1),
     points(logFC, -log10(P.Value), cex=0.8, pch=20, col="turquoise3"))
dev.copy(png, "VolcanoPlot_M_LPS_v_M_LA.png", width=700, height=700)
dev.off()
```

M_LPS v M_LP

```{r uneval_volcano}
res <- read.csv("topTab_M_LPSvM_LP_CDS_limmabatchcorrection_20171120rev.csv", header=TRUE)
head(res)
with(res, plot(logFC, -log10(P.Value), cex=0.8, pch=20,
               main="Volcano plot M_LPS v M_LP", xlim=c(-7, 7)))

## Add colored points: red if padj<0.05, orange of log2FC>1, green if both)
with(subset(res, adj.P.Val<.05 ),
     points(logFC, -log10(P.Value), cex=0.8, pch=20, col="violetred2"))
with(subset(res, abs(logFC)>1),
     points(logFC, -log10(P.Value), cex=0.8, pch=20, col="orange"))
with(subset(res, adj.P.Val<.05 & abs(logFC)>1),
     points(logFC, -log10(P.Value), cex=0.8, pch=20, col="turquoise3"))
dev.copy(png, "VolcanoPlot_M_LPS_v_M_LP.png", width=700, height=700)
dev.off()
```

## Rich Factor Graphs

## GO terms shared LA LP

```{r}
GOSharedpathways <- read.table("GO MF FDR .05 TOP 5 only-2.txt", header=TRUE, sep='\t')
GOSharedpathways <- GOSharedpathways[order(GOSharedpathways$p.Value, decreasing=TRUE), ]

colorRamp <- colorRampPalette(c("turquoise1", "mediumblue"))(50)
par(mar=c(5, 18, 3, 8))
plot(GOSharedpathways$Rich.Factor, c(1:5), axes=F,
     panel.first=abline(h=1:5, v=seq(0.1, 0.6, 0.1), col="grey90"),
     cex=as.numeric(GOSharedpathways$Gene.Number)/4,
     col=colorRamp[cut(as.numeric(GOSharedpathways$p.Value), breaks=50)],
     pch=20, ylab=" ", xlab="Rich Factor", main="Pathways Enriched",
     xlim=c(0, 0.04))
axis(side=2, at=1:5, labels=as.character(GOSharedpathways$GeneSet), las=2, cex.axis=0.5)
axis(side=1, tick=GOSharedpathways[["Rich.Factor"]])
```

### KEGG LA

```{r, eval=FALSE}
LApathways <- read.table("Cytoscape Pathways/LA v LPS.txt", header=TRUE, sep='\t')
LApathways <- LApathways[order(LApathways$p.Value, decreasing=TRUE), ]
#plot x v. y, change size, assign color to p-value
colorRamp <- colorRampPalette(c("turquoise1", "mediumblue"))(50)
par(mar=c(5, 18, 3, 8))
plot(LApathways$Rich.Factor, c(1:28), axes=F,
     panel.first=abline(h=1:28, v=seq(0.1, 0.4, 0.1), col="grey90"),
     cex=as.numeric(LApathways$Gene.Number)/10,
     col=colorRamp[cut(as.numeric(LApathways$p.Value), breaks=50)],
     pch=20, ylab=" ", xlab="Rich Factor", main="Pathways Enriched",
     xlim=c(0, 0.4))
axis(side=2, at=1:28, labels=as.character(LApathways$Pathway), las=2, cex.axis=0.8)
axis(side=1, tick=LApathways$Rich.Factor, pos=0.4)
```

### GO LA

```{r, eval=FALSE}
GOLApathways <- read.table("Cytoscape Pathways/GO BP LA whole network.txt", header=TRUE, sep='\t')
GOLApathways <- GOLApathways[order(GOLApathways$p.Value, decreasing=TRUE), ]
##plot x v. y, change size, assign color to p-value
colorRamp <- colorRampPalette(c("turquoise1", "mediumblue"))(50)
par(mar=c(5, 20, 3, 8))
plot(GOLApathways$Rich.Factor, c(1:57), axes=F,
     panel.first=abline(h=1:57, v=seq(0.1, 0.5, 0.1), col="grey90"),
     cex=as.numeric(GOLApathways$Gene.Number)/10,
     col=colorRamp[cut(as.numeric(GOLApathways$p.Value), breaks=50)],
     pch=20, ylab=" ", xlab="Rich Factor", main="Pathways Enriched",
     xlim=c(0, 0.5))
axis(side=2,at=1:57, labels=as.character(GOLApathways$Pathway), las=2, cex.axis=0.8)
axis(side=1, tick=GOLApathways$Rich.Factor, pos=0.4)
```

### GO LP

```{r, eval=FALSE}
GOLPpathways <- read.table("Cytoscape Pathways/GO_BP_LPvLPS.txt", header=TRUE, sep='\t')
GOLPpathways <- GOLPpathways[order(GOLPpathways$p.Value, decreasing=TRUE), ]

colorRamp <- colorRampPalette(c("turquoise1", "mediumblue"))(50)
par(mar=c(5, 18, 3, 8))
plot(GOLPpathways$Rich.Factor, c(1:35), axes=F,
     panel.first=abline(h=1:35, v=seq(0.1, 0.6, 0.1), col="grey90"),
     cex=as.numeric(GOLPpathways$Gene.Number)/20,
     col=colorRamp[cut(as.numeric(GOLPpathways$p.Value), breaks=50)],
     pch=20, ylab=" ", xlab="Rich Factor", main="Pathways Enriched",
     xlim=c(0, 0.6))
axis(side=2, at=1:35, labels=as.character(GOLPpathways$Pathway), las=2, cex.axis=0.5)
axis(side=1, tick=GOLPpathways$Rich.Factor, pos=0.4)
```

# Volcano plot M_LPS_4 v M_LP_4

```{r first_volcano}
res <- read.csv("topTab_M_LPSvM_LP_CDS_limmabatchcorrection_20171120rev.csv",
                header=TRUE)
head(res)
with(res, plot(logFC, -log10(P.Value), cex=0.8,
               pch=20, main="Volcano plot M_LPS v M_LP", xlim=c(-7, 7)))
## Add colored points: red if padj<0.05, orange of log2FC>1, green if both)
with(subset(res, adj.P.Val<.05 ), points(logFC, -log10(P.Value), cex=0.8,
                                         pch=20, col="violetred2"))
with(subset(res, abs(logFC)>1), points(logFC, -log10(P.Value), cex=0.8,
                                       pch=20, col="orange"))
with(subset(res, adj.P.Val<.05 & abs(logFC)>1),
     points(logFC, -log10(P.Value),
            cex=0.8, pch=20, col="turquoise3"))
dev.copy(png, "VolcanoPlot_M_LPS_vs_M_LP.png", width=700, height=700)
dev.off()
```

# Volcano plot M_LPS_4 v M_LA_4

```{r second_volcano}
##res <- read.table("topTab_M_LPSvM_LA_CDS_limmabatchcorrection_20171120.txt",
##                  header=TRUE)
res <- read.csv(lps_la_table)
head(res)
with(res, plot(logFC, -log10(P.Value), cex=0.8, pch=20,
               main="Volcano plot M_LPS v M_LA", xlim=c(-7, 7)))
## Add colored points: red if padj<0.05, orange of log2FC>1, green if both)
with(subset(res, adj.P.Val<.05 ), points(logFC, -log10(P.Value), cex=0.8,
                                         pch=20, col="violetred2"))
with(subset(res, abs(logFC)>1), points(logFC, -log10(P.Value), cex=0.8,
                                       pch=20, col="orange"))
with(subset(res, adj.P.Val<.05 & abs(logFC)>1),
     points(logFC, -log10(P.Value),
            cex=0.8, pch=20, col="turquoise3"))
dev.copy(png, "VolcanoPlot_M_LPS_v_M_LA.png", width=700, height=700)
dev.off()
```

# Volcano plot GM_LPS_4 v GM_LA_4

```{r third_volcano}
res <- read.csv(lps_la_table)
##res <- read.table("GM_LPS v GM_LA/topTab_GM_LPSvGM_LA_CDS_limmabatchcorrection_20171023.txt",
##header=TRUE)
head(res)
with(res, plot(logFC, -log10(P.Value), cex=0.8, pch=20,
               main="Volcano plot GM_LPS_4 v GM_LA_4", xlim=c(-7, 7)))
## Add colored points: red if padj<0.05, orange of log2FC>1, green if both)
with(subset(res, adj.P.Val<.05 ), points(logFC, -log10(P.Value), cex=0.8,
                                         pch=20, col="violetred2"))
with(subset(res, abs(logFC)>1), points(logFC, -log10(P.Value), cex=0.8,
                                       pch=20, col="orange"))
with(subset(res, adj.P.Val<.05 & abs(logFC)>1),
     points(logFC, -log10(P.Value), cex=0.8, pch=20, col="turquoise3"))
dev.copy(png, "VolcanoPlot GM_LPS v GM_LA", width=700, height=700)
dev.off()
```

# Volcano plot GM_LPS_4 v GM_LP_4

```{r fourth_volcano}
##res <- read.table("topTab_GM_LPSvGM_LP_CDS_limmabatchcorrection_20171023.txt",
##                  header=TRUE)
res <- read.csv(gm_lps_lp_table)
head(res)
with(res, plot(logFC, -log10(P.Value), cex=0.8, pch=20,
               main="Volcano plot GM_LPS v GM_LP", xlim=c(-7, 7)))
## Add colored points: red if padj<0.05, orange of log2FC>1, green if both)
with(subset(res, adj.P.Val<.05 ), points(logFC, -log10(P.Value), cex=0.8,
                                         pch=20, col="violetred2"))
with(subset(res, abs(logFC)>1), points(logFC, -log10(P.Value), cex=0.8,
                                       pch=20, col="orange"))
with(subset(res, adj.P.Val<.05 & abs(logFC)>1),
     points(logFC, -log10(P.Value), cex=0.8, pch=20, col="turquoise3"))
dev.copy(png, "VolcanoPlot GM_LPS v GM_LP", width=700, height=700)
dev.off()
```
