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.

1.8 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

1.12 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 Euclidian Distance Heat Map

1.15 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.

1.16 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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1.26 GM_LPS v. GM_NS

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1.27 GM_LPS v GM_LP

This contrast was written in the opposite order as the others, I think this is the reason some of my plots keep looking backwards/upsidedown… I am going to comment out the original line and rewrite it so that it is identical in order to what I found in: M_LP.M_LP.contr.mat.

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1.28 GM_LPS v GM_LA

I think I observe the same flipping here.

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1.29 Barplots of Fold-Changes, e.g. Figure 3C/D

It appears to me that these figures are created by merging the M/GM tables for the LP-LPS and LA-LPS contrasts, taking the top ~20 for the M table, and plotting the log2FC on the linear scale. I believe I can trivially modify this to add the logFC / t-stat. The caveat will be that doing this gives me a log2 error bar, not linear… My inclination therefore is to just plot the logFC rather than convert it to linear, but whatever.

1.29.2 Figure 3C top-left

Kajal’s variables to create this are…

  1. M_LPS.M_LP.topTab
  2. GM_LPS.GM_LP.topTab

These were passed to biomart to get the gene names rather than ensembl IDs. I am going to be lazy and just copy/paste Kajal’s code for these tasks.

Having done the top-left piece of this, it seems to me that if you are going to take the top 20 genes and exclude based on the M-CSF adjusted p-value, perhaps you should also exclude based on the GM-CSF adjusted p-value, but the way this was done, only the M is used. In its current state, I am only using the M as per the figures in their current state.

Note, that if you want error bars from the standard error, then it is waaaay easier to stay on the log2 scale rather than convert back to linear because the math for converting the standard error back to linear is weird. If the table had a few more parameters in it, I could do it without struggling, but it doesn’t.

## Cache found
colnames(desc) <- c("ID", "Symbol", "Description", "Type")
fig3c_lp_df <- merge(fig3c_lp_df, desc, by="ID", all.x=TRUE)

tl_order_idx <- order(fig3c_lp_df[["logFC.x"]], decreasing=TRUE)
fig3c_tl_df <- fig3c_lp_df[tl_order_idx, ]
sig_idx <- fig3c_tl_df[["adj.P.Val.x"]] <= 0.05
fig3c_tl_df <- head(fig3c_tl_df[sig_idx, ], n=25)
fig3c_tl_df <- fig3c_tl_df[, wanted_columns]
colnames(fig3c_tl_df) <- renamed_columns
rownames(fig3c_tl_df) <- fig3c_tl_df[["ID"]]
fig3c_tl_df[["ID"]] <- NULL
fig3c_tl_df[["m_lfcerr"]] <- fig3c_tl_df[["m_logfc"]] / fig3c_tl_df[["m_t"]]
fig3c_tl_df[["gm_lfcerr"]] <- fig3c_tl_df[["gm_logfc"]] / fig3c_tl_df[["gm_t"]]

fig3c_tl_m <- fig3c_tl_df[, c("m_logfc", "symbol", "m_lfcerr")]
fig3c_tl_m[["type"]] <- "m"
colnames(fig3c_tl_m) <- c("logfc", "symbol", "lfcerr", "type")
fig3c_tl_gm <- fig3c_tl_df[, c("gm_logfc", "symbol", "gm_lfcerr")]
fig3c_tl_gm[["type"]] <- "gm"
colnames(fig3c_tl_gm) <- c("logfc", "symbol", "lfcerr", "type")
melted_fig3c_tl <- rbind(fig3c_tl_m, fig3c_tl_gm)
melted_fig3c_tl[["symbol"]] <- factor(melted_fig3c_tl[["symbol"]],
                                      levels=fig3c_tl_m[["symbol"]])
melted_fig3c_tl[["type"]] <- factor(melted_fig3c_tl[["type"]],
                                    levels=c("m", "gm"))

p <- ggplot(data=melted_fig3c_tl, aes(x=symbol, y=logfc, fill=type)) +
  geom_bar(stat="identity", color="black", position=position_dodge())+
  scale_fill_manual(values=c("cornflowerblue", "darkgrey")) +
  geom_errorbar(aes(ymin=logfc - (lfcerr / 2), ymax=logfc + (lfcerr / 2)), width=.2,
                position=position_dodge(0.9)) +
  theme_minimal() +
  theme(axis.text.x=element_text(angle=45, hjust=1, vjust=0.5))
p

##            Symbol   P.Value adj.P.Val
## LYPD3       LYPD3 5.612e-02  0.622381
## ACKR3       ACKR3 3.963e-02  0.553822
## AREG         AREG 3.531e-02  0.529726
## KBTBD11   KBTBD11 2.706e-07  0.001740
## IGLON5     IGLON5 2.474e-04  0.067686
## CXCR4       CXCR4 3.205e-06  0.006153
## LIPN         LIPN 4.199e-02  0.562948
## GNG4         GNG4 7.213e-02  0.672602
## DUSP4       DUSP4 9.261e-01  0.998546
## ALPK2       ALPK2 9.460e-01  0.998546
## THBS1       THBS1 9.879e-01  0.999432
## KRT17       KRT17 9.604e-01  0.998546
## CD300E     CD300E 3.864e-01  0.949738
## HAS1         HAS1 9.206e-02  0.723115
## NR4A2       NR4A2 2.170e-02  0.441905
## CMSS1       CMSS1 9.074e-04  0.115520
## ADAMTS17 ADAMTS17 3.629e-01  0.942674
## CRLF2       CRLF2 9.997e-01  0.999882
## ZNF331     ZNF331 9.113e-05  0.043085
## FFAR3       FFAR3 2.981e-01  0.926399
## RGS2         RGS2 8.436e-01  0.998041
## CREM         CREM 1.370e-01  0.799699
## ABHD17C   ABHD17C 1.694e-02  0.408765
## THAP2       THAP2 6.660e-02  0.651783
## SLC16A10 SLC16A10 5.415e-03  0.260782

1.29.3 Figure 3C bottom-left

##          Symbol   P.Value adj.P.Val
## IL12B     IL12B 1.029e-04  0.043085
## CCL3L1   CCL3L1 1.685e-03  0.147786
## CCL3L3   CCL3L3 1.685e-03  0.147786
## CCL8       CCL8 2.933e-02  0.492300
## TIFAB     TIFAB 1.334e-03  0.138546
## CCL4L2   CCL4L2 1.543e-05  0.018032
## CCL2       CCL2 1.434e-02  0.388755
## IL36G     IL36G 1.229e-02  0.359328
## TNF         TNF 3.561e-04  0.080319
## CCL15     CCL15 1.284e-03  0.137488
## P2RX7     P2RX7 1.745e-03  0.149671
## CCL3       CCL3 2.702e-05  0.024818
## CCL1       CCL1 1.894e-02  0.427142
## ZACN       ZACN 1.614e-03  0.146148
## CISH       CISH 1.314e-04  0.049681
## RGS16     RGS16 2.319e-03  0.175535
## CCL4       CCL4 2.820e-03  0.194541
## F3           F3 1.874e-04  0.066918
## IL27       IL27 8.285e-03  0.304500
## ANKRD22 ANKRD22 2.656e-06  0.006153

1.29.4 Figure 3C top-right

The right side of the plot is LA rather than LP, otherwise this is the same.

## Cache found
colnames(desc) <- c("ID", "Symbol", "Description", "Type")
fig3c_la_df <- merge(fig3c_la_df, desc, by="ID", all.x=TRUE)

tr_order_idx <- order(fig3c_la_df[["logFC.x"]], decreasing=TRUE)
fig3c_tr_df <- fig3c_la_df[tr_order_idx, ]
sig_idx <- fig3c_tr_df[["adj.P.Val.x"]] <= 0.05
fig3c_tr_df <- head(fig3c_tr_df[sig_idx, ], n=25)
fig3c_tr_df <- fig3c_tr_df[, wanted_columns]
colnames(fig3c_tr_df) <- renamed_columns
rownames(fig3c_tr_df) <- fig3c_tr_df[["ID"]]
fig3c_tr_df[["ID"]] <- NULL
fig3c_tr_df[["m_lfcerr"]] <- fig3c_tr_df[["m_logfc"]] / fig3c_tr_df[["m_t"]]
fig3c_tr_df[["gm_lfcerr"]] <- fig3c_tr_df[["gm_logfc"]] / fig3c_tr_df[["gm_t"]]

fig3c_tr_m <- fig3c_tr_df[, c("m_logfc", "symbol", "m_lfcerr")]
fig3c_tr_m[["type"]] <- "m"
colnames(fig3c_tr_m) <- c("logfc", "symbol", "lfcerr", "type")
fig3c_tr_gm <- fig3c_tr_df[, c("gm_logfc", "symbol", "gm_lfcerr")]
fig3c_tr_gm[["type"]] <- "gm"
colnames(fig3c_tr_gm) <- c("logfc", "symbol", "lfcerr", "type")
melted_fig3c_tr <- rbind(fig3c_tr_m, fig3c_tr_gm)
melted_fig3c_tr[["symbol"]] <- factor(melted_fig3c_tr[["symbol"]],
                                      levels=fig3c_tr_m[["symbol"]])
melted_fig3c_tr[["type"]] <- factor(melted_fig3c_tr[["type"]],
                                    levels=c("m", "gm"))

p <- ggplot(data=melted_fig3c_tr, aes(x=symbol, y=logfc, fill=type)) +
  geom_bar(stat="identity", color="black", position=position_dodge())+
  scale_fill_manual(values=c("cornflowerblue", "darkgrey")) +
  geom_errorbar(aes(ymin=logfc-lfcerr, ymax=logfc+lfcerr), width=.2,
                position=position_dodge(0.9)) +
  theme_minimal() +
  theme(axis.text.x=element_text(angle=45, hjust=1, vjust=0.5))
p

##            Symbol   P.Value adj.P.Val
## LYPD3       LYPD3 5.624e-03   0.45401
## IGLON5     IGLON5 5.678e-05   0.09926
## LIPN         LIPN 6.852e-03   0.47369
## KRT17       KRT17 5.861e-01   0.99598
## ADAMTS17 ADAMTS17 1.844e-01   0.99433
## KBTBD11   KBTBD11 6.888e-06   0.04428
## CMSS1       CMSS1 1.561e-04   0.13098
## CREM         CREM 4.686e-04   0.15856
## MYO5C       MYO5C 9.445e-03   0.53818
## GPR3         GPR3 9.244e-02   0.92149
## GNG4         GNG4 2.665e-01   0.99433
## THAP2       THAP2 6.334e-03   0.46871
## SV2B         SV2B 3.161e-01   0.99433
## CASP9       CASP9 3.884e-04   0.14458
## SGIP1       SGIP1 4.732e-02   0.82196
## COBL         COBL 5.187e-03   0.44874
## TGFA         TGFA 4.926e-02   0.82814
## AREG         AREG 9.583e-02   0.92296
## CXCR4       CXCR4 1.016e-04   0.11555
## TGFB2       TGFB2 1.503e-01   0.99433
## ALPK2       ALPK2 2.187e-01   0.99433
## RBKS         RBKS 1.971e-01   0.99433
## MYOM2       MYOM2 4.319e-04   0.15009
## AKAP12     AKAP12 1.345e-02   0.58700
## BEX1         BEX1 8.732e-02   0.91475

1.29.5 Figure 3C bottom-right

##            Symbol   P.Value adj.P.Val
## IL12B       IL12B 6.948e-05   0.09926
## CCL3L1     CCL3L1 1.038e-03   0.24713
## CCL3L3     CCL3L3 1.038e-03   0.24713
## CCL8         CCL8 9.417e-04   0.23733
## CCL1         CCL1 1.647e-02   0.63204
## TIFAB       TIFAB 3.711e-03   0.41853
## CSF2         CSF2 4.480e-03   0.44388
## UNC13A     UNC13A 1.836e-02   0.65262
## IL6           IL6 2.380e-02   0.70136
## CCL4L2     CCL4L2 1.147e-03   0.24991
## CMKLR1     CMKLR1 1.508e-03   0.29448
## SERPINE1 SERPINE1 1.581e-02   0.62443
## KCNA3       KCNA3 2.625e-03   0.38893
## CCL2         CCL2 2.135e-02   0.67555
## F3             F3 1.078e-04   0.11555
## CCL15       CCL15 5.612e-04   0.18039
## CFAP46     CFAP46 1.137e-02   0.56691
## CASS4       CASS4 1.121e-02   0.56691
## OCSTAMP   OCSTAMP 7.040e-04   0.19903
## GP1BA       GP1BA 1.109e-02   0.56575

1.29.6 Figure 2A

Kajal responded to my query about this figure, and I am a little embarrassed to say that I should have seen what it is. It seems to me that it is a bit redundant though with 3c, as it is precisely a subset of that data, though slightly reordered.

1.29.7 Figure 2D

I created a table of shared DEG between L+PGE2/LPS and L+Ado/LPS (table attached below) by merging the 2 DE tables based on common gene symbol. In this table, logFC.x corresponds to L+Ado/LPS and logFC.y corresponds to L+PGE2/LPS. I averaged the fold changes between logFC.x and logFC.y in order to determine the rank order of the genes in the figure but plotted the fold changes individually. Instead of plotting log2FC I manually converted those values to just FC. I don’t know how to calculate standard error if I am plotting just FC. It would be nice if this could all be done in R.

2 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

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2.1 Rich Factor Graphs

2.2 GO terms shared LA LP

3 Volcano plot M_LPS_4 v M_LP_4

3.1 Figure 3B top-left

##                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

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3.1.1 The number of up significant genes

## [1] 232
## [1] "ENSG00000121966" "ENSG00000182568" "ENSG00000176595" "ENSG00000198814"
## [5] "ENSG00000112394" "ENSG00000109321"

3.1.2 The number of down significant genes

## [1] 257
## [1] "ENSG00000166068" "ENSG00000089041" "ENSG00000140968" "ENSG00000196449"
## [5] "ENSG00000181915" "ENSG00000104549"

4 Volcano plot M_LPS_4 v M_LA_4

4.1 Figure 3B top-right

##                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

4.1.1 The number of up significant genes

## [1] 128
## [1] "ENSG00000139112" "ENSG00000095794" "ENSG00000132906" "ENSG00000124466"
## [5] "ENSG00000163235" "ENSG00000164674"

4.1.2 The number of down significant genes

## [1] 128
## [1] "ENSG00000068305" "ENSG00000140968" "ENSG00000125657" "ENSG00000089041"
## [5] "ENSG00000149635" "ENSG00000117525"

5 Volcano plot GM_LPS_4 v GM_LA_4

5.1 Figure 3B bottom-right

##                ID   logFC AveExpr      t   P.Value adj.P.Val     B
## 1 ENSG00000163235  1.6278  4.9419  6.048 1.005e-06  0.006463 5.477
## 2 ENSG00000099625  1.9849  0.7463  6.726 1.459e-07  0.001876 4.817
## 3 ENSG00000108702 -1.4830  2.3455 -5.497 4.914e-06  0.021061 4.032
## 4 ENSG00000095794  1.3592  6.0086  5.201 1.155e-05  0.026752 3.310
## 5 ENSG00000071205 -0.6386  5.8815 -5.174 1.248e-05  0.026752 3.245
## 6 ENSG00000166886 -1.0612  4.2945 -5.092 1.584e-05  0.029088 2.971

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

5.1.1 The number of up significant genes

## [1] 5
## [1] "ENSG00000163235" "ENSG00000099625" "ENSG00000095794" "ENSG00000197555"
## [5] "ENSG00000128422"

5.1.2 The number of down significant genes

## [1] 2
## [1] "ENSG00000108702" "ENSG00000166886"

6 Volcano plot GM_LPS_4 v GM_LP_4

6.1 Figure 3B bottom-left

So, it appears I get the inverse of what is in the figure. Did the contrast get reversed?

I went back to the section which defines this, it has the heading ‘GM_LPS_v_GM_LP’ and the contrast appears to me to be correct. At least it is written like this:

GM_LPS.GM_LP.contr.mat <- makeContrasts(GM_LPSvGM_LP=((condGM_LPS-condGM_NS)-(condGM_LP-condGM_NS)), levels=v$design)

My inclination is therefore that this got flipped?

##                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

6.1.1 The number of up significant genes

## [1] 104
## [1] "ENSG00000163235" "ENSG00000095794" "ENSG00000099625" "ENSG00000120875"
## [5] "ENSG00000188042" "ENSG00000197872"

6.1.2 The number of down significant genes

## [1] 22
## [1] "ENSG00000089041" "ENSG00000108702" "ENSG00000136997" "ENSG00000105810"
## [5] "ENSG00000181856" "ENSG00000244398"

6.1.3 Figure 2B

I think the code I used for the venn diagrams is in the Rmarkdown file towards the end. However, I have 2 files saved for the venn diagrams that are different from what I have in figure 2B. I think I need to swap the current 2B with the venn diagrams I have attached below because the numbers here match with the volcano plots. Are these the numbers you are getting also?

6.1.3.3 Correlation of logFCs for LP/LPS and LA/LPS

One request in our conversation was to have a calculation of the correlation coefficient between the LP/LPS and LA/LPS comparisons.

## 
##  Pearson's product-moment correlation
## 
## data:  m_cor_table[["logFC.x"]] and m_cor_table[["logFC.y"]]
## t = 138, df = 12856, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7646 0.7786
## sample estimates:
##    cor 
## 0.7717
## 
##  Pearson's product-moment correlation
## 
## data:  gm_cor_table[["logFC.x"]] and gm_cor_table[["logFC.y"]]
## t = 74, df = 12856, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5368 0.5610
## sample estimates:
##   cor 
## 0.549

---
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 libraries, warning=FALSE, message=FALSE, include=FALSE}
library(devtools)
library(tximport)
library(biomaRt)
library(hpgltools)
library(DESeq2)
library(gplots)
library(ggplot2)
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("sample_sheets/MetaData only 4 hour.txt", header=TRUE, 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$counts, "TPM_MvGM_CDS_20180711.txt",
            col.names=TRUE, row.names=FALSE, quote=FALSE)
write.csv(txi.kallisto.tsv$counts, "csv/TPM_MvGM_20180711.csv",
          row.names=TRUE, quote=FALSE)

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")
```

## My Barplot of counts

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

plot_libsize(stim_expt)$plot
```

## 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
```

## 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
```

## 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)
```

## 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
```

## 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, ]
```

## 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$Patient), start=0, end=0.8)
patient.color <- rep(0, length(design$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$Patient), start=0, end=0.8)
stim_patient.color <- rep(0, length(stim_design$Patient))
for (i in 1:length(stim_design$Patient)) {
  idx <- stim_design$Patient[i] == levels(stim_design$Patient)
  stim_patient.color[i] <- stim_vec.patient[idx]
}
stim_vec.condition <- c("green", "lightblue", "pink", "purple")
stim_condition.color <- rep(0, length(stim_design$Stimulation))
for (i in 1:length(stim_design$Stimulation)) {
  idx <- stim_design$Stimulation[i] == levels(stim_design$Stimulation)
  stim_condition.color[i] <- stim_vec.condition[idx]
}
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_expt)
stim_disheat <- plot_disheat(stim_expt)
```

## 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
```

## 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 <- "csv/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, "csv/DEG_M_LPSvM_NS_CDS_limmabatchcorrection_20171120.txt",
            col.names=TRUE, row.names=FALSE, quote=FALSE)
write.csv(DEG_LPS, "csv/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 <- "csv/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, "images/MAplot_M_LPSvM_LA_CDS_limmabatchcorrection_20171120rev.pdf",
         width=8, height=8)
dev.off()
dev.copy(png, "images/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, "csv/DEG_M_LPSvM_LA_CDS_limmabatchcorrection_20171120rev.txt",
            col.names=TRUE, row.names=FALSE, quote=FALSE)
write.csv(DEG, "csv/DEG_M_LPSvM_LA_CDS_limmabatchcorrection_20171120rev.csv",
          row.names=FALSE, quote=FALSE)
```

## 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 <- "csv/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, "images/MAplot_M_LPSvM_LP_CDS_limmabatchcorrection_20171120rev.pdf",
         width=8, height=8)
dev.off()
dev.copy(png, "images/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, "csv/DEG_M_LPSvM_LP_CDS_limmabatchcorrection_20171120rev.txt",
            col.names=TRUE, row.names=FALSE, quote=FALSE)
write.csv(DEG, "csv/DEG_M_LPSvM_LP_CDS_limmabatchcorrection_20171120rev.csv",
          row.names=FALSE, quote=FALSE)
```

## 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 <- "csv/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, "images/MAplot_M_LPvM_NS_CDS_limmabatchcorrection_20171018.pdf",
         width=8, height=8)
dev.off()
dev.copy(png, "images/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, "csv/DEG_M_LPvM_NS_CDS_limmabatchcorrection_20171018.txt",
            col.names=TRUE, row.names=FALSE, quote=FALSE)
write.csv(DEG_LP4, "csv/DEG_M_LPvM_NS_CDS_limmabatchcorrection_20171018.csv",
          row.names=FALSE, quote=FALSE)
```

## 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 <- "csv/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, "images/MAplot_M_LAvM_NS_CDS_limmabatchcorrection_20171120.pdf", width=8, height=8)
dev.off()
dev.copy(png, "images/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, "csv/DEG_M_LAvM_NS_CDS_limmabatchcorrection_20171120.txt",
            col.names=TRUE, row.names=FALSE, quote=FALSE)
write.csv(DEG_LA4, "csv/DEG_M_LAvM_NS_CDS_limmabatchcorrection_20171120.csv",
          row.names=FALSE, quote=FALSE)
```

## 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 <- "csv/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, "images/MAplot_GM_LPSvGM_NS_CDS_limmabatchcorrection_20171120.pdf",
         width=8, height=8)
dev.off()
dev.copy(png, "images/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, "csv/DEG_GM_LPSvGM_NS_CDS_limmabatchcorrection_20171120.txt",
            col.names=TRUE, row.names=FALSE, quote=FALSE)
write.csv(DEG, "csv/DEG_GM_LPSvGM_NS_CDS_limmabatchcorrection_20171120.csv",
          row.names=FALSE, quote=FALSE)
```

## GM_LPS v GM_LP

This contrast was written in the opposite order as the others, I think this is
the reason some of my plots keep looking backwards/upsidedown...  I am going to
comment out the original line and rewrite it so that it is identical in order to
what I found in: M_LP.M_LP.contr.mat.

```{r}
#eBayes finds an F-statistic from the set of t-statistics for that gene

## Here is the original line
## look here ----------------------------------------------->   v                     v
##GM_LPS.GM_LP.contr.mat <- makeContrasts(GM_LPSvGM_LP=((condGM_LPS-condGM_NS)-(condGM_LP-condGM_NS)),
##                                        levels=v$design)
##

## And here is my version:
## look here ----------------------------------------------->  v                     v
GM_LPS.GM_LP.contr.mat <- makeContrasts(
    GM_LPSvGM_LP=((condGM_LP - condGM_NS) - (condGM_LPS - condGM_NS)),
    levels=v$design)
## End of my change.

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 <- "csv/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, "images/MAplot_GM_LPSvGM_LP_CDS_limmabatchcorrection_20171120.pdf",
         width=8, height=8)
dev.off()
dev.copy(png, "images/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, "csv/DEG_GM_LPSvGM_LP_CDS_limmabatchcorrection_20171120.txt",
            col.names=TRUE, row.names=FALSE, quote=FALSE)
write.csv(DEG, "csv/DEG_GM_LPSvGM_LP_CDS_limmabatchcorrection_20171120.csv",
          row.names=FALSE, quote=FALSE)
```

## GM_LPS v GM_LA

I think I observe the same flipping here.

```{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$design)
GM_LPS.GM_LA.contr.mat <- makeContrasts(
    GM_LPSvGM_LA=((condGM_LA - condGM_NS) - (condGM_LPS - condGM_NS)),
    levels=v$design)
## End of my change
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 <- "csv/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, "images/MAplot_GM_LPSvGM_LA_CDS_limmabatchcorrection_20171120.pdf",
         width=8, height=8)
dev.off()
dev.copy(png, "images/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, "csv/DEG_GM_LPSvGM_LA_CDS_limmabatchcorrection_20171120.txt",
            col.names=TRUE, row.names=FALSE, quote=FALSE)
write.csv(DEG, "csv/DEG_GM_LPSvGM_LA_CDS_limmabatchcorrection_20171120.csv",
          row.names=FALSE, quote=FALSE)
```

## Barplots of Fold-Changes, e.g. Figure 3C/D

It appears to me that these figures are created by merging the M/GM tables for
the LP-LPS and LA-LPS contrasts, taking the top ~20 for the M table, and
plotting the log2FC on the linear scale.  I believe I can trivially modify this
to add the logFC / t-stat.  The caveat will be that doing this gives me a log2
error bar, not linear...  My inclination therefore is to just plot the logFC
rather than convert it to linear, but whatever.

### Figure 3C

Najib and Dave want some significance test between the M/GM bars for these 20
genes.  As I think about it, I think the only valid way to do this is to
calculate the logFC of GM(PG/LP) / M(PG/LP).  Then let limma calculate the t
statistic and p-value using its voom() modified values.

```{r my_fig3c_stats}
GM_PG.M_PG.contr.mat <- makeContrasts(
    GM_PGvM_PG=((condGM_LP - condGM_LPS) - (condM_LP - condM_LPS)),
    levels=v$design)
GM_PG.M_PG.fit <- contrasts.fit(fit, GM_PG.M_PG.contr.mat)
GM_PG.M_PG.eb <- eBayes(GM_PG.M_PG.fit)
GM_PG.M_PG.topTab <- topTable(GM_PG.M_PG.eb, coef="GM_PGvM_PG", number=nrow(v$E))

desc <- getBM(attributes=c("ensembl_gene_id", "hgnc_symbol", "description", "gene_biotype"),
              filters="ensembl_gene_id", values=rownames(GM_PG.M_PG.topTab), mart=ensembl)
colnames(desc) <- c("ID", "Symbol", "Description", "Type")
gene_symbols <- desc[, c("ID", "Symbol")]

GM_PG.M_PG.topTab <- merge(gene_symbols, GM_PG.M_PG.topTab, by.x="ID", by.y="row.names")

GM_LA.M_LA.contr.mat <- makeContrasts(
    GM_LAvM_LA=((condGM_LA - condGM_LPS) - (condM_LA - condM_LPS)),
    levels=v$design)
GM_LA.M_LA.fit <- contrasts.fit(fit, GM_LA.M_LA.contr.mat)
GM_LA.M_LA.eb <- eBayes(GM_LA.M_LA.fit)
GM_LA.M_LA.topTab <- topTable(GM_LA.M_LA.eb, coef="GM_LAvM_LA", number=nrow(v$E))

GM_LA.M_LA.topTab <- merge(gene_symbols, GM_LA.M_LA.topTab, by.x="ID", by.y="row.names")
```

### Figure 3C top-left

Kajal's variables to create this are...

1.  M_LPS.M_LP.topTab
2.  GM_LPS.GM_LP.topTab

These were passed to biomart to get the gene names rather than ensembl IDs.
I am going to be lazy and just copy/paste Kajal's code for these tasks.

Having done the top-left piece of this, it seems to me that if you are going to
take the top 20 genes and exclude based on the M-CSF adjusted p-value, perhaps
you should also exclude based on the GM-CSF adjusted p-value, but the way this
was done, only the M is used.  In its current state, I am only using the M as
per the figures in their current state.

Note, that if you want error bars from the standard error, then it is waaaay
easier to stay on the log2 scale rather than convert back to linear because the
math for converting the standard error back to linear is weird.  If the table
had a few more parameters in it, I could do it without struggling, but it doesn't.

```{r fig3c_topleft}
wanted_columns <- c("ID", "logFC.x", "logFC.y", "t.x", "t.y", "Symbol")
renamed_columns <- c("ID", "m_logfc", "gm_logfc", "m_t", "gm_t", "symbol")

fig3c_lp_df <- merge(M_LPS.M_LP.topTab, GM_LPS.GM_LP.topTab, by="ID")
desc <- getBM(attributes=c("ensembl_gene_id", "hgnc_symbol", "description", "gene_biotype"),
              filters="ensembl_gene_id", values=fig3c_lp_df[["ID"]], mart=ensembl)
colnames(desc) <- c("ID", "Symbol", "Description", "Type")
fig3c_lp_df <- merge(fig3c_lp_df, desc, by="ID", all.x=TRUE)

tl_order_idx <- order(fig3c_lp_df[["logFC.x"]], decreasing=TRUE)
fig3c_tl_df <- fig3c_lp_df[tl_order_idx, ]
sig_idx <- fig3c_tl_df[["adj.P.Val.x"]] <= 0.05
fig3c_tl_df <- head(fig3c_tl_df[sig_idx, ], n=25)
fig3c_tl_df <- fig3c_tl_df[, wanted_columns]
colnames(fig3c_tl_df) <- renamed_columns
rownames(fig3c_tl_df) <- fig3c_tl_df[["ID"]]
fig3c_tl_df[["ID"]] <- NULL
fig3c_tl_df[["m_lfcerr"]] <- fig3c_tl_df[["m_logfc"]] / fig3c_tl_df[["m_t"]]
fig3c_tl_df[["gm_lfcerr"]] <- fig3c_tl_df[["gm_logfc"]] / fig3c_tl_df[["gm_t"]]

fig3c_tl_m <- fig3c_tl_df[, c("m_logfc", "symbol", "m_lfcerr")]
fig3c_tl_m[["type"]] <- "m"
colnames(fig3c_tl_m) <- c("logfc", "symbol", "lfcerr", "type")
fig3c_tl_gm <- fig3c_tl_df[, c("gm_logfc", "symbol", "gm_lfcerr")]
fig3c_tl_gm[["type"]] <- "gm"
colnames(fig3c_tl_gm) <- c("logfc", "symbol", "lfcerr", "type")
melted_fig3c_tl <- rbind(fig3c_tl_m, fig3c_tl_gm)
melted_fig3c_tl[["symbol"]] <- factor(melted_fig3c_tl[["symbol"]],
                                      levels=fig3c_tl_m[["symbol"]])
melted_fig3c_tl[["type"]] <- factor(melted_fig3c_tl[["type"]],
                                    levels=c("m", "gm"))

p <- ggplot(data=melted_fig3c_tl, aes(x=symbol, y=logfc, fill=type)) +
  geom_bar(stat="identity", color="black", position=position_dodge())+
  scale_fill_manual(values=c("cornflowerblue", "darkgrey")) +
  geom_errorbar(aes(ymin=logfc - (lfcerr / 2), ymax=logfc + (lfcerr / 2)), width=.2,
                position=position_dodge(0.9)) +
  theme_minimal() +
  theme(axis.text.x=element_text(angle=45, hjust=1, vjust=0.5))
p

sig_idx <- GM_PG.M_PG.topTab[["Symbol"]] %in% fig3c_tl_gm[["symbol"]]
sig_values <- GM_PG.M_PG.topTab[sig_idx, c("Symbol", "P.Value", "adj.P.Val")]
rownames(sig_values) <- sig_values[["Symbol"]]
sig_values[levels(melted_fig3c_tl[["symbol"]]), ]
```

### Figure 3C bottom-left

```{r fig3C_bottomleft}
bl_order_idx <- order(fig3c_lp_df[["logFC.x"]], decreasing=FALSE)
fig3c_bl_df <- fig3c_lp_df[bl_order_idx, ]
sig_idx <- fig3c_bl_df[["adj.P.Val.x"]] <= 0.05
fig3c_bl_df <- head(fig3c_bl_df[sig_idx, ], n=20)
fig3c_bl_df <- fig3c_bl_df[, wanted_columns]
colnames(fig3c_bl_df) <- renamed_columns
fig3c_bl_df[["ID"]] <- NULL
fig3c_bl_df[["m_lfcerr"]] <- fig3c_bl_df[["m_logfc"]] / fig3c_bl_df[["m_t"]]
fig3c_bl_df[["gm_lfcerr"]] <- fig3c_bl_df[["gm_logfc"]] / fig3c_bl_df[["gm_t"]]

fig3c_bl_m <- fig3c_bl_df[, c("m_logfc", "symbol", "m_lfcerr")]
fig3c_bl_m[["type"]] <- "m"
colnames(fig3c_bl_m) <- c("logfc", "symbol", "lfcerr", "type")
fig3c_bl_gm <- fig3c_bl_df[, c("gm_logfc", "symbol", "gm_lfcerr")]
fig3c_bl_gm[["type"]] <- "gm"
colnames(fig3c_bl_gm) <- c("logfc", "symbol", "lfcerr", "type")
melted_fig3c_bl <- rbind(fig3c_bl_m, fig3c_bl_gm)
melted_fig3c_bl[["symbol"]] <- factor(melted_fig3c_bl[["symbol"]],
                                      levels=fig3c_bl_m[["symbol"]])
melted_fig3c_bl[["type"]] <- factor(melted_fig3c_bl[["type"]],
                                    levels=c("m", "gm"))

p <- ggplot(data=melted_fig3c_bl, aes(x=symbol, y=logfc, fill=type)) +
  geom_bar(stat="identity", color="black", position=position_dodge())+
  scale_fill_manual(values=c("cornflowerblue", "darkgrey")) +
  geom_errorbar(aes(ymin=logfc-lfcerr, ymax=logfc+lfcerr), width=.2,
                position=position_dodge(0.9)) +
  theme_minimal() +
  theme(axis.text.x=element_text(angle=45, hjust=1, vjust=0.5))
p

sig_idx <- GM_PG.M_PG.topTab[["Symbol"]] %in% fig3c_bl_gm[["symbol"]]
sig_values <- GM_PG.M_PG.topTab[sig_idx, c("Symbol", "P.Value", "adj.P.Val")]
rownames(sig_values) <- sig_values[["Symbol"]]
sig_values[levels(melted_fig3c_bl[["symbol"]]), ]
```

### Figure 3C top-right

The right side of the plot is LA rather than LP, otherwise this is the same.

```{r fig3c_topright}
wanted_columns <- c("ID", "logFC.x", "logFC.y", "t.x", "t.y", "Symbol")
renamed_columns <- c("ID", "m_logfc", "gm_logfc", "m_t", "gm_t", "symbol")

fig3c_la_df <- merge(M_LPS.M_LA.topTab, GM_LPS.GM_LA.topTab, by="ID")
desc <- getBM(attributes=c("ensembl_gene_id", "hgnc_symbol", "description", "gene_biotype"),
              filters="ensembl_gene_id", values=fig3c_la_df[["ID"]], mart=ensembl)
colnames(desc) <- c("ID", "Symbol", "Description", "Type")
fig3c_la_df <- merge(fig3c_la_df, desc, by="ID", all.x=TRUE)

tr_order_idx <- order(fig3c_la_df[["logFC.x"]], decreasing=TRUE)
fig3c_tr_df <- fig3c_la_df[tr_order_idx, ]
sig_idx <- fig3c_tr_df[["adj.P.Val.x"]] <= 0.05
fig3c_tr_df <- head(fig3c_tr_df[sig_idx, ], n=25)
fig3c_tr_df <- fig3c_tr_df[, wanted_columns]
colnames(fig3c_tr_df) <- renamed_columns
rownames(fig3c_tr_df) <- fig3c_tr_df[["ID"]]
fig3c_tr_df[["ID"]] <- NULL
fig3c_tr_df[["m_lfcerr"]] <- fig3c_tr_df[["m_logfc"]] / fig3c_tr_df[["m_t"]]
fig3c_tr_df[["gm_lfcerr"]] <- fig3c_tr_df[["gm_logfc"]] / fig3c_tr_df[["gm_t"]]

fig3c_tr_m <- fig3c_tr_df[, c("m_logfc", "symbol", "m_lfcerr")]
fig3c_tr_m[["type"]] <- "m"
colnames(fig3c_tr_m) <- c("logfc", "symbol", "lfcerr", "type")
fig3c_tr_gm <- fig3c_tr_df[, c("gm_logfc", "symbol", "gm_lfcerr")]
fig3c_tr_gm[["type"]] <- "gm"
colnames(fig3c_tr_gm) <- c("logfc", "symbol", "lfcerr", "type")
melted_fig3c_tr <- rbind(fig3c_tr_m, fig3c_tr_gm)
melted_fig3c_tr[["symbol"]] <- factor(melted_fig3c_tr[["symbol"]],
                                      levels=fig3c_tr_m[["symbol"]])
melted_fig3c_tr[["type"]] <- factor(melted_fig3c_tr[["type"]],
                                    levels=c("m", "gm"))

p <- ggplot(data=melted_fig3c_tr, aes(x=symbol, y=logfc, fill=type)) +
  geom_bar(stat="identity", color="black", position=position_dodge())+
  scale_fill_manual(values=c("cornflowerblue", "darkgrey")) +
  geom_errorbar(aes(ymin=logfc-lfcerr, ymax=logfc+lfcerr), width=.2,
                position=position_dodge(0.9)) +
  theme_minimal() +
  theme(axis.text.x=element_text(angle=45, hjust=1, vjust=0.5))
p

sig_idx <- GM_LA.M_LA.topTab[["Symbol"]] %in% fig3c_tr_gm[["symbol"]]
sig_values <- GM_LA.M_LA.topTab[sig_idx, c("Symbol", "P.Value", "adj.P.Val")]
rownames(sig_values) <- sig_values[["Symbol"]]
sig_values[levels(melted_fig3c_tr[["symbol"]]), ]
```

### Figure 3C bottom-right

```{r fig3C_bottomright}
br_order_idx <- order(fig3c_la_df[["logFC.x"]], decreasing=FALSE)
fig3c_br_df <- fig3c_la_df[br_order_idx, ]
sig_idx <- fig3c_br_df[["adj.P.Val.x"]] <= 0.05
fig3c_br_df <- head(fig3c_br_df[sig_idx, ], n=20)
fig3c_br_df <- fig3c_br_df[, wanted_columns]
colnames(fig3c_br_df) <- renamed_columns
fig3c_br_df[["ID"]] <- NULL
fig3c_br_df[["m_lfcerr"]] <- fig3c_br_df[["m_logfc"]] / fig3c_br_df[["m_t"]]
fig3c_br_df[["gm_lfcerr"]] <- fig3c_br_df[["gm_logfc"]] / fig3c_br_df[["gm_t"]]

fig3c_br_m <- fig3c_br_df[, c("m_logfc", "symbol", "m_lfcerr")]
fig3c_br_m[["type"]] <- "m"
colnames(fig3c_br_m) <- c("logfc", "symbol", "lfcerr", "type")
fig3c_br_gm <- fig3c_br_df[, c("gm_logfc", "symbol", "gm_lfcerr")]
fig3c_br_gm[["type"]] <- "gm"
colnames(fig3c_br_gm) <- c("logfc", "symbol", "lfcerr", "type")
melted_fig3c_br <- rbind(fig3c_br_m, fig3c_br_gm)
melted_fig3c_br[["symbol"]] <- factor(melted_fig3c_br[["symbol"]],
                                      levels=fig3c_br_m[["symbol"]])
melted_fig3c_br[["type"]] <- factor(melted_fig3c_br[["type"]],
                                    levels=c("m", "gm"))

p <- ggplot(data=melted_fig3c_br, aes(x=symbol, y=logfc, fill=type)) +
  geom_bar(stat="identity", color="black", position=position_dodge())+
  scale_fill_manual(values=c("cornflowerblue", "darkgrey")) +
  geom_errorbar(aes(ymin=logfc-lfcerr, ymax=logfc+lfcerr), width=.2,
                position=position_dodge(0.9)) +
  theme_minimal() +
  theme(axis.text.x=element_text(angle=45, hjust=1, vjust=0.5))
p

sig_idx <- GM_LA.M_LA.topTab[["Symbol"]] %in% fig3c_br_gm[["symbol"]]
sig_values <- GM_LA.M_LA.topTab[sig_idx, c("Symbol", "P.Value", "adj.P.Val")]
rownames(sig_values) <- sig_values[["Symbol"]]
sig_values[levels(melted_fig3c_br[["symbol"]]), ]
```

### Figure 2A

Kajal responded to my query about this figure, and I am a little embarrassed to
say that I should have seen what it is.  It seems to me that it is a bit
redundant though with 3c, as it is precisely a subset of that data, though
slightly reordered.

#### Figure 2A Left

```{r fig2a1}
fig2a_df <- M_LPS.M_LP.sigGenes
order_idx <- order(fig2a_df[["logFC"]], decreasing=FALSE)
fig2a_df <- fig2a_df[order_idx, ]
top <- head(fig2a_df, n=10)
bot <- tail(fig2a_df, n=10)
plotted <- rbind(top, bot)
rownames(plotted) <- plotted[["ID"]]
desc_idx <- desc[["ID"]] %in% plotted[["ID"]]
this_desc <- desc[desc_idx, ]
current <- rownames(plotted)
plotted <- merge(this_desc, plotted, by.x="ID", by.y="ID", all=TRUE)
rownames(plotted) <- make.names(plotted[["ID"]], unique=TRUE)
plotted <- plotted[current, ]
plotted[["lfcerr"]] <- plotted[["logFC"]] / plotted[["t"]]
plotted[["Symbol"]] <- factor(plotted[["Symbol"]], levels=plotted[["Symbol"]])

p <- ggplot(data=plotted, aes(x=Symbol, y=logFC)) +
  geom_bar(stat="identity", fill="#89A8DD", color="black", position=position_dodge())+
  geom_errorbar(aes(ymin=logFC - (lfcerr / 2), ymax=logFC + (lfcerr / 2)), width=0.2,
                position=position_dodge(0.9)) +
  coord_flip() +
  theme_minimal() +
  theme(axis.text.x=element_text(angle=45, hjust=1, vjust=0.5))
p
```

#### Figure 2A Right

```{r fig2a}
fig2a_df <- M_LPS.M_LA.sigGenes
order_idx <- order(fig2a_df[["logFC"]], decreasing=FALSE)
fig2a_df <- fig2a_df[order_idx, ]
top <- head(fig2a_df, n=10)
bot <- tail(fig2a_df, n=10)
plotted <- rbind(top, bot)
rownames(plotted) <- plotted[["ID"]]
desc_idx <- desc[["ID"]] %in% plotted[["ID"]]
this_desc <- desc[desc_idx, ]
current <- rownames(plotted)
plotted <- merge(this_desc, plotted, by.x="ID", by.y="ID", all=TRUE)
rownames(plotted) <- make.names(plotted[["ID"]], unique=TRUE)
plotted <- plotted[current, ]
plotted[["lfcerr"]] <- plotted[["logFC"]] / plotted[["t"]]
plotted[["Symbol"]] <- factor(plotted[["Symbol"]], levels=plotted[["Symbol"]])

p <- ggplot(data=plotted, aes(x=Symbol, y=logFC)) +
  geom_bar(stat="identity", fill="#7BD19C", color="black", position=position_dodge())+
  geom_errorbar(aes(ymin=logFC - (lfcerr / 2), ymax=logFC + (lfcerr / 2)), width=0.2,
                position=position_dodge(0.9)) +
  coord_flip() +
  theme_minimal() +
  theme(axis.text.x=element_text(angle=45, hjust=1, vjust=0.5))
p
```

### Figure 2D

I created a table of shared DEG between L+PGE2/LPS and L+Ado/LPS (table attached
below) by merging the 2 DE tables based on common gene symbol.  In this table,
logFC.x corresponds to L+Ado/LPS and logFC.y corresponds to L+PGE2/LPS.  I
averaged the fold changes between logFC.x and logFC.y in order to determine the
rank order of the genes in the figure but plotted the fold changes
individually.  Instead of plotting log2FC I manually converted those values to
just FC. I don’t know how to calculate standard error if I am plotting just FC.
It would be nice if this could all be done in R.

```{r fig2dt}
fig2d_df <- merge(M_LPS.M_LP.sigGenes, M_LPS.M_LA.sigGenes, by="ID")
fig2d_df <- merge(fig2d_df, desc, by="ID")
fig2d_df[["avg"]] <- (fig2d_df[["logFC.x"]] + fig2d_df[["logFC.y"]]) / 2

order_idx <- order(fig2d_df[["avg"]], decreasing=TRUE)
fig2dt_df <- head(fig2d_df[order_idx, ], n=22)
fig2dt_df[["lp_lfcerr"]] <- fig2dt_df[["logFC.x"]] / fig2dt_df[["t.x"]]
fig2dt_df[["la_lfcerr"]] <- fig2dt_df[["logFC.y"]] / fig2dt_df[["t.y"]]

fig2dt_lp <- fig2dt_df[, c("Symbol", "logFC.x", "lp_lfcerr")]
colnames(fig2dt_lp) <- c("symbol", "logfc", "lfcerr")
fig2dt_lp[["type"]] <- "LPS+PGE"
fig2dt_la <- fig2dt_df[, c("Symbol", "logFC.y", "la_lfcerr")]
colnames(fig2dt_la) <- c("symbol", "logfc", "lfcerr")
fig2dt_la[["type"]] <- "LPS+Ado"
melted_fig2dt <- rbind(fig2dt_la, fig2dt_lp)
melted_fig2dt[["symbol"]] <- factor(melted_fig2dt[["symbol"]],
                                    levels=unique(melted_fig2dt[["symbol"]]))
melted_fig2dt[["type"]] <- factor(melted_fig2dt[["type"]], levels=c("LPS+Ado", "LPS+PGE"))

p <- ggplot(data=melted_fig2dt, aes(x=symbol, y=logfc, fill=type)) +
  geom_bar(stat="identity", color="black", position=position_dodge())+
  scale_fill_manual(values=c("#7BD19C", "#89A8DD")) +
  geom_errorbar(aes(ymin=logfc - (lfcerr / 2), ymax=logfc + (lfcerr / 2)), width=0.2,
                position=position_dodge(0.9)) +
  theme_minimal() +
  theme(axis.text.x=element_text(angle=45, hjust=1, vjust=0.5))
p
```

```{r fig2db}
fig2d_df <- merge(M_LPS.M_LP.sigGenes, M_LPS.M_LA.sigGenes, by="ID")
fig2d_df <- merge(fig2d_df, desc, by="ID")
fig2d_df[["avg"]] <- (fig2d_df[["logFC.x"]] + fig2d_df[["logFC.y"]]) / 2

order_idx <- order(fig2d_df[["avg"]], decreasing=FALSE)
fig2db_df <- head(fig2d_df[order_idx, ], n=22)
fig2db_df[["lp_lfcerr"]] <- fig2db_df[["logFC.x"]] / fig2db_df[["t.x"]]
fig2db_df[["la_lfcerr"]] <- fig2db_df[["logFC.y"]] / fig2db_df[["t.y"]]

fig2db_lp <- fig2db_df[, c("Symbol", "logFC.x", "lp_lfcerr")]
colnames(fig2db_lp) <- c("symbol", "logfc", "lfcerr")
fig2db_lp[["type"]] <- "LPS+PGE"
fig2db_la <- fig2db_df[, c("Symbol", "logFC.y", "la_lfcerr")]
colnames(fig2db_la) <- c("symbol", "logfc", "lfcerr")
fig2db_la[["type"]] <- "LPS+Ado"
melted_fig2db <- rbind(fig2db_la, fig2db_lp)
melted_fig2db[["symbol"]] <- factor(melted_fig2db[["symbol"]],
                                    levels=unique(melted_fig2db[["symbol"]]))
melted_fig2db[["type"]] <- factor(melted_fig2db[["type"]], levels=c("LPS+Ado", "LPS+PGE"))

p <- ggplot(data=melted_fig2db, aes(x=symbol, y=logfc, fill=type)) +
  geom_bar(stat="identity", color="black", position=position_dodge())+
  scale_fill_manual(values=c("#7BD19C", "#89A8DD")) +
  geom_errorbar(aes(ymin=logfc - (lfcerr / 2), ymax=logfc + (lfcerr / 2)), width=0.2,
                position=position_dodge(0.9)) +
  theme_minimal() +
  theme(axis.text.x=element_text(angle=45, hjust=1, vjust=0.5))
p
```

## 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)
Vennerable::plot(v_data, doWeights=FALSE)
## 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)
Vennerable::plot(v_data, doWeights=FALSE)
## 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)
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 < 0.05 ),
     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 < 0.05 & abs(logFC) > 1),
     points(logFC, -log10(P.Value), cex=0.8, pch=20, col="turquoise3"))
dev.copy(png, "images/VolcanoPlot_M_LPS_v_M_LA.png", width=700, height=700)
dev.off()
```

```{r print_numbers_downla}
sig_up_idx <- res[, "adj.P.Val"] < 0.05 & res[, "logFC"] > 1
sum(sig_up_idx)
sig_up_ids <- res[sig_up_idx, "ID"]
m_lps_m_la_up_ids <- sig_up_ids

sig_down_idx <- res[, "adj.P.Val"] < 0.05 & res[, "logFC"] < -1
sum(sig_down_idx)
sig_down_ids <- res[sig_down_idx, "ID"]
m_lps_m_la_down_ids <- sig_down_ids
```

# M_LPS v M_LP

```{r uneval_volcano}
res <- read.csv("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 < 0.05 ),
     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 < 0.05 & abs(logFC) > 1),
     points(logFC, -log10(P.Value), cex=0.8, pch=20, col="turquoise3"))
dev.copy(png, "images/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("csv/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$Rich.Factor, c(1:5), axes=FALSE,
     panel.first=abline(h=1:5, v=seq(0.1, 0.6, 0.1), col="grey90"),
     cex=as.numeric(GOSharedpathways$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("csv/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=FALSE,
     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("csv/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("csv/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$Rich.Factor, c(1:35), axes=FALSE,
     panel.first=abline(h=1:35, v=seq(0.1, 0.6, 0.1), col="grey90"),
     cex=as.numeric(GOLPpathways$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

## Figure 3B top-left

```{r first_volcano}
res <- read.csv("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 < 0.05 ), 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 < 0.05 & abs(logFC) > 1),
     points(logFC, -log10(P.Value),
            cex=0.8, pch=20, col="turquoise3"))
dev.copy(png, "images/VolcanoPlot_M_LPS_vs_M_LP.png", width=700, height=700)
dev.off()
```

### The number of up significant genes

```{r print_numbers_up1}
sig_up_idx <- res[, "adj.P.Val"] < 0.05 & res[, "logFC"] > 1
sum(sig_up_idx)
sig_up_ids <- res[sig_up_idx, "ID"]
head(sig_up_ids)
m_lps_m_lp_up_ids <- sig_up_ids
```

### The number of down significant genes

```{r print_numbers_down1}
sig_down_idx <- res[, "adj.P.Val"] < 0.05 & res[, "logFC"] < -1
sum(sig_down_idx)
sig_down_ids <- res[sig_down_idx, "ID"]
head(sig_down_ids)
m_lps_m_lp_down_ids <- sig_down_ids
```

# Volcano plot M_LPS_4 v M_LA_4

## Figure 3B top-right

```{r second_volcano}
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 < 0.05 ), 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, "images/VolcanoPlot_M_LPS_v_M_LA.png", width=700, height=700)
dev.off()
```

### The number of up significant genes

```{r print_numbers_up2}
sig_up_idx <- res[, "adj.P.Val"] < 0.05 & res[, "logFC"] > 1
sum(sig_up_idx)
sig_up_ids <- res[sig_up_idx, "ID"]
head(sig_up_ids)
m_lps_m_la_sig_ids <- sig_up_ids
```

### The number of down significant genes

```{r print_numbers_down2}
sig_down_idx <- res[, "adj.P.Val"] < 0.05 & res[, "logFC"] < -1
sum(sig_down_idx)
sig_down_ids <- res[sig_down_idx, "ID"]
head(sig_down_ids)
```

# Volcano plot GM_LPS_4 v GM_LA_4

## Figure 3B bottom-right

```{r third_volcano}
res <- read.csv(gm_lps_la_table)
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 < 0.05), 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 < 0.05 & abs(logFC) > 1),
     points(logFC, -log10(P.Value), cex=0.8, pch=20, col="turquoise3"))
dev.copy(png, "images/VolcanoPlot GM_LPS v GM_LA", width=700, height=700)
dev.off()
```

### The number of up significant genes

```{r print_numbers_up3}
sig_up_idx <- res[, "adj.P.Val"] < 0.05 & res[, "logFC"] > 1
sum(sig_up_idx)
sig_up_ids <- res[sig_up_idx, "ID"]
head(sig_up_ids)
```

### The number of down significant genes

```{r print_numbers_down3}
sig_down_idx <- res[, "adj.P.Val"] < 0.05 & res[, "logFC"] < -1
sum(sig_down_idx)
sig_down_ids <- res[sig_down_idx, "ID"]
head(sig_down_ids)
```

# Volcano plot GM_LPS_4 v GM_LP_4

## Figure 3B bottom-left

So, it appears I get the inverse of what is in the figure.  Did the contrast get reversed?

I went back to the section which defines this, it has the heading
'GM_LPS_v_GM_LP' and the contrast appears to me to be correct.  At least it is
written like this:

<code>
GM_LPS.GM_LP.contr.mat <- makeContrasts(GM_LPSvGM_LP=((condGM_LPS-condGM_NS)-(condGM_LP-condGM_NS)),
levels=v$design)
</code>

My inclination is therefore that this got flipped?

```{r fourth_volcano}
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 < 0.05 ), 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 < 0.05 & abs(logFC) > 1),
     points(logFC, -log10(P.Value), cex=0.8, pch=20, col="turquoise3"))
dev.copy(png, "images/VolcanoPlot GM_LPS v GM_LP", width=700, height=700)
dev.off()
```

### The number of up significant genes

```{r print_numbers_up4}
sig_up_idx <- res[, "adj.P.Val"] < 0.05 & res[, "logFC"] > 1
sum(sig_up_idx)
sig_up_ids <- res[sig_up_idx, "ID"]
head(sig_up_ids)
```

### The number of down significant genes

```{r print_numbers_down4}
sig_down_idx <- res[, "adj.P.Val"] < 0.05 & res[, "logFC"] < -1
sum(sig_down_idx)
sig_down_ids <- res[sig_down_idx, "ID"]
head(sig_down_ids)
```

### Figure 2B

I think the code I used for the venn diagrams is in the Rmarkdown file towards
the end.  However, I have 2 files saved for the venn diagrams that are different
from what I have in figure 2B.  I think I need to swap the current 2B with the
venn diagrams I have attached below because the numbers here match with the
volcano plots.  Are these the numbers you are getting also?

#### Attempt 1

This is just pulling the M LP/LPS LA/LPS significant genes.
This appears to not be the answer.

```{r fig2b}
lps_pge_sig <- M_LPS.M_LP.sigGenes
up_idx <- lps_pge_sig[["logFC"]] > 0
down_idx <- lps_pge_sig[["logFC"]] < 0
lps_pge_up_ids <- lps_pge_sig[up_idx, "ID"]
lps_pge_down_ids <- lps_pge_sig[down_idx, "ID"]
lps_a_sig <- M_LPS.M_LA.sigGenes
up_idx <- lps_a_sig[["logFC"]] > 0
down_idx <- lps_a_sig[["logFC"]] < 0
lps_a_up_ids <- lps_a_sig[up_idx, "ID"]
lps_a_down_ids <- lps_a_sig[down_idx, "ID"]

v_data <- list(lps_pge_up_ids, lps_a_up_ids)
v_data <- Venn(v_data, SetNames=c("LPS+PGE2", "LPS+Ado"), numberOfSets=2)
Vennerable::plot(v_data, doWeights=FALSE)

v_data <- list(lps_pge_down_ids, lps_a_down_ids)
v_data <- Venn(v_data, SetNames=c("LPS+PGE2", "LPS+Ado"), numberOfSets=2)
Vennerable::plot(v_data, doWeights=FALSE)
```

#### Attempt 2

Lets assume there is a hint in the text and that I should pull from the volcano
plot numbers for M LP/LPS and M LA/LPS

```{r attempt2}
v_data <- Venn(list(m_lps_m_lp_up_ids, m_lps_m_la_up_ids),
               SetNames=c("LPS+PGE2", "LPS+Ado"))
Vennerable::plot(v_data)

v_data <- Venn(list(m_lps_m_lp_down_ids, m_lps_m_la_down_ids),
               SetNames=c("LPS+PGE2", "LPS+Ado"))
Vennerable::plot(v_data)
```

#### Correlation of logFCs for LP/LPS and LA/LPS

One request in our conversation was to have a calculation of the correlation
coefficient between the LP/LPS and LA/LPS comparisons.

```{r lpla_cor}
m_cor_table <- merge(M_LPS.M_LP.topTab, M_LPS.M_LA.topTab, by="ID")
cor.test(x=m_cor_table[["logFC.x"]], y=m_cor_table[["logFC.y"]])

gm_cor_table <- merge(GM_LPS.GM_LP.topTab, GM_LPS.GM_LA.topTab, by="ID")
cor.test(x=gm_cor_table[["logFC.x"]], y=gm_cor_table[["logFC.y"]])

m_scatter <- plot_linear_scatter(m_cor_table[, c("logFC.x", "logFC.y")])
m_scatter$scatter

gm_scatter <- plot_linear_scatter(gm_cor_table[, c("logFC.x", "logFC.y")])
gm_scatter$scatter
```
