5.1 No batch adjust

This first plot makes no attempt to handle the various batch effects in the data.

mbio_norm <- normalize_expt(mbio_expt, transform="log2", convert="cpm", filter=TRUE, norm="quant")

## This function will replace the expt$expressionset slot with:

## log2(cpm(quant(hpgl(data))))

## It backs up the current data into a slot named:
##  expt$backup_expressionset. It will also save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep the libsizes in mind
##  when invoking limma.  The appropriate libsize is the 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

## 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: hpgl

## Removing 7814 low-count genes (11815 remaining).

## Step 2: normalizing the data with quant.

## Using normalize.quantiles.robust due to a thread error in preprocessCore.

## Step 3: converting the data with cpm.

## Step 4: transforming the data with log2.

## transform_counts: Found 1337 values equal to 0, adding 1 to the matrix.

## Step 5: not doing batch correction.

mbio_pca <- plot_pca(mbio_norm, size_column="expttime", plot_labels=FALSE,
                     size_order=c("t4h", "t24h", "t48h", "t72h"))

## Not putting labels on the plot.

##mbio_pca <- plot_pca(mbio_norm)
mbio_pca$plot

5.2 limma batch adjust

In this iteration, we use limma’s function to remove batch effect, which I think is what was used in order to make the figure in the paper. This is borne out by the fact that the image generated is nearly identical to the one in the paper.

mbio_batch1 <- normalize_expt(mbio_expt, transform="log2", convert="cpm",
                              filter=TRUE, norm="quant", batch="limma")

## This function will replace the expt$expressionset slot with:

## log2(limma(cpm(quant(hpgl(data)))))

## It backs up the current data into a slot named:
##  expt$backup_expressionset. It will also save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep the libsizes in mind
##  when invoking limma.  The appropriate libsize is the 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

## Step 1: performing count filter with option: hpgl

## Removing 7814 low-count genes (11815 remaining).

## Step 2: normalizing the data with quant.

## Using normalize.quantiles.robust due to a thread error in preprocessCore.

## Step 3: converting the data with cpm.

## Step 4: transforming the data with log2.

## transform_counts: Found 1337 values equal to 0, adding 1 to the matrix.

## Step 5: doing batch correction with limma.

## Note to self:  If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.

## batch_counts: Before batch correction, 47487 entries 0<=x<1.

## batch_counts: Before batch correction, 1337 entries are >= 0.

## Passing off to all_adjusters.

## batch_counts: Before batch/surrogate estimation, 1337 entries are x<=0.

## The be method chose 9 surrogate variable(s).

## batch_counts: Using limma's removeBatchEffect to remove batch effect.

## If you receive a warning: 'NANs produced', one potential reason is that the data was quantile normalized.

## The number of elements which are < 0 after batch correction is: 1776

## The variable low_to_zero sets whether to change <0 values to 0 and is: FALSE

mbio_pca1 <- plot_pca(mbio_batch1, size_column="expttime", plot_labels=FALSE,
                      size_order=c("t4h", "t24h", "t48h", "t72h"))

## Not putting labels on the plot.

##mbio_pca <- plot_pca(mbio_norm)
mbio_pca1$plot

5.3 svaseq batch adjust

Finally, I employ my favorite method: svaseq(). This squishes the time-based differences in the data and highlights the differences between the various infection states.

mbio_batch2 <- normalize_expt(mbio_expt, transform="log2", convert="cpm",
                              filter=TRUE, batch="svaseq")

## This function will replace the expt$expressionset slot with:

## log2(svaseq(cpm(hpgl(data))))

## It backs up the current data into a slot named:
##  expt$backup_expressionset. It will also save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep the libsizes in mind
##  when invoking limma.  The appropriate libsize is the 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 unnormalized.  This is necessary for DESeq, but
##  EdgeR/limma might benefit from normalization.  Good choices include quantile,
##  size-factor, tmm, etc.

## Step 1: performing count filter with option: hpgl

## Removing 7814 low-count genes (11815 remaining).

## Step 2: not normalizing the data.

## Step 3: converting the data with cpm.

## Step 4: transforming the data with log2.

## transform_counts: Found 5536 values equal to 0, adding 1 to the matrix.

## Step 5: doing batch correction with svaseq.

## Note to self:  If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.

## batch_counts: Before batch correction, 46466 entries 0<=x<1.

## batch_counts: Before batch correction, 5536 entries are >= 0.

## Passing off to all_adjusters.

## batch_counts: Before batch/surrogate estimation, 5536 entries are x<=0.

## The be method chose 8 surrogate variable(s).

## Attempting svaseq estimation with 8 surrogates.

## The number of elements which are < 0 after batch correction is: 1084

## The variable low_to_zero sets whether to change <0 values to 0 and is: FALSE

mbio_pca2 <- plot_pca(mbio_batch2, size_column="expttime", plot_labels=FALSE,
                      size_order=c("t4h", "t24h", "t48h", "t72h"))

## Not putting labels on the plot.

##mbio_pca <- plot_pca(mbio_norm)
mbio_pca2$plot

5.4 Bead effect

I do not recall what methods were used to estimate the ‘bead effect’ in the data. Therefore I am copy/pasting the relevant logs from Laura and will then try to recapitulate the tasks performed separately.

I think the makeTab() function is what was used to regenerate the p-values for the bead-adjusted data.

Following the function definition is a representative invocation performed by Laura. (I copy/pasted from her log with minor formatting changes).

## Quantile normalize counts
countsSubQ <- qNorm(counts)
## Specify model
mod = model.matrix(~0+condition+batch)
## Use voom to transform quantile-normalized count data to log2-counts per million, estimate mean-variance relationship
## and use m-v relationship to computer appropriate observational-level weights
v <- voom(countsSubQ, mod)
## Fit a linear model for each gene using the specified design contained in v
fit <- lmFit(v)

makeTab <- function(contrFit, coef1, coef2, ...) {
  ## Compute test statistic
  stat <- pmin(abs(contrFit$t[, coef1]), abs(contrFit$t[, coef2]))
  ## Compute pvalue for stat
  pval <- pmax(contrFit$p.value[, coef1], contrFit$p.value[, coef2])
  ## Adjust pvalue for multiple testing
  adj.pval <- p.adjust(pval, method="BH")
  ## Make the toptable
  tab <- topTable(contrFit, coef=coef1, sort.by="none", ...)
  coef1_name <- colnames(contrFit$coef)[coef1]
  coef2_name <- colnames(contrFit$coef)[coef2]
  new_tab <- data.frame(tab$ID, tab$logFC, contrFit$coef[, coef2], tab$AveExpr,
                        tab$t, contrFit$t[, coef2], stat, pval, adj.pval)
  new_tab <- new_tab[order(-stat), ]

  colnames(new_tab) <- c("ID", paste0("logFC_", coef1_name),
                         paste0("logFC_", coef2_name),
                         "AveExpr",
                         paste0("t_", coef1_name),
                         paste0("t_", coef2_name),
                         "stat",
                         "P.Value",
                         "adj.P.Value")
  new_tab
}

## eBayes finds an F-statistic from the set of t-statistics for that gene
beads24.infLM24.contr.mat <- makeContrasts(uninf_inf=(conditioninfLM24-conditionuninf24),
                                           beads_inf=(conditioninfLM24-conditionbeads24),
                                           levels=v$design)
beads24.infLM24.fit <- contrasts.fit(fit, beads24.infLM24.contr.mat)
beads24.infLM24.eb <- eBayes(beads24.infLM24.fit)

beads24.infLM24.topTab <- makeTab(beads24.infLM24.eb, 1, 2, number=nrow(v$E))

As far as the above goes, it mostly makes sense. My question is, how do we get the modified logFC values? Presumably that is later down in the log.

5.5 makeTab2

Looking further down I found the following invocations, which partially but incompletely answer my question.

## Define makeTab2 function
## construct a DE result table for infection vs. uninfected and beads
## contrFit: the result of eBayes after conrasts.fit
## cellmeansFit: the cell means fit (lmFit(v) above)
## conjContrasts: the 'conjuctive' null test (infection vs. uninf AND infect vs. beads)
## disjContrast: the 'other' test (beads vs. uninf)
makeTab2 <- function(contrFit, cellmeansFit, conjContrasts, disjContrast) {
  ## Get average expression for all relevant terms
  contr_level_counts <- rowSums(contrFit$contrasts[, c(conjContrasts, disjContrast)] != 0)
  ## Define the condition levels involved in the tests
  levels_to_use <- names(contr_level_counts)[contr_level_counts > 0]
  ## Extract the average counts for each, make into table
  ave_expression_mat <- cellmeansFit$coef[, levels_to_use]
  exp_table <- data.frame(ID=rownames(ave_expression_mat))
  exp_table <- cbind(exp_table, as.data.frame(ave_expression_mat))
  names(exp_table)[-1] <- paste(
    "AveExpr", gsub("condition","",levels_to_use),
    sep=":")
  ## Compute test statistic, adjusted pval, and logFC for conjuctive test
  ## Add to table
  stat <- rowMins(abs(contrFit$t[, conjContrasts]))
  pval <- rowMaxs(contrFit$p.value[, conjContrasts])
  adj.pval <- p.adjust(pval, method="BH")
  fcs <- as.data.frame(contrFit$coef[, conjContrasts])
  names(fcs) <- paste("logFC", names(fcs), sep=":")
  conj_pvals <- as.data.frame(apply(contrFit$p.value[, conjContrasts], 2,
                                    p.adjust, method="BH"))
  names(conj_pvals) <- paste("adj.P.Val", names(conj_pvals), sep=":")
  conj_table <- data.frame(ID=rownames(contrFit))
  conj_table <- cbind(conj_table, fcs, conj_pvals, stat=stat, adj.P.Value=adj.pval)
  names(conj_table)[seq(2 + 2 * length(conjContrasts), ncol(conj_table))] <- paste(
    c("stat","adj.P.Value"),
    paste(conjContrasts,collapse=":"),
    sep=":")
  ## Make the table for the 'other' test
  disj_table <- data.frame(ID=rownames(contrFit),
                           logFC=contrFit$coef[, disjContrast],
                           adj.P.Value=p.adjust(contrFit$p.value[, disjContrast], method="BH"))
  names(disj_table)[-1] <- paste(c("logFC", "adj.P.Value"), disjContrast, sep=":")
  ## Combine tables, making sure all tables are in the same order
  stopifnot(all(exp_table$ID == conj_table$ID & exp_table$ID == disj_table$ID))
  out_table <- cbind(exp_table, conj_table[, -1], disj_table[, -1])

  ## order output table by the statistic in the disjunctive test
  o <- order(-stat)
  out_table[o,]
}

infLM4.infLM24.contr.mat <- makeContrasts(uninf_inf=((conditioninfLM24-conditionuninf24)-(conditioninfLM4-conditionuninf4)),
                                          beads_inf=((conditioninfLM24-conditionbeads24)-(conditioninfLM4-conditionbeads4)),
                                          uninf_beads=((conditionbeads24-conditionuninf24)-(conditionbeads4-conditionuninf4)), levels=v$design)
infLM4.infLM24.fit <- contrasts.fit(fit, infLM4.infLM24.contr.mat)
infLM4.infLM24.eb <- eBayes(infLM4.infLM24.fit)

infLM4.infLM24.topTab <- makeTab2(infLM4.infLM24.eb, fit, c("uninf_inf", "beads_inf"),
                                  c("uninf_beads"))

I think that is everything performed. If I understand what I see, then it is doing the following:

• quantile normalize and filter the count table.
• perform voom with a condition+batch experimental model.
• invoke lmFit on the result.
• set up a set of contrasts which include:
  • uninf_inf = (infected at time y - uninfected at time y) - (infected at time x - uninfected at time x)
  • beads_inf = (infected at time y - beads at time y) - (infected at time x - beads at time x)
  • uninf_beads = (beads at time y - uninfected at time y) - (beads at time x - uninfected at time x)
• Invoke contrasts.fit and eBayes
• Invoke the makeTab(2)() function with the eBayes result as arg1, the lmFit result as arg2, and the character list of c(“uninf_inf”, “beads_inf”) as the third arg and finally “uninf_beads” as the final argument. makeTab2() does the following:
  • uses the original fit to extract the average expression values.
  • finds the minimum t statistic and maximum pvalue for each gene from the uninf_inf and beads_inf columns.
  • uses p.adjust on this set of maximized pvalues.
  • pulls the logFC values for each of the uninf_inf and beads_inf columns.
  • does p.adjust on the pvalues of the contrast pvalues.
  • uses cbind on these pieces to make a single table.
  • uses cbind on the expression table, the newly created table (conj) and the disjunct table.
  • orders them according to the t statistic.

I do not see how this set of operations gives us a better picture of the effect of beads during an infection. The primary thing I see in it is the modification of the p-values and the compound contrast of (infy-uninfy)-(infx-uninfx) It seems to me that this is the perfect time for an interaction model?

6.1 hpgltools method

keepers <- list(
  "4hpi_uninf" = c("lm_infected_t4h", "uninfected_t4h"),
  "4hpi_beads" = c("lm_infected_t4h", "bead_t4h")
)
mbio_filt <- set_expt_conditions(mbio_expt, new_condition)
mbio_filt <- normalize_expt(mbio_filt, filter=TRUE)

## This function will replace the expt$expressionset slot with:

## hpgl(data)

## It backs up the current data into a slot named:
##  expt$backup_expressionset. It will also save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep the libsizes in mind
##  when invoking limma.  The appropriate libsize is the 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 in its current base format, keep in mind that
##  some metrics are easier to see when the data is log2 transformed, but
##  EdgeR/DESeq do not accept transformed data.

## 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).

## Leaving the data unnormalized.  This is necessary for DESeq, but
##  EdgeR/limma might benefit from normalization.  Good choices include quantile,
##  size-factor, tmm, etc.

## 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: hpgl

## Removing 7814 low-count genes (11815 remaining).

## Step 2: not normalizing the data.

## Step 3: not converting the data.

## Step 4: not transforming the data.

## Step 5: not doing batch correction.

## Something weird happened, my counts are somehow getting cast as non-integers!
## Thus I am invoking force=TRUE until I figure out what is going on.
mbio_pairwise <- all_pairwise(mbio_filt, model_batch=TRUE,
                              do_ebseq=FALSE, force=TRUE)

## Using limma's removeBatchEffect to visualize with/out batch inclusion.

## Finished running DE analyses, collecting outputs.

## Comparing analyses 1/120: bead_t48h_vs_bead_t24h

## Comparing analyses 2/120: bead_t4h_vs_bead_t24h

## Comparing analyses 3/120: bead_t72h_vs_bead_t24h

## Comparing analyses 4/120: la_infected_t24h_vs_bead_t24h

## Comparing analyses 5/120: la_infected_t48h_vs_bead_t24h

## Comparing analyses 6/120: la_infected_t4h_vs_bead_t24h

## Comparing analyses 7/120: la_infected_t72h_vs_bead_t24h

## Comparing analyses 8/120: lm_infected_t24h_vs_bead_t24h

## Comparing analyses 9/120: lm_infected_t48h_vs_bead_t24h

## Comparing analyses 10/120: lm_infected_t4h_vs_bead_t24h

## Comparing analyses 11/120: lm_infected_t72h_vs_bead_t24h

## Comparing analyses 12/120: uninfected_t24h_vs_bead_t24h

## Comparing analyses 13/120: uninfected_t48h_vs_bead_t24h

## Comparing analyses 14/120: uninfected_t4h_vs_bead_t24h

## Comparing analyses 15/120: uninfected_t72h_vs_bead_t24h

## Comparing analyses 16/120: bead_t4h_vs_bead_t48h

## Comparing analyses 17/120: bead_t72h_vs_bead_t48h

## Comparing analyses 18/120: la_infected_t24h_vs_bead_t48h

## Comparing analyses 19/120: la_infected_t48h_vs_bead_t48h

## Comparing analyses 20/120: la_infected_t4h_vs_bead_t48h

## Comparing analyses 21/120: la_infected_t72h_vs_bead_t48h

## Comparing analyses 22/120: lm_infected_t24h_vs_bead_t48h

## Comparing analyses 23/120: lm_infected_t48h_vs_bead_t48h

## Comparing analyses 24/120: lm_infected_t4h_vs_bead_t48h

## Comparing analyses 25/120: lm_infected_t72h_vs_bead_t48h

## Comparing analyses 26/120: uninfected_t24h_vs_bead_t48h

## Comparing analyses 27/120: uninfected_t48h_vs_bead_t48h

## Comparing analyses 28/120: uninfected_t4h_vs_bead_t48h

## Comparing analyses 29/120: uninfected_t72h_vs_bead_t48h

## Comparing analyses 30/120: bead_t72h_vs_bead_t4h

## Comparing analyses 31/120: la_infected_t24h_vs_bead_t4h

## Comparing analyses 32/120: la_infected_t48h_vs_bead_t4h

## Comparing analyses 33/120: la_infected_t4h_vs_bead_t4h

## Comparing analyses 34/120: la_infected_t72h_vs_bead_t4h

## Comparing analyses 35/120: lm_infected_t24h_vs_bead_t4h

## Comparing analyses 36/120: lm_infected_t48h_vs_bead_t4h

## Comparing analyses 37/120: lm_infected_t4h_vs_bead_t4h

## Comparing analyses 38/120: lm_infected_t72h_vs_bead_t4h

## Comparing analyses 39/120: uninfected_t24h_vs_bead_t4h

## Comparing analyses 40/120: uninfected_t48h_vs_bead_t4h

## Comparing analyses 41/120: uninfected_t4h_vs_bead_t4h

## Comparing analyses 42/120: uninfected_t72h_vs_bead_t4h

## Comparing analyses 43/120: la_infected_t24h_vs_bead_t72h

## Comparing analyses 44/120: la_infected_t48h_vs_bead_t72h

## Comparing analyses 45/120: la_infected_t4h_vs_bead_t72h

## Comparing analyses 46/120: la_infected_t72h_vs_bead_t72h

## Comparing analyses 47/120: lm_infected_t24h_vs_bead_t72h

## Comparing analyses 48/120: lm_infected_t48h_vs_bead_t72h

## Comparing analyses 49/120: lm_infected_t4h_vs_bead_t72h

## Comparing analyses 50/120: lm_infected_t72h_vs_bead_t72h

## Comparing analyses 51/120: uninfected_t24h_vs_bead_t72h

## Comparing analyses 52/120: uninfected_t48h_vs_bead_t72h

## Comparing analyses 53/120: uninfected_t4h_vs_bead_t72h

## Comparing analyses 54/120: uninfected_t72h_vs_bead_t72h

## Comparing analyses 55/120: la_infected_t48h_vs_la_infected_t24h

## Comparing analyses 56/120: la_infected_t4h_vs_la_infected_t24h

## Comparing analyses 57/120: la_infected_t72h_vs_la_infected_t24h

## Comparing analyses 58/120: lm_infected_t24h_vs_la_infected_t24h

## Comparing analyses 59/120: lm_infected_t48h_vs_la_infected_t24h

## Comparing analyses 60/120: lm_infected_t4h_vs_la_infected_t24h

## Comparing analyses 61/120: lm_infected_t72h_vs_la_infected_t24h

## Comparing analyses 62/120: uninfected_t24h_vs_la_infected_t24h

## Comparing analyses 63/120: uninfected_t48h_vs_la_infected_t24h

## Comparing analyses 64/120: uninfected_t4h_vs_la_infected_t24h

## Comparing analyses 65/120: uninfected_t72h_vs_la_infected_t24h

## Comparing analyses 66/120: la_infected_t4h_vs_la_infected_t48h

## Comparing analyses 67/120: la_infected_t72h_vs_la_infected_t48h

## Comparing analyses 68/120: lm_infected_t24h_vs_la_infected_t48h

## Comparing analyses 69/120: lm_infected_t48h_vs_la_infected_t48h

## Comparing analyses 70/120: lm_infected_t4h_vs_la_infected_t48h

## Comparing analyses 71/120: lm_infected_t72h_vs_la_infected_t48h

## Comparing analyses 72/120: uninfected_t24h_vs_la_infected_t48h

## Comparing analyses 73/120: uninfected_t48h_vs_la_infected_t48h

## Comparing analyses 74/120: uninfected_t4h_vs_la_infected_t48h

## Comparing analyses 75/120: uninfected_t72h_vs_la_infected_t48h

## Comparing analyses 76/120: la_infected_t72h_vs_la_infected_t4h

## Comparing analyses 77/120: lm_infected_t24h_vs_la_infected_t4h

## Comparing analyses 78/120: lm_infected_t48h_vs_la_infected_t4h

## Comparing analyses 79/120: lm_infected_t4h_vs_la_infected_t4h

## Comparing analyses 80/120: lm_infected_t72h_vs_la_infected_t4h

## Comparing analyses 81/120: uninfected_t24h_vs_la_infected_t4h

## Comparing analyses 82/120: uninfected_t48h_vs_la_infected_t4h

## Comparing analyses 83/120: uninfected_t4h_vs_la_infected_t4h

## Comparing analyses 84/120: uninfected_t72h_vs_la_infected_t4h

## Comparing analyses 85/120: lm_infected_t24h_vs_la_infected_t72h

## Comparing analyses 86/120: lm_infected_t48h_vs_la_infected_t72h

## Comparing analyses 87/120: lm_infected_t4h_vs_la_infected_t72h

## Comparing analyses 88/120: lm_infected_t72h_vs_la_infected_t72h

## Comparing analyses 89/120: uninfected_t24h_vs_la_infected_t72h

## Comparing analyses 90/120: uninfected_t48h_vs_la_infected_t72h

## Comparing analyses 91/120: uninfected_t4h_vs_la_infected_t72h

## Comparing analyses 92/120: uninfected_t72h_vs_la_infected_t72h

## Comparing analyses 93/120: lm_infected_t48h_vs_lm_infected_t24h

## Comparing analyses 94/120: lm_infected_t4h_vs_lm_infected_t24h

## Comparing analyses 95/120: lm_infected_t72h_vs_lm_infected_t24h

## Comparing analyses 96/120: uninfected_t24h_vs_lm_infected_t24h

## Comparing analyses 97/120: uninfected_t48h_vs_lm_infected_t24h

## Comparing analyses 98/120: uninfected_t4h_vs_lm_infected_t24h

## Comparing analyses 99/120: uninfected_t72h_vs_lm_infected_t24h

## Comparing analyses 100/120: lm_infected_t4h_vs_lm_infected_t48h

## Comparing analyses 101/120: lm_infected_t72h_vs_lm_infected_t48h

## Comparing analyses 102/120: uninfected_t24h_vs_lm_infected_t48h

## Comparing analyses 103/120: uninfected_t48h_vs_lm_infected_t48h

## Comparing analyses 104/120: uninfected_t4h_vs_lm_infected_t48h

## Comparing analyses 105/120: uninfected_t72h_vs_lm_infected_t48h

## Comparing analyses 106/120: lm_infected_t72h_vs_lm_infected_t4h

## Comparing analyses 107/120: uninfected_t24h_vs_lm_infected_t4h

## Comparing analyses 108/120: uninfected_t48h_vs_lm_infected_t4h

## Comparing analyses 109/120: uninfected_t4h_vs_lm_infected_t4h

## Comparing analyses 110/120: uninfected_t72h_vs_lm_infected_t4h

## Comparing analyses 111/120: uninfected_t24h_vs_lm_infected_t72h

## Comparing analyses 112/120: uninfected_t48h_vs_lm_infected_t72h

## Comparing analyses 113/120: uninfected_t4h_vs_lm_infected_t72h

## Comparing analyses 114/120: uninfected_t72h_vs_lm_infected_t72h

## Comparing analyses 115/120: uninfected_t48h_vs_uninfected_t24h

## Comparing analyses 116/120: uninfected_t4h_vs_uninfected_t24h

## Comparing analyses 117/120: uninfected_t72h_vs_uninfected_t24h

## Comparing analyses 118/120: uninfected_t4h_vs_uninfected_t48h

## Comparing analyses 119/120: uninfected_t72h_vs_uninfected_t48h

## Comparing analyses 120/120: uninfected_t72h_vs_uninfected_t4h

## Comparing analyses 1/120: bead_t48h_vs_bead_t24h

## Comparing analyses 2/120: bead_t4h_vs_bead_t24h

## Comparing analyses 3/120: bead_t72h_vs_bead_t24h

## Comparing analyses 4/120: la_infected_t24h_vs_bead_t24h

## Comparing analyses 5/120: la_infected_t48h_vs_bead_t24h

## Comparing analyses 6/120: la_infected_t4h_vs_bead_t24h

## Comparing analyses 7/120: la_infected_t72h_vs_bead_t24h

## Comparing analyses 8/120: lm_infected_t24h_vs_bead_t24h

## Comparing analyses 9/120: lm_infected_t48h_vs_bead_t24h

## Comparing analyses 10/120: lm_infected_t4h_vs_bead_t24h

## Comparing analyses 11/120: lm_infected_t72h_vs_bead_t24h

## Comparing analyses 12/120: uninfected_t24h_vs_bead_t24h

## Comparing analyses 13/120: uninfected_t48h_vs_bead_t24h

## Comparing analyses 14/120: uninfected_t4h_vs_bead_t24h

## Comparing analyses 15/120: uninfected_t72h_vs_bead_t24h

## Comparing analyses 16/120: bead_t4h_vs_bead_t48h

## Comparing analyses 17/120: bead_t72h_vs_bead_t48h

## Comparing analyses 18/120: la_infected_t24h_vs_bead_t48h

## Comparing analyses 19/120: la_infected_t48h_vs_bead_t48h

## Comparing analyses 20/120: la_infected_t4h_vs_bead_t48h

## Comparing analyses 21/120: la_infected_t72h_vs_bead_t48h

## Comparing analyses 22/120: lm_infected_t24h_vs_bead_t48h

## Comparing analyses 23/120: lm_infected_t48h_vs_bead_t48h

## Comparing analyses 24/120: lm_infected_t4h_vs_bead_t48h

## Comparing analyses 25/120: lm_infected_t72h_vs_bead_t48h

## Comparing analyses 26/120: uninfected_t24h_vs_bead_t48h

## Comparing analyses 27/120: uninfected_t48h_vs_bead_t48h

## Comparing analyses 28/120: uninfected_t4h_vs_bead_t48h

## Comparing analyses 29/120: uninfected_t72h_vs_bead_t48h

## Comparing analyses 30/120: bead_t72h_vs_bead_t4h

## Comparing analyses 31/120: la_infected_t24h_vs_bead_t4h

## Comparing analyses 32/120: la_infected_t48h_vs_bead_t4h

## Comparing analyses 33/120: la_infected_t4h_vs_bead_t4h

## Comparing analyses 34/120: la_infected_t72h_vs_bead_t4h

## Comparing analyses 35/120: lm_infected_t24h_vs_bead_t4h

## Comparing analyses 36/120: lm_infected_t48h_vs_bead_t4h

## Comparing analyses 37/120: lm_infected_t4h_vs_bead_t4h

## Comparing analyses 38/120: lm_infected_t72h_vs_bead_t4h

## Comparing analyses 39/120: uninfected_t24h_vs_bead_t4h

## Comparing analyses 40/120: uninfected_t48h_vs_bead_t4h

## Comparing analyses 41/120: uninfected_t4h_vs_bead_t4h

## Comparing analyses 42/120: uninfected_t72h_vs_bead_t4h

## Comparing analyses 43/120: la_infected_t24h_vs_bead_t72h

## Comparing analyses 44/120: la_infected_t48h_vs_bead_t72h

## Comparing analyses 45/120: la_infected_t4h_vs_bead_t72h

## Comparing analyses 46/120: la_infected_t72h_vs_bead_t72h

## Comparing analyses 47/120: lm_infected_t24h_vs_bead_t72h

## Comparing analyses 48/120: lm_infected_t48h_vs_bead_t72h

## Comparing analyses 49/120: lm_infected_t4h_vs_bead_t72h

## Comparing analyses 50/120: lm_infected_t72h_vs_bead_t72h

## Comparing analyses 51/120: uninfected_t24h_vs_bead_t72h

## Comparing analyses 52/120: uninfected_t48h_vs_bead_t72h

## Comparing analyses 53/120: uninfected_t4h_vs_bead_t72h

## Comparing analyses 54/120: uninfected_t72h_vs_bead_t72h

## Comparing analyses 55/120: la_infected_t48h_vs_la_infected_t24h

## Comparing analyses 56/120: la_infected_t4h_vs_la_infected_t24h

## Comparing analyses 57/120: la_infected_t72h_vs_la_infected_t24h

## Comparing analyses 58/120: lm_infected_t24h_vs_la_infected_t24h

## Comparing analyses 59/120: lm_infected_t48h_vs_la_infected_t24h

## Comparing analyses 60/120: lm_infected_t4h_vs_la_infected_t24h

## Comparing analyses 61/120: lm_infected_t72h_vs_la_infected_t24h

## Comparing analyses 62/120: uninfected_t24h_vs_la_infected_t24h

## Comparing analyses 63/120: uninfected_t48h_vs_la_infected_t24h

## Comparing analyses 64/120: uninfected_t4h_vs_la_infected_t24h

## Comparing analyses 65/120: uninfected_t72h_vs_la_infected_t24h

## Comparing analyses 66/120: la_infected_t4h_vs_la_infected_t48h

## Comparing analyses 67/120: la_infected_t72h_vs_la_infected_t48h

## Comparing analyses 68/120: lm_infected_t24h_vs_la_infected_t48h

## Comparing analyses 69/120: lm_infected_t48h_vs_la_infected_t48h

## Comparing analyses 70/120: lm_infected_t4h_vs_la_infected_t48h

## Comparing analyses 71/120: lm_infected_t72h_vs_la_infected_t48h

## Comparing analyses 72/120: uninfected_t24h_vs_la_infected_t48h

## Comparing analyses 73/120: uninfected_t48h_vs_la_infected_t48h

## Comparing analyses 74/120: uninfected_t4h_vs_la_infected_t48h

## Comparing analyses 75/120: uninfected_t72h_vs_la_infected_t48h

## Comparing analyses 76/120: la_infected_t72h_vs_la_infected_t4h

## Comparing analyses 77/120: lm_infected_t24h_vs_la_infected_t4h

## Comparing analyses 78/120: lm_infected_t48h_vs_la_infected_t4h

## Comparing analyses 79/120: lm_infected_t4h_vs_la_infected_t4h

## Comparing analyses 80/120: lm_infected_t72h_vs_la_infected_t4h

## Comparing analyses 81/120: uninfected_t24h_vs_la_infected_t4h

## Comparing analyses 82/120: uninfected_t48h_vs_la_infected_t4h

## Comparing analyses 83/120: uninfected_t4h_vs_la_infected_t4h

## Comparing analyses 84/120: uninfected_t72h_vs_la_infected_t4h

## Comparing analyses 85/120: lm_infected_t24h_vs_la_infected_t72h

## Comparing analyses 86/120: lm_infected_t48h_vs_la_infected_t72h

## Comparing analyses 87/120: lm_infected_t4h_vs_la_infected_t72h

## Comparing analyses 88/120: lm_infected_t72h_vs_la_infected_t72h

## Comparing analyses 89/120: uninfected_t24h_vs_la_infected_t72h

## Comparing analyses 90/120: uninfected_t48h_vs_la_infected_t72h

## Comparing analyses 91/120: uninfected_t4h_vs_la_infected_t72h

## Comparing analyses 92/120: uninfected_t72h_vs_la_infected_t72h

## Comparing analyses 93/120: lm_infected_t48h_vs_lm_infected_t24h

## Comparing analyses 94/120: lm_infected_t4h_vs_lm_infected_t24h

## Comparing analyses 95/120: lm_infected_t72h_vs_lm_infected_t24h

## Comparing analyses 96/120: uninfected_t24h_vs_lm_infected_t24h

## Comparing analyses 97/120: uninfected_t48h_vs_lm_infected_t24h

## Comparing analyses 98/120: uninfected_t4h_vs_lm_infected_t24h

## Comparing analyses 99/120: uninfected_t72h_vs_lm_infected_t24h

## Comparing analyses 100/120: lm_infected_t4h_vs_lm_infected_t48h

## Comparing analyses 101/120: lm_infected_t72h_vs_lm_infected_t48h

## Comparing analyses 102/120: uninfected_t24h_vs_lm_infected_t48h

## Comparing analyses 103/120: uninfected_t48h_vs_lm_infected_t48h

## Comparing analyses 104/120: uninfected_t4h_vs_lm_infected_t48h

## Comparing analyses 105/120: uninfected_t72h_vs_lm_infected_t48h

## Comparing analyses 106/120: lm_infected_t72h_vs_lm_infected_t4h

## Comparing analyses 107/120: uninfected_t24h_vs_lm_infected_t4h

## Comparing analyses 108/120: uninfected_t48h_vs_lm_infected_t4h

## Comparing analyses 109/120: uninfected_t4h_vs_lm_infected_t4h

## Comparing analyses 110/120: uninfected_t72h_vs_lm_infected_t4h

## Comparing analyses 111/120: uninfected_t24h_vs_lm_infected_t72h

## Comparing analyses 112/120: uninfected_t48h_vs_lm_infected_t72h

## Comparing analyses 113/120: uninfected_t4h_vs_lm_infected_t72h

## Comparing analyses 114/120: uninfected_t72h_vs_lm_infected_t72h

## Comparing analyses 115/120: uninfected_t48h_vs_uninfected_t24h

## Comparing analyses 116/120: uninfected_t4h_vs_uninfected_t24h

## Comparing analyses 117/120: uninfected_t72h_vs_uninfected_t24h

## Comparing analyses 118/120: uninfected_t4h_vs_uninfected_t48h

## Comparing analyses 119/120: uninfected_t72h_vs_uninfected_t48h

## Comparing analyses 120/120: uninfected_t72h_vs_uninfected_t4h

## Comparing analyses 1/120: bead_t48h_vs_bead_t24h

## Comparing analyses 2/120: bead_t4h_vs_bead_t24h

## Comparing analyses 3/120: bead_t72h_vs_bead_t24h

## Comparing analyses 4/120: la_infected_t24h_vs_bead_t24h

## Comparing analyses 5/120: la_infected_t48h_vs_bead_t24h

## Comparing analyses 6/120: la_infected_t4h_vs_bead_t24h

## Comparing analyses 7/120: la_infected_t72h_vs_bead_t24h

## Comparing analyses 8/120: lm_infected_t24h_vs_bead_t24h

## Comparing analyses 9/120: lm_infected_t48h_vs_bead_t24h

## Comparing analyses 10/120: lm_infected_t4h_vs_bead_t24h

## Comparing analyses 11/120: lm_infected_t72h_vs_bead_t24h

## Comparing analyses 12/120: uninfected_t24h_vs_bead_t24h

## Comparing analyses 13/120: uninfected_t48h_vs_bead_t24h

## Comparing analyses 14/120: uninfected_t4h_vs_bead_t24h

## Comparing analyses 15/120: uninfected_t72h_vs_bead_t24h

## Comparing analyses 16/120: bead_t4h_vs_bead_t48h

## Comparing analyses 17/120: bead_t72h_vs_bead_t48h

## Comparing analyses 18/120: la_infected_t24h_vs_bead_t48h

## Comparing analyses 19/120: la_infected_t48h_vs_bead_t48h

## Comparing analyses 20/120: la_infected_t4h_vs_bead_t48h

## Comparing analyses 21/120: la_infected_t72h_vs_bead_t48h

## Comparing analyses 22/120: lm_infected_t24h_vs_bead_t48h

## Comparing analyses 23/120: lm_infected_t48h_vs_bead_t48h

## Comparing analyses 24/120: lm_infected_t4h_vs_bead_t48h

## Comparing analyses 25/120: lm_infected_t72h_vs_bead_t48h

## Comparing analyses 26/120: uninfected_t24h_vs_bead_t48h

## Comparing analyses 27/120: uninfected_t48h_vs_bead_t48h

## Comparing analyses 28/120: uninfected_t4h_vs_bead_t48h

## Comparing analyses 29/120: uninfected_t72h_vs_bead_t48h

## Comparing analyses 30/120: bead_t72h_vs_bead_t4h

## Comparing analyses 31/120: la_infected_t24h_vs_bead_t4h

## Comparing analyses 32/120: la_infected_t48h_vs_bead_t4h

## Comparing analyses 33/120: la_infected_t4h_vs_bead_t4h

## Comparing analyses 34/120: la_infected_t72h_vs_bead_t4h

## Comparing analyses 35/120: lm_infected_t24h_vs_bead_t4h

## Comparing analyses 36/120: lm_infected_t48h_vs_bead_t4h

## Comparing analyses 37/120: lm_infected_t4h_vs_bead_t4h

## Comparing analyses 38/120: lm_infected_t72h_vs_bead_t4h

## Comparing analyses 39/120: uninfected_t24h_vs_bead_t4h

## Comparing analyses 40/120: uninfected_t48h_vs_bead_t4h

## Comparing analyses 41/120: uninfected_t4h_vs_bead_t4h

## Comparing analyses 42/120: uninfected_t72h_vs_bead_t4h

## Comparing analyses 43/120: la_infected_t24h_vs_bead_t72h

## Comparing analyses 44/120: la_infected_t48h_vs_bead_t72h

## Comparing analyses 45/120: la_infected_t4h_vs_bead_t72h

## Comparing analyses 46/120: la_infected_t72h_vs_bead_t72h

## Comparing analyses 47/120: lm_infected_t24h_vs_bead_t72h

## Comparing analyses 48/120: lm_infected_t48h_vs_bead_t72h

## Comparing analyses 49/120: lm_infected_t4h_vs_bead_t72h

## Comparing analyses 50/120: lm_infected_t72h_vs_bead_t72h

## Comparing analyses 51/120: uninfected_t24h_vs_bead_t72h

## Comparing analyses 52/120: uninfected_t48h_vs_bead_t72h

## Comparing analyses 53/120: uninfected_t4h_vs_bead_t72h

## Comparing analyses 54/120: uninfected_t72h_vs_bead_t72h

## Comparing analyses 55/120: la_infected_t48h_vs_la_infected_t24h

## Comparing analyses 56/120: la_infected_t4h_vs_la_infected_t24h

## Comparing analyses 57/120: la_infected_t72h_vs_la_infected_t24h

## Comparing analyses 58/120: lm_infected_t24h_vs_la_infected_t24h

## Comparing analyses 59/120: lm_infected_t48h_vs_la_infected_t24h

## Comparing analyses 60/120: lm_infected_t4h_vs_la_infected_t24h

## Comparing analyses 61/120: lm_infected_t72h_vs_la_infected_t24h

## Comparing analyses 62/120: uninfected_t24h_vs_la_infected_t24h

## Comparing analyses 63/120: uninfected_t48h_vs_la_infected_t24h

## Comparing analyses 64/120: uninfected_t4h_vs_la_infected_t24h

## Comparing analyses 65/120: uninfected_t72h_vs_la_infected_t24h

## Comparing analyses 66/120: la_infected_t4h_vs_la_infected_t48h

## Comparing analyses 67/120: la_infected_t72h_vs_la_infected_t48h

## Comparing analyses 68/120: lm_infected_t24h_vs_la_infected_t48h

## Comparing analyses 69/120: lm_infected_t48h_vs_la_infected_t48h

## Comparing analyses 70/120: lm_infected_t4h_vs_la_infected_t48h

## Comparing analyses 71/120: lm_infected_t72h_vs_la_infected_t48h

## Comparing analyses 72/120: uninfected_t24h_vs_la_infected_t48h

## Comparing analyses 73/120: uninfected_t48h_vs_la_infected_t48h

## Comparing analyses 74/120: uninfected_t4h_vs_la_infected_t48h

## Comparing analyses 75/120: uninfected_t72h_vs_la_infected_t48h

## Comparing analyses 76/120: la_infected_t72h_vs_la_infected_t4h

## Comparing analyses 77/120: lm_infected_t24h_vs_la_infected_t4h

## Comparing analyses 78/120: lm_infected_t48h_vs_la_infected_t4h

## Comparing analyses 79/120: lm_infected_t4h_vs_la_infected_t4h

## Comparing analyses 80/120: lm_infected_t72h_vs_la_infected_t4h

## Comparing analyses 81/120: uninfected_t24h_vs_la_infected_t4h

## Comparing analyses 82/120: uninfected_t48h_vs_la_infected_t4h

## Comparing analyses 83/120: uninfected_t4h_vs_la_infected_t4h

## Comparing analyses 84/120: uninfected_t72h_vs_la_infected_t4h

## Comparing analyses 85/120: lm_infected_t24h_vs_la_infected_t72h

## Comparing analyses 86/120: lm_infected_t48h_vs_la_infected_t72h

## Comparing analyses 87/120: lm_infected_t4h_vs_la_infected_t72h

## Comparing analyses 88/120: lm_infected_t72h_vs_la_infected_t72h

## Comparing analyses 89/120: uninfected_t24h_vs_la_infected_t72h

## Comparing analyses 90/120: uninfected_t48h_vs_la_infected_t72h

## Comparing analyses 91/120: uninfected_t4h_vs_la_infected_t72h

## Comparing analyses 92/120: uninfected_t72h_vs_la_infected_t72h

## Comparing analyses 93/120: lm_infected_t48h_vs_lm_infected_t24h

## Comparing analyses 94/120: lm_infected_t4h_vs_lm_infected_t24h

## Comparing analyses 95/120: lm_infected_t72h_vs_lm_infected_t24h

## Comparing analyses 96/120: uninfected_t24h_vs_lm_infected_t24h

## Comparing analyses 97/120: uninfected_t48h_vs_lm_infected_t24h

## Comparing analyses 98/120: uninfected_t4h_vs_lm_infected_t24h

## Comparing analyses 99/120: uninfected_t72h_vs_lm_infected_t24h

## Comparing analyses 100/120: lm_infected_t4h_vs_lm_infected_t48h

## Comparing analyses 101/120: lm_infected_t72h_vs_lm_infected_t48h

## Comparing analyses 102/120: uninfected_t24h_vs_lm_infected_t48h

## Comparing analyses 103/120: uninfected_t48h_vs_lm_infected_t48h

## Comparing analyses 104/120: uninfected_t4h_vs_lm_infected_t48h

## Comparing analyses 105/120: uninfected_t72h_vs_lm_infected_t48h

## Comparing analyses 106/120: lm_infected_t72h_vs_lm_infected_t4h

## Comparing analyses 107/120: uninfected_t24h_vs_lm_infected_t4h

## Comparing analyses 108/120: uninfected_t48h_vs_lm_infected_t4h

## Comparing analyses 109/120: uninfected_t4h_vs_lm_infected_t4h

## Comparing analyses 110/120: uninfected_t72h_vs_lm_infected_t4h

## Comparing analyses 111/120: uninfected_t24h_vs_lm_infected_t72h

## Comparing analyses 112/120: uninfected_t48h_vs_lm_infected_t72h

## Comparing analyses 113/120: uninfected_t4h_vs_lm_infected_t72h

## Comparing analyses 114/120: uninfected_t72h_vs_lm_infected_t72h

## Comparing analyses 115/120: uninfected_t48h_vs_uninfected_t24h

## Comparing analyses 116/120: uninfected_t4h_vs_uninfected_t24h

## Comparing analyses 117/120: uninfected_t72h_vs_uninfected_t24h

## Comparing analyses 118/120: uninfected_t4h_vs_uninfected_t48h

## Comparing analyses 119/120: uninfected_t72h_vs_uninfected_t48h

## Comparing analyses 120/120: uninfected_t72h_vs_uninfected_t4h

## Comparing analyses 1/120: bead_t48h_vs_bead_t24h

## Comparing analyses 2/120: bead_t4h_vs_bead_t24h

## Comparing analyses 3/120: bead_t72h_vs_bead_t24h

## Comparing analyses 4/120: la_infected_t24h_vs_bead_t24h

## Comparing analyses 5/120: la_infected_t48h_vs_bead_t24h

## Comparing analyses 6/120: la_infected_t4h_vs_bead_t24h

## Comparing analyses 7/120: la_infected_t72h_vs_bead_t24h

## Comparing analyses 8/120: lm_infected_t24h_vs_bead_t24h

## Comparing analyses 9/120: lm_infected_t48h_vs_bead_t24h

## Comparing analyses 10/120: lm_infected_t4h_vs_bead_t24h

## Comparing analyses 11/120: lm_infected_t72h_vs_bead_t24h

## Comparing analyses 12/120: uninfected_t24h_vs_bead_t24h

## Comparing analyses 13/120: uninfected_t48h_vs_bead_t24h

## Comparing analyses 14/120: uninfected_t4h_vs_bead_t24h

## Comparing analyses 15/120: uninfected_t72h_vs_bead_t24h

## Comparing analyses 16/120: bead_t4h_vs_bead_t48h

## Comparing analyses 17/120: bead_t72h_vs_bead_t48h

## Comparing analyses 18/120: la_infected_t24h_vs_bead_t48h

## Comparing analyses 19/120: la_infected_t48h_vs_bead_t48h

## Comparing analyses 20/120: la_infected_t4h_vs_bead_t48h

## Comparing analyses 21/120: la_infected_t72h_vs_bead_t48h

## Comparing analyses 22/120: lm_infected_t24h_vs_bead_t48h

## Comparing analyses 23/120: lm_infected_t48h_vs_bead_t48h

## Comparing analyses 24/120: lm_infected_t4h_vs_bead_t48h

## Comparing analyses 25/120: lm_infected_t72h_vs_bead_t48h

## Comparing analyses 26/120: uninfected_t24h_vs_bead_t48h

## Comparing analyses 27/120: uninfected_t48h_vs_bead_t48h

## Comparing analyses 28/120: uninfected_t4h_vs_bead_t48h

## Comparing analyses 29/120: uninfected_t72h_vs_bead_t48h

## Comparing analyses 30/120: bead_t72h_vs_bead_t4h

## Comparing analyses 31/120: la_infected_t24h_vs_bead_t4h

## Comparing analyses 32/120: la_infected_t48h_vs_bead_t4h

## Comparing analyses 33/120: la_infected_t4h_vs_bead_t4h

## Comparing analyses 34/120: la_infected_t72h_vs_bead_t4h

## Comparing analyses 35/120: lm_infected_t24h_vs_bead_t4h

## Comparing analyses 36/120: lm_infected_t48h_vs_bead_t4h

## Comparing analyses 37/120: lm_infected_t4h_vs_bead_t4h

## Comparing analyses 38/120: lm_infected_t72h_vs_bead_t4h

## Comparing analyses 39/120: uninfected_t24h_vs_bead_t4h

## Comparing analyses 40/120: uninfected_t48h_vs_bead_t4h

## Comparing analyses 41/120: uninfected_t4h_vs_bead_t4h

## Comparing analyses 42/120: uninfected_t72h_vs_bead_t4h

## Comparing analyses 43/120: la_infected_t24h_vs_bead_t72h

## Comparing analyses 44/120: la_infected_t48h_vs_bead_t72h

## Comparing analyses 45/120: la_infected_t4h_vs_bead_t72h

## Comparing analyses 46/120: la_infected_t72h_vs_bead_t72h

## Comparing analyses 47/120: lm_infected_t24h_vs_bead_t72h

## Comparing analyses 48/120: lm_infected_t48h_vs_bead_t72h

## Comparing analyses 49/120: lm_infected_t4h_vs_bead_t72h

## Comparing analyses 50/120: lm_infected_t72h_vs_bead_t72h

## Comparing analyses 51/120: uninfected_t24h_vs_bead_t72h

## Comparing analyses 52/120: uninfected_t48h_vs_bead_t72h

## Comparing analyses 53/120: uninfected_t4h_vs_bead_t72h

## Comparing analyses 54/120: uninfected_t72h_vs_bead_t72h

## Comparing analyses 55/120: la_infected_t48h_vs_la_infected_t24h

## Comparing analyses 56/120: la_infected_t4h_vs_la_infected_t24h

## Comparing analyses 57/120: la_infected_t72h_vs_la_infected_t24h

## Comparing analyses 58/120: lm_infected_t24h_vs_la_infected_t24h

## Comparing analyses 59/120: lm_infected_t48h_vs_la_infected_t24h

## Comparing analyses 60/120: lm_infected_t4h_vs_la_infected_t24h

## Comparing analyses 61/120: lm_infected_t72h_vs_la_infected_t24h

## Comparing analyses 62/120: uninfected_t24h_vs_la_infected_t24h

## Comparing analyses 63/120: uninfected_t48h_vs_la_infected_t24h

## Comparing analyses 64/120: uninfected_t4h_vs_la_infected_t24h

## Comparing analyses 65/120: uninfected_t72h_vs_la_infected_t24h

## Comparing analyses 66/120: la_infected_t4h_vs_la_infected_t48h

## Comparing analyses 67/120: la_infected_t72h_vs_la_infected_t48h

## Comparing analyses 68/120: lm_infected_t24h_vs_la_infected_t48h

## Comparing analyses 69/120: lm_infected_t48h_vs_la_infected_t48h

## Comparing analyses 70/120: lm_infected_t4h_vs_la_infected_t48h

## Comparing analyses 71/120: lm_infected_t72h_vs_la_infected_t48h

## Comparing analyses 72/120: uninfected_t24h_vs_la_infected_t48h

## Comparing analyses 73/120: uninfected_t48h_vs_la_infected_t48h

## Comparing analyses 74/120: uninfected_t4h_vs_la_infected_t48h

## Comparing analyses 75/120: uninfected_t72h_vs_la_infected_t48h

## Comparing analyses 76/120: la_infected_t72h_vs_la_infected_t4h

## Comparing analyses 77/120: lm_infected_t24h_vs_la_infected_t4h

## Comparing analyses 78/120: lm_infected_t48h_vs_la_infected_t4h

## Comparing analyses 79/120: lm_infected_t4h_vs_la_infected_t4h

## Comparing analyses 80/120: lm_infected_t72h_vs_la_infected_t4h

## Comparing analyses 81/120: uninfected_t24h_vs_la_infected_t4h

## Comparing analyses 82/120: uninfected_t48h_vs_la_infected_t4h

## Comparing analyses 83/120: uninfected_t4h_vs_la_infected_t4h

## Comparing analyses 84/120: uninfected_t72h_vs_la_infected_t4h

## Comparing analyses 85/120: lm_infected_t24h_vs_la_infected_t72h

## Comparing analyses 86/120: lm_infected_t48h_vs_la_infected_t72h

## Comparing analyses 87/120: lm_infected_t4h_vs_la_infected_t72h

## Comparing analyses 88/120: lm_infected_t72h_vs_la_infected_t72h

## Comparing analyses 89/120: uninfected_t24h_vs_la_infected_t72h

## Comparing analyses 90/120: uninfected_t48h_vs_la_infected_t72h

## Comparing analyses 91/120: uninfected_t4h_vs_la_infected_t72h

## Comparing analyses 92/120: uninfected_t72h_vs_la_infected_t72h

## Comparing analyses 93/120: lm_infected_t48h_vs_lm_infected_t24h

## Comparing analyses 94/120: lm_infected_t4h_vs_lm_infected_t24h

## Comparing analyses 95/120: lm_infected_t72h_vs_lm_infected_t24h

## Comparing analyses 96/120: uninfected_t24h_vs_lm_infected_t24h

## Comparing analyses 97/120: uninfected_t48h_vs_lm_infected_t24h

## Comparing analyses 98/120: uninfected_t4h_vs_lm_infected_t24h

## Comparing analyses 99/120: uninfected_t72h_vs_lm_infected_t24h

## Comparing analyses 100/120: lm_infected_t4h_vs_lm_infected_t48h

## Comparing analyses 101/120: lm_infected_t72h_vs_lm_infected_t48h

## Comparing analyses 102/120: uninfected_t24h_vs_lm_infected_t48h

## Comparing analyses 103/120: uninfected_t48h_vs_lm_infected_t48h

## Comparing analyses 104/120: uninfected_t4h_vs_lm_infected_t48h

## Comparing analyses 105/120: uninfected_t72h_vs_lm_infected_t48h

## Comparing analyses 106/120: lm_infected_t72h_vs_lm_infected_t4h

## Comparing analyses 107/120: uninfected_t24h_vs_lm_infected_t4h

## Comparing analyses 108/120: uninfected_t48h_vs_lm_infected_t4h

## Comparing analyses 109/120: uninfected_t4h_vs_lm_infected_t4h

## Comparing analyses 110/120: uninfected_t72h_vs_lm_infected_t4h

## Comparing analyses 111/120: uninfected_t24h_vs_lm_infected_t72h

## Comparing analyses 112/120: uninfected_t48h_vs_lm_infected_t72h

## Comparing analyses 113/120: uninfected_t4h_vs_lm_infected_t72h

## Comparing analyses 114/120: uninfected_t72h_vs_lm_infected_t72h

## Comparing analyses 115/120: uninfected_t48h_vs_uninfected_t24h

## Comparing analyses 116/120: uninfected_t4h_vs_uninfected_t24h

## Comparing analyses 117/120: uninfected_t72h_vs_uninfected_t24h

## Comparing analyses 118/120: uninfected_t4h_vs_uninfected_t48h

## Comparing analyses 119/120: uninfected_t72h_vs_uninfected_t48h

## Comparing analyses 120/120: uninfected_t72h_vs_uninfected_t4h

## Comparing analyses 1/120: bead_t48h_vs_bead_t24h

## Comparing analyses 2/120: bead_t4h_vs_bead_t24h

## Comparing analyses 3/120: bead_t72h_vs_bead_t24h

## Comparing analyses 4/120: la_infected_t24h_vs_bead_t24h

## Comparing analyses 5/120: la_infected_t48h_vs_bead_t24h

## Comparing analyses 6/120: la_infected_t4h_vs_bead_t24h

## Comparing analyses 7/120: la_infected_t72h_vs_bead_t24h

## Comparing analyses 8/120: lm_infected_t24h_vs_bead_t24h

## Comparing analyses 9/120: lm_infected_t48h_vs_bead_t24h

## Comparing analyses 10/120: lm_infected_t4h_vs_bead_t24h

## Comparing analyses 11/120: lm_infected_t72h_vs_bead_t24h

## Comparing analyses 12/120: uninfected_t24h_vs_bead_t24h

## Comparing analyses 13/120: uninfected_t48h_vs_bead_t24h

## Comparing analyses 14/120: uninfected_t4h_vs_bead_t24h

## Comparing analyses 15/120: uninfected_t72h_vs_bead_t24h

## Comparing analyses 16/120: bead_t4h_vs_bead_t48h

## Comparing analyses 17/120: bead_t72h_vs_bead_t48h

## Comparing analyses 18/120: la_infected_t24h_vs_bead_t48h

## Comparing analyses 19/120: la_infected_t48h_vs_bead_t48h

## Comparing analyses 20/120: la_infected_t4h_vs_bead_t48h

## Comparing analyses 21/120: la_infected_t72h_vs_bead_t48h

## Comparing analyses 22/120: lm_infected_t24h_vs_bead_t48h

## Comparing analyses 23/120: lm_infected_t48h_vs_bead_t48h

## Comparing analyses 24/120: lm_infected_t4h_vs_bead_t48h

## Comparing analyses 25/120: lm_infected_t72h_vs_bead_t48h

## Comparing analyses 26/120: uninfected_t24h_vs_bead_t48h

## Comparing analyses 27/120: uninfected_t48h_vs_bead_t48h

## Comparing analyses 28/120: uninfected_t4h_vs_bead_t48h

## Comparing analyses 29/120: uninfected_t72h_vs_bead_t48h

## Comparing analyses 30/120: bead_t72h_vs_bead_t4h

## Comparing analyses 31/120: la_infected_t24h_vs_bead_t4h

## Comparing analyses 32/120: la_infected_t48h_vs_bead_t4h

## Comparing analyses 33/120: la_infected_t4h_vs_bead_t4h

## Comparing analyses 34/120: la_infected_t72h_vs_bead_t4h

## Comparing analyses 35/120: lm_infected_t24h_vs_bead_t4h

## Comparing analyses 36/120: lm_infected_t48h_vs_bead_t4h

## Comparing analyses 37/120: lm_infected_t4h_vs_bead_t4h

## Comparing analyses 38/120: lm_infected_t72h_vs_bead_t4h

## Comparing analyses 39/120: uninfected_t24h_vs_bead_t4h

## Comparing analyses 40/120: uninfected_t48h_vs_bead_t4h

## Comparing analyses 41/120: uninfected_t4h_vs_bead_t4h

## Comparing analyses 42/120: uninfected_t72h_vs_bead_t4h

## Comparing analyses 43/120: la_infected_t24h_vs_bead_t72h

## Comparing analyses 44/120: la_infected_t48h_vs_bead_t72h

## Comparing analyses 45/120: la_infected_t4h_vs_bead_t72h

## Comparing analyses 46/120: la_infected_t72h_vs_bead_t72h

## Comparing analyses 47/120: lm_infected_t24h_vs_bead_t72h

## Comparing analyses 48/120: lm_infected_t48h_vs_bead_t72h

## Comparing analyses 49/120: lm_infected_t4h_vs_bead_t72h

## Comparing analyses 50/120: lm_infected_t72h_vs_bead_t72h

## Comparing analyses 51/120: uninfected_t24h_vs_bead_t72h

## Comparing analyses 52/120: uninfected_t48h_vs_bead_t72h

## Comparing analyses 53/120: uninfected_t4h_vs_bead_t72h

## Comparing analyses 54/120: uninfected_t72h_vs_bead_t72h

## Comparing analyses 55/120: la_infected_t48h_vs_la_infected_t24h

## Comparing analyses 56/120: la_infected_t4h_vs_la_infected_t24h

## Comparing analyses 57/120: la_infected_t72h_vs_la_infected_t24h

## Comparing analyses 58/120: lm_infected_t24h_vs_la_infected_t24h

## Comparing analyses 59/120: lm_infected_t48h_vs_la_infected_t24h

## Comparing analyses 60/120: lm_infected_t4h_vs_la_infected_t24h

## Comparing analyses 61/120: lm_infected_t72h_vs_la_infected_t24h

## Comparing analyses 62/120: uninfected_t24h_vs_la_infected_t24h

## Comparing analyses 63/120: uninfected_t48h_vs_la_infected_t24h

## Comparing analyses 64/120: uninfected_t4h_vs_la_infected_t24h

## Comparing analyses 65/120: uninfected_t72h_vs_la_infected_t24h

## Comparing analyses 66/120: la_infected_t4h_vs_la_infected_t48h

## Comparing analyses 67/120: la_infected_t72h_vs_la_infected_t48h

## Comparing analyses 68/120: lm_infected_t24h_vs_la_infected_t48h

## Comparing analyses 69/120: lm_infected_t48h_vs_la_infected_t48h

## Comparing analyses 70/120: lm_infected_t4h_vs_la_infected_t48h

## Comparing analyses 71/120: lm_infected_t72h_vs_la_infected_t48h

## Comparing analyses 72/120: uninfected_t24h_vs_la_infected_t48h

## Comparing analyses 73/120: uninfected_t48h_vs_la_infected_t48h

## Comparing analyses 74/120: uninfected_t4h_vs_la_infected_t48h

## Comparing analyses 75/120: uninfected_t72h_vs_la_infected_t48h

## Comparing analyses 76/120: la_infected_t72h_vs_la_infected_t4h

## Comparing analyses 77/120: lm_infected_t24h_vs_la_infected_t4h

## Comparing analyses 78/120: lm_infected_t48h_vs_la_infected_t4h

## Comparing analyses 79/120: lm_infected_t4h_vs_la_infected_t4h

## Comparing analyses 80/120: lm_infected_t72h_vs_la_infected_t4h

## Comparing analyses 81/120: uninfected_t24h_vs_la_infected_t4h

## Comparing analyses 82/120: uninfected_t48h_vs_la_infected_t4h

## Comparing analyses 83/120: uninfected_t4h_vs_la_infected_t4h

## Comparing analyses 84/120: uninfected_t72h_vs_la_infected_t4h

## Comparing analyses 85/120: lm_infected_t24h_vs_la_infected_t72h

## Comparing analyses 86/120: lm_infected_t48h_vs_la_infected_t72h

## Comparing analyses 87/120: lm_infected_t4h_vs_la_infected_t72h

## Comparing analyses 88/120: lm_infected_t72h_vs_la_infected_t72h

## Comparing analyses 89/120: uninfected_t24h_vs_la_infected_t72h

## Comparing analyses 90/120: uninfected_t48h_vs_la_infected_t72h

## Comparing analyses 91/120: uninfected_t4h_vs_la_infected_t72h

## Comparing analyses 92/120: uninfected_t72h_vs_la_infected_t72h

## Comparing analyses 93/120: lm_infected_t48h_vs_lm_infected_t24h

## Comparing analyses 94/120: lm_infected_t4h_vs_lm_infected_t24h

## Comparing analyses 95/120: lm_infected_t72h_vs_lm_infected_t24h

## Comparing analyses 96/120: uninfected_t24h_vs_lm_infected_t24h

## Comparing analyses 97/120: uninfected_t48h_vs_lm_infected_t24h

## Comparing analyses 98/120: uninfected_t4h_vs_lm_infected_t24h

## Comparing analyses 99/120: uninfected_t72h_vs_lm_infected_t24h

## Comparing analyses 100/120: lm_infected_t4h_vs_lm_infected_t48h

## Comparing analyses 101/120: lm_infected_t72h_vs_lm_infected_t48h

## Comparing analyses 102/120: uninfected_t24h_vs_lm_infected_t48h

## Comparing analyses 103/120: uninfected_t48h_vs_lm_infected_t48h

## Comparing analyses 104/120: uninfected_t4h_vs_lm_infected_t48h

## Comparing analyses 105/120: uninfected_t72h_vs_lm_infected_t48h

## Comparing analyses 106/120: lm_infected_t72h_vs_lm_infected_t4h

## Comparing analyses 107/120: uninfected_t24h_vs_lm_infected_t4h

## Comparing analyses 108/120: uninfected_t48h_vs_lm_infected_t4h

## Comparing analyses 109/120: uninfected_t4h_vs_lm_infected_t4h

## Comparing analyses 110/120: uninfected_t72h_vs_lm_infected_t4h

## Comparing analyses 111/120: uninfected_t24h_vs_lm_infected_t72h

## Comparing analyses 112/120: uninfected_t48h_vs_lm_infected_t72h

## Comparing analyses 113/120: uninfected_t4h_vs_lm_infected_t72h

## Comparing analyses 114/120: uninfected_t72h_vs_lm_infected_t72h

## Comparing analyses 115/120: uninfected_t48h_vs_uninfected_t24h

## Comparing analyses 116/120: uninfected_t4h_vs_uninfected_t24h

## Comparing analyses 117/120: uninfected_t72h_vs_uninfected_t24h

## Comparing analyses 118/120: uninfected_t4h_vs_uninfected_t48h

## Comparing analyses 119/120: uninfected_t72h_vs_uninfected_t48h

## Comparing analyses 120/120: uninfected_t72h_vs_uninfected_t4h

## Comparing analyses 1/120: bead_t48h_vs_bead_t24h

## Comparing analyses 2/120: bead_t4h_vs_bead_t24h

## Comparing analyses 3/120: bead_t72h_vs_bead_t24h

## Comparing analyses 4/120: la_infected_t24h_vs_bead_t24h

## Comparing analyses 5/120: la_infected_t48h_vs_bead_t24h

## Comparing analyses 6/120: la_infected_t4h_vs_bead_t24h

## Comparing analyses 7/120: la_infected_t72h_vs_bead_t24h

## Comparing analyses 8/120: lm_infected_t24h_vs_bead_t24h

## Comparing analyses 9/120: lm_infected_t48h_vs_bead_t24h

## Comparing analyses 10/120: lm_infected_t4h_vs_bead_t24h

## Comparing analyses 11/120: lm_infected_t72h_vs_bead_t24h

## Comparing analyses 12/120: uninfected_t24h_vs_bead_t24h

## Comparing analyses 13/120: uninfected_t48h_vs_bead_t24h

## Comparing analyses 14/120: uninfected_t4h_vs_bead_t24h

## Comparing analyses 15/120: uninfected_t72h_vs_bead_t24h

## Comparing analyses 16/120: bead_t4h_vs_bead_t48h

## Comparing analyses 17/120: bead_t72h_vs_bead_t48h

## Comparing analyses 18/120: la_infected_t24h_vs_bead_t48h

## Comparing analyses 19/120: la_infected_t48h_vs_bead_t48h

## Comparing analyses 20/120: la_infected_t4h_vs_bead_t48h

## Comparing analyses 21/120: la_infected_t72h_vs_bead_t48h

## Comparing analyses 22/120: lm_infected_t24h_vs_bead_t48h

## Comparing analyses 23/120: lm_infected_t48h_vs_bead_t48h

## Comparing analyses 24/120: lm_infected_t4h_vs_bead_t48h

## Comparing analyses 25/120: lm_infected_t72h_vs_bead_t48h

## Comparing analyses 26/120: uninfected_t24h_vs_bead_t48h

## Comparing analyses 27/120: uninfected_t48h_vs_bead_t48h

## Comparing analyses 28/120: uninfected_t4h_vs_bead_t48h

## Comparing analyses 29/120: uninfected_t72h_vs_bead_t48h

## Comparing analyses 30/120: bead_t72h_vs_bead_t4h

## Comparing analyses 31/120: la_infected_t24h_vs_bead_t4h

## Comparing analyses 32/120: la_infected_t48h_vs_bead_t4h

## Comparing analyses 33/120: la_infected_t4h_vs_bead_t4h

## Comparing analyses 34/120: la_infected_t72h_vs_bead_t4h

## Comparing analyses 35/120: lm_infected_t24h_vs_bead_t4h

## Comparing analyses 36/120: lm_infected_t48h_vs_bead_t4h

## Comparing analyses 37/120: lm_infected_t4h_vs_bead_t4h

## Comparing analyses 38/120: lm_infected_t72h_vs_bead_t4h

## Comparing analyses 39/120: uninfected_t24h_vs_bead_t4h

## Comparing analyses 40/120: uninfected_t48h_vs_bead_t4h

## Comparing analyses 41/120: uninfected_t4h_vs_bead_t4h

## Comparing analyses 42/120: uninfected_t72h_vs_bead_t4h

## Comparing analyses 43/120: la_infected_t24h_vs_bead_t72h

## Comparing analyses 44/120: la_infected_t48h_vs_bead_t72h

## Comparing analyses 45/120: la_infected_t4h_vs_bead_t72h

## Comparing analyses 46/120: la_infected_t72h_vs_bead_t72h

## Comparing analyses 47/120: lm_infected_t24h_vs_bead_t72h

## Comparing analyses 48/120: lm_infected_t48h_vs_bead_t72h

## Comparing analyses 49/120: lm_infected_t4h_vs_bead_t72h

## Comparing analyses 50/120: lm_infected_t72h_vs_bead_t72h

## Comparing analyses 51/120: uninfected_t24h_vs_bead_t72h

## Comparing analyses 52/120: uninfected_t48h_vs_bead_t72h

## Comparing analyses 53/120: uninfected_t4h_vs_bead_t72h

## Comparing analyses 54/120: uninfected_t72h_vs_bead_t72h

## Comparing analyses 55/120: la_infected_t48h_vs_la_infected_t24h

## Comparing analyses 56/120: la_infected_t4h_vs_la_infected_t24h

## Comparing analyses 57/120: la_infected_t72h_vs_la_infected_t24h

## Comparing analyses 58/120: lm_infected_t24h_vs_la_infected_t24h

## Comparing analyses 59/120: lm_infected_t48h_vs_la_infected_t24h

## Comparing analyses 60/120: lm_infected_t4h_vs_la_infected_t24h

## Comparing analyses 61/120: lm_infected_t72h_vs_la_infected_t24h

## Comparing analyses 62/120: uninfected_t24h_vs_la_infected_t24h

## Comparing analyses 63/120: uninfected_t48h_vs_la_infected_t24h

## Comparing analyses 64/120: uninfected_t4h_vs_la_infected_t24h

## Comparing analyses 65/120: uninfected_t72h_vs_la_infected_t24h

## Comparing analyses 66/120: la_infected_t4h_vs_la_infected_t48h

## Comparing analyses 67/120: la_infected_t72h_vs_la_infected_t48h

## Comparing analyses 68/120: lm_infected_t24h_vs_la_infected_t48h

## Comparing analyses 69/120: lm_infected_t48h_vs_la_infected_t48h

## Comparing analyses 70/120: lm_infected_t4h_vs_la_infected_t48h

## Comparing analyses 71/120: lm_infected_t72h_vs_la_infected_t48h

## Comparing analyses 72/120: uninfected_t24h_vs_la_infected_t48h

## Comparing analyses 73/120: uninfected_t48h_vs_la_infected_t48h

## Comparing analyses 74/120: uninfected_t4h_vs_la_infected_t48h

## Comparing analyses 75/120: uninfected_t72h_vs_la_infected_t48h

## Comparing analyses 76/120: la_infected_t72h_vs_la_infected_t4h

## Comparing analyses 77/120: lm_infected_t24h_vs_la_infected_t4h

## Comparing analyses 78/120: lm_infected_t48h_vs_la_infected_t4h

## Comparing analyses 79/120: lm_infected_t4h_vs_la_infected_t4h

## Comparing analyses 80/120: lm_infected_t72h_vs_la_infected_t4h

## Comparing analyses 81/120: uninfected_t24h_vs_la_infected_t4h

## Comparing analyses 82/120: uninfected_t48h_vs_la_infected_t4h

## Comparing analyses 83/120: uninfected_t4h_vs_la_infected_t4h

## Comparing analyses 84/120: uninfected_t72h_vs_la_infected_t4h

## Comparing analyses 85/120: lm_infected_t24h_vs_la_infected_t72h

## Comparing analyses 86/120: lm_infected_t48h_vs_la_infected_t72h

## Comparing analyses 87/120: lm_infected_t4h_vs_la_infected_t72h

## Comparing analyses 88/120: lm_infected_t72h_vs_la_infected_t72h

## Comparing analyses 89/120: uninfected_t24h_vs_la_infected_t72h

## Comparing analyses 90/120: uninfected_t48h_vs_la_infected_t72h

## Comparing analyses 91/120: uninfected_t4h_vs_la_infected_t72h

## Comparing analyses 92/120: uninfected_t72h_vs_la_infected_t72h

## Comparing analyses 93/120: lm_infected_t48h_vs_lm_infected_t24h

## Comparing analyses 94/120: lm_infected_t4h_vs_lm_infected_t24h

## Comparing analyses 95/120: lm_infected_t72h_vs_lm_infected_t24h

## Comparing analyses 96/120: uninfected_t24h_vs_lm_infected_t24h

## Comparing analyses 97/120: uninfected_t48h_vs_lm_infected_t24h

## Comparing analyses 98/120: uninfected_t4h_vs_lm_infected_t24h

## Comparing analyses 99/120: uninfected_t72h_vs_lm_infected_t24h

## Comparing analyses 100/120: lm_infected_t4h_vs_lm_infected_t48h

## Comparing analyses 101/120: lm_infected_t72h_vs_lm_infected_t48h

## Comparing analyses 102/120: uninfected_t24h_vs_lm_infected_t48h

## Comparing analyses 103/120: uninfected_t48h_vs_lm_infected_t48h

## Comparing analyses 104/120: uninfected_t4h_vs_lm_infected_t48h

## Comparing analyses 105/120: uninfected_t72h_vs_lm_infected_t48h

## Comparing analyses 106/120: lm_infected_t72h_vs_lm_infected_t4h

## Comparing analyses 107/120: uninfected_t24h_vs_lm_infected_t4h

## Comparing analyses 108/120: uninfected_t48h_vs_lm_infected_t4h

## Comparing analyses 109/120: uninfected_t4h_vs_lm_infected_t4h

## Comparing analyses 110/120: uninfected_t72h_vs_lm_infected_t4h

## Comparing analyses 111/120: uninfected_t24h_vs_lm_infected_t72h

## Comparing analyses 112/120: uninfected_t48h_vs_lm_infected_t72h

## Comparing analyses 113/120: uninfected_t4h_vs_lm_infected_t72h

## Comparing analyses 114/120: uninfected_t72h_vs_lm_infected_t72h

## Comparing analyses 115/120: uninfected_t48h_vs_uninfected_t24h

## Comparing analyses 116/120: uninfected_t4h_vs_uninfected_t24h

## Comparing analyses 117/120: uninfected_t72h_vs_uninfected_t24h

## Comparing analyses 118/120: uninfected_t4h_vs_uninfected_t48h

## Comparing analyses 119/120: uninfected_t72h_vs_uninfected_t48h

## Comparing analyses 120/120: uninfected_t72h_vs_uninfected_t4h

excel_file <- glue::glue("excel/{rundate}_mbio_pairwise_tables-v{ver}.xlsx")
mbio_tables <- combine_de_tables(mbio_pairwise, keepers=keepers, excel=excel_file)

## Deleting the file excel/20190128_mbio_pairwise_tables-v20190124.xlsx before writing the tables.

## Writing a legend of columns.

## Printing a pca plot before/after surrogates/batch estimation.

## Working on 1/2: 4hpi_uninf which is: lm_infected_t4h/uninfected_t4h.

## Found inverse table with uninfected_t4h_vs_lm_infected_t4h

## The ebseq table is null.

## 20181210 a pthread error in normalize.quantiles leads me to robust.

## Used Bon Ferroni corrected t test(s) between columns.
## Used Bon Ferroni corrected t test(s) between columns.
## Used Bon Ferroni corrected t test(s) between columns.

## Working on 2/2: 4hpi_beads which is: lm_infected_t4h/bead_t4h.

## Found table with lm_infected_t4h_vs_bead_t4h

## The ebseq table is null.

## 20181210 a pthread error in normalize.quantiles leads me to robust.

## Used Bon Ferroni corrected t test(s) between columns.
## Used Bon Ferroni corrected t test(s) between columns.
## Used Bon Ferroni corrected t test(s) between columns.

## Adding venn plots for 4hpi_uninf.

## Limma expression coefficients for 4hpi_uninf; R^2: 0.943; equation: y = 0.986x - 0.035

## Edger expression coefficients for 4hpi_uninf; R^2: 0.944; equation: y = 1.05x - 0.438

## DESeq2 expression coefficients for 4hpi_uninf; R^2: 0.944; equation: y = 1.05x - 0.437

## Adding venn plots for 4hpi_beads.

## Limma expression coefficients for 4hpi_beads; R^2: 0.951; equation: y = 0.995x - 0.0683

## Edger expression coefficients for 4hpi_beads; R^2: 0.952; equation: y = 1.04x - 0.344

## DESeq2 expression coefficients for 4hpi_beads; R^2: 0.952; equation: y = 1.04x - 0.335

## Writing summary information.

## Performing save of the workbook.

6.2 Compare against a sheet I downloaded from the paper.

I saved the worksheet ‘infLM4_before’ as inline-supplementary-material-5_infLM4_before.csv It is 4 hpi / uninfected, which is happily a contrast I performed.

old_table <- readr::read_csv("excel/inline-supplementary-material-5_infLM4_before.csv")

## Warning: Missing column names filled in: 'X2' [2], 'X3' [3], 'X4' [4],
## 'X5' [5]

## Parsed with column specification:
## cols(
##   `DE genes in L. major-infected human macrophages relative to uninfected controls, 4 hpi, not accounting for phagocytosis` = col_character(),
##   X2 = col_character(),
##   X3 = col_character(),
##   X4 = col_character(),
##   X5 = col_character()
## )

colnames(old_table) <- old_table[1, ]
old_table <- as.data.frame(old_table[-1, ])
rownames(old_table) <- old_table[["ID"]]
new_table <- mbio_tables[["data"]][["4hpi_uninf"]]
common <- merge(old_table, new_table, by="row.names")
dim(common)

## [1] 5118   52

common[["Fold change"]] <- log2(as.numeric(common[["Fold change"]]))

## Warning: NaNs produced

cor.test(common[["Fold change"]], common[["limma_logfc"]])

## 
##  Pearson's product-moment correlation
## 
## data:  common[["Fold change"]] and common[["limma_logfc"]]
## t = 160, df = 2000, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.9610 0.9672
## sample estimates:
##    cor 
## 0.9642

test <- plot_linear_scatter(common[, c("Fold change", "limma_logfc")])

## Used Bon Ferroni corrected t test(s) between columns.

test$scatter

old_table <- readr::read_csv("excel/inline-supplementary-material-5_infLM4_after.csv")

## Warning: Missing column names filled in: 'X2' [2], 'X3' [3], 'X4' [4],
## 'X5' [5], 'X6' [6]

## Parsed with column specification:
## cols(
##   `DE genes in L. major-infected human macrophages relative to uninfected controls, 4 hpi, with accounting for phagocytosis` = col_character(),
##   X2 = col_character(),
##   X3 = col_character(),
##   X4 = col_character(),
##   X5 = col_character(),
##   X6 = col_character()
## )

colnames(old_table) <- old_table[1, ]
old_table <- as.data.frame(old_table[-1, ])
rownames(old_table) <- old_table[["ID"]]
new_table <- mbio_tables[["data"]][["4hpi_beads"]]
common <- merge(old_table, new_table, by="row.names")
dim(common)

## [1] 2955   53

common[["Fold change beads v inf"]] <- log2(as.numeric(common[["Fold change beads v inf"]]))

## Warning: NaNs produced

cor.test(common[["Fold change beads v inf"]], common[["limma_logfc"]])

## 
##  Pearson's product-moment correlation
## 
## data:  common[["Fold change beads v inf"]] and common[["limma_logfc"]]
## t = 110, df = 1200, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.9451 0.9561
## sample estimates:
##    cor 
## 0.9509

test <- plot_linear_scatter(common[, c("Fold change beads v inf", "limma_logfc")])

## Used Bon Ferroni corrected t test(s) between columns.

test$scatter

pander::pander(sessionInfo())

R version 3.5.2 (2018-12-20)

Platform: x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=en_US.utf8, LC_NUMERIC=C, LC_TIME=en_US.utf8, LC_COLLATE=en_US.utf8, LC_MONETARY=en_US.utf8, LC_MESSAGES=en_US.utf8, LC_PAPER=en_US.utf8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.utf8 and LC_IDENTIFICATION=C

attached base packages: parallel, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: ruv(v.0.9.7), bindrcpp(v.0.2.2), hpgltools(v.2018.11), Biobase(v.2.42.0) and BiocGenerics(v.0.28.0)

loaded via a namespace (and not attached): tidyselect(v.0.2.5), lme4(v.1.1-19), htmlwidgets(v.1.3), RSQLite(v.2.1.1), AnnotationDbi(v.1.44.0), grid(v.3.5.2), BiocParallel(v.1.16.5), devtools(v.2.0.1), munsell(v.0.5.0), codetools(v.0.2-16), preprocessCore(v.1.45.0), units(v.0.6-2), withr(v.2.1.2), colorspace(v.1.4-0), GOSemSim(v.2.8.0), knitr(v.1.21), rstudioapi(v.0.9.0), stats4(v.3.5.2), Vennerable(v.3.1.0.9000), robustbase(v.0.93-3), DOSE(v.3.8.2), labeling(v.0.3), urltools(v.1.7.1), tximport(v.1.10.1), GenomeInfoDbData(v.1.2.0), bit64(v.0.9-7), farver(v.1.1.0), rprojroot(v.1.3-2), xfun(v.0.4), R6(v.2.3.0), doParallel(v.1.0.14), GenomeInfoDb(v.1.18.1), locfit(v.1.5-9.1), bitops(v.1.0-6), fgsea(v.1.8.0), gridGraphics(v.0.3-0), DelayedArray(v.0.8.0), assertthat(v.0.2.0), scales(v.1.0.0), ggraph(v.1.0.2), nnet(v.7.3-12), enrichplot(v.1.2.0), gtable(v.0.2.0), sva(v.3.30.1), processx(v.3.2.1), rlang(v.0.3.1), genefilter(v.1.64.0), splines(v.3.5.2), rtracklayer(v.1.42.1), lazyeval(v.0.2.1), acepack(v.1.4.1), europepmc(v.0.3), checkmate(v.1.9.1), yaml(v.2.2.0), reshape2(v.1.4.3), GenomicFeatures(v.1.34.1), backports(v.1.1.3), qvalue(v.2.14.1), Hmisc(v.4.1-1), RBGL(v.1.58.1), clusterProfiler(v.3.10.1), tools(v.3.5.2), usethis(v.1.4.0), ggplotify(v.0.0.3), ggplot2(v.3.1.0), gplots(v.3.0.1), RColorBrewer(v.1.1-2), sessioninfo(v.1.1.1), ggridges(v.0.5.1), Rcpp(v.1.0.0), plyr(v.1.8.4), base64enc(v.0.1-3), progress(v.1.2.0), zlibbioc(v.1.28.0), purrr(v.0.2.5), RCurl(v.1.95-4.11), ps(v.1.3.0), prettyunits(v.1.0.2), rpart(v.4.1-13), viridis(v.0.5.1), cowplot(v.0.9.4), S4Vectors(v.0.20.1), SummarizedExperiment(v.1.12.0), ggrepel(v.0.8.0), cluster(v.2.0.7-1), colorRamps(v.2.3), fs(v.1.2.6), variancePartition(v.1.12.1), magrittr(v.1.5), data.table(v.1.12.0), DO.db(v.2.9), openxlsx(v.4.1.0), triebeard(v.0.3.0), packrat(v.0.5.0), matrixStats(v.0.54.0), pkgload(v.1.0.2), hms(v.0.4.2), evaluate(v.0.12), xtable(v.1.8-3), pbkrtest(v.0.4-7), XML(v.3.98-1.16), IRanges(v.2.16.0), gridExtra(v.2.3), testthat(v.2.0.1), compiler(v.3.5.2), biomaRt(v.2.38.0), tibble(v.2.0.1), KernSmooth(v.2.23-15), crayon(v.1.3.4), minqa(v.1.2.4), htmltools(v.0.3.6), mgcv(v.1.8-26), corpcor(v.1.6.9), Formula(v.1.2-3), geneplotter(v.1.60.0), tidyr(v.0.8.2), DBI(v.1.0.0), tweenr(v.1.0.1), MASS(v.7.3-51.1), Matrix(v.1.2-15), readr(v.1.3.1), cli(v.1.0.1), gdata(v.2.18.0), bindr(v.0.1.1), igraph(v.1.2.2), GenomicRanges(v.1.34.0), pkgconfig(v.2.0.2), rvcheck(v.0.1.3), GenomicAlignments(v.1.18.1), foreign(v.0.8-71), xml2(v.1.2.0), foreach(v.1.4.4), annotate(v.1.60.0), XVector(v.0.22.0), stringr(v.1.3.1), callr(v.3.1.1), digest(v.0.6.18), graph(v.1.60.0), Biostrings(v.2.50.2), rmarkdown(v.1.11), fastmatch(v.1.1-0), htmlTable(v.1.13.1), edgeR(v.3.24.3), Rsamtools(v.1.34.0), gtools(v.3.8.1), nloptr(v.1.2.1), nlme(v.3.1-137), jsonlite(v.1.6), desc(v.1.2.0), viridisLite(v.0.3.0), limma(v.3.38.3), pillar(v.1.3.1), lattice(v.0.20-38), DEoptimR(v.1.0-8), httr(v.1.4.0), pkgbuild(v.1.0.2), survival(v.2.43-3), GO.db(v.3.7.0), glue(v.1.3.0), remotes(v.2.0.2), zip(v.1.0.0), UpSetR(v.1.3.3), iterators(v.1.0.10), pander(v.0.6.3), bit(v.1.1-14), ggforce(v.0.1.3), stringi(v.1.2.4), blob(v.1.1.1), DESeq2(v.1.22.2), latticeExtra(v.0.6-28), caTools(v.1.17.1.1), memoise(v.1.1.0) and dplyr(v.0.7.8)

message(paste0("This is hpgltools commit: ", get_git_commit()))

## If you wish to reproduce this exact build of hpgltools, invoke the following:

## > git clone http://github.com/abelew/hpgltools.git

## > git reset b0e11455e9f02944597c1bb5027f8ecbeb14201b

## This is hpgltools commit: Fri Jan 18 13:42:11 2019 -0500: b0e11455e9f02944597c1bb5027f8ecbeb14201b

## message(paste0("Saving to ", savefile))
## tmp <- sm(saveme(filename=savefile))