20190124 Recapitulating previous results: which genes are DE in human macrophages at 4hrs upon infection with L. major?
atb abelew@gmail.com
2019-01-25
1 Gather annotation data
## The biomart annotations file already exists, loading from it.rownames(hs_annot) <- make.names(
paste0(hs_annot[["ensembl_transcript_id"]], ".",
hs_annot[["transcript_version"]]),
unique=TRUE)
hs_tx_gene <- hs_annot[, c("ensembl_gene_id", "ensembl_transcript_id")]
hs_tx_gene[["id"]] <- rownames(hs_tx_gene)
hs_tx_gene <- hs_tx_gene[, c("id", "ensembl_gene_id")]
new_hs_annot <- hs_annot
rownames(new_hs_annot) <- make.names(hs_annot[["ensembl_gene_id"]], unique=TRUE)2 Create expressionset
Note that as of 20190124, two samples are still missing: hpgl0914 and hpgl0749. I am rerunning their mapping with salmon now with the assumption that I just missed them previously. I noted their status in the ‘skipped’ column of the online sample sheet.
Note 20190125: hpgl0749 mapped; but hpgl0914 has some weirdness still. It looks like the forward reads have a gzip CRC error, so I used zcat to extract the available data and will then retrim/remap.
Another note, I am using the ‘state’ column, which was missing the field ‘la_infected’ for the Leishmania amazonensis samples; this resulted in a set of ‘NA’ conditions. I therefore added la_infected to the relevant fields to the sample sheet online and my working sheet.
I also found an error in the time attribution for sample hpgl0461. The time was set to undefined, while in the study it was t24h. That has been changed in both the online and my copy of the sample sheet.
sample_sheet <- "sample_sheets/all_leishmania_samples_20190124.xlsx"
lots <- create_expt(sample_sheet,
gene_info=new_hs_annot,
tx_gene_map=hs_tx_gene)## Reading the sample metadata.## The sample definitions comprises: 291 rows(samples) and 35 columns(metadata fields).## Reading count tables.## Using the transcript to gene mapping.## Reading salmon data with tximport.## Finished reading count tables.## Matched 19629 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'.3 Extract the mbio data
Now we have a 291 sample data set, but we only want the samples from the human portion of the mBio paper, which Najib helpfully defined in the ‘study’ column of the sample sheet as ‘mBio’.
Thus I will pull those samples from the sample sheet and set the conditions/batches to what I am assuming are reasonable values. An important caveat: we need to concatenate the existing columns: ‘expt_time’ and ‘state’ in order to get useful values for the condition.
In addition, I am removing the L. amazonensis samples for the moment.
## There were 272, now there are 82 samples.## There were 82, now there are 66 samples.##mbio_expt <- subset_expt(mbio_expt, subset="pathogenspecies!='lamazonensis'")
##mbio_expt <- subset_expt(mbio_expt, subset="donor!='thp1'")
mbio_expt <- set_expt_batches(mbio_expt, "studybatch")
metadata <- pData(mbio_expt)
new_condition <- paste0(metadata[["state"]], "_", metadata[["expttime"]])
mbio_expt <- set_expt_conditions(mbio_expt, "state")4 Perform a few metrics
Now make some plots and see if I get similar ones to those observed in the paper.
Here is the link with the PCA plots and such: https://mbio.asm.org/content/7/3/e00027-16/figures-only
Unless I am mistaken, the only things I have to compare against are some fancy PCA plots in the main paper and a few raw-ish ones in the supplemental.
5 Normalize and plot more
5.1 No batch adjust
This first plot makes no attempt to handle the various batch effects in the data.
## 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.
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: FALSEmbio_pca1 <- plot_pca(mbio_batch1, size_column="expttime", plot_labels=FALSE,
size_order=c("t4h", "t24h", "t48h", "t72h"))## Not putting labels on the 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: FALSEmbio_pca2 <- plot_pca(mbio_batch2, size_column="expttime", plot_labels=FALSE,
size_order=c("t4h", "t24h", "t48h", "t72h"))## Not putting labels on the plot.
6 Try DE
## 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.mbio_filt <- set_expt_conditions(mbio_filt, new_condition)
## 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="svaseq", parallel=FALSE,
do_ebseq=FALSE, force=TRUE)## batch_counts: Before batch/surrogate estimation, 5536 entries are x<=0.## The be method chose 6 surrogate variable(s).## Attempting svaseq estimation with 6 surrogates.## Using svaseq to visualize before/after batch inclusion.## Performing a test normalization with: raw## Starting basic pairwise comparison.## Leaving the data alone, regardless of normalization state.## Basic step 0/3: Transforming data.## Basic step 1/3: Creating median and variance tables.## Basic step 2/3: Performing 136 comparisons.## Basic step 3/3: Creating faux DE Tables.## Basic: Returning tables.## Starting DESeq2 pairwise comparisons.## About to round the data, this is a pretty terrible thing to do. But if you, like me, want to see what happens when you put non-standard data into deseq, then here you go.## Warning in choose_binom_dataset(input, force = force): This data was
## inappropriately forced into integers.## Including batch estimates from sva/ruv/pca in the model.## Choosing the non-intercept containing model.## DESeq2 step 1/5: Including a matrix of batch estimates in the deseq model.## converting counts to integer mode## DESeq2 step 2/5: Estimate size factors.## DESeq2 step 3/5: Estimate dispersions.## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## Using a parametric fitting seems to have worked.## DESeq2 step 4/5: nbinomWaldTest.## Starting edgeR pairwise comparisons.## About to round the data, this is a pretty terrible thing to do. But if you, like me, want to see what happens when you put non-standard data into deseq, then here you go.## Warning in choose_binom_dataset(input, force = force): This data was
## inappropriately forced into integers.## Including batch estimates from sva/ruv/pca in the model.## Choosing the non-intercept containing model.## EdgeR step 1/9: Importing and normalizing data.## EdgeR step 2/9: Estimating the common dispersion.## EdgeR step 3/9: Estimating dispersion across genes.## EdgeR step 4/9: Estimating GLM Common dispersion.## EdgeR step 5/9: Estimating GLM Trended dispersion.## EdgeR step 6/9: Estimating GLM Tagged dispersion.## EdgeR step 7/9: Running glmFit, switch to glmQLFit by changing the argument 'edger_test'.## EdgeR step 8/9: Making pairwise contrasts.
## Starting limma pairwise comparison.## Leaving the data alone, regardless of normalization state.## libsize was not specified, this parameter has profound effects on limma's result.## Using the libsize from expt$best_libsize.## Limma step 1/6: choosing model.## Including batch estimates from sva/ruv/pca in the model.## Choosing the non-intercept containing model.## Limma step 2/6: running limma::voom(), switch with the argument 'which_voom'.## Using normalize.method=quantile for voom.
## Limma step 3/6: running lmFit with method: ls.## Limma step 4/6: making and fitting contrasts with no intercept. (~ 0 + factors)## Limma step 5/6: Running eBayes with robust=FALSE and trend=FALSE.## Limma step 6/6: Writing limma outputs.## Limma step 6/6: 1/120: Creating table: bead_t48h_vs_bead_t24h. Adjust=BH## Limma step 6/6: 2/120: Creating table: bead_t4h_vs_bead_t24h. Adjust=BH## Limma step 6/6: 3/120: Creating table: bead_t72h_vs_bead_t24h. Adjust=BH## Limma step 6/6: 4/120: Creating table: la_infected_t24h_vs_bead_t24h. Adjust=BH## Limma step 6/6: 5/120: Creating table: la_infected_t48h_vs_bead_t24h. Adjust=BH## Limma step 6/6: 6/120: Creating table: la_infected_t4h_vs_bead_t24h. Adjust=BH## Limma step 6/6: 7/120: Creating table: la_infected_t72h_vs_bead_t24h. Adjust=BH## Limma step 6/6: 8/120: Creating table: lm_infected_t24h_vs_bead_t24h. Adjust=BH## Limma step 6/6: 9/120: Creating table: lm_infected_t48h_vs_bead_t24h. Adjust=BH## Limma step 6/6: 10/120: Creating table: lm_infected_t4h_vs_bead_t24h. Adjust=BH## Limma step 6/6: 11/120: Creating table: lm_infected_t72h_vs_bead_t24h. Adjust=BH## Limma step 6/6: 12/120: Creating table: uninfected_t24h_vs_bead_t24h. Adjust=BH## Limma step 6/6: 13/120: Creating table: uninfected_t48h_vs_bead_t24h. Adjust=BH## Limma step 6/6: 14/120: Creating table: uninfected_t4h_vs_bead_t24h. Adjust=BH## Limma step 6/6: 15/120: Creating table: uninfected_t72h_vs_bead_t24h. Adjust=BH## Limma step 6/6: 16/120: Creating table: bead_t4h_vs_bead_t48h. Adjust=BH## Limma step 6/6: 17/120: Creating table: bead_t72h_vs_bead_t48h. Adjust=BH## Limma step 6/6: 18/120: Creating table: la_infected_t24h_vs_bead_t48h. Adjust=BH## Limma step 6/6: 19/120: Creating table: la_infected_t48h_vs_bead_t48h. Adjust=BH## Limma step 6/6: 20/120: Creating table: la_infected_t4h_vs_bead_t48h. Adjust=BH## Limma step 6/6: 21/120: Creating table: la_infected_t72h_vs_bead_t48h. Adjust=BH## Limma step 6/6: 22/120: Creating table: lm_infected_t24h_vs_bead_t48h. Adjust=BH## Limma step 6/6: 23/120: Creating table: lm_infected_t48h_vs_bead_t48h. Adjust=BH## Limma step 6/6: 24/120: Creating table: lm_infected_t4h_vs_bead_t48h. Adjust=BH## Limma step 6/6: 25/120: Creating table: lm_infected_t72h_vs_bead_t48h. Adjust=BH## Limma step 6/6: 26/120: Creating table: uninfected_t24h_vs_bead_t48h. Adjust=BH## Limma step 6/6: 27/120: Creating table: uninfected_t48h_vs_bead_t48h. Adjust=BH## Limma step 6/6: 28/120: Creating table: uninfected_t4h_vs_bead_t48h. Adjust=BH## Limma step 6/6: 29/120: Creating table: uninfected_t72h_vs_bead_t48h. Adjust=BH## Limma step 6/6: 30/120: Creating table: bead_t72h_vs_bead_t4h. Adjust=BH## Limma step 6/6: 31/120: Creating table: la_infected_t24h_vs_bead_t4h. Adjust=BH## Limma step 6/6: 32/120: Creating table: la_infected_t48h_vs_bead_t4h. Adjust=BH## Limma step 6/6: 33/120: Creating table: la_infected_t4h_vs_bead_t4h. Adjust=BH## Limma step 6/6: 34/120: Creating table: la_infected_t72h_vs_bead_t4h. Adjust=BH## Limma step 6/6: 35/120: Creating table: lm_infected_t24h_vs_bead_t4h. Adjust=BH## Limma step 6/6: 36/120: Creating table: lm_infected_t48h_vs_bead_t4h. Adjust=BH## Limma step 6/6: 37/120: Creating table: lm_infected_t4h_vs_bead_t4h. Adjust=BH## Limma step 6/6: 38/120: Creating table: lm_infected_t72h_vs_bead_t4h. Adjust=BH## Limma step 6/6: 39/120: Creating table: uninfected_t24h_vs_bead_t4h. Adjust=BH## Limma step 6/6: 40/120: Creating table: uninfected_t48h_vs_bead_t4h. Adjust=BH## Limma step 6/6: 41/120: Creating table: uninfected_t4h_vs_bead_t4h. Adjust=BH## Limma step 6/6: 42/120: Creating table: uninfected_t72h_vs_bead_t4h. Adjust=BH## Limma step 6/6: 43/120: Creating table: la_infected_t24h_vs_bead_t72h. Adjust=BH## Limma step 6/6: 44/120: Creating table: la_infected_t48h_vs_bead_t72h. Adjust=BH## Limma step 6/6: 45/120: Creating table: la_infected_t4h_vs_bead_t72h. Adjust=BH## Limma step 6/6: 46/120: Creating table: la_infected_t72h_vs_bead_t72h. Adjust=BH## Limma step 6/6: 47/120: Creating table: lm_infected_t24h_vs_bead_t72h. Adjust=BH## Limma step 6/6: 48/120: Creating table: lm_infected_t48h_vs_bead_t72h. Adjust=BH## Limma step 6/6: 49/120: Creating table: lm_infected_t4h_vs_bead_t72h. Adjust=BH## Limma step 6/6: 50/120: Creating table: lm_infected_t72h_vs_bead_t72h. Adjust=BH## Limma step 6/6: 51/120: Creating table: uninfected_t24h_vs_bead_t72h. Adjust=BH## Limma step 6/6: 52/120: Creating table: uninfected_t48h_vs_bead_t72h. Adjust=BH## Limma step 6/6: 53/120: Creating table: uninfected_t4h_vs_bead_t72h. Adjust=BH## Limma step 6/6: 54/120: Creating table: uninfected_t72h_vs_bead_t72h. Adjust=BH## Limma step 6/6: 55/120: Creating table: la_infected_t48h_vs_la_infected_t24h. Adjust=BH## Limma step 6/6: 56/120: Creating table: la_infected_t4h_vs_la_infected_t24h. Adjust=BH## Limma step 6/6: 57/120: Creating table: la_infected_t72h_vs_la_infected_t24h. Adjust=BH## Limma step 6/6: 58/120: Creating table: lm_infected_t24h_vs_la_infected_t24h. Adjust=BH## Limma step 6/6: 59/120: Creating table: lm_infected_t48h_vs_la_infected_t24h. Adjust=BH## Limma step 6/6: 60/120: Creating table: lm_infected_t4h_vs_la_infected_t24h. Adjust=BH## Limma step 6/6: 61/120: Creating table: lm_infected_t72h_vs_la_infected_t24h. Adjust=BH## Limma step 6/6: 62/120: Creating table: uninfected_t24h_vs_la_infected_t24h. Adjust=BH## Limma step 6/6: 63/120: Creating table: uninfected_t48h_vs_la_infected_t24h. Adjust=BH## Limma step 6/6: 64/120: Creating table: uninfected_t4h_vs_la_infected_t24h. Adjust=BH## Limma step 6/6: 65/120: Creating table: uninfected_t72h_vs_la_infected_t24h. Adjust=BH## Limma step 6/6: 66/120: Creating table: la_infected_t4h_vs_la_infected_t48h. Adjust=BH## Limma step 6/6: 67/120: Creating table: la_infected_t72h_vs_la_infected_t48h. Adjust=BH## Limma step 6/6: 68/120: Creating table: lm_infected_t24h_vs_la_infected_t48h. Adjust=BH## Limma step 6/6: 69/120: Creating table: lm_infected_t48h_vs_la_infected_t48h. Adjust=BH## Limma step 6/6: 70/120: Creating table: lm_infected_t4h_vs_la_infected_t48h. Adjust=BH## Limma step 6/6: 71/120: Creating table: lm_infected_t72h_vs_la_infected_t48h. Adjust=BH## Limma step 6/6: 72/120: Creating table: uninfected_t24h_vs_la_infected_t48h. Adjust=BH## Limma step 6/6: 73/120: Creating table: uninfected_t48h_vs_la_infected_t48h. Adjust=BH## Limma step 6/6: 74/120: Creating table: uninfected_t4h_vs_la_infected_t48h. Adjust=BH## Limma step 6/6: 75/120: Creating table: uninfected_t72h_vs_la_infected_t48h. Adjust=BH## Limma step 6/6: 76/120: Creating table: la_infected_t72h_vs_la_infected_t4h. Adjust=BH## Limma step 6/6: 77/120: Creating table: lm_infected_t24h_vs_la_infected_t4h. Adjust=BH## Limma step 6/6: 78/120: Creating table: lm_infected_t48h_vs_la_infected_t4h. Adjust=BH## Limma step 6/6: 79/120: Creating table: lm_infected_t4h_vs_la_infected_t4h. Adjust=BH## Limma step 6/6: 80/120: Creating table: lm_infected_t72h_vs_la_infected_t4h. Adjust=BH## Limma step 6/6: 81/120: Creating table: uninfected_t24h_vs_la_infected_t4h. Adjust=BH## Limma step 6/6: 82/120: Creating table: uninfected_t48h_vs_la_infected_t4h. Adjust=BH## Limma step 6/6: 83/120: Creating table: uninfected_t4h_vs_la_infected_t4h. Adjust=BH## Limma step 6/6: 84/120: Creating table: uninfected_t72h_vs_la_infected_t4h. Adjust=BH## Limma step 6/6: 85/120: Creating table: lm_infected_t24h_vs_la_infected_t72h. Adjust=BH## Limma step 6/6: 86/120: Creating table: lm_infected_t48h_vs_la_infected_t72h. Adjust=BH## Limma step 6/6: 87/120: Creating table: lm_infected_t4h_vs_la_infected_t72h. Adjust=BH## Limma step 6/6: 88/120: Creating table: lm_infected_t72h_vs_la_infected_t72h. Adjust=BH## Limma step 6/6: 89/120: Creating table: uninfected_t24h_vs_la_infected_t72h. Adjust=BH## Limma step 6/6: 90/120: Creating table: uninfected_t48h_vs_la_infected_t72h. Adjust=BH## Limma step 6/6: 91/120: Creating table: uninfected_t4h_vs_la_infected_t72h. Adjust=BH## Limma step 6/6: 92/120: Creating table: uninfected_t72h_vs_la_infected_t72h. Adjust=BH## Limma step 6/6: 93/120: Creating table: lm_infected_t48h_vs_lm_infected_t24h. Adjust=BH## Limma step 6/6: 94/120: Creating table: lm_infected_t4h_vs_lm_infected_t24h. Adjust=BH## Limma step 6/6: 95/120: Creating table: lm_infected_t72h_vs_lm_infected_t24h. Adjust=BH## Limma step 6/6: 96/120: Creating table: uninfected_t24h_vs_lm_infected_t24h. Adjust=BH## Limma step 6/6: 97/120: Creating table: uninfected_t48h_vs_lm_infected_t24h. Adjust=BH## Limma step 6/6: 98/120: Creating table: uninfected_t4h_vs_lm_infected_t24h. Adjust=BH## Limma step 6/6: 99/120: Creating table: uninfected_t72h_vs_lm_infected_t24h. Adjust=BH## Limma step 6/6: 100/120: Creating table: lm_infected_t4h_vs_lm_infected_t48h. Adjust=BH## Limma step 6/6: 101/120: Creating table: lm_infected_t72h_vs_lm_infected_t48h. Adjust=BH## Limma step 6/6: 102/120: Creating table: uninfected_t24h_vs_lm_infected_t48h. Adjust=BH## Limma step 6/6: 103/120: Creating table: uninfected_t48h_vs_lm_infected_t48h. Adjust=BH## Limma step 6/6: 104/120: Creating table: uninfected_t4h_vs_lm_infected_t48h. Adjust=BH## Limma step 6/6: 105/120: Creating table: uninfected_t72h_vs_lm_infected_t48h. Adjust=BH## Limma step 6/6: 106/120: Creating table: lm_infected_t72h_vs_lm_infected_t4h. Adjust=BH## Limma step 6/6: 107/120: Creating table: uninfected_t24h_vs_lm_infected_t4h. Adjust=BH## Limma step 6/6: 108/120: Creating table: uninfected_t48h_vs_lm_infected_t4h. Adjust=BH## Limma step 6/6: 109/120: Creating table: uninfected_t4h_vs_lm_infected_t4h. Adjust=BH## Limma step 6/6: 110/120: Creating table: uninfected_t72h_vs_lm_infected_t4h. Adjust=BH## Limma step 6/6: 111/120: Creating table: uninfected_t24h_vs_lm_infected_t72h. Adjust=BH## Limma step 6/6: 112/120: Creating table: uninfected_t48h_vs_lm_infected_t72h. Adjust=BH## Limma step 6/6: 113/120: Creating table: uninfected_t4h_vs_lm_infected_t72h. Adjust=BH## Limma step 6/6: 114/120: Creating table: uninfected_t72h_vs_lm_infected_t72h. Adjust=BH## Limma step 6/6: 115/120: Creating table: uninfected_t48h_vs_uninfected_t24h. Adjust=BH## Limma step 6/6: 116/120: Creating table: uninfected_t4h_vs_uninfected_t24h. Adjust=BH## Limma step 6/6: 117/120: Creating table: uninfected_t72h_vs_uninfected_t24h. Adjust=BH## Limma step 6/6: 118/120: Creating table: uninfected_t4h_vs_uninfected_t48h. Adjust=BH## Limma step 6/6: 119/120: Creating table: uninfected_t72h_vs_uninfected_t48h. Adjust=BH## Limma step 6/6: 120/120: Creating table: uninfected_t72h_vs_uninfected_t4h. Adjust=BH## Limma step 6/6: 1/16: Creating table: bead_t24h. Adjust=BH## Limma step 6/6: 2/16: Creating table: bead_t48h. Adjust=BH## Limma step 6/6: 3/16: Creating table: bead_t4h. Adjust=BH## Limma step 6/6: 4/16: Creating table: bead_t72h. Adjust=BH## Limma step 6/6: 5/16: Creating table: la_infected_t24h. Adjust=BH## Limma step 6/6: 6/16: Creating table: la_infected_t48h. Adjust=BH## Limma step 6/6: 7/16: Creating table: la_infected_t4h. Adjust=BH## Limma step 6/6: 8/16: Creating table: la_infected_t72h. Adjust=BH## Limma step 6/6: 9/16: Creating table: lm_infected_t24h. Adjust=BH## Limma step 6/6: 10/16: Creating table: lm_infected_t48h. Adjust=BH## Limma step 6/6: 11/16: Creating table: lm_infected_t4h. Adjust=BH## Limma step 6/6: 12/16: Creating table: lm_infected_t72h. Adjust=BH## Limma step 6/6: 13/16: Creating table: uninfected_t24h. Adjust=BH## Limma step 6/6: 14/16: Creating table: uninfected_t48h. Adjust=BH## Limma step 6/6: 15/16: Creating table: uninfected_t4h. Adjust=BH## Limma step 6/6: 16/16: Creating table: uninfected_t72h. Adjust=BH## 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
## Writing a legend of columns.## Working on table 1/120: bead_t48h_vs_bead_t24h## 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.## Working on table 2/120: bead_t4h_vs_bead_t24h## 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.## Working on table 3/120: bead_t72h_vs_bead_t24h## 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.## Working on table 4/120: la_infected_t24h_vs_bead_t24h## 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.## Working on table 5/120: la_infected_t48h_vs_bead_t24h## 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.## Working on table 6/120: la_infected_t4h_vs_bead_t24h## 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.## Working on table 7/120: la_infected_t72h_vs_bead_t24h## 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.## Working on table 8/120: lm_infected_t24h_vs_bead_t24h## 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.## Working on table 9/120: lm_infected_t48h_vs_bead_t24h## 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.## Working on table 10/120: lm_infected_t4h_vs_bead_t24h## 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.## Working on table 11/120: lm_infected_t72h_vs_bead_t24h## 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.## Working on table 12/120: uninfected_t24h_vs_bead_t24h## 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.## Working on table 13/120: uninfected_t48h_vs_bead_t24h## 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.## Working on table 14/120: uninfected_t4h_vs_bead_t24h## 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.## Working on table 15/120: uninfected_t72h_vs_bead_t24h## 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.## Working on table 16/120: bead_t4h_vs_bead_t48h## 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.## Working on table 17/120: bead_t72h_vs_bead_t48h## 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.## Working on table 18/120: la_infected_t24h_vs_bead_t48h## 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.## Working on table 19/120: la_infected_t48h_vs_bead_t48h## 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.## Working on table 20/120: la_infected_t4h_vs_bead_t48h## 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.## Working on table 21/120: la_infected_t72h_vs_bead_t48h## 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.## Working on table 22/120: lm_infected_t24h_vs_bead_t48h## 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.## Working on table 23/120: lm_infected_t48h_vs_bead_t48h## 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.## Working on table 24/120: lm_infected_t4h_vs_bead_t48h## 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.## Working on table 25/120: lm_infected_t72h_vs_bead_t48h## 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.## Working on table 26/120: uninfected_t24h_vs_bead_t48h## 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.## Working on table 27/120: uninfected_t48h_vs_bead_t48h## 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.## Working on table 28/120: uninfected_t4h_vs_bead_t48h## 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.## Working on table 29/120: uninfected_t72h_vs_bead_t48h## 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.## Working on table 30/120: bead_t72h_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.## Working on table 31/120: la_infected_t24h_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.## Working on table 32/120: la_infected_t48h_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.## Working on table 33/120: la_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.## Working on table 34/120: la_infected_t72h_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.## Working on table 35/120: lm_infected_t24h_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.## Working on table 36/120: lm_infected_t48h_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.## Working on table 37/120: 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.## Working on table 38/120: lm_infected_t72h_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.## Working on table 39/120: uninfected_t24h_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.## Working on table 40/120: uninfected_t48h_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.## Working on table 41/120: uninfected_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.## Working on table 42/120: uninfected_t72h_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.## Working on table 43/120: la_infected_t24h_vs_bead_t72h## 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.## Working on table 44/120: la_infected_t48h_vs_bead_t72h## 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.## Working on table 45/120: la_infected_t4h_vs_bead_t72h## 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.## Working on table 46/120: la_infected_t72h_vs_bead_t72h## 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.## Working on table 47/120: lm_infected_t24h_vs_bead_t72h## 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.## Working on table 48/120: lm_infected_t48h_vs_bead_t72h## 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.## Working on table 49/120: lm_infected_t4h_vs_bead_t72h## 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.## Working on table 50/120: lm_infected_t72h_vs_bead_t72h## 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.## Working on table 51/120: uninfected_t24h_vs_bead_t72h## 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.## Working on table 52/120: uninfected_t48h_vs_bead_t72h## 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.## Working on table 53/120: uninfected_t4h_vs_bead_t72h## 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.## Working on table 54/120: uninfected_t72h_vs_bead_t72h## 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.## Working on table 55/120: la_infected_t48h_vs_la_infected_t24h## 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.## Working on table 56/120: la_infected_t4h_vs_la_infected_t24h## 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.## Working on table 57/120: la_infected_t72h_vs_la_infected_t24h## 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.## Working on table 58/120: lm_infected_t24h_vs_la_infected_t24h## 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.## Working on table 59/120: lm_infected_t48h_vs_la_infected_t24h## 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.## Working on table 60/120: lm_infected_t4h_vs_la_infected_t24h## 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.## Working on table 61/120: lm_infected_t72h_vs_la_infected_t24h## 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.## Working on table 62/120: uninfected_t24h_vs_la_infected_t24h## 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.## Working on table 63/120: uninfected_t48h_vs_la_infected_t24h## 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.## Working on table 64/120: uninfected_t4h_vs_la_infected_t24h## 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.## Working on table 65/120: uninfected_t72h_vs_la_infected_t24h## 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.## Working on table 66/120: la_infected_t4h_vs_la_infected_t48h## 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.## Working on table 67/120: la_infected_t72h_vs_la_infected_t48h## 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.## Working on table 68/120: lm_infected_t24h_vs_la_infected_t48h## 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.## Working on table 69/120: lm_infected_t48h_vs_la_infected_t48h## 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.## Working on table 70/120: lm_infected_t4h_vs_la_infected_t48h## 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.## Working on table 71/120: lm_infected_t72h_vs_la_infected_t48h## 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.## Working on table 72/120: uninfected_t24h_vs_la_infected_t48h## 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.## Working on table 73/120: uninfected_t48h_vs_la_infected_t48h## 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.## Working on table 74/120: uninfected_t4h_vs_la_infected_t48h## 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.## Working on table 75/120: uninfected_t72h_vs_la_infected_t48h## 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.## Working on table 76/120: la_infected_t72h_vs_la_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.## Working on table 77/120: lm_infected_t24h_vs_la_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.## Working on table 78/120: lm_infected_t48h_vs_la_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.## Working on table 79/120: lm_infected_t4h_vs_la_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.## Working on table 80/120: lm_infected_t72h_vs_la_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.## Working on table 81/120: uninfected_t24h_vs_la_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.## Working on table 82/120: uninfected_t48h_vs_la_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.## Working on table 83/120: uninfected_t4h_vs_la_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.## Working on table 84/120: uninfected_t72h_vs_la_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.## Working on table 85/120: lm_infected_t24h_vs_la_infected_t72h## 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.## Working on table 86/120: lm_infected_t48h_vs_la_infected_t72h## 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.## Working on table 87/120: lm_infected_t4h_vs_la_infected_t72h## 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.## Working on table 88/120: lm_infected_t72h_vs_la_infected_t72h## 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.## Working on table 89/120: uninfected_t24h_vs_la_infected_t72h## 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.## Working on table 90/120: uninfected_t48h_vs_la_infected_t72h## 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.## Working on table 91/120: uninfected_t4h_vs_la_infected_t72h## 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.## Working on table 92/120: uninfected_t72h_vs_la_infected_t72h## 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.## Working on table 93/120: lm_infected_t48h_vs_lm_infected_t24h## 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.## Working on table 94/120: lm_infected_t4h_vs_lm_infected_t24h## 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.## Working on table 95/120: lm_infected_t72h_vs_lm_infected_t24h## 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.## Working on table 96/120: uninfected_t24h_vs_lm_infected_t24h## 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.## Working on table 97/120: uninfected_t48h_vs_lm_infected_t24h## 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.## Working on table 98/120: uninfected_t4h_vs_lm_infected_t24h## 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.## Working on table 99/120: uninfected_t72h_vs_lm_infected_t24h## 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.## Working on table 100/120: lm_infected_t4h_vs_lm_infected_t48h## 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.## Working on table 101/120: lm_infected_t72h_vs_lm_infected_t48h## 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.## Working on table 102/120: uninfected_t24h_vs_lm_infected_t48h## 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.## Working on table 103/120: uninfected_t48h_vs_lm_infected_t48h## 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.## Working on table 104/120: uninfected_t4h_vs_lm_infected_t48h## 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.## Working on table 105/120: uninfected_t72h_vs_lm_infected_t48h## 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.## Working on table 106/120: lm_infected_t72h_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.## Working on table 107/120: uninfected_t24h_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.## Working on table 108/120: uninfected_t48h_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.## Working on table 109/120: 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.## Working on table 110/120: uninfected_t72h_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.## Working on table 111/120: uninfected_t24h_vs_lm_infected_t72h## 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.## Working on table 112/120: uninfected_t48h_vs_lm_infected_t72h## 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.## Working on table 113/120: uninfected_t4h_vs_lm_infected_t72h## 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.## Working on table 114/120: uninfected_t72h_vs_lm_infected_t72h## 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.## Working on table 115/120: uninfected_t48h_vs_uninfected_t24h## 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.## Working on table 116/120: uninfected_t4h_vs_uninfected_t24h## 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.## Working on table 117/120: uninfected_t72h_vs_uninfected_t24h## 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.## Working on table 118/120: uninfected_t4h_vs_uninfected_t48h## 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.## Working on table 119/120: uninfected_t72h_vs_uninfected_t48h## 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.## Working on table 120/120: uninfected_t72h_vs_uninfected_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.6.1 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.
##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$E))As far as the above goes, it mostly makes sense. My question is, how do we get the modified logFC values? Presumably that is later down in the log.
6.2 makeTab2
Looking further down I found the following invocations, which partially but incompletely answer my question.
##Define makeTab2 function
## construct a DE result table for infection vs. uninfected and beads
## contrFit: the result of eBayes after conrasts.fit
## cellmeansFit: the cell means fit (lmFit(v) above)
## conjContrasts: the 'conjuctive' null test (infection vs. uninf AND infect vs. beads)
## disjContrast: the 'other' test (beads vs. uninf)
makeTab2 <- function(contrFit, cellmeansFit, conjContrasts, disjContrast) {
## Get average expression for all relevant terms
contr_level_counts <- rowSums(contrFit$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?
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: edgeR(v.3.24.3), 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), 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), 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), Biostrings(v.2.50.2), rmarkdown(v.1.11), fastmatch(v.1.1-0), htmlTable(v.1.13.1), 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)
## 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: 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