Experiments to analyze

We are doing the same set of differential expression analyses as per the sample metric separations previously.

Eg.

  1. thyexpress: RNASeq alone: Compare gene expression of 3 time points in 5448; exponential, transition, and stationary.
    1. Include all Type==RNASeq
  2. thytnseq: TNSeq in THY: Use differential expression tools to assess gene fitness with respect to time in THY.
    1. Include all samples TNSeq which are also condition==t0|t1|t2|t3
  3. thyrnatn: RNASeq+TNSeq in THY: Contrast changing fitness and gene expression in THY with respect to time; even though the time scales of the two experiments are very different.
    1. Include all Type==RNASeq as well as condition==t0|t1|t2|t3
  4. subcu: 5448 fitness during subcutaneous infection in SKH1 mice; use DE tools for fitness vs. time.
    1. Include all condition beginning with “subcut”
  5. pmnexp1: 5448 fitness while being attacked by pmn cells experiment 1 (why is 1 different than 2?)
    1. Include samples: pmnlavt0, pmnlavt1, pmnylbt0, pmnylbt1
  6. pmnexp2: 5448 fitness while being attacked by pmn cells experiment 2 (because the first one had 24 hours outgrowth while this experiment has only 4 hours outgrowth)
    1. Include samples: pmnlavt0v2, pmnlavexp1v2, pmnlavexp2v2, pmnlavplasmav2, pmnlavt0v2, pmnylbexpv2, pmnylbplasmav2, pmnylbt0v2
  7. abscess: 5448 fitness within a rabbit abscess
    1. Include all samples starting with ‘abscess’

THY Expression

The name of the variables previously created are: thyexpress, thyexpress_norm, thyexpress_normbatch

## First try a normal condition + batch model
thyexpress_pairwise <- all_pairwise(thyexpress)
## Starting limma pairwise comparison.
## libsize was not specified, this parameter has profound effects on limma's result.
## Using the libsize from expt$libsize.
## Choosing the intercept containing model.
## Limma step 1/6: choosing model.
## Limma step 2/6: running voom
## The voom input was not cpm, converting now.
## The voom input was not log2, transforming now.
## Limma step 3/6: running lmFit
## Limma step 4/6: making and fitting contrasts.
## Limma step 5/6: Running eBayes and topTable.
## Limma step 6/6: Writing limma outputs.
## Limma step 6/6: 1/15: Printing table: rna_el.
## Limma step 6/6: 2/15: Printing table: rna_ll.
## Limma step 6/6: 3/15: Printing table: rna_st.
## Limma step 6/6: 4/15: Printing table: rpoe_comp.
## Limma step 6/6: 5/15: Printing table: rpoe_mut.
## Limma step 6/6: 6/15: Printing table: rna_ll_vs_rna_el.
## Limma step 6/6: 7/15: Printing table: rna_st_vs_rna_el.
## Limma step 6/6: 8/15: Printing table: rpoe_comp_vs_rna_el.
## Limma step 6/6: 9/15: Printing table: rpoe_mut_vs_rna_el.
## Limma step 6/6: 10/15: Printing table: rna_st_vs_rna_ll.
## Limma step 6/6: 11/15: Printing table: rpoe_comp_vs_rna_ll.
## Limma step 6/6: 12/15: Printing table: rpoe_mut_vs_rna_ll.
## Limma step 6/6: 13/15: Printing table: rpoe_comp_vs_rna_st.
## Limma step 6/6: 14/15: Printing table: rpoe_mut_vs_rna_st.
## Limma step 6/6: 15/15: Printing table: rpoe_mut_vs_rpoe_comp.
## Starting DESeq2 pairwise comparisons.
## The data should be suitable for EdgeR/DESeq.
## If EdgeR/DESeq freaks out, check the state of the count table and ensure that it is in integer counts.
## Choosing the intercept containing model.
## DESeq2 step 1/5: Including batch and condition in the deseq model.
## DESeq2 step 2/5: Estimate size factors.
## DESeq2 step 3/5: Estimate dispersions.
## DESeq2 step 4/5: nbinomWaldTest.
## DESeq2 step 5/5: 1/10: Printing table: rna_ll_vs_rna_el
## DESeq2 step 5/5: 2/10: Printing table: rna_st_vs_rna_el
## DESeq2 step 5/5: 3/10: Printing table: rpoe_comp_vs_rna_el
## DESeq2 step 5/5: 4/10: Printing table: rpoe_mut_vs_rna_el
## DESeq2 step 5/5: 5/10: Printing table: rna_st_vs_rna_ll
## DESeq2 step 5/5: 6/10: Printing table: rpoe_comp_vs_rna_ll
## DESeq2 step 5/5: 7/10: Printing table: rpoe_mut_vs_rna_ll
## DESeq2 step 5/5: 8/10: Printing table: rpoe_comp_vs_rna_st
## DESeq2 step 5/5: 9/10: Printing table: rpoe_mut_vs_rna_st
## DESeq2 step 5/5: 10/10: Printing table: rpoe_mut_vs_rpoe_comp
## Collected coefficients for: rna_el
## Collected coefficients for: rna_ll
## Collected coefficients for: rna_st
## Collected coefficients for: rpoe_comp
## Collected coefficients for: rpoe_mut
## Starting edgeR pairwise comparisons.
## The data should be suitable for EdgeR/DESeq.
## If EdgeR/DESeq freaks out, check the state of the count table and ensure that it is in integer counts.
## Choosing the intercept containing model.
## EdgeR step 1/9: normalizing data.
## EdgeR step 9/9: 1/10: Printing table: rna_ll_vs_rna_el.
## EdgeR step 9/9: 2/10: Printing table: rna_st_vs_rna_el.
## EdgeR step 9/9: 3/10: Printing table: rpoe_comp_vs_rna_el.
## EdgeR step 9/9: 4/10: Printing table: rpoe_mut_vs_rna_el.
## EdgeR step 9/9: 5/10: Printing table: rna_st_vs_rna_ll.
## EdgeR step 9/9: 6/10: Printing table: rpoe_comp_vs_rna_ll.
## EdgeR step 9/9: 7/10: Printing table: rpoe_mut_vs_rna_ll.
## EdgeR step 9/9: 8/10: Printing table: rpoe_comp_vs_rna_st.
## EdgeR step 9/9: 9/10: Printing table: rpoe_mut_vs_rna_st.
## EdgeR step 9/9: 10/10: Printing table: rpoe_mut_vs_rpoe_comp.
## Starting basic pairwise comparison.
## This basic pairwise function assumes log2, converted, normalized counts, normalizing now.
## Basic step 1/3: Creating median and variance tables.
## Basic step 2/3: Performing comparisons.
## Basic step 2/3: 1/10: Performing log2 subtraction: rna_ll_vs_rna_el
## Basic step 2/3: 2/10: Performing log2 subtraction: rna_st_vs_rna_el
## Basic step 2/3: 3/10: Performing log2 subtraction: rpoe_comp_vs_rna_el
## Basic step 2/3: 4/10: Performing log2 subtraction: rpoe_mut_vs_rna_el
## Basic step 2/3: 5/10: Performing log2 subtraction: rna_st_vs_rna_ll
## Basic step 2/3: 6/10: Performing log2 subtraction: rpoe_comp_vs_rna_ll
## Basic step 2/3: 7/10: Performing log2 subtraction: rpoe_mut_vs_rna_ll
## Basic step 2/3: 8/10: Performing log2 subtraction: rpoe_comp_vs_rna_st
## Basic step 2/3: 9/10: Performing log2 subtraction: rpoe_mut_vs_rna_st
## Basic step 2/3: 10/10: Performing log2 subtraction: rpoe_mut_vs_rpoe_comp
## Basic step 3/3: Creating faux DE Tables.
## Basic: Returning tables.
## Comparing analyses 1/10: rna_ll_vs_rna_el
## Comparing analyses 2/10: rna_st_vs_rna_el
## Comparing analyses 3/10: rpoe_comp_vs_rna_el
## Comparing analyses 4/10: rpoe_mut_vs_rna_el
## Comparing analyses 5/10: rna_st_vs_rna_ll
## Comparing analyses 6/10: rpoe_comp_vs_rna_ll
## Comparing analyses 7/10: rpoe_mut_vs_rna_ll
## Comparing analyses 8/10: rpoe_comp_vs_rna_st
## Comparing analyses 9/10: rpoe_mut_vs_rna_st
## Comparing analyses 10/10: rpoe_mut_vs_rpoe_comp

keepers <- list(
    "ll_el" = c("rna_ll", "rna_el"),
    "st_el" = c("rna_st", "rna_el"))
thyexpress_tables <- combine_de_tables(thyexpress_pairwise, keepers=keepers,
                                       excel=paste0("excel/thyexpress_tables-", file_version, ".xlsx"))
## Writing a legend of columns.
## Working on 1/2: ll_el
## Found table with rna_ll_vs_rna_el
## Running create_combined_table with inverse=FALSE
## The table is: rna_ll_vs_rna_el
## Working on 2/2: st_el
## Found table with rna_st_vs_rna_el
## Running create_combined_table with inverse=FALSE
## The table is: rna_st_vs_rna_el
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## Attempting to add a coefficient plot for ll_el at column 55
## Warning: Removed 85 rows containing non-finite values (stat_smooth).
## Warning: Removed 85 rows containing missing values (geom_point).
## Warning: Removed 45 rows containing missing values (geom_point).
## Warning: Removed 22 rows containing missing values (geom_point).
## Warning: Removed 85 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing missing values (geom_point).
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## Attempting to add a coefficient plot for st_el at column 55
## Warning: Removed 93 rows containing non-finite values (stat_smooth).
## Warning: Removed 93 rows containing missing values (geom_point).
## Warning: Removed 10 rows containing missing values (geom_point).
## Warning: Removed 17 rows containing missing values (geom_point).
## Warning: Removed 93 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Writing summary information.
## The sheet pairwise_summary already exists, it will get overwritten
## Attempting to add the comparison plot to pairwise_summary at row: 20 and column: 1
## Performing save of the workbook.

THY TNSeq

THY Expression and TNSeq

Subcutaneous infection

PMN experiment 1

PMN experiment 2

Rabbit abscess

RPOE

## First try a normal condition + batch model
rpoe_pairwise <- all_pairwise(rpoe)
## Starting limma pairwise comparison.
## libsize was not specified, this parameter has profound effects on limma's result.
## Using the libsize from expt$libsize.
## The condition+batch model failed.  Does your experimental design support both condition and batch?
## Using only a conditional model.
## Choosing the intercept containing model.
## Limma step 1/6: choosing model.
## Limma step 2/6: running voom
## The voom input was not cpm, converting now.
## The voom input was not log2, transforming now.
## Limma step 3/6: running lmFit
## Limma step 4/6: making and fitting contrasts.
## Limma step 5/6: Running eBayes and topTable.
## Limma step 6/6: Writing limma outputs.
## Limma step 6/6: 1/6: Printing table: rna_ll.
## Limma step 6/6: 2/6: Printing table: rpoe_comp.
## Limma step 6/6: 3/6: Printing table: rpoe_mut.
## Limma step 6/6: 4/6: Printing table: rpoe_comp_vs_rna_ll.
## Limma step 6/6: 5/6: Printing table: rpoe_mut_vs_rna_ll.
## Limma step 6/6: 6/6: Printing table: rpoe_mut_vs_rpoe_comp.
## Starting DESeq2 pairwise comparisons.
## The data should be suitable for EdgeR/DESeq.
## If EdgeR/DESeq freaks out, check the state of the count table and ensure that it is in integer counts.
## The condition+batch model failed.  Does your experimental design support both condition and batch?
## Using only a conditional model.
## Choosing the intercept containing model.
## DESeq2 step 1/5: Including batch and condition in the deseq model.
## DESeq2 step 2/5: Estimate size factors.
## DESeq2 step 3/5: Estimate dispersions.
## DESeq2 step 4/5: nbinomWaldTest.
## DESeq2 step 5/5: 1/3: Printing table: rpoe_comp_vs_rna_ll
## DESeq2 step 5/5: 2/3: Printing table: rpoe_mut_vs_rna_ll
## DESeq2 step 5/5: 3/3: Printing table: rpoe_mut_vs_rpoe_comp
## Collected coefficients for: rna_ll
## Collected coefficients for: rpoe_comp
## Collected coefficients for: rpoe_mut
## Starting edgeR pairwise comparisons.
## The data should be suitable for EdgeR/DESeq.
## If EdgeR/DESeq freaks out, check the state of the count table and ensure that it is in integer counts.
## The condition+batch model failed.  Does your experimental design support both condition and batch?
## Using only a conditional model.
## Choosing the intercept containing model.
## EdgeR step 1/9: normalizing data.
## EdgeR step 9/9: 1/3: Printing table: rpoe_comp_vs_rna_ll.
## EdgeR step 9/9: 2/3: Printing table: rpoe_mut_vs_rna_ll.
## EdgeR step 9/9: 3/3: Printing table: rpoe_mut_vs_rpoe_comp.
## Starting basic pairwise comparison.
## This basic pairwise function assumes log2, converted, normalized counts, normalizing now.
## Basic step 1/3: Creating median and variance tables.
## Basic step 2/3: Performing comparisons.
## Basic step 2/3: 1/3: Performing log2 subtraction: rpoe_comp_vs_rna_ll
## Basic step 2/3: 2/3: Performing log2 subtraction: rpoe_mut_vs_rna_ll
## Basic step 2/3: 3/3: Performing log2 subtraction: rpoe_mut_vs_rpoe_comp
## Basic step 3/3: Creating faux DE Tables.
## Basic: Returning tables.
## Comparing analyses 1/3: rpoe_comp_vs_rna_ll
## Comparing analyses 2/3: rpoe_mut_vs_rna_ll
## Comparing analyses 3/3: rpoe_mut_vs_rpoe_comp
keepers <- list(
    "mut_wt" = c("rpoe_mut", "rna_ll"),
    "comp_wt" = c("rpoe_comp","rna_ll"),
    "mut_comp" = c("rpoe_mut", "rpoe_comp"))
rpoe_tables <- combine_de_tables(rpoe_pairwise, keepers=keepers, extra_annot=Biobase::exprs(rpoe_norm$expressionset),
                                 excel=paste0("excel/rpoe_tables-", file_version, ".xlsx"))
## Writing a legend of columns.
## Working on 1/3: mut_wt
## Found table with rpoe_mut_vs_rna_ll
## Running create_combined_table with inverse=FALSE
## The table is: rpoe_mut_vs_rna_ll
## Working on 2/3: comp_wt
## Found table with rpoe_comp_vs_rna_ll
## Running create_combined_table with inverse=FALSE
## The table is: rpoe_comp_vs_rna_ll
## Working on 3/3: mut_comp
## Found table with rpoe_mut_vs_rpoe_comp
## Running create_combined_table with inverse=FALSE
## The table is: rpoe_mut_vs_rpoe_comp
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## Attempting to add a coefficient plot for mut_wt at column 67
## Warning: Removed 83 rows containing non-finite values (stat_smooth).
## Warning: Removed 83 rows containing missing values (geom_point).
## Warning: Removed 17 rows containing missing values (geom_point).
## Warning: Removed 13 rows containing missing values (geom_point).
## Warning: Removed 83 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing missing values (geom_point).
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## Attempting to add a coefficient plot for comp_wt at column 67
## Warning: Removed 99 rows containing non-finite values (stat_smooth).
## Warning: Removed 99 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 29 rows containing missing values (geom_point).
## Warning: Removed 99 rows containing missing values (geom_point).
## Warning: Removed 6 rows containing non-finite values (stat_smooth).
## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## Attempting to add a coefficient plot for mut_comp at column 67
## Warning: Removed 98 rows containing non-finite values (stat_smooth).
## Warning: Removed 98 rows containing missing values (geom_point).
## Warning: Removed 38 rows containing missing values (geom_point).
## Warning: Removed 10 rows containing missing values (geom_point).
## Warning: Removed 98 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).

## Warning: Removed 7 rows containing missing values (geom_point).
## Writing summary information.
## The sheet pairwise_summary already exists, it will get overwritten
## Attempting to add the comparison plot to pairwise_summary at row: 21 and column: 1
## Performing save of the workbook.
rpoe_sig <- extract_significant_genes(rpoe_tables, according_to="all",
                                      excel=paste0("excel/rpoe_sig-", file_version, ".xlsx"))
## Writing excel data sheet 1/6
## Assuming the fold changes are on the log scale and so taking >< 0
## After (adj)p filter, the up genes table has 997 genes.
## After (adj)p filter, the down genes table has 350 genes.
## Assuming the fold changes are on the log scale and so taking -1 * fc
## After fold change filter, the up genes table has 413 genes.
## After fold change filter, the down genes table has 126 genes.
## Writing excel data sheet 2/6
## Assuming the fold changes are on the log scale and so taking >< 0
## After (adj)p filter, the up genes table has 366 genes.
## After (adj)p filter, the down genes table has 762 genes.
## Assuming the fold changes are on the log scale and so taking -1 * fc
## After fold change filter, the up genes table has 101 genes.
## After fold change filter, the down genes table has 176 genes.
## Writing excel data sheet 3/6
## Assuming the fold changes are on the log scale and so taking >< 0
## After (adj)p filter, the up genes table has 1117 genes.
## After (adj)p filter, the down genes table has 290 genes.
## Assuming the fold changes are on the log scale and so taking -1 * fc
## After fold change filter, the up genes table has 504 genes.
## After fold change filter, the down genes table has 93 genes.
## Printing significant genes to the file: excel/rpoe_sig-07.xlsx
## Converted change_counts_up to characters.
## Converted change_counts_down to characters.
## 1/3: Writing excel data sheet up_limma_mut_wt
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 1/3: Writing excel data sheet down_limma_mut_wt
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 2/3: Writing excel data sheet up_limma_comp_wt
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 2/3: Writing excel data sheet down_limma_comp_wt
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 3/3: Writing excel data sheet up_limma_mut_comp
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 3/3: Writing excel data sheet down_limma_mut_comp
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## Warning in max(nchar(data[[col]]), na.rm = TRUE): no non-missing arguments to max;
## returning -Inf
## Writing changed genes summary on last sheet.
## Writing excel data sheet 4/6
## Assuming the fold changes are on the log scale and so taking >< 0
## After (adj)p filter, the up genes table has 620 genes.
## After (adj)p filter, the down genes table has 639 genes.
## Assuming the fold changes are on the log scale and so taking -1 * fc
## After fold change filter, the up genes table has 200 genes.
## After fold change filter, the down genes table has 222 genes.
## Writing excel data sheet 5/6
## Assuming the fold changes are on the log scale and so taking >< 0
## After (adj)p filter, the up genes table has 557 genes.
## After (adj)p filter, the down genes table has 534 genes.
## Assuming the fold changes are on the log scale and so taking -1 * fc
## After fold change filter, the up genes table has 132 genes.
## After fold change filter, the down genes table has 112 genes.
## Writing excel data sheet 6/6
## Assuming the fold changes are on the log scale and so taking >< 0
## After (adj)p filter, the up genes table has 591 genes.
## After (adj)p filter, the down genes table has 724 genes.
## Assuming the fold changes are on the log scale and so taking -1 * fc
## After fold change filter, the up genes table has 238 genes.
## After fold change filter, the down genes table has 255 genes.
## Printing significant genes to the file: excel/rpoe_sig-07.xlsx
## The sheet number_changed_genes already exists, it will get overwritten
## Converted change_counts_up to characters.
## Converted change_counts_down to characters.
## 1/3: Writing excel data sheet up_edger_mut_wt
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 1/3: Writing excel data sheet down_edger_mut_wt
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 2/3: Writing excel data sheet up_edger_comp_wt
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 2/3: Writing excel data sheet down_edger_comp_wt
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 3/3: Writing excel data sheet up_edger_mut_comp
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 3/3: Writing excel data sheet down_edger_mut_comp
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## Writing changed genes summary on last sheet.
## Writing excel data sheet 7/6
## Assuming the fold changes are on the log scale and so taking >< 0
## After (adj)p filter, the up genes table has 647 genes.
## After (adj)p filter, the down genes table has 626 genes.
## Assuming the fold changes are on the log scale and so taking -1 * fc
## After fold change filter, the up genes table has 198 genes.
## After fold change filter, the down genes table has 213 genes.
## Writing excel data sheet 8/6
## Assuming the fold changes are on the log scale and so taking >< 0
## After (adj)p filter, the up genes table has 529 genes.
## After (adj)p filter, the down genes table has 583 genes.
## Assuming the fold changes are on the log scale and so taking -1 * fc
## After fold change filter, the up genes table has 125 genes.
## After fold change filter, the down genes table has 116 genes.
## Writing excel data sheet 9/6
## Assuming the fold changes are on the log scale and so taking >< 0
## After (adj)p filter, the up genes table has 650 genes.
## After (adj)p filter, the down genes table has 681 genes.
## Assuming the fold changes are on the log scale and so taking -1 * fc
## After fold change filter, the up genes table has 253 genes.
## After fold change filter, the down genes table has 234 genes.
## Printing significant genes to the file: excel/rpoe_sig-07.xlsx
## The sheet number_changed_genes already exists, it will get overwritten
## Converted change_counts_up to characters.
## Converted change_counts_down to characters.
## 1/3: Writing excel data sheet up_deseq_mut_wt
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 1/3: Writing excel data sheet down_deseq_mut_wt
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 2/3: Writing excel data sheet up_deseq_comp_wt
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 2/3: Writing excel data sheet down_deseq_comp_wt
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 3/3: Writing excel data sheet up_deseq_mut_comp
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 3/3: Writing excel data sheet down_deseq_mut_comp
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## Writing changed genes summary on last sheet.
## Writing excel data sheet 10/6
## Assuming the fold changes are on the log scale and so taking >< 0
## After (adj)p filter, the up genes table has 571 genes.
## After (adj)p filter, the down genes table has 509 genes.
## Assuming the fold changes are on the log scale and so taking -1 * fc
## After fold change filter, the up genes table has 153 genes.
## After fold change filter, the down genes table has 202 genes.
## Writing excel data sheet 11/6
## Assuming the fold changes are on the log scale and so taking >< 0
## After (adj)p filter, the up genes table has 455 genes.
## After (adj)p filter, the down genes table has 471 genes.
## Assuming the fold changes are on the log scale and so taking -1 * fc
## After fold change filter, the up genes table has 124 genes.
## After fold change filter, the down genes table has 99 genes.
## Writing excel data sheet 12/6
## Assuming the fold changes are on the log scale and so taking >< 0
## After (adj)p filter, the up genes table has 546 genes.
## After (adj)p filter, the down genes table has 559 genes.
## Assuming the fold changes are on the log scale and so taking -1 * fc
## After fold change filter, the up genes table has 164 genes.
## After fold change filter, the down genes table has 212 genes.
## Printing significant genes to the file: excel/rpoe_sig-07.xlsx
## The sheet number_changed_genes already exists, it will get overwritten
## Converted change_counts_up to characters.
## Converted change_counts_down to characters.
## 1/3: Writing excel data sheet up_basic_mut_wt
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 1/3: Writing excel data sheet down_basic_mut_wt
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 2/3: Writing excel data sheet up_basic_comp_wt
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 2/3: Writing excel data sheet down_basic_comp_wt
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 3/3: Writing excel data sheet up_basic_mut_comp
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## 3/3: Writing excel data sheet down_basic_mut_comp
## Converted seqnames to characters.
## Converted strand to characters.
## Converted dbxref to characters.
## Converted genesynonym to characters.
## Writing changed genes summary on last sheet.

## rpoe_goseq <-