L. major 2017: differential expression of Leishmania samples (201803).
atb abelew@gmail.com
2018-04-16
1 Differential Expression version: 20180410
2 L. major differential expression
lm_filt <- normalize_expt(lm_expt, filter=TRUE)## This function will replace the expt$expressionset slot with:## hpgl(data)## It backs up the current data into a slot named:
## expt$backup_expressionset. It will also save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep the libsizes in mind
## when invoking limma. The appropriate libsize is the non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: performing count filter with option: hpgl## Removing 53 low-count genes (7917 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.lm_pairwise <- all_pairwise(input=lm_filt, model_batch=TRUE, parallel=FALSE)## Using limma's removeBatchEffect to visualize before/after batch inclusion.## Starting limma pairwise comparison.## 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.## 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: 1/15: Creating table: pmn12hinf_vs_ama. Adjust=BH## Limma step 6/6: 2/15: Creating table: pmn12huninf_vs_ama. Adjust=BH## Limma step 6/6: 3/15: Creating table: pmn14dinf_vs_ama. Adjust=BH## Limma step 6/6: 4/15: Creating table: pmn14duninf_vs_ama. Adjust=BH## Limma step 6/6: 5/15: Creating table: pro_vs_ama. Adjust=BH## Limma step 6/6: 6/15: Creating table: pmn12huninf_vs_pmn12hinf. Adjust=BH## Limma step 6/6: 7/15: Creating table: pmn14dinf_vs_pmn12hinf. Adjust=BH## Limma step 6/6: 8/15: Creating table: pmn14duninf_vs_pmn12hinf. Adjust=BH## Limma step 6/6: 9/15: Creating table: pro_vs_pmn12hinf. Adjust=BH## Limma step 6/6: 10/15: Creating table: pmn14dinf_vs_pmn12huninf. Adjust=BH## Limma step 6/6: 11/15: Creating table: pmn14duninf_vs_pmn12huninf. Adjust=BH## Limma step 6/6: 12/15: Creating table: pro_vs_pmn12huninf. Adjust=BH## Limma step 6/6: 13/15: Creating table: pmn14duninf_vs_pmn14dinf. Adjust=BH## Limma step 6/6: 14/15: Creating table: pro_vs_pmn14dinf. Adjust=BH## Limma step 6/6: 15/15: Creating table: pro_vs_pmn14duninf. Adjust=BH## Limma step 6/6: 1/6: Creating table: ama. Adjust=BH## Limma step 6/6: 2/6: Creating table: pmn12hinf. Adjust=BH## Limma step 6/6: 3/6: Creating table: pmn12huninf. Adjust=BH## Limma step 6/6: 4/6: Creating table: pmn14dinf. Adjust=BH## Limma step 6/6: 5/6: Creating table: pmn14duninf. Adjust=BH## Limma step 6/6: 6/6: Creating table: pro. Adjust=BH## 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 non-intercept containing model.## DESeq2 step 1/5: Including batch and condition in the deseq model.## using counts and average transcript lengths from tximport## DESeq2 step 2/5: Estimate size factors.## using 'avgTxLength' from assays(dds), correcting for library size## 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.## Plotting dispersions.
##
|
| | 0%
|
|==== | 7%
|
|========= | 13%
|
|============= | 20%
|
|================= | 27%
|
|====================== | 33%
|
|========================== | 40%
|
|============================== | 47%
|
|=================================== | 53%
|
|======================================= | 60%
|
|=========================================== | 67%
|
|================================================ | 73%
|
|==================================================== | 80%
|
|======================================================== | 87%
|
|============================================================= | 93%
|
|=================================================================| 100%## 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 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.
##
|
| | 0%
|
|==== | 7%
|
|========= | 13%
|
|============= | 20%
|
|================= | 27%
|
|====================== | 33%
|
|========================== | 40%
|
|============================== | 47%
|
|=================================== | 53%
|
|======================================= | 60%
|
|=========================================== | 67%
|
|================================================ | 73%
|
|==================================================== | 80%
|
|======================================================== | 87%
|
|============================================================= | 93%
|
|=================================================================| 100%## Starting basic pairwise comparison.## Basic step 0/3: Normalizing data.## Basic step 0/3: Converting data.## Basic step 0/3: Transforming data.## Basic step 1/3: Creating median and variance tables.## Basic step 2/3: Performing 21 comparisons.##
|
| | 0%
|
|==== | 7%
|
|========= | 13%
|
|============= | 20%
|
|================= | 27%
|
|====================== | 33%
|
|========================== | 40%
|
|============================== | 47%
|
|=================================== | 53%
|
|======================================= | 60%
|
|=========================================== | 67%
|
|================================================ | 73%
|
|==================================================== | 80%
|
|======================================================== | 87%
|
|============================================================= | 93%
|
|=================================================================| 100%## Basic step 3/3: Creating faux DE Tables.## Basic: Returning tables.## Comparing analyses 1/15: pmn12hinf_vs_ama## Comparing analyses 2/15: pmn12huninf_vs_ama## Comparing analyses 3/15: pmn14dinf_vs_ama## Comparing analyses 4/15: pmn14duninf_vs_ama## Comparing analyses 5/15: pro_vs_ama## Comparing analyses 6/15: pmn12huninf_vs_pmn12hinf## Comparing analyses 7/15: pmn14dinf_vs_pmn12hinf## Comparing analyses 8/15: pmn14duninf_vs_pmn12hinf## Comparing analyses 9/15: pro_vs_pmn12hinf## Comparing analyses 10/15: pmn14dinf_vs_pmn12huninf## Comparing analyses 11/15: pmn14duninf_vs_pmn12huninf## Comparing analyses 12/15: pro_vs_pmn12huninf## Comparing analyses 13/15: pmn14duninf_vs_pmn14dinf## Comparing analyses 14/15: pro_vs_pmn14dinf## Comparing analyses 15/15: pro_vs_pmn14duninf
keepers <- list(
"12hr_infuninf" = c("pmn12hinf", "pmn12huninf"),
"14d_infuninf" = c("pmn14dinf", "pmn14duninf"),
"ama_pro" = c("ama", "pro"),
"inf_14d12hr" = c("pmn14dinf", "pmn12hinf"),
"uninf_14d12hr" = c("pmn14duninf", "pmn12huninf")
)
lm_tables <- combine_de_tables(lm_pairwise, keepers=keepers,
excel=paste0("excel/lm_pairwise-v", ver, ".xlsx"))## Writing a legend of columns.## Printing a pca plot before/after surrogates/batch estimation.## Working on 1/5: 12hr_infuninf## Found inverse table with pmn12huninf_vs_pmn12hinf## Working on 2/5: 14d_infuninf## Found inverse table with pmn14duninf_vs_pmn14dinf## Working on 3/5: ama_pro## Found inverse table with pro_vs_ama## Working on 4/5: inf_14d12hr## Found table with pmn14dinf_vs_pmn12hinf## Working on 5/5: uninf_14d12hr## Found table with pmn14duninf_vs_pmn12huninf## Adding venn plots for pmn12hinf_vs_pmn12huninf.
## Limma expression coefficients for pmn12hinf_vs_pmn12huninf; R^2: 0.238; equation: y = 1.14x - 1.18## Edger expression coefficients for pmn12hinf_vs_pmn12huninf; R^2: 0.259; equation: y = 0.196x + 11.2## DESeq2 expression coefficients for pmn12hinf_vs_pmn12huninf; R^2: 0.389; equation: y = 0.653x + 2.16## Adding venn plots for pmn14dinf_vs_pmn14duninf.
## Limma expression coefficients for pmn14dinf_vs_pmn14duninf; R^2: 0.601; equation: y = 1.09x - 0.678## Edger expression coefficients for pmn14dinf_vs_pmn14duninf; R^2: 0.618; equation: y = 0.574x + 5.95## DESeq2 expression coefficients for pmn14dinf_vs_pmn14duninf; R^2: 0.663; equation: y = 0.81x + 1.2## Adding venn plots for ama_vs_pro.
## Limma expression coefficients for ama_vs_pro; R^2: 0.579; equation: y = 0.766x + 1.15## Edger expression coefficients for ama_vs_pro; R^2: 0.66; equation: y = 0.864x + 1.26## DESeq2 expression coefficients for ama_vs_pro; R^2: 0.6; equation: y = 0.817x + 0.509## Adding venn plots for pmn14dinf_vs_pmn12hinf.
## Limma expression coefficients for pmn14dinf_vs_pmn12hinf; R^2: 0.768; equation: y = 0.882x + 0.57## Edger expression coefficients for pmn14dinf_vs_pmn12hinf; R^2: 0.796; equation: y = 0.987x + 0.0971## DESeq2 expression coefficients for pmn14dinf_vs_pmn12hinf; R^2: 0.78; equation: y = 0.958x + 0.024## Adding venn plots for pmn14duninf_vs_pmn12huninf.
## Limma expression coefficients for pmn14duninf_vs_pmn12huninf; R^2: 0.304; equation: y = 0.894x + 0.214## Edger expression coefficients for pmn14duninf_vs_pmn12huninf; R^2: 0.187; equation: y = 0.24x + 10.2## DESeq2 expression coefficients for pmn14duninf_vs_pmn12huninf; R^2: 0.509; equation: y = 0.944x + 0.202## Writing summary information.## Attempting to add the comparison plot to pairwise_summary at row: 23 and column: 1##
|
| | 0%
|
|============= | 20%
|
|========================== | 40%
|
|======================================= | 60%
|
|==================================================== | 80%
|
|=================================================================| 100%## Performing save of the workbook.
lm_sig <- extract_significant_genes(combined=lm_tables,
excel=paste0("excel/lm_sig-v", ver, ".xlsx"))## Writing a legend of columns.## Writing excel data for pmn12hinf_vs_pmn12huninf: 1/20.## After (adj)p filter, the up genes table has 575 genes.## After (adj)p filter, the down genes table has 1233 genes.## After fold change filter, the up genes table has 575 genes.## After fold change filter, the down genes table has 1233 genes.## Writing excel data for pmn14dinf_vs_pmn14duninf: 2/20.## After (adj)p filter, the up genes table has 54 genes.## After (adj)p filter, the down genes table has 251 genes.## After fold change filter, the up genes table has 54 genes.## After fold change filter, the down genes table has 251 genes.## Writing excel data for ama_vs_pro: 3/20.## After (adj)p filter, the up genes table has 218 genes.## After (adj)p filter, the down genes table has 249 genes.## After fold change filter, the up genes table has 218 genes.## After fold change filter, the down genes table has 249 genes.## Writing excel data for pmn14dinf_vs_pmn12hinf: 4/20.## After (adj)p filter, the up genes table has 2 genes.## After (adj)p filter, the down genes table has 10 genes.## After fold change filter, the up genes table has 2 genes.## After fold change filter, the down genes table has 10 genes.## Writing excel data for pmn14duninf_vs_pmn12huninf: 5/20.## After (adj)p filter, the up genes table has 56 genes.## After (adj)p filter, the down genes table has 6 genes.## After fold change filter, the up genes table has 56 genes.## After fold change filter, the down genes table has 6 genes.## Printing significant genes to the file: excel/lm_sig-v20180410.xlsx## 1/5: Creating significant table up_1limma_pmn12hinf_vs_pmn12huninf## The sheet name was too long for Excel, truncating it by removing vowels.
## The sheet name was too long for Excel, truncating it by removing vowels.## 2/5: Creating significant table up_2limma_pmn14dinf_vs_pmn14duninf## The sheet name was too long for Excel, truncating it by removing vowels.
## The sheet name was too long for Excel, truncating it by removing vowels.## 3/5: Creating significant table up_3limma_ama_vs_pro## 4/5: Creating significant table up_4limma_pmn14dinf_vs_pmn12hinf## The sheet name was too long for Excel, truncating it by removing vowels.
## The sheet name was too long for Excel, truncating it by removing vowels.## 5/5: Creating significant table up_5limma_pmn14duninf_vs_pmn12huninf## The sheet name was too long for Excel, truncating it by removing vowels.
## The sheet name was too long for Excel, truncating it by removing vowels.## Writing excel data for pmn12hinf_vs_pmn12huninf: 6/20.## After (adj)p filter, the up genes table has 128 genes.## After (adj)p filter, the down genes table has 43 genes.## After fold change filter, the up genes table has 128 genes.## After fold change filter, the down genes table has 43 genes.## Writing excel data for pmn14dinf_vs_pmn14duninf: 7/20.## After (adj)p filter, the up genes table has 14 genes.## After (adj)p filter, the down genes table has 6 genes.## After fold change filter, the up genes table has 14 genes.## After fold change filter, the down genes table has 6 genes.## Writing excel data for ama_vs_pro: 8/20.## After (adj)p filter, the up genes table has 79 genes.## After (adj)p filter, the down genes table has 127 genes.## After fold change filter, the up genes table has 79 genes.## After fold change filter, the down genes table has 127 genes.## Writing excel data for pmn14dinf_vs_pmn12hinf: 9/20.## After (adj)p filter, the up genes table has 1 genes.## After (adj)p filter, the down genes table has 3 genes.## After fold change filter, the up genes table has 1 genes.## After fold change filter, the down genes table has 3 genes.## Writing excel data for pmn14duninf_vs_pmn12huninf: 10/20.## After (adj)p filter, the up genes table has 85 genes.## After (adj)p filter, the down genes table has 76 genes.## After fold change filter, the up genes table has 85 genes.## After fold change filter, the down genes table has 76 genes.## Printing significant genes to the file: excel/lm_sig-v20180410.xlsx## 1/5: Creating significant table up_1edger_pmn12hinf_vs_pmn12huninf## The sheet name was too long for Excel, truncating it by removing vowels.
## The sheet name was too long for Excel, truncating it by removing vowels.## 2/5: Creating significant table up_2edger_pmn14dinf_vs_pmn14duninf## The sheet name was too long for Excel, truncating it by removing vowels.
## The sheet name was too long for Excel, truncating it by removing vowels.## 3/5: Creating significant table up_3edger_ama_vs_pro## 4/5: Creating significant table up_4edger_pmn14dinf_vs_pmn12hinf## The sheet name was too long for Excel, truncating it by removing vowels.
## The sheet name was too long for Excel, truncating it by removing vowels.## 5/5: Creating significant table up_5edger_pmn14duninf_vs_pmn12huninf## The sheet name was too long for Excel, truncating it by removing vowels.
## The sheet name was too long for Excel, truncating it by removing vowels.## Writing excel data for pmn12hinf_vs_pmn12huninf: 11/20.## After (adj)p filter, the up genes table has 229 genes.## After (adj)p filter, the down genes table has 60 genes.## After fold change filter, the up genes table has 229 genes.## After fold change filter, the down genes table has 60 genes.## Writing excel data for pmn14dinf_vs_pmn14duninf: 12/20.## After (adj)p filter, the up genes table has 32 genes.## After (adj)p filter, the down genes table has 13 genes.## After fold change filter, the up genes table has 32 genes.## After fold change filter, the down genes table has 13 genes.## Writing excel data for ama_vs_pro: 13/20.## After (adj)p filter, the up genes table has 91 genes.## After (adj)p filter, the down genes table has 271 genes.## After fold change filter, the up genes table has 91 genes.## After fold change filter, the down genes table has 271 genes.## Writing excel data for pmn14dinf_vs_pmn12hinf: 14/20.## After (adj)p filter, the up genes table has 3 genes.## After (adj)p filter, the down genes table has 6 genes.## After fold change filter, the up genes table has 3 genes.## After fold change filter, the down genes table has 6 genes.## Writing excel data for pmn14duninf_vs_pmn12huninf: 15/20.## After (adj)p filter, the up genes table has 149 genes.## After (adj)p filter, the down genes table has 86 genes.## After fold change filter, the up genes table has 149 genes.## After fold change filter, the down genes table has 86 genes.## Printing significant genes to the file: excel/lm_sig-v20180410.xlsx## 1/5: Creating significant table up_1deseq_pmn12hinf_vs_pmn12huninf## The sheet name was too long for Excel, truncating it by removing vowels.
## The sheet name was too long for Excel, truncating it by removing vowels.## 2/5: Creating significant table up_2deseq_pmn14dinf_vs_pmn14duninf## The sheet name was too long for Excel, truncating it by removing vowels.
## The sheet name was too long for Excel, truncating it by removing vowels.## 3/5: Creating significant table up_3deseq_ama_vs_pro## 4/5: Creating significant table up_4deseq_pmn14dinf_vs_pmn12hinf## The sheet name was too long for Excel, truncating it by removing vowels.
## The sheet name was too long for Excel, truncating it by removing vowels.## 5/5: Creating significant table up_5deseq_pmn14duninf_vs_pmn12huninf## The sheet name was too long for Excel, truncating it by removing vowels.
## The sheet name was too long for Excel, truncating it by removing vowels.## Writing excel data for pmn12hinf_vs_pmn12huninf: 16/20.## After (adj)p filter, the up genes table has 259 genes.## After (adj)p filter, the down genes table has 264 genes.## After fold change filter, the up genes table has 246 genes.## After fold change filter, the down genes table has 262 genes.## Writing excel data for pmn14dinf_vs_pmn14duninf: 17/20.## After (adj)p filter, the up genes table has 0 genes.## After (adj)p filter, the down genes table has 0 genes.## After fold change filter, the up genes table has 0 genes.## After fold change filter, the down genes table has 0 genes.## Writing excel data for ama_vs_pro: 18/20.## After (adj)p filter, the up genes table has 6 genes.## After (adj)p filter, the down genes table has 12 genes.## After fold change filter, the up genes table has 5 genes.## After fold change filter, the down genes table has 12 genes.## Writing excel data for pmn14dinf_vs_pmn12hinf: 19/20.## After (adj)p filter, the up genes table has 0 genes.## After (adj)p filter, the down genes table has 0 genes.## After fold change filter, the up genes table has 0 genes.## After fold change filter, the down genes table has 0 genes.## Writing excel data for pmn14duninf_vs_pmn12huninf: 20/20.## After (adj)p filter, the up genes table has 3 genes.## After (adj)p filter, the down genes table has 0 genes.## After fold change filter, the up genes table has 3 genes.## After fold change filter, the down genes table has 0 genes.## Printing significant genes to the file: excel/lm_sig-v20180410.xlsx## 1/5: Creating significant table up_1basic_pmn12hinf_vs_pmn12huninf## The sheet name was too long for Excel, truncating it by removing vowels.
## The sheet name was too long for Excel, truncating it by removing vowels.## 2/5: Creating significant table up_2basic_pmn14dinf_vs_pmn14duninf## The sheet name was too long for Excel, truncating it by removing vowels.
## The sheet name was too long for Excel, truncating it by removing vowels.## 3/5: Creating significant table up_3basic_ama_vs_pro## 4/5: Creating significant table up_4basic_pmn14dinf_vs_pmn12hinf## The sheet name was too long for Excel, truncating it by removing vowels.
## The sheet name was too long for Excel, truncating it by removing vowels.## 5/5: Creating significant table up_5basic_pmn14duninf_vs_pmn12huninf## The sheet name was too long for Excel, truncating it by removing vowels.
## The sheet name was too long for Excel, truncating it by removing vowels.## Adding significance bar plots.if (!isTRUE(get0("skip_load"))) {
pander::pander(sessionInfo())
message(paste0("This is hpgltools commit: ", get_git_commit()))
this_save <- paste0(gsub(pattern="\\.Rmd", replace="", x=rmd_file), "-v", ver, ".rda.xz")
message(paste0("Saving to ", this_save))
tmp <- sm(saveme(filename=this_save))
}## If you wish to reproduce this exact build of hpgltools, invoke the following:## > git clone http://github.com/abelew/hpgltools.git## > git reset 7de4503f6bb5724c28cce24af5dbee22bb1c0cae## R> packrat::restore()## This is hpgltools commit: Thu Apr 12 22:08:53 2018 -0400: 7de4503f6bb5724c28cce24af5dbee22bb1c0cae## Saving to 03_differential_expression_lmajor_201804-v20180410.rda.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