Mature miRNA M.musculus samples.

index.html preprocessing.html

1 Raw global metrics

## Graph the raw metrics of all samples mapped against the transcripts and miRNAs
mmmi_mature_metrics <- sm(graph_metrics(mmmi_mature))

mmmi_mature_norm <- default_norm(mmmi_mature, transform="log2")
mmmi_mature_norm_metrics <- sm(graph_metrics(mmmi_mature_norm))

mmmi_mature_writer <- write_expt(mmmi_mature,  excel=paste0("excel/mature_data-v", ver, ".xlsx"))

## Writing the legend.

## The sheet: legend is in legend.

## Writing the raw reads.

## Graphing the raw reads.

## Warning: Transformation introduced infinite values in continuous x-axis

## Warning: Transformation introduced infinite values in continuous y-axis

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

## log2(cpm(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

## Filter is false, this should likely be set to something, good
##  choices include cbcb, kofa, pofa (anything but FALSE).  If you want this to
##  stay FALSE, keep in mind that if other normalizations are performed, then the
##  resulting libsizes are likely to be strange (potentially negative!)

## 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: not doing count filtering.

## Step 2: not normalizing the data.

## Step 3: converting the data with cpm.

## Step 4: transforming the data with log2.

## transform_counts: Found 5653 values equal to 0, adding 1 to the matrix.

## Step 5: not doing batch correction.

## The sheet: raw_graphs is in legend, raw_reads, raw_graphs.

## The sheet: raw_graphs is in legend, raw_reads, raw_graphs.

## Writing the normalized reads.

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

## log2(sva(cpm(quant(cbcb(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

## Warning in normalize_expt(expt = expt, transform = transform, norm = norm, : Quantile
## normalization and sva do not always play well together.

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

## Removing 801 low-count genes (427 remaining).

## Step 2: normalizing the data with quant.

## Step 3: converting the data with cpm.

## Step 4: transforming the data with log2.

## transform_counts: Found 2 values equal to 0, adding 1 to the matrix.

## Step 5: doing batch correction with sva.

## Note to self:  If you get an error like 'x contains missing values'; I think this means that the data has too many 0's and needs to have a better low-count filter applied.

## Note to self:  I keep forgetting this, but the most common batch correction performed in Dr. El-Sayed's lab is implemented here as 'limmaresid'.

## batch_counts: Before batch correction, 390 entries 0<x<1.

## batch_counts: Before batch correction, 2 entries are >= 0.

## Passing the batch method to get_model_adjust().

## It currently understands: 'sva_(un)supervised', 'ruv_empirical', 'svaseq', 'pca', 'ruv_supervised', and 'ruv_residuals'.

## Not able to discern the state of the data.  Going to use a simplistic metric to guess if it is log scale.

## The be method chose 1 surrogate variable(s).

## Estimate type 'sva' is shorthand for 'sva_unsupervised'.

## Other sva options include: sva_supervised and svaseq.

## Attempting sva unsupervised surrogate estimation.

## The number of elements which are < 0 after batch correction is: 383

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

## Graphing the normalized reads.

## Warning in self$trans$transform(x): NaNs produced

## Warning in self$trans$transform(x): Transformation introduced infinite values in
## continuous x-axis

## Warning: Transformation introduced infinite values in continuous y-axis

## The sheet: norm_graphs is in legend, raw_reads, raw_graphs, norm_data, norm_graphs.

## The sheet: norm_graphs is in legend, raw_reads, raw_graphs, norm_data, norm_graphs.

## Writing the median reads by factor.

## The factor cell_mirna_mut has 2 rows.

## The factor cell_mirna_wt has 2 rows.

## The factor exo_mirna_mut has 2 rows.

## The factor exo_mirna_wt has 2 rows.

1.1 A Legend!

First, do not forget to print a legend showing the colors used and what they mean:

mmmi_mature_metrics$legend$plot

## This should be the same for the mm_mi and mm_tx objects.

1.2 Start with some global metrics

mmmi_mature_metrics$libsize

mmmi_mature_norm_metrics$corheat

mmmi_mature_norm_metrics$pcaplot

mmmi_mature_de <- all_pairwise(mmmi_mature, model_batch="sva")

## The be method chose 1 surrogate variable(s).

## Estimate type 'sva' is shorthand for 'sva_unsupervised'.

## Other sva options include: sva_supervised and svaseq.

## Attempting sva unsupervised surrogate estimation.

## Finished running DE analyses, collecting outputs.

## Comparing analyses 1/6: cell_mirna_wt_vs_cell_mirna_mut

## Comparing analyses 2/6: exo_mirna_mut_vs_cell_mirna_mut

## Comparing analyses 3/6: exo_mirna_wt_vs_cell_mirna_mut

## Comparing analyses 4/6: exo_mirna_mut_vs_cell_mirna_wt

## Comparing analyses 5/6: exo_mirna_wt_vs_cell_mirna_wt

## Comparing analyses 6/6: exo_mirna_wt_vs_exo_mirna_mut

mature_keepers <- list(
    "mutvwt_cell" = c("cell_mirna_mut", "cell_mirna_wt"),
    "mutvwt_exo" = c("exo_mirna_mut", "exo_mirna_wt"),
    "exovcell_wt" = c("exo_mirna_wt", "cell_mirna_wt"),
    "exovcell_mut" = c("exo_mirna_mut", "cell_mirna_mut"))
mmmi_mature_tables <- combine_de_tables(mmmi_mature_de,
                                        excel=paste0("excel/mature", ver, ".xlsx"),
                                        keepers=mature_keepers)

## Deleting the file excel/mature15.xlsx before writing the tables.

## Writing a legend of columns.

## Working on 1/4: mutvwt_cell

## Found inverse table with cell_mirna_wt_vs_cell_mirna_mut

## Working on 2/4: mutvwt_exo

## Found inverse table with exo_mirna_wt_vs_exo_mirna_mut

## Working on 3/4: exovcell_wt

## Found table with exo_mirna_wt_vs_cell_mirna_wt

## Working on 4/4: exovcell_mut

## Found table with exo_mirna_mut_vs_cell_mirna_mut

## Adding venn plots for mutvwt_cell.

## Limma expression coefficients for mutvwt_cell; R^2: 0.975; equation: y = 0.969x - 0.161

## Warning: Removed 838 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_vline).

## Warning: Removed 1 rows containing missing values (geom_vline).

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 1 rows containing missing values (geom_vline).

## Warning: Removed 838 rows containing missing values (geom_point).

## Warning: Removed 212 rows containing missing values (geom_point).

## Warning: Removed 73 rows containing missing values (geom_point).

## Warning: Removed 838 rows containing missing values (geom_point).

## Edger expression coefficients for mutvwt_cell; R^2: 0.971; equation: y = 0.993x + 0.195

## DESeq2 expression coefficients for mutvwt_cell; R^2: 0.985; equation: y = 1.01x - 0.04

## Warning: Removed 237 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_vline).

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 237 rows containing missing values (geom_point).

## Warning: Removed 125 rows containing missing values (geom_point).

## Warning: Removed 64 rows containing missing values (geom_point).

## Warning: Removed 237 rows containing missing values (geom_point).

## Adding venn plots for mutvwt_exo.

## Limma expression coefficients for mutvwt_exo; R^2: 0.252; equation: y = 0.0113x - 1.26

## Warning: Removed 887 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_vline).

## Warning: Removed 1 rows containing missing values (geom_vline).

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 1 rows containing missing values (geom_vline).

## Warning: Removed 887 rows containing missing values (geom_point).

## Warning: Removed 198 rows containing missing values (geom_point).

## Warning: Removed 66 rows containing missing values (geom_point).

## Warning: Removed 887 rows containing missing values (geom_point).

## Edger expression coefficients for mutvwt_exo; R^2: 0.351; equation: y = -0.009x + 17.5

## DESeq2 expression coefficients for mutvwt_exo; R^2: 0.997; equation: y = 0.986x + 0.043

## Warning: Removed 232 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_vline).

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 232 rows containing missing values (geom_point).

## Warning: Removed 47 rows containing missing values (geom_point).

## Warning: Removed 123 rows containing missing values (geom_point).

## Warning: Removed 232 rows containing missing values (geom_point).

## Adding venn plots for exovcell_wt.

## Limma expression coefficients for exovcell_wt; R^2: 0.903; equation: y = 1.11x - 0.685

## Warning: Removed 861 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_vline).

## Warning: Removed 1 rows containing missing values (geom_vline).

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 1 rows containing missing values (geom_vline).

## Warning: Removed 861 rows containing missing values (geom_point).

## Warning: Removed 23 rows containing missing values (geom_point).

## Warning: Removed 199 rows containing missing values (geom_point).

## Warning: Removed 861 rows containing missing values (geom_point).

## Edger expression coefficients for exovcell_wt; R^2: 0.998; equation: y = 1.05x - 0.898

## DESeq2 expression coefficients for exovcell_wt; R^2: 0.989; equation: y = 0.98x - 0.0233

## Warning: Removed 232 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_vline).

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 232 rows containing missing values (geom_point).

## Warning: Removed 87 rows containing missing values (geom_point).

## Warning: Removed 56 rows containing missing values (geom_point).

## Warning: Removed 232 rows containing missing values (geom_point).

## Adding venn plots for exovcell_mut.

## Limma expression coefficients for exovcell_mut; R^2: 0.989; equation: y = 1.24x - 1.78

## Warning: Removed 889 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_vline).

## Warning: Removed 1 rows containing missing values (geom_vline).

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 1 rows containing missing values (geom_vline).

## Warning: Removed 889 rows containing missing values (geom_point).

## Warning: Removed 38 rows containing missing values (geom_point).

## Warning: Removed 241 rows containing missing values (geom_point).

## Warning: Removed 889 rows containing missing values (geom_point).

## Edger expression coefficients for exovcell_mut; R^2: 0.893; equation: y = 0.939x + 0.605

## DESeq2 expression coefficients for exovcell_mut; R^2: 0.988; equation: y = 0.997x - 0.0747

## Warning: Removed 249 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_vline).

## Warning: Removed 1 rows containing missing values (geom_hline).

## Warning: Removed 249 rows containing missing values (geom_point).

## Warning: Removed 137 rows containing missing values (geom_point).

## Warning: Removed 30 rows containing missing values (geom_point).

## Warning: Removed 249 rows containing missing values (geom_point).

## Writing summary information.

## The sheet: pairwise_summary is in legend, mutvwt_cell, mutvwt_exo, exovcell_wt, exovcell_mut, pairwise_summary.

## Attempting to add the comparison plot to pairwise_summary at row: 22 and column: 1

## Performing save of the workbook.

saveme(filename="mature.rda.xz")

## The savefile is: /cbcb/nelsayed-scratch/atb/small_rna/mmusculus_exosomev2/savefiles/mature.rda.xz

## Renaming /cbcb/nelsayed-scratch/atb/small_rna/mmusculus_exosomev2/savefiles/mature.rda.xz to /cbcb/nelsayed-scratch/atb/small_rna/mmusculus_exosomev2/savefiles/mature.rda.xz.01.

## The save string is: con <- base::pipe(paste0('pxz -T4 > /cbcb/nelsayed-scratch/atb/small_rna/mmusculus_exosomev2/savefiles/mature.rda.xz'), 'wb');
##  save(list=ls(all.names=TRUE, envir=globalenv()), envir=globalenv(), file=con, compress=FALSE);
##  close(con)

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