S. cerevisiae sample estimation, merged sample edition.

1 S. cerevisiae sample estimation of all samples (old and new)

This document should make clear the suitability of our yeast data for differential expression analyses. It should also give some ideas about the depth and distribution of the data.

2 Gathering samples

merged_expt <- create_expt(metadata="sample_sheets/all_samples.xlsx",
                           gene_info=sc_all_annotations,
                           file_column="allfile")

## preprocessing/v1/hpgl0564/hpgl0564_scerevisiae.count.xz contains 7131 rows.

## preprocessing/v1/hpgl0565/hpgl0565_scerevisiae.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v1/hpgl0566/hpgl0566_scerevisiae.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v1/hpgl0567/hpgl0567_scerevisiae.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v1/hpgl0568/hpgl0568_scerevisiae.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v1/hpgl0569/hpgl0569_scerevisiae.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v1/hpgl0570/hpgl0570_scerevisiae.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v1/hpgl0571/hpgl0571_scerevisiae.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v2/hpgl0774/outputs/bowtie2_scerevisiae/hpgl0774_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v2/hpgl0775/outputs/bowtie2_scerevisiae/hpgl0775_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v2/hpgl0776/outputs/bowtie2_scerevisiae/hpgl0776_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v2/hpgl0777/outputs/bowtie2_scerevisiae/hpgl0777_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v2/hpgl0778/outputs/bowtie2_scerevisiae/hpgl0778_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v2/hpgl0779/outputs/bowtie2_scerevisiae/hpgl0779_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v2/hpgl0780/outputs/bowtie2_scerevisiae/hpgl0780_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v2/hpgl0781/outputs/bowtie2_scerevisiae/hpgl0781_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v2/hpgl0782/outputs/bowtie2_scerevisiae/hpgl0782_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v2/hpgl0783/outputs/bowtie2_scerevisiae/hpgl0783_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v2/hpgl0784/outputs/bowtie2_scerevisiae/hpgl0784_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v2/hpgl0785/outputs/bowtie2_scerevisiae/hpgl0785_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v2/hpgl0786/outputs/bowtie2_scerevisiae/hpgl0786_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v2/hpgl0787/outputs/bowtie2_scerevisiae/hpgl0787_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v2/hpgl0788/outputs/bowtie2_scerevisiae/hpgl0788_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.

## preprocessing/v2/hpgl0789/outputs/bowtie2_scerevisiae/hpgl0789_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.

## Bringing together the count matrix and gene information.

## Some annotations were lost in merging, setting them to 'undefined'.

merged_nor <- subset_expt(merged_expt, subset="batch!='r'")
merged_nos <- subset_expt(merged_expt, subset="batch!='s'")

3 Visualizing raw data

There are lots of toys we have learned to use to play with with raw data and explore stuff like batch effects or non-canonical distributions or skewed counts. hpgltools provides some functionality to make this process easier. The graphs shown below and many more are generated with the wrapper ‘graph_metrics()’ but that takes away the chance to explain the graphs as I generate them.

merged_filt <- sm(normalize_expt(merged_expt, filter=TRUE))
merged_libsize <- plot_libsize(merged_expt)
merged_libsize

merged_nonzero <- plot_nonzero(merged_filt)
merged_nonzero

merged_density <- plot_density(merged_filt)

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 1152 zero count features.

merged_density

merged_boxplot <- plot_boxplot(merged_filt)

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 1152 zero count features.

merged_boxplot

merged_nor_filt <- sm(normalize_expt(merged_nor, filter=TRUE))
merged_nor_libsize <- plot_libsize(merged_nor_filt)
merged_nor_libsize

merged_nor_nonzero <- plot_nonzero(merged_nor_filt)
merged_nor_nonzero

merged_nor_density <- plot_density(merged_nor_filt)

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 575 zero count features.

merged_nor_density

merged_nor_boxplot <- plot_boxplot(merged_nor_filt)

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 575 zero count features.

merged_nor_boxplot

merged_nos_filt <- sm(normalize_expt(merged_nos, filter=TRUE))
merged_nos_libsize <- plot_libsize(merged_nos_filt)
merged_nos_libsize

merged_nos_nonzero <- plot_nonzero(merged_nos_filt)
merged_nos_nonzero

merged_nos_density <- plot_density(merged_nos_filt)

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 578 zero count features.

merged_nos_density

merged_nos_boxplot <- plot_boxplot(merged_nos_filt)

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 578 zero count features.

merged_nos_boxplot

4 Normalize and visualize

merged_norm <- sm(normalize_expt(merged_expt, transform="log2", norm="quant", convert="cpm", filter=TRUE))
merged_metrics <- sm(graph_metrics(merged_norm))

merged_nor_norm <- sm(normalize_expt(merged_nor, transform="log2", norm="quant", convert="cpm", filter=TRUE))
merged_nor_metrics <- sm(graph_metrics(merged_nor_norm))

merged_nos_norm <- sm(normalize_expt(merged_nos, transform="log2", norm="quant", convert="cpm", filter=TRUE))
merged_nos_metrics <- sm(graph_metrics(merged_nos_norm))

The data should now be normalized, lets view some metrics post-facto.

merged_metrics$corheat

## It appears that just the normalization is sufficient to split the samples completely by type and deeply separate them from the heterologous samples

merged_metrics$smc

## The samples are very well behaved, none fall below the red line.

merged_metrics$pcaplot

## The homogeneous wt/mutant are nicely separated, and what is more, the exogeneous samples also split wt/mutant, that might prove to be quite useful.

merged_nor_metrics$corheat

merged_nor_metrics$smc

merged_nor_metrics$pcaplot

merged_nos_metrics$corheat

merged_nos_metrics$smc

merged_nos_metrics$pcaplot

5 Try a batch estimation

merged_pcabatch <- sm(normalize_expt(merged_expt, transform="log2",
                                filter=TRUE, batch="pca", low_to_zero=TRUE))
merged_pcabatch_metrics <- graph_metrics(merged_pcabatch)

## Graphing number of non-zero genes with respect to CPM by library.

## Graphing library sizes.

## Graphing a boxplot.

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 1028 zero count features.

## Graphing a correlation heatmap.

## Graphing a standard median correlation.

## Performing correlation.

## Graphing a distance heatmap.

## Graphing a standard median distance.

## Performing distance.

## Graphing a PCA plot.

## Graphing a T-SNE plot.

## Plotting a density plot.

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 1028 zero count features.

## Printing a color to condition legend.

merged_pcabatch_metrics$pcaplot

merged_nor_pcabatch <- sm(normalize_expt(merged_nor, transform="log2",
                                filter=TRUE, batch="pca", low_to_zero=TRUE))
merged_nor_pcabatch_metrics <- graph_metrics(merged_nor_pcabatch)

## Graphing number of non-zero genes with respect to CPM by library.

## Graphing library sizes.

## Graphing a boxplot.

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 349 zero count features.

## Graphing a correlation heatmap.

## Graphing a standard median correlation.

## Performing correlation.

## Graphing a distance heatmap.

## Graphing a standard median distance.

## Performing distance.

## Graphing a PCA plot.

## Graphing a T-SNE plot.

## Plotting a density plot.

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 349 zero count features.

## Printing a color to condition legend.

merged_nor_pcabatch_metrics$pcaplot

merged_nos_pcabatch <- sm(normalize_expt(merged_nos, transform="log2",
                                filter=TRUE, batch="pca", low_to_zero=TRUE))
merged_nos_pcabatch_metrics <- graph_metrics(merged_nos_pcabatch)

## Graphing number of non-zero genes with respect to CPM by library.

## Graphing library sizes.

## Graphing a boxplot.

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 1156 zero count features.

## Graphing a correlation heatmap.

## Graphing a standard median correlation.

## Performing correlation.

## Graphing a distance heatmap.

## Graphing a standard median distance.

## Performing distance.

## Graphing a PCA plot.

## Graphing a T-SNE plot.

## Plotting a density plot.

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 1156 zero count features.

## Printing a color to condition legend.

merged_nos_pcabatch_metrics$pcaplot

merged_sva <- sm(normalize_expt(merged_expt, transform="log2",
                                filter=TRUE, batch="sva", low_to_zero=TRUE))
merged_sva_metrics <- graph_metrics(merged_sva)

## Graphing number of non-zero genes with respect to CPM by library.

## Graphing library sizes.

## Graphing a boxplot.

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 794 zero count features.

## Graphing a correlation heatmap.

## Graphing a standard median correlation.

## Performing correlation.

## Graphing a distance heatmap.

## Graphing a standard median distance.

## Performing distance.

## Graphing a PCA plot.

## Graphing a T-SNE plot.

## Plotting a density plot.

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 794 zero count features.

## Printing a color to condition legend.

merged_sva_metrics$pcaplot

merged_nor_sva <- sm(normalize_expt(merged_nor, transform="log2",
                                filter=TRUE, batch="sva", low_to_zero=TRUE))
merged_nor_sva_metrics <- graph_metrics(merged_nor_sva)

## Graphing number of non-zero genes with respect to CPM by library.

## Graphing library sizes.

## Graphing a boxplot.

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 218 zero count features.

## Graphing a correlation heatmap.

## Graphing a standard median correlation.

## Performing correlation.

## Graphing a distance heatmap.

## Graphing a standard median distance.

## Performing distance.

## Graphing a PCA plot.

## Graphing a T-SNE plot.

## Plotting a density plot.

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 218 zero count features.

## Printing a color to condition legend.

merged_nor_sva_metrics$pcaplot

merged_nos_sva <- sm(normalize_expt(merged_nos, transform="log2",
                                filter=TRUE, batch="sva", low_to_zero=TRUE))
merged_nos_sva_metrics <- graph_metrics(merged_nos_sva)

## Graphing number of non-zero genes with respect to CPM by library.

## Graphing library sizes.

## Graphing a boxplot.

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 1922 zero count features.

## Graphing a correlation heatmap.

## Graphing a standard median correlation.

## Performing correlation.

## Graphing a distance heatmap.

## Graphing a standard median distance.

## Performing distance.

## Graphing a PCA plot.

## Graphing a T-SNE plot.

## Plotting a density plot.

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 1922 zero count features.

## Printing a color to condition legend.

merged_nos_sva_metrics$pcaplot

merged_combat <- sm(normalize_expt(merged_expt, transform="log2",
                                   filter=TRUE, batch="combat_scale", low_to_zero=TRUE))
merged_combat_metrics <- graph_metrics(merged_combat)

## Graphing number of non-zero genes with respect to CPM by library.

## Graphing library sizes.

## Graphing a boxplot.

## Graphing a correlation heatmap.

## Graphing a standard median correlation.

## Performing correlation.

## Graphing a distance heatmap.

## Graphing a standard median distance.

## Performing distance.

## Graphing a PCA plot.

## Graphing a T-SNE plot.

## Plotting a density plot.

## Printing a color to condition legend.

merged_combat_metrics$pcaplot

merged_nor_combat <- sm(normalize_expt(merged_nor, transform="log2",
                                   filter=TRUE, batch="combat_scale", low_to_zero=TRUE))
merged_nor_combat_metrics <- graph_metrics(merged_nor_combat)

## Graphing number of non-zero genes with respect to CPM by library.

## Graphing library sizes.

## Graphing a boxplot.

## Graphing a correlation heatmap.

## Graphing a standard median correlation.

## Performing correlation.

## Graphing a distance heatmap.

## Graphing a standard median distance.

## Performing distance.

## Graphing a PCA plot.

## Graphing a T-SNE plot.

## Plotting a density plot.

## Printing a color to condition legend.

merged_nor_combat_metrics$pcaplot

merged_nos_combat <- sm(normalize_expt(merged_nos, transform="log2",
                                   filter=TRUE, batch="combat_scale", low_to_zero=TRUE))
merged_nos_combat_metrics <- graph_metrics(merged_nos_combat)

## Graphing number of non-zero genes with respect to CPM by library.

## Graphing library sizes.

## Graphing a boxplot.

## Graphing a correlation heatmap.

## Graphing a standard median correlation.

## Performing correlation.

## Graphing a distance heatmap.

## Graphing a standard median distance.

## Performing distance.

## Graphing a PCA plot.

## Graphing a T-SNE plot.

## Plotting a density plot.

## Printing a color to condition legend.

merged_nos_combat_metrics$pcaplot

merged_limma <- normalize_expt(merged_expt, transform="log2",
                                   filter=TRUE, batch="limmaresid", low_to_zero=TRUE)

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

## log2(limmaresid(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 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.

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

## Removing 1030 low-count genes (6095 remaining).

## Step 2: not normalizing the data.

## Step 3: not converting the data.

## Step 4: transforming the data with log2.

## transform_counts: Found 1152 values equal to 0, adding 1 to the matrix.

## Step 5: doing batch correction with limmaresid.

## 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.

## batch_counts: Before batch correction, 1152 entries are >= 0.

## batch_counts: Using residuals of limma's lmfit to remove batch effect.

## The number of elements which are < 0 after batch correction is: 72276

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

merged_limma_metrics <- graph_metrics(merged_limma)

## Graphing number of non-zero genes with respect to CPM by library.

## Graphing library sizes.

## Graphing a boxplot.

## Graphing a correlation heatmap.

## Graphing a standard median correlation.

## Performing correlation.

## Graphing a distance heatmap.

## Graphing a standard median distance.

## Performing distance.

## Graphing a PCA plot.

## Graphing a T-SNE plot.

## Plotting a density plot.

## Printing a color to condition legend.

merged_limma_metrics$pcaplot

merged_nor_limma <- normalize_expt(merged_nor, transform="log2",
                                   filter=TRUE, batch="limmaresid", low_to_zero=TRUE)

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

## log2(limmaresid(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 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.

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

## Removing 1116 low-count genes (6009 remaining).

## Step 2: not normalizing the data.

## Step 3: not converting the data.

## Step 4: transforming the data with log2.

## transform_counts: Found 575 values equal to 0, adding 1 to the matrix.

## Step 5: doing batch correction with limmaresid.

## 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.

## batch_counts: Before batch correction, 575 entries are >= 0.

## batch_counts: Using residuals of limma's lmfit to remove batch effect.

## The number of elements which are < 0 after batch correction is: 47507

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

merged_nor_limma_metrics <- graph_metrics(merged_nor_limma)

## Graphing number of non-zero genes with respect to CPM by library.

## Graphing library sizes.

## Graphing a boxplot.

## Graphing a correlation heatmap.

## Graphing a standard median correlation.

## Performing correlation.

## Graphing a distance heatmap.

## Graphing a standard median distance.

## Performing distance.

## Graphing a PCA plot.

## Graphing a T-SNE plot.

## Plotting a density plot.

## Printing a color to condition legend.

merged_nor_limma_metrics$pcaplot

merged_nos_limma <- normalize_expt(merged_nos, transform="log2",
                                   filter=TRUE, batch="limmaresid", low_to_zero=TRUE)

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

## log2(limmaresid(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 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.

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

## Removing 1069 low-count genes (6056 remaining).

## Step 2: not normalizing the data.

## Step 3: not converting the data.

## Step 4: transforming the data with log2.

## transform_counts: Found 578 values equal to 0, adding 1 to the matrix.

## Step 5: doing batch correction with limmaresid.

## 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.

## batch_counts: Before batch correction, 578 entries are >= 0.

## batch_counts: Using residuals of limma's lmfit to remove batch effect.

## The number of elements which are < 0 after batch correction is: 47866

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

merged_nos_limma_metrics <- graph_metrics(merged_nos_limma)

## Graphing number of non-zero genes with respect to CPM by library.

## Graphing library sizes.

## Graphing a boxplot.

## Graphing a correlation heatmap.

## Graphing a standard median correlation.

## Performing correlation.

## Graphing a distance heatmap.

## Graphing a standard median distance.

## Performing distance.

## Graphing a PCA plot.

## Graphing a T-SNE plot.

## Plotting a density plot.

## Printing a color to condition legend.

merged_nos_limma_metrics$pcaplot

merged_fsva <- sm(normalize_expt(merged_expt, transform="log2",
                              filter=TRUE, batch="fsva", low_to_zero=TRUE))
merged_fsva_metrics <- graph_metrics(merged_fsva)

## Graphing number of non-zero genes with respect to CPM by library.

## Graphing library sizes.

## Graphing a boxplot.

## Graphing a correlation heatmap.

## Graphing a standard median correlation.

## Performing correlation.

## Graphing a distance heatmap.

## Graphing a standard median distance.

## Performing distance.

## Graphing a PCA plot.

## Graphing a T-SNE plot.

## Plotting a density plot.

## Printing a color to condition legend.

merged_fsva_metrics$pcaplot

merged_nor_fsva <- sm(normalize_expt(merged_nor, transform="log2",
                              filter=TRUE, batch="fsva", low_to_zero=TRUE))
merged_nor_fsva_metrics <- graph_metrics(merged_nor_fsva)

## Graphing number of non-zero genes with respect to CPM by library.

## Graphing library sizes.

## Graphing a boxplot.

## Graphing a correlation heatmap.

## Graphing a standard median correlation.

## Performing correlation.

## Graphing a distance heatmap.

## Graphing a standard median distance.

## Performing distance.

## Graphing a PCA plot.

## Graphing a T-SNE plot.

## Plotting a density plot.

## Printing a color to condition legend.

merged_nor_fsva_metrics$pcaplot

merged_nos_fsva <- sm(normalize_expt(merged_nos, transform="log2",
                              filter=TRUE, batch="fsva", low_to_zero=TRUE))
merged_nos_fsva_metrics <- graph_metrics(merged_nos_fsva)

## Graphing number of non-zero genes with respect to CPM by library.

## Graphing library sizes.

## Graphing a boxplot.

## Graphing a correlation heatmap.

## Graphing a standard median correlation.

## Performing correlation.

## Graphing a distance heatmap.

## Graphing a standard median distance.

## Performing distance.

## Graphing a PCA plot.

## Graphing a T-SNE plot.

## Plotting a density plot.

## Printing a color to condition legend.

merged_nos_fsva_metrics$pcaplot

6 Write the expt

fun <- write_expt(merged_expt, excel=paste0("excel/samples_written_mergedv1v2-v", ver, ".xlsx"),
                  filter=TRUE, norm="raw", batch="fsva", transform="log2")

## Writing the legend.

## Writing the raw reads.

## Graphing the raw reads.

## Writing the normalized reads.

## Graphing the normalized reads.

## Writing the median reads by factor.

## The factor mtc_mtu has 4 rows.

## The factor mtc_wtu has 8 rows.

## The factor wtc_mtu has 4 rows.

## The factor wtc_wtu has 8 rows.

fun_nor <- write_expt(merged_nor, excel=paste0("excel/samples_written_merged_nor-v", ver, ".xlsx"),
                      filter=TRUE, norm="raw", batch="fsva", transform="log2")

## Writing the legend.

## Writing the raw reads.

## Graphing the raw reads.

## Writing the normalized reads.

## Graphing the normalized reads.

## Writing the median reads by factor.

## The factor mtc_mtu has 2 rows.

## The factor mtc_wtu has 6 rows.

## The factor wtc_mtu has 2 rows.

## The factor wtc_wtu has 6 rows.

fun_nos <- write_expt(merged_nos, excel=paste0("excel/samples_written_merged_nos-v", ver, ".xlsx"),
                  filter=TRUE, norm="raw", batch="fsva", transform="log2")

## Writing the legend.

## Writing the raw reads.

## Graphing the raw reads.

## Writing the normalized reads.

## Graphing the normalized reads.

## Writing the median reads by factor.

## The factor mtc_mtu has 2 rows.

## The factor mtc_wtu has 6 rows.

## The factor wtc_mtu has 2 rows.

## The factor wtc_wtu has 6 rows.