ED Project: Pseudomonas RNASeq dataset.

1 Introduction

This dataset contains multiple experiments.

2 Annotations

pa14_gff <- load_gff_annotations("reference/paeruginosa_pa14.gff", id_col="gene_id")

## Trying attempt: rtracklayer::import.gff3(gff, sequenceRegionsAsSeqinfo = TRUE)

## Had a successful gff import with rtracklayer::import.gff3(gff, sequenceRegionsAsSeqinfo = TRUE)

## Returning a df with 16 columns and 11946 rows.

rownames(pa14_gff) <- pa14_gff[["gene_id"]]
## The Alias column has PA14_00010

pa14_microbes <- as.data.frame(load_microbesonline_annotations("PA14"))

## Found 1 entry.

## Pseudomonas aeruginosa UCBPP-PA14Proteobacteria2006-11-22yes105972208963

## The species being downloaded is: Pseudomonas aeruginosa UCBPP-PA14

## Downloading: http://www.microbesonline.org/cgi-bin/genomeInfo.cgi?tId=208963;export=tab

## The sysName column has PA14_0010

pa14_annot <- merge(pa14_gff, pa14_microbes, by.x="Alias", by.y="sysName")
rownames(pa14_annot) <- pa14_annot[["gene_id"]]

## The identifiers are a bit odd, so we need to do a little work
pa14_length <- pa14_annot[, c("gene_id", "width", "Alias")]
pa14_go <- load_microbesonline_go(species="PA14", id_column="sysName")

## Found 1 entry.

## Pseudomonas aeruginosa UCBPP-PA14Proteobacteria2006-11-22yes105972208963

## The species being downloaded is: Pseudomonas aeruginosa UCBPP-PA14 and is being downloaded as 208963.tab.

pa14_go_length <- merge(pa14_go, pa14_length, by.x="sysName", by.y="Alias")
pa14_go <- pa14_go_length[, c("gene_id", "GO")]
colnames(pa14_go) <- c("ID", "GO")
pa14_length <- pa14_go_length[, c("gene_id", "width")]
pa14_length_ids <- unique(pa14_length)[["gene_id"]]
pa14_length <- pa14_length[pa14_length_ids, ]
rownames(pa14_length) <- make.names(pa14_length[["gene_id"]], unique=TRUE)
colnames(pa14_length) <- c("ID", "width")

3 Make the expressionset

Given the above annotations, now lets pull in the counts.

I am switching to the sheet all_samples_modified_gcd.xlsx for the moment because I added a space in the strain name for the gcd sampl

pa14_expt <- create_expt("sample_sheets/all_samples_modified_gcd.xlsx",
                         gene_info=pa14_annot, file_column="hisatcounttable")

## Reading the sample metadata.

## Did not find the condition column in the sample sheet.

## Filling it in as undefined.

## Did not find the batch column in the sample sheet.

## Filling it in as undefined.

## The sample definitions comprises: 105 rows(samples) and 31 columns(metadata fields).

## Matched 5972 annotations and counts.

## Bringing together the count matrix and gene information.

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

## Saving the expressionset to 'expt.rda'.

## The final expressionset has 5979 features and 105 samples.

4 Quick global look at the data

While we are at it, lets drop the two sad samples.

pa14_libsize <- plot_libsize(pa14_expt)
pa14_libsize$plot

pa14_nonzero <- plot_nonzero(pa14_expt)
pa14_nonzero$plot

## Warning: ggrepel: 100 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

pa14_expt <- subset_expt(pa14_expt, nonzero=5500)

## The samples (and read coverage) removed when filtering 5500 non-zero genes are:

##  SM040  SM048 
##  43984 316632

## subset_expt(): There were 105, now there are 103 samples.

4.1 A few queries

I know a priori that April is interested to see how the 4 library preparations look with respect to each other. Let us therefore create a data structure to look explicitly at that.

pa14_libprep <- set_expt_conditions(pa14_expt, fact="libraryprepbatch") %>%
  set_expt_batches(fact="organisms")

pa14_lib_norm <- normalize_expt(pa14_libprep, filter=TRUE,
                                convert="cpm", norm="quant", transform="log2")

## Removing 6 low-count genes (5973 remaining).

## transform_counts: Found 35 values equal to 0, adding 1 to the matrix.

plot_pca(pa14_lib_norm)$plot

## plot labels was not set and there are more than 100 samples, disabling it.

## Error: Continuous value supplied to discrete scale

plot_corheat(pa14_lib_norm)$plot

libprep_pca_info <- pca_information(
    pa14_lib_norm, plot_pcas=TRUE,
    expt_factors=c("libraryprepbatch", "organisms", "strains", "media", "bioreplicate"))

## plot labels was not set and there are more than 100 samples, disabling it.

libprep_pca_info$anova_f_heatmap

libprep_pca_info$pca_plots[[2]]

## Error: Continuous value supplied to discrete scale

libprep_pca_info$pca_plots[[3]]

## Error: Continuous value supplied to discrete scale

libprep_pca_info$pca_plots[[4]]

## Error: Continuous value supplied to discrete scale

libprep_pca_info$pca_plots[[5]]

## Error: Continuous value supplied to discrete scale

To my eyes they look reasonably mixed, suggesting that library prep batch is not a dominant factor in the data.

At this point, I am thinking that we should separate the data by the experiments, but I will first just show the relationships among all the data.

5 Looking at other factors

As far as I see, there are three factors which are of primary interest:

1. PA14 strain
2. Media used
3. Bioreplicate

The last will be used as batch in the following plots.

pa14_media <- set_expt_conditions(pa14_expt, fact="media") %>%
  set_expt_batches(fact="bioreplicate")
pa14_media_norm <- normalize_expt(pa14_media, transform="log2", convert="cpm",
                                  filter=TRUE, norm="quant")

## Removing 6 low-count genes (5973 remaining).

## transform_counts: Found 35 values equal to 0, adding 1 to the matrix.

plot_pca(pa14_media_norm)$plot

## plot labels was not set and there are more than 100 samples, disabling it.

pa14_strains <- set_expt_conditions(pa14_expt, fact="strains") %>%
  set_expt_batches(fact="bioreplicate")
pa14_strains_norm <- normalize_expt(pa14_strains, transform="log2", convert="cpm",
                                  filter=TRUE, norm="quant")

## Removing 6 low-count genes (5973 remaining).
## transform_counts: Found 35 values equal to 0, adding 1 to the matrix.

plot_pca(pa14_strains_norm)$plot

## plot labels was not set and there are more than 100 samples, disabling it.

Disregarding the various experiments performed, I think we can state that media separates the data in a fashion which is more interesting than strain. Given the number of (what I assume are) closely related strains, I am thinking it might prove to be a good idea to perform my variant search tool on this data and see how well they held up with respect to the reference strain.

6 Separate the experiments

I have been told repeatedly that there are multiple experiments in this data, but apparently I have not paid proper attention because I cannot remember which is which, and to my eyes it is not obvious in the sample sheet.

With this in mind, I spoke with Solomon briefly and have an idea of the 3 logical groups in his data. Let us therefore separate and examine those first.

6.1 Metabolism and infection

For the moment, I am going to call Solomon’s samples ‘metabolism and infection.’ I will also complicate the ‘condition’ of the data by combining the media and strain, but shortly thereafter will split that back. I think the reason why will become clear.

initials_factor <- gsub(x=rownames(pData(pa14_expt)), pattern="^(..).*$", replacement="\\1")
pData(pa14_expt)[["initials"]] <- as.factor(initials_factor)
strain_media <- paste0(pData(pa14_expt)[["strains"]], "_",
                       pData(pa14_expt)[["media"]])
pData(pa14_expt)[["strain_media"]] <- strain_media

## Lets set some colors
## WT: grayscale, eda: blue, edd: green, gcd: purple, pgl: red, zwf: yellow
colors_by_strain <- list(
    "PA14 WT" = "#000000",
    "PA14 eda" = "#0000dd",
    "PA14 edd" = "#00dd00",
    "PA14 gcd" = "#dd00dd",
    "PA14 pgl" = "#dd0000",
    "PA14 zwf" = "#dddd00")

infect_metabolism <- subset_expt(pa14_expt, subset="initials=='SM'") %>%
  set_expt_conditions(fact="strains") %>%
  set_expt_batches(fact="media")

## subset_expt(): There were 103, now there are 58 samples.

plot_legend(infect_metabolism)$plot

metabolism_control <- subset_expt(infect_metabolism,
                                  subset="media=='LB'|media=='LB + 0.5 M urea'") %>%
  set_expt_conditions(fact="media") %>%
  set_expt_batches("bioreplicate")

## subset_expt(): There were 58, now there are 6 samples.

metabolism_starvation <- subset_expt(infect_metabolism,
                                     subset="media=='PBST'|media=='Urine'") %>%
  set_expt_colors(colors=colors_by_strain) %>%
  set_expt_conditions(fact="media") %>%
  set_expt_batches(fact="strains")

## subset_expt(): There were 58, now there are 35 samples.

metabolism_starvation_strain <- set_expt_conditions(metabolism_starvation, fact="strains") %>%
  set_expt_batches(fact="media")

metabolism_exudate <- subset_expt(infect_metabolism,
                                  subset="media=='Instilled'")

## subset_expt(): There were 58, now there are 17 samples.

6.2 Glance at these 4 subsets

As a whole group, these samples are a bit confusing. The mouse instiled samples are prety obvious, but the other sources of variance remain a bit of a mystery to me.

global_norm <- normalize_expt(infect_metabolism, filter=TRUE, convert="cpm",
                              norm="quant", transform="log2") %>%
  set_expt_conditions(fact="media")

## Removing 13 low-count genes (5966 remaining).

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

plot_pca(global_norm)$plot

## Warning: ggrepel: 16 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

tmp <- global_norm %>%
  set_expt_conditions(fact="strains")
plot_pca(tmp)$plot

## Warning: ggrepel: 48 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

7 The smallest experiment: LB vs. LB+urea

This is a group of 6 samples, 3 in LB and three in LB+urea. This should therefore be the most straight forward comparison.

mc_norm <- normalize_expt(metabolism_control, transform="log2",
                          convert="cpm", norm="quant", filter=TRUE)

## Removing 73 low-count genes (5906 remaining).

plot_pca(mc_norm)$plot

7.1 Metabolism control experiment, DE

mc_san <- sanitize_expt(metabolism_control)
mc_de <- all_pairwise(mc_san, model_batch=TRUE, filter=TRUE)

## Using limma's removeBatchEffect to visualize with(out) batch inclusion.

## Finished running DE analyses, collecting outputs.

## Comparing analyses.

mc_tables <- combine_de_tables(
    mc_de,
    excel=glue::glue("excel/metabolism_control_tables-v{ver}.xlsx"))

## Deleting the file excel/metabolism_control_tables-v20220512.xlsx before writing the tables.

mc_sig <- extract_significant_genes(
    mc_tables,
    excel=glue::glue("excel/metabolism_control_sig-v{ver}.xlsx"))

## Deleting the file excel/metabolism_control_sig-v20220512.xlsx before writing the tables.

8 Starving strains

The second group is a little more complex, it seeks to simultaneously compare the strains (WT vs. mutants) and the environment (PBS vs. urine).

This design is complex enough that I think we need to choose colors more carefully.

Here is a query from Solomon:

Could you please generate me a table for the metabolism starvation strain analyses where the urine is the numerator and the PBST is the denominator?

I am interpreting this to mean we should have an experimental design in which we ignore the strains and just compare all urine and pbst samples. When I ran the following block, it looks to me that we currently return this in the following block:

ms_norm <- normalize_expt(metabolism_starvation, filter=TRUE, norm="quant",
                          convert="cpm", transform="log2")

## Removing 30 low-count genes (5949 remaining).

plot_pca(ms_norm)$plot

ms_de <- all_pairwise(metabolism_starvation, model_batch=TRUE)

## Using limma's removeBatchEffect to visualize with(out) batch inclusion.

## Finished running DE analyses, collecting outputs.

## Comparing analyses.

ms_de$comparison$comp

##                Urine_vs_PBST
## limma_vs_deseq        0.9830
## limma_vs_edger        0.9829
## limma_vs_ebseq        0.9772
## limma_vs_basic        0.9996
## deseq_vs_edger        0.9999
## deseq_vs_ebseq        0.9953
## deseq_vs_basic        0.9833
## edger_vs_ebseq        0.9963
## edger_vs_basic        0.9833
## ebseq_vs_basic        0.9810

ms_tables <- combine_de_tables(
    ms_de,
    excel=glue::glue("excel/metabolism_starvation_tables-v{ver}.xlsx"))

## Deleting the file excel/metabolism_starvation_tables-v20220512.xlsx before writing the tables.

ms_sig <- extract_significant_genes(
    ms_tables,
    excel=glue::glue("excel/metabolism_starvation_sig-v{ver}.xlsx"))

## Deleting the file excel/metabolism_starvation_sig-v20220512.xlsx before writing the tables.

9 Metabolism Starvation GO

ms_up <- ms_tables[["significant"]][["deseq"]][["ups"]][[1]]
ms_down <- ms_tables[["significant"]][["deseq"]][["downs"]][[1]]

## The go data from microbesonline is keyed by the gene name
## (e.g. dnaA), not gene ID or PA id or whatever.
pa14_lengths <- pa14_annot[, c("name.x", "width")]
colnames(pa14_lengths) <- c("ID", "width")

rownames(ms_up) <- make.names(ms_up[["namex"]], unique=TRUE)

## Error in `rownames<-`(`*tmp*`, value = character(0)): attempt to set 'rownames' on an object with no dimensions

ms_up_goseq <- simple_goseq(ms_up, go_db=pa14_go, length_db=pa14_lengths)

## Error in simple_goseq(ms_up, go_db = pa14_go, length_db = pa14_lengths): Not sure how to handle your set of significant gene ids.

ms_up_goseq[["pvalue_plots"]][["bpp_plot_over"]]

## Error in eval(expr, envir, enclos): object 'ms_up_goseq' not found

ms_up_goseq[["pvalue_plots"]][["mfp_plot_over"]]

## Error in eval(expr, envir, enclos): object 'ms_up_goseq' not found

rownames(ms_down) <- make.names(ms_down[["namex"]], unique=TRUE)

## Error in `rownames<-`(`*tmp*`, value = character(0)): attempt to set 'rownames' on an object with no dimensions

ms_down_goseq <- simple_goseq(ms_down, go_db=pa14_go, length_db=pa14_lengths)

## Error in simple_goseq(ms_down, go_db = pa14_go, length_db = pa14_lengths): Not sure how to handle your set of significant gene ids.

ms_down_goseq[["pvalue_plots"]][["bpp_plot_over"]]

## Error in eval(expr, envir, enclos): object 'ms_down_goseq' not found

ms_down_goseq[["pvalue_plots"]][["mfp_plot_over"]]

## Error in eval(expr, envir, enclos): object 'ms_down_goseq' not found

9.1 Compare strains

This time let us compare the strains and lower the variance from media.

mss_norm <- normalize_expt(metabolism_starvation_strain, filter=TRUE, convert="cpm", norm="quant",
                           transform="log2")

## Removing 30 low-count genes (5949 remaining).

plot_pca(mss_norm)$plot

mss_urine <- subset_expt(metabolism_starvation_strain, subset="batch=='Urine'")

## subset_expt(): There were 35, now there are 18 samples.

mss_urine_norm <- normalize_expt(mss_urine, filter=TRUE, convert="cpm", norm="quant",
                                 batch="svaseq", transform="log2")

## Warning in normalize_expt(mss_urine, filter = TRUE, convert = "cpm", norm =
## "quant", : Quantile normalization and sva do not always play well together.

## Removing 38 low-count genes (5941 remaining).

## Setting 5 low elements to zero.

## transform_counts: Found 5 values equal to 0, adding 1 to the matrix.

plot_pca(mss_urine_norm)$plot

mss_pbst <- subset_expt(metabolism_starvation_strain, subset="batch=='PBST'")

## subset_expt(): There were 35, now there are 17 samples.

mss_pbst_norm <- normalize_expt(mss_pbst, filter=TRUE, convert="cpm",
                                batch="svaseq", transform="log2")

## Removing 57 low-count genes (5922 remaining).

## Setting 14 low elements to zero.

## transform_counts: Found 14 values equal to 0, adding 1 to the matrix.

plot_pca(mss_pbst_norm)$plot

interesting <- list(
    "eda_vs_wt" = c("PA14eda", "PA14WT"),
    "edd_vs_wt" = c("PA14edd", "PA14WT"),
    "gcd_vs_wt" = c("PA14gcd", "PA14WT"),
    "pgl_vs_wt" = c("PA14pgl", "PA14WT"),
    "zfw_vs_wt" = c("PA14zwf", "PA14WT"))
mss_urine_de <- all_pairwise(mss_urine, model_batch="svaseq", filter=TRUE)

## Removing 0 low-count genes (5941 remaining).

## Setting 11 low elements to zero.

## transform_counts: Found 11 values equal to 0, adding 1 to the matrix.

## Finished running DE analyses, collecting outputs.

## Comparing analyses.

mss_urine_table <- combine_de_tables(
    mss_urine_de, keepers=interesting,
    excel=glue::glue("excel/metabolism_starvation_strain_tables-v{ver}.xlsx"))

## Deleting the file excel/metabolism_starvation_strain_tables-v20220512.xlsx before writing the tables.

mss_urine_sig <- extract_significant_genes(
    mss_urine_table,
    excel=glue::glue("excel/metabolism_starvation_strain_sig-v{ver}.xlsx"))

## Deleting the file excel/metabolism_starvation_strain_sig-v20220512.xlsx before writing the tables.

Given that the strains are so similar, we can comfortably compare them across media (PBST/urine).

msm <- set_expt_conditions(metabolism_starvation_strain, fact="batch") %>%
  set_expt_batches(fact="bioreplicate")
msm_norm <- normalize_expt(msm, filter=TRUE, convert="cpm", norm="quant",
                           transform="log2")

## Removing 30 low-count genes (5949 remaining).

plot_pca(msm_norm)$plot

10 Exudate

Compare the strains during the instillation process

exudate_norm <- normalize_expt(metabolism_exudate, filter=TRUE, convert="cpm",
                               norm="quant", transform="log2")

## Removing 69 low-count genes (5910 remaining).

## transform_counts: Found 76 values equal to 0, adding 1 to the matrix.

pp(file="images/compare_strains_exudate.pdf", image=plot_pca(exudate_norm)$plot)

exudate_nb <- normalize_expt(metabolism_exudate, filter=TRUE, convert="cpm",
                             transform="log2", batch="svaseq")

## Removing 69 low-count genes (5910 remaining).

## Setting 393 low elements to zero.

## transform_counts: Found 393 values equal to 0, adding 1 to the matrix.

plot_pca(exudate_nb)$plot

pp(file="images/compare_strains_exudate_sva.pdf", image=plot_pca(exudate_nb)$plot)

10.1 Exudate DE

exudate_de <- all_pairwise(metabolism_exudate, model_batch=TRUE, filter=TRUE)

## Using limma's removeBatchEffect to visualize with(out) batch inclusion.

## Finished running DE analyses, collecting outputs.

## Comparing analyses.

exudate_tables <- combine_de_tables(
    exudate_de, keepers=interesting,
    excel=glue::glue("excel/exudate_tables-v{ver}.xlsx"))
exudate_sig <- extract_significant_genes(
    exudate_tables,
    excel=glue::glue("excel/exudate_sig-v{ver}.xlsx"))

wanted_table <- exudate_tables[["data"]][["eda_vs_wt"]]
eda_wt_volcano <- plot_volcano_de(wanted_table, logfc=2, fc_col="deseq_logfc", p_col="deseq_adjp")
pp(file="images/wt_vs_eda_de_volcano_instilled.pdf",
   image=eda_wt_volcano$plot)

11 Instilled vs. PBST/Urine

Vince and Najib are interested in a slightly different question:

instilled_vs <- subset_expt(
    pa14_expt,
    subset='media=="PBST"|media=="Urine"|media=="Instilled"') %>%
  set_expt_conditions(fact="media") %>%
  set_expt_batches(fact="bioreplicate")

## subset_expt(): There were 103, now there are 52 samples.

instilled_vs_norm <- normalize_expt(instilled_vs, transform="log2", convert="cpm",
                                    norm="quant", filter=TRUE)

## Removing 13 low-count genes (5966 remaining).

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

inst_pca <- plot_pca(instilled_vs_norm)$plot
pp(file="images/instilled_vs_pca.pdf", image=inst_pca)

## Warning: ggrepel: 10 unlabeled data points (too many overlaps). Consider increasing max.overlaps
## ggrepel: 10 unlabeled data points (too many overlaps). Consider increasing max.overlaps

inst_vs_de <- all_pairwise(instilled_vs, filter=TRUE)

## Using limma's removeBatchEffect to visualize with(out) batch inclusion.

## Finished running DE analyses, collecting outputs.

## Comparing analyses.

inst_vs_tables <- combine_de_tables(
    inst_vs_de,
    excel=glue::glue("excel/instilled_vs_tables-v{ver}.xlsx"))
inst_vs_sig <- extract

## Error in eval(expr, envir, enclos): object 'extract' not found

pp(file="images/pbst_vs_instilled_de_volcano.pdf",
   image=inst_vs_tables[["plots"]][["PBST_vs_Instilled"]][["deseq_vol_plots"]][["plot"]])

12 Check mouse counts

mm_expt <- create_expt("sample_sheets/all_samples_modified2.xlsx",
                       gene_info=pa14_annot, file_column="mousetable")

## Reading the sample metadata.

## Did not find the condition column in the sample sheet.

## Filling it in as undefined.

## Did not find the batch column in the sample sheet.

## Filling it in as undefined.

## The sample definitions comprises: 105 rows(samples) and 32 columns(metadata fields).

## Warning in create_expt("sample_sheets/all_samples_modified2.xlsx", gene_info
## = pa14_annot, : Some samples were removed when cross referencing the samples
## against the count data.

## Warning in create_expt("sample_sheets/all_samples_modified2.xlsx", gene_info =
## pa14_annot, : Even after changing the rownames in gene info, they do not match
## the count table.

## Even after changing the rownames in gene info, they do not match the count table.

## Here are the first few rownames from the count tables:

## gene:ENSMUSG00000000001, gene:ENSMUSG00000000003, gene:ENSMUSG00000000028, gene:ENSMUSG00000000037, gene:ENSMUSG00000000049, gene:ENSMUSG00000000056

## Here are the first few rownames from the gene information table:

## gene1650835, gene1650837, gene1650839, gene1650841, gene1650843, gene1650845

## Bringing together the count matrix and gene information.

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

## Warning in create_expt("sample_sheets/all_samples_modified2.xlsx", gene_info =
## pa14_annot, : The following samples have no counts! SM029SM032SM038SM040

## Saving the expressionset to 'expt.rda'.

## The final expressionset has 25753 features and 35 samples.

plot_libsize(mm_expt)$plot

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

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

## Warning: Removed 4 rows containing missing values (geom_bar).

13 Fun with circos

control_table <- mc_tables[["data"]][["LB05Murea_vs_LB"]]
urine_eda <- mss_urine_table[["data"]][["eda_vs_wt"]]
urine_edd <- mss_urine_table[["data"]][["edd_vs_wt"]]
urine_gcd <- mss_urine_table[["data"]][["gcd_vs_wt"]]
urine_pgl <- mss_urine_table[["data"]][["pgl_vs_wt"]]
urine_zfw <- mss_urine_table[["data"]][["zfw_vs_wt"]]

exudate_eda <- exudate_tables[["data"]][["eda_vs_wt"]]
exudate_edd <- exudate_tables[["data"]][["edd_vs_wt"]]
exudate_gcd <- exudate_tables[["data"]][["gcd_vs_wt"]]
exudate_pgl <- exudate_tables[["data"]][["pgl_vs_wt"]]
exudate_zfw <- exudate_tables[["data"]][["zfw_vs_wt"]]

pa14_annot[["chromosome"]] <- "Pseudomonas_aeruginosa_UCBPP_PA14"

sm_cfg <- circos_prefix(pa14_annot, name="sm", cog_column = "COGFun",
                        start_column="start.x", end_column="end", strand_column="strand.x",
                        chr_column="chromosome", id_column="gene_id")

## This assumes you have a colors.conf in circos/colors/ and fonts.conf in circos/fonts/

## It also assumes you have conf/ideogram.conf, conf/ticks.conf, and conf/housekeeping.conf

## It will write circos/conf/sm.conf with a reasonable first approximation config file.

## Wrote karyotype to circos/conf/ideograms/sm.conf

## This should match the ideogram= line in sm.conf

## Wrote ticks to circos/conf/ticks_sm.conf

sm_kary <- circos_karyotype(sm_cfg, fasta="reference/paeruginosah_pa14.fasta")

## Error in .Call2("new_input_filexp", filepath, PACKAGE = "XVector"): cannot open file 'reference/paeruginosah_pa14.fasta'

sm_plus_minus <- circos_plus_minus(sm_cfg, width=0.06, thickness=40)

## Writing data file: circos/data/sm_plus_go.txt with the + strand GO data.

## Writing data file: circos/data/sm_minus_go.txt with the - strand GO data.

## Wrote the +/- config files.  Appending their inclusion to the master file.

## Returning the inner width: 0.88.  Use it as the outer for the next ring.

## Put the plots here
sm_first_heat <- circos_heatmap(sm_cfg, control_table, colname="deseq_logfc",
                                basename="control", outer=sm_plus_minus, width=0.05)

## Assuming the input is a dataframe.

## Writing data file: circos/data/smdeseq_logfc_heatmap.txt with the controldeseq_logfc column.

## Returning the inner width: 0.81.  Use it as the outer for the next ring.

##sm_eda_hist <- circos_hist(sm_cfg, urine_eda, colname="deseq_logfc",
##                           basename="ureda", outer=sm_first_hist, spacing=-0.05)
##sm_edd_hist <- circos_hist(sm_cfg, urine_edd, colname="deseq_logfc",
##                           basename="uredd", outer=sm_eda_hist, spacing=-0.05)
##sm_gcd_hist <- circos_hist(sm_cfg, urine_gcd, colname="deseq_logfc",
##                           basename="urgcd", outer=sm_edd_hist, spacing=-0.05)
##sm_pgl_hist <- circos_hist(sm_cfg, urine_pgl, colname="deseq_logfc",
##                           basename="urpgl", outer=sm_gcd_hist, spacing=-0.05)
##sm_zfw_hist <- circos_hist(sm_cfg, urine_zfw, colname="deseq_logfc",
##                           basename="urzfw", outer=sm_pgl_hist, spacing=-0.05)
##ex_eda_hist <- circos_hist(sm_cfg, exudate_eda, colname="deseq_logfc",
##                           basename="exeda", outer=sm_zfw_hist, spacing=-0.05)
##ex_edd_hist <- circos_hist(sm_cfg, exudate_edd, colname="deseq_logfc",
##                           basename="exedd", outer=ex_eda_hist, spacing=-0.05)
##ex_gcd_hist <- circos_hist(sm_cfg, exudate_gcd, colname="deseq_logfc",
##                           basename="exgcd", outer=ex_edd_hist, spacing=-0.05)
##ex_pgl_hist <- circos_hist(sm_cfg, exudate_pgl, colname="deseq_logfc",
##                           basename="expgl", outer=ex_gcd_hist, spacing=-0.05)
##ex_zfw_hist <- circos_hist(sm_cfg, exudate_zfw, colname="deseq_logfc",
##                           basename="exzfw", outer=ex_pgl_hist, spacing=-0.05)
sm_finish <- circos_suffix(sm_cfg)
sm_made <- circos_make(sm_cfg, target="sm")

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))