L.panamensis 201909: Antimonial Response Sample Estimation.

1 RNA sequencing of L. panamensis: Antimonial responses.

This document seeks to explore the differences in samples which did and did-not respond to drug treatment during infection.

2 Antimonial host responses

Extract the antimonial samples from the experiment.

ant <- subset_expt(hs_cds_expt, subset="condition=='respond_nil'|condition=='respond_inf'|condition=='fail_nil'|condition=='fail_inf'")

## Using a subset expression.

## There were 42, now there are 12 samples.

ant$colors <- gsub(pattern="CD960C", replacement="005500", x=ant$colors)
ant$colors <- gsub(pattern="AA7A1A", replacement="880000", x=ant$colors)
ant$colors <- gsub(pattern="886E3E", replacement="0000EE", x=ant$colors)
ant$colors <- gsub(pattern="666666", replacement="777777", x=ant$colors)

2.1 Graph antimonial host metrics

ant_met <- suppressMessages(graph_metrics(ant))

Lets take a look at the data before normalization.

ant_met$libsize

ant_met$nonzero

3 Normalize antimonial host samples

Perform an initial sample normalization and see how they look.

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

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

## log2(cpm(quant(cbcb(data))))

## It will save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep libsizes in mind
##  when invoking limma.  The appropriate libsize is non-log(cpm(normalized)).
##  This is most likely kept at:
##  'new_expt$normalized$intermediate_counts$normalization$libsizes'
##  A copy of this may also be found at:
##  new_expt$best_libsize

## 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: cbcb

## Removing 6998 low-count genes (11849 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: not doing batch correction.

anorm_met <- graph_metrics(anorm)

## 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.

## Plotting a CV plot.

## Naively calculating coefficient of variation/dispersion with respect to condition.

## Finished calculating dispersion estimates.

## Plotting the representation of the top-n genes.

## Plotting the expression of the top-n PC loaded genes.

## Printing a color to condition legend.

Do these samples segregate in ways which make sense?

norm_pca <- plot_pca(anorm)
norm_pca$plot

knitr::kable(norm_pca$table)

	sampleid	condition	batch	batch_int	colors	labels	PC1	PC2	pc_1	pc_2	pc_3	pc_4	pc_5	pc_6	pc_7	pc_8	pc_9	pc_10	pc_11
hpgl0315	HPGL0315	respond_nil	d202	1	#8068AE	HPGL0315	-0.0338	-0.6025	-0.0338	-0.6025	0.1303	-0.2138	0.1463	-0.2340	0.1992	-0.2157	0.2115	-0.5258	0.0791
hpgl0316	HPGL0316	respond_inf	d202	1	#D03792	HPGL0316	0.0290	-0.3744	0.0290	-0.3744	-0.4013	-0.0489	0.2854	-0.3512	-0.0279	0.2744	-0.2251	0.5296	0.0106
hpgl0317	HPGL0317	respond_nil	d209	2	#8068AE	HPGL0317	0.1650	-0.0435	0.1650	-0.0435	0.3056	0.3768	-0.3640	-0.2161	-0.3055	0.1443	0.1904	0.1009	0.5589
hpgl0318	HPGL0318	respond_inf	d209	2	#D03792	HPGL0318	0.2562	0.1285	0.2562	0.1285	-0.1629	0.3698	-0.4091	-0.3222	0.3184	-0.2234	-0.1481	-0.0842	-0.4687
hpgl0319	HPGL0319	fail_nil	d218	3	#A66753	HPGL0319	0.0351	-0.3389	0.0351	-0.3389	0.1651	0.2846	0.1072	0.5343	-0.3611	-0.1955	0.1263	0.1934	-0.4164
hpgl0320	HPGL0320	fail_inf	d218	3	#7FA718	HPGL0320	0.1647	-0.0126	0.1647	-0.0126	-0.3753	0.1494	-0.0171	0.5871	0.3742	0.2216	-0.1444	-0.2110	0.3561
hpgl0321	HPGL0321	fail_nil	d119	4	#A66753	HPGL0321	0.3916	0.1902	0.3916	0.1902	0.3407	-0.3311	0.0911	-0.0069	-0.1481	0.5585	-0.1577	-0.2289	-0.2863
hpgl0322	HPGL0322	fail_inf	d119	4	#7FA718	HPGL0322	0.4296	0.3207	0.4296	0.3207	0.0206	-0.3733	0.1964	0.0381	0.0453	-0.5583	0.1086	0.2799	0.2135
hpgl0640	HPGL0640	fail_nil	d1025	5	#A66753	HPGL0640	-0.4513	-0.0217	-0.4513	-0.0217	0.2727	-0.3649	-0.4382	0.1494	0.3733	0.1025	0.0788	0.3615	-0.0628
hpgl0641	HPGL0641	fail_inf	d1025	5	#7FA718	HPGL0641	-0.2927	0.1332	-0.2927	0.1332	-0.4542	-0.2947	-0.3076	0.0027	-0.5700	-0.1275	-0.1183	-0.2653	0.0050
hpgl0642	HPGL0642	respond_nil	d1018	6	#8068AE	HPGL0642	-0.4142	0.2290	-0.4142	0.2290	0.3286	0.2494	0.3634	-0.0673	0.0287	-0.1770	-0.5732	-0.0942	0.1276
hpgl0643	HPGL0643	respond_inf	d1018	6	#D03792	HPGL0643	-0.2793	0.3920	-0.2793	0.3920	-0.1698	0.1966	0.3462	-0.1138	0.0735	0.1960	0.6513	-0.0560	-0.1165

write.csv(file=glue::glue("excel/antimonial_pca_norm_table-v{ver}.csv"),
          norm_pca$table)

anorm_met$corheat

3.1 Batch normalize antimonial host samples

The batch effect is pretty severe, what happens if we try combat on it?

abatch_combat <- sm(normalize_expt(ant, filter=TRUE, norm="quant", batch="combat"))
abatch_scale <- sm(normalize_expt(ant, filter=TRUE, norm="quant", batch="combat_scale"))
abatch_sva <- sm(normalize_expt(ant, filter=TRUE, norm="quant", batch="svaseq", convert="cpm"))
abatch_pca <- plot_pca(abatch_scale)
abatch_pca$plot

knitr::kable(abatch_pca$table)

	sampleid	condition	batch	batch_int	colors	labels	PC1	PC2	pc_1	pc_2	pc_3	pc_4	pc_5	pc_6	pc_7	pc_8	pc_9	pc_10	pc_11
hpgl0315	HPGL0315	respond_nil	d202	1	#8068AE	HPGL0315	-0.3774	0.2881	-0.3774	0.2881	-0.1487	0.4487	0.3389	-0.0322	-0.0374	-0.1513	-0.3670	-0.3888	-0.2044
hpgl0316	HPGL0316	respond_inf	d202	1	#D03792	HPGL0316	-0.1377	-0.2621	-0.1377	-0.2621	0.1236	-0.4758	-0.3688	0.0316	0.4082	-0.0960	-0.3680	-0.3221	-0.1882
hpgl0317	HPGL0317	respond_nil	d209	2	#8068AE	HPGL0317	-0.4103	-0.4970	-0.4103	-0.4970	0.3046	0.0760	0.2029	0.1918	-0.1735	0.3255	0.3140	0.1158	-0.2769
hpgl0318	HPGL0318	respond_inf	d209	2	#D03792	HPGL0318	-0.1065	0.4579	-0.1065	0.4579	-0.3220	-0.1205	-0.1105	-0.2202	0.3602	0.4080	0.3574	0.1664	-0.2552
hpgl0319	HPGL0319	fail_nil	d218	3	#A66753	HPGL0319	0.1198	0.2314	0.1198	0.2314	0.3218	0.2202	-0.5008	-0.0235	-0.3830	0.3835	-0.3525	0.1397	0.0888
hpgl0320	HPGL0320	fail_inf	d218	3	#7FA718	HPGL0320	0.3896	-0.2161	0.3896	-0.2161	-0.2836	-0.1892	0.5043	0.0106	0.0254	0.3283	-0.4241	0.2195	0.1054
hpgl0321	HPGL0321	fail_nil	d119	4	#A66753	HPGL0321	0.1396	-0.3143	0.1396	-0.3143	-0.4978	0.0592	-0.2508	-0.1714	-0.3503	0.0126	0.2930	-0.4535	0.2013
hpgl0322	HPGL0322	fail_inf	d119	4	#7FA718	HPGL0322	0.3777	0.2525	0.3777	0.2525	0.4887	-0.0526	0.2697	0.1485	0.1602	0.0462	0.3115	-0.4407	0.2343
hpgl0640	HPGL0640	fail_nil	d1025	5	#A66753	HPGL0640	0.1264	0.1341	0.1264	0.1341	0.1541	-0.3528	0.1303	-0.3912	-0.4254	-0.4681	0.0757	0.2027	-0.3429
hpgl0641	HPGL0641	fail_inf	d1025	5	#7FA718	HPGL0641	0.3960	-0.0986	0.3960	-0.0986	-0.1283	0.3975	-0.1947	0.4460	0.2125	-0.3522	0.0916	0.2524	-0.3124
hpgl0642	HPGL0642	respond_nil	d1018	6	#8068AE	HPGL0642	-0.3682	0.2365	-0.3682	0.2365	-0.1727	-0.3045	-0.0131	0.5093	-0.1530	-0.2157	0.0411	0.2196	0.4725
hpgl0643	HPGL0643	respond_inf	d1018	6	#D03792	HPGL0643	-0.1489	-0.2125	-0.1489	-0.2125	0.1601	0.2939	-0.0074	-0.4994	0.3562	-0.2207	0.0274	0.2890	0.4776

write.csv(file=glue::glue("excel/antimonial_pca_batch_table-v{ver}.csv"),
          abatch_pca$table)

asva_pca <- plot_pca(abatch_sva)
asva_pca$plot

knitr::kable(asva_pca$table)

	sampleid	condition	batch	batch_int	colors	labels	PC1	PC2	pc_1	pc_2	pc_3	pc_4	pc_5	pc_6	pc_7	pc_8	pc_9	pc_10	pc_11
hpgl0315	HPGL0315	respond_nil	d202	1	#8068AE	HPGL0315	-0.1724	-0.1313	-0.1724	-0.1313	0.0850	0.3823	0.2579	0.1408	0.1885	0.3931	0.3326	-0.1472	0.5546
hpgl0316	HPGL0316	respond_inf	d202	1	#D03792	HPGL0316	0.1530	-0.4233	0.1530	-0.4233	0.1281	-0.0442	-0.3911	-0.2939	-0.3162	-0.2511	-0.2465	-0.0906	0.4737
hpgl0317	HPGL0317	respond_nil	d209	2	#8068AE	HPGL0317	-0.3327	0.3810	-0.3327	0.3810	0.4853	-0.3070	-0.4018	-0.1349	0.1432	0.0344	0.2918	0.2111	0.0027
hpgl0318	HPGL0318	respond_inf	d209	2	#D03792	HPGL0318	0.4605	0.3580	0.4605	0.3580	0.4121	0.1045	0.4365	-0.1383	0.0418	0.0189	-0.3998	0.1530	0.0273
hpgl0319	HPGL0319	fail_nil	d218	3	#A66753	HPGL0319	-0.1350	0.4275	-0.1350	0.4275	-0.4226	0.1166	-0.2395	0.4566	0.1241	-0.2387	-0.3552	0.0406	0.2396
hpgl0320	HPGL0320	fail_inf	d218	3	#7FA718	HPGL0320	0.3112	0.1516	0.3112	0.1516	-0.3119	-0.3970	0.1437	0.1468	-0.5456	0.1669	0.3898	0.1324	0.0684
hpgl0321	HPGL0321	fail_nil	d119	4	#A66753	HPGL0321	-0.4615	-0.4070	-0.4615	-0.4070	0.0722	-0.0268	0.2408	0.2140	-0.2039	0.0634	-0.2710	0.5076	-0.2274
hpgl0322	HPGL0322	fail_inf	d119	4	#7FA718	HPGL0322	0.2039	-0.1102	0.2039	-0.1102	-0.4058	0.2749	-0.0911	-0.4357	0.3578	-0.1094	0.2424	0.4482	-0.1577
hpgl0640	HPGL0640	fail_nil	d1025	5	#A66753	HPGL0640	-0.3903	0.1696	-0.3903	0.1696	-0.2955	-0.1252	0.1962	-0.5260	-0.0806	0.1818	-0.2153	-0.4568	-0.1512
hpgl0641	HPGL0641	fail_inf	d1025	5	#7FA718	HPGL0641	0.1354	-0.3103	0.1354	-0.3103	0.0088	-0.5378	0.2112	0.2144	0.5261	-0.2638	0.0486	-0.2650	-0.0562
hpgl0642	HPGL0642	respond_nil	d1018	6	#8068AE	HPGL0642	-0.0559	0.0370	-0.0559	0.0370	0.1855	0.4169	0.0788	0.1290	-0.2734	-0.5351	0.3236	-0.3073	-0.3475
hpgl0643	HPGL0643	respond_inf	d1018	6	#D03792	HPGL0643	0.2839	-0.1427	0.2839	-0.1427	0.0588	0.1429	-0.4415	0.2272	0.0382	0.5395	-0.1410	-0.2260	-0.4264

write.csv(file=glue::glue("excel/antimonial_pca_sva_table-v{ver}.csv"),
          asva_pca$table)

abatch_met <- graph_metrics(abatch_scale)

## 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 data are negative.  We are on log scale, setting them to 0.

## Changed 1929 negative features.

## Some entries are 0.  We are on log scale, adding 1 to the data.

## Changed 1929 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 data are negative.  We are on log scale, setting them to 0.5.

## Changed 1929 negative features.

## Plotting a CV plot.

## Naively calculating coefficient of variation/dispersion with respect to condition.

## Finished calculating dispersion estimates.

## Plotting the representation of the top-n genes.

## Plotting the expression of the top-n PC loaded genes.

## Printing a color to condition legend.

The PCA plot of the combat adjusted data suggests that it might actually work out ok. Let us look at the other metrics.

abatch_met$density

## The quantile normalization smashed the differences in distribution
abatch_met$corheat

abatch_met$smc

4 Differential Expression of antimonial host samples

anti_host_combat_de <- sm(all_pairwise(abatch_scale, force=TRUE, model_batch=FALSE))

anti_host_combat_de$comparison$heat
limma_table <- anti_host_combat_de$limma$all_tables$respond_inf_vs_fail_inf
de_plots <- extract_de_plots(anti_host_combat_de, table="respond_inf_vs_fail_inf")
de_plots$ma$plot

antimony_contrasts <- list(
    "fail_respond_inf" = c("fail_inf", "respond_inf"),
    "fail_respond_nil" = c("fail_nil", "respond_nil"),
    "fail" = c("fail_inf", "fail_nil"),
    "respond" = c("respond_inf", "respond_nil"))
antimony_tables <- sm(combine_de_tables(
  anti_host_combat_de,
  excel=glue::glue("excel/antimony_combat_tables-v{ver}.xlsx"),
  keepers=antimony_contrasts, annot_df=hs_annotations))

##antimony_tables$plots[["fail"]]
antimony_sig_tables <- sm(extract_significant_genes(
  antimony_tables,
  excel=glue::glue("excel/antimony_significant_genes-v{ver}.xlsx"),
  according_to="all"))

anti_host_sva <- sm(all_pairwise(ant, model_batch="svaseq", filter=TRUE))

anti_host_tables <- sm(combine_de_tables(
  anti_host_sva,
  excel=glue::glue("excel/antimony_sva_tables-v{ver}.xlsx"),
  keepers=antimony_contrasts))

anti_host_plots <- extract_de_plots(anti_host_tables, table="fail", type="deseq",
                                    label=5)
anti_host_plots$volcano$plot

anti_comparison <- compare_de_results(antimony_tables, anti_host_tables)

## Testing method: limma.

## Adding method: limma to the set.

## Testing method: deseq.

## Adding method: deseq to the set.

## Testing method: edger.

## Adding method: edger to the set.

## Testing method: ebseq.

## Adding method: ebseq to the set.

## Testing method: basic.

## Adding method: basic to the set.

## Warning in cor(x = fs[["x"]], y = fs[["y"]], method = cor_method): the
## standard deviation is zero

## Warning in cor(x = fs[["x"]], y = fs[["y"]], method = cor_method): the
## standard deviation is zero

anti_comparison$logfc

## fail_respond_inf fail_respond_nil             fail          respond 
##           0.2371           0.1971           0.6620           0.6337

5 Try ontology searches with the host data.

upgenes_fail_respond_infected <- antimony_sig_tables[["deseq"]][["ups"]][["fail_respond_inf"]]
fail_respond_inf_up_gp <- simple_gprofiler(upgenes_fail_respond_infected, first_col="deseq_logfc")

## Performing gProfiler GO search of 622 genes against hsapiens.

## GO search found 187 hits.

## Performing gProfiler KEGG search of 622 genes against hsapiens.

## KEGG search found 7 hits.

## Performing gProfiler REAC search of 622 genes against hsapiens.

## REAC search found 31 hits.

## Performing gProfiler MI search of 622 genes against hsapiens.

## MI search found 0 hits.

## Performing gProfiler TF search of 622 genes against hsapiens.

## TF search found 84 hits.

## Performing gProfiler CORUM search of 622 genes against hsapiens.

## CORUM search found 1 hits.

## Performing gProfiler HP search of 622 genes against hsapiens.

## HP search found 1 hits.

fail_respond_inf_up_gp$pvalue_plots$mfp_plot_over

fail_respond_inf_up_gp$pvalue_plots$kegg_plot_over

fail_respond_inf_up_gp$pvalue_plots$tf_plot_over

fail_respond_inf_up_gp$pvalue_plots$reactome_plot_over

fail_respond_inf_up_gp$pvalue_plots$bpp_plot_over

dngenes_fail_respond_infected <- antimony_sig_tables[["deseq"]][["downs"]][["fail_respond_inf"]]
fail_respond_inf_dn_gp <- simple_gprofiler(dngenes_fail_respond_infected, first_col="deseq_logfc")

## Performing gProfiler GO search of 1329 genes against hsapiens.

## GO search found 213 hits.

## Performing gProfiler KEGG search of 1329 genes against hsapiens.

## KEGG search found 8 hits.

## Performing gProfiler REAC search of 1329 genes against hsapiens.

## REAC search found 12 hits.

## Performing gProfiler MI search of 1329 genes against hsapiens.

## MI search found 0 hits.

## Performing gProfiler TF search of 1329 genes against hsapiens.

## TF search found 409 hits.

## Performing gProfiler CORUM search of 1329 genes against hsapiens.

## CORUM search found 1 hits.

## Performing gProfiler HP search of 1329 genes against hsapiens.

## HP search found 18 hits.

fail_respond_inf_dn_gp$pvalue_plots$mfp_plot_over

fail_respond_inf_dn_gp$pvalue_plots$kegg_plot_over

fail_respond_inf_dn_gp$pvalue_plots$reactome_plot_over

fail_respond_inf_dn_gp$pvalue_plots$bpp_plot_over

upgenes_fail_respond_uninfected <- antimony_sig_tables[["deseq"]][["ups"]][["fail_respond_nil"]]
fail_respond_uninf_up_gp <- simple_gprofiler(upgenes_fail_respond_uninfected, first_col="deseq_logfc")

## Performing gProfiler GO search of 1033 genes against hsapiens.

## GO search found 366 hits.

## Performing gProfiler KEGG search of 1033 genes against hsapiens.

## KEGG search found 15 hits.

## Performing gProfiler REAC search of 1033 genes against hsapiens.

## REAC search found 44 hits.

## Performing gProfiler MI search of 1033 genes against hsapiens.

## MI search found 0 hits.

## Performing gProfiler TF search of 1033 genes against hsapiens.

## TF search found 135 hits.

## Performing gProfiler CORUM search of 1033 genes against hsapiens.

## CORUM search found 3 hits.

## Performing gProfiler HP search of 1033 genes against hsapiens.

## HP search found 2 hits.

fail_respond_uninf_up_gp$pvalue_plots$mfp_plot_over

fail_respond_uninf_up_gp$pvalue_plots$kegg_plot_over

fail_respond_uninf_up_gp$pvalue_plots$tf_plot_over

fail_respond_uninf_up_gp$pvalue_plots$reactome_plot_over

fail_respond_uninf_up_gp$pvalue_plots$bpp_plot_over

dngenes_fail_respond_uninfected <- antimony_sig_tables[["deseq"]][["downs"]][["fail_respond_nil"]]
fail_respond_uninf_dn_gp <- simple_gprofiler(dngenes_fail_respond_uninfected, first_col="deseq_logfc")

## Performing gProfiler GO search of 658 genes against hsapiens.

## GO search found 136 hits.

## Performing gProfiler KEGG search of 658 genes against hsapiens.

## KEGG search found 5 hits.

## Performing gProfiler REAC search of 658 genes against hsapiens.

## REAC search found 7 hits.

## Performing gProfiler MI search of 658 genes against hsapiens.

## MI search found 0 hits.

## Performing gProfiler TF search of 658 genes against hsapiens.

## TF search found 151 hits.

## Performing gProfiler CORUM search of 658 genes against hsapiens.

## CORUM search found 0 hits.

## Performing gProfiler HP search of 658 genes against hsapiens.

## HP search found 0 hits.

fail_respond_uninf_dn_gp$pvalue_plots$mfp_plot_over

fail_respond_uninf_dn_gp$pvalue_plots$kegg_plot_over

fail_respond_uninf_dn_gp$pvalue_plots$tf_plot_over

fail_respond_uninf_dn_gp$pvalue_plots$reactome_plot_over

fail_respond_uninf_dn_gp$pvalue_plots$bpp_plot_over

6 Try a search for cell types and GSEA

ant_filt <- normalize_expt(ant, filter=TRUE)

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

## cbcb(data)

## It will save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep libsizes in mind
##  when invoking limma.  The appropriate libsize is non-log(cpm(normalized)).
##  This is most likely kept at:
##  'new_expt$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: cbcb

## Removing 6998 low-count genes (11849 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.

fail_gsc <- make_gsc_from_ids(first_ids=rownames(upgenes_fail_respond_infected),
                              second_ids=rownames(dngenes_fail_respond_infected),
                              study_name="antimony_response",
                              category_name="response")

## Converting the rownames() of the expressionset to ENTREZID.

fail_gsva <- simple_gsva(ant_filt)

## Converting the rownames() of the expressionset to ENTREZID.

## Before conversion, the expressionset has 11849 entries.

## After conversion, the expressionset has 11900 entries.

## Mapping identifiers between gene sets and feature names
## Estimating GSVA scores for 2990 gene sets.
## Computing observed enrichment scores
## Estimating ECDFs with Poisson kernels
## Allocating cluster
## Estimating enrichment scores in parallel
## Taking diff of max KS.
## Cleaning up

expt <- fail_gsva$expt
ex <- expt$expressionset
reactome_idx <- grep(x=rownames(exprs(ex)), pattern="^REACTOME")
reactome_ids <- rownames(exprs(ex))[reactome_idx]
ex_rownames <- rownames(exprs(ex))
fd_rownames <- rownames(fData(ex))
extra <- fd_rownames %in% ex_rownames
fData(ex) <- fData(ex)[extra, ]
expt$expressionset <- ex
reactome <- exclude_genes_expt(expt, method="keep", ids=reactome_ids)

## Before removal, there were 2990 entries.

## Now there are 428 entries.

## Percent kept: 21.055, -38.837, 6.811, 19.056, 2.511, 5.844, 15.271, 12.715, -11.998, -24.376, -22.480, 18.239

## Percent removed: 78.945, 138.837, 93.189, 80.944, 97.489, 94.156, 84.729, 87.285, 111.998, 124.376, 122.480, 81.761

row_labels <- gsub(x=rownames(exprs(reactome)), pattern="^([[:alpha:]]+_)?(.*)$", replacement="\\2")
gsva_heat <- plot_sample_heatmap(reactome, row_label=row_labels)

ant_xcell <- simple_xcell(ant)

## The biomart annotations file already exists, loading from it.

## xCell strongly perfers rpkm values, re-normalizing now.

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

## rpkm(data)

## It will save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep libsizes in mind
##  when invoking limma.  The appropriate libsize is non-log(cpm(normalized)).
##  This is most likely kept at:
##  'new_expt$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 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 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 rpkm.

## Step 4: not transforming the data.

## Step 5: not doing batch correction.

## Loading required namespace: xCell

ant_xcell$heatmap

7 Repeat using the parasite data

## Reread the parasite sample sheet to play with other covariants
## Wow, from the parasite perspective, it seems they cluster much more tightly by strain
parasite_expt <- sm(create_expt(
  "sample_sheets/old/all_samples-lpanamensis.xlsx",
  gene_info=lp_annotations))
antipara_expt <- subset_expt(parasite_expt, subset="condition=='ant_good'|condition=='ant_bad'")

## Using a subset expression.

## There were 33, now there are 6 samples.

antipara_expt <- set_expt_batches(antipara_expt, fact="strain")
antipara_metrics <- sm(graph_metrics(antipara_expt))

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

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

## log2(cpm(quant(cbcb(data))))

## It will save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep libsizes in mind
##  when invoking limma.  The appropriate libsize is non-log(cpm(normalized)).
##  This is most likely kept at:
##  'new_expt$normalized$intermediate_counts$normalization$libsizes'
##  A copy of this may also be found at:
##  new_expt$best_libsize

## 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: cbcb

## Removing 0 low-count genes (8841 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 93 values equal to 0, adding 1 to the matrix.

## Step 5: not doing batch correction.

plot_pca(antipara_norm)$plot

antipara_sva <- normalize_expt(antipara_expt,
                               transform="log2",
                               filter="simple",
                               batch="svaseq")

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

## log2(svaseq(simple(data)))

## It will save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep libsizes in mind
##  when invoking limma.  The appropriate libsize is non-log(cpm(normalized)).
##  This is most likely kept at:
##  'new_expt$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: simple

## Removing 114 low-count genes (8727 remaining).

## Step 2: not normalizing the data.

## Step 3: not converting the data.

## Step 4: transforming the data with log2.

## transform_counts: Found 7670 values equal to 0, adding 1 to the matrix.

## Step 5: doing batch correction with svaseq.

## Note to self:  If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.

## Passing off to all_adjusters.

## batch_counts: Before batch/surrogate estimation, 40937 entries are x>1: 78.2%.

## batch_counts: Before batch/surrogate estimation, 7670 entries are x==0: 14.6%.

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

## Attempting svaseq estimation with 1 surrogates.

## There are 15939 (30.4%) elements which are < 0 after batch correction.

plot_pca(antipara_sva)$plot

7.0.1 Also try antimonials

human_expt <- create_expt(file="all_samples-hsapiens.xlsx")
antimonial_expt <- expt_subset(human_expt, subset="condition=='respond_nil'|condition=='respond_inf'|condition=='fail_nil'|condition=='fail_inf'")
antimonial_expt$colors <- c("blue","blue","red","red","black","black","green","green","red","green","blue","black")
antimonial_metrics <- graph_metrics(antimonial_expt)
antimonial_metrics$libsize
antimonial_norm <- normalize_expt(antimonial_expt, transform="log2", convert="cpm", filter_low=TRUE, norm="quant")
antimonialnorm_metrics <- graph_metrics(antimonial_norm)
antimonialnorm_metrics$pcaplot
antimonialnorm_metrics$corheat
antimonialnorm_metrics$disheat
antimonial_nb <- normalize_expt(antimonial_expt, transform="log2", convert="cpm", filter_low=TRUE, norm="quant", batch="combat")
antimonialnb_metrics <- graph_metrics(antimonial_nb)
antimonialnb_metrics$pcaplot
pp("images/antimonial_batch_corheat.png")
antimonialnb_metrics$corheat
dev.off()

## Reread the parasite sample sheet to play with other covariants
## Wow, from the parasite perspective, it seems they cluster much more tightly by strain
parasite_expt <- create_expt(file="all_samples-lpanamensis_patientbatch.xlsx")
antipara_expt <-  expt_subset(parasite_expt, subset="condition=='ant_good'|condition=='ant_bad'")
antipara_metrics <- graph_metrics(antipara_expt)
antipara_metrics$pcaplot
antipara_norm <- normalize_expt(antipara_expt, transform="log2", convert="cpm", filter_low=TRUE, norm="quant", batch="combat")
antiparanorm_metrics <- graph_metrics(antipara_norm)
antiparanorm_metrics$pcaplot
antiparanorm_metrics$corheat

library(Biobase)
library(Heatplus)
infection_norm <- sm(normalize_expt(parasite_expt, transform="raw",
                                    convert="cpm", filter="pofa",
                                    norm="quant", batch="sva"))
infect_data <- exprs(infection_norm$expressionset)
correlations <- hpgl_cor(infect_data)
distances <- as.matrix(dist(t(infect_data), method="mink"))

design <- as.data.frame(infection_norm$design)
rownames(correlations) <- paste0(rownames(correlations), "_", as.character(design$condition))
colnames(correlations) <- paste0(colnames(correlations), "_", as.character(design$batch))

col_data <- as.data.frame(pData(infection_norm)[, c("strain", "condition")])
row_data <- design[, c("batch","condition")]
colnames(col_data) <- c("strain", "selfheal")
colnames(row_data) <- c("volunteer","selfheal")
row_data$selfheal <- as.numeric(row_data$selfheal)
row_data$selfheal <- ifelse(row_data$selfheal == 2, TRUE, FALSE)
myannot <- list(Col=list(data=col_data), Row=list(data=row_data))
mylabels <- list(Row=list(nrow=10), Col=list(nrow=10))

map1 <- annHeatmap2(distances,
                    cluster=list(cuth=7000),
                    ann=myannot,
                    labels=mylabels)
## 3 numbers, dendrogram, plot, annotation width/height
plot(map1)

8 Something silly

words <- as.character(unlist(as.data.frame(read.csv("sample_sheets/testing.csv"))))
numbers <- gsub(x=words, pattern="[[:punct:]]", replacement="")
numbers <- gsub(x=numbers, pattern="[[:alpha:]]", replacement="")
numbers <- as.factor(as.numeric(unlist(strsplit(x=numbers, split=""))))

## Warning in strsplit(x = numbers, split = ""): input string 1914 is invalid
## UTF-8

## Warning in strsplit(x = numbers, split = ""): input string 1915 is invalid
## UTF-8

## Warning in strsplit(x = numbers, split = ""): input string 1916 is invalid
## UTF-8

## Warning in strsplit(x = numbers, split = ""): input string 1917 is invalid
## UTF-8

numbers <- numbers[!is.na(numbers)]

9 Save the results.

message(paste0("This is hpgltools commit: ", get_git_commit()))

## If you wish to reproduce this exact build of hpgltools, invoke the following:

## > git clone http://github.com/abelew/hpgltools.git

## > git reset c2881f6d97e1ec981fd1481cf46d6bc875fac423

## This is hpgltools commit: Tue Oct 22 10:22:30 2019 -0400: c2881f6d97e1ec981fd1481cf46d6bc875fac423

this_save <- paste0(gsub(pattern="\\.Rmd", replace="", x=rmd_file), "-v", ver, ".rda.xz")
message(paste0("Saving to ", this_save))

## Saving to 02_antimonial_estimation_20190914-v20190914.rda.xz

##tmp <- sm(saveme(filename=this_save))
pander::pander(sessionInfo())

R version 3.6.1 (2019-07-05)

Platform: x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=en_US.UTF-8, LC_NUMERIC=C, LC_TIME=en_US.UTF-8, LC_COLLATE=en_US.UTF-8, LC_MONETARY=en_US.UTF-8, LC_MESSAGES=en_US.UTF-8, LC_PAPER=en_US.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.UTF-8 and LC_IDENTIFICATION=C

attached base packages: parallel, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: Heatplus(v.2.31.0), GSVAdata(v.1.21.0), org.Hs.eg.db(v.3.8.2), foreach(v.1.4.7), ruv(v.0.9.7.1), hpgltools(v.1.0), Biobase(v.2.45.1) and BiocGenerics(v.0.31.6)

loaded via a namespace (and not attached): snow(v.0.4-3), backports(v.1.1.5), Hmisc(v.4.2-0), BiocFileCache(v.1.9.1), plyr(v.1.8.4), lazyeval(v.0.2.2), GSEABase(v.1.47.0), splines(v.3.6.1), BiocParallel(v.1.19.4), GenomeInfoDb(v.1.21.2), ggplot2(v.3.2.1), sva(v.3.33.1), digest(v.0.6.22), htmltools(v.0.4.0), gdata(v.2.18.0), magrittr(v.1.5), checkmate(v.1.9.4), memoise(v.1.1.0), cluster(v.2.1.0), doParallel(v.1.0.15), openxlsx(v.4.1.0.1), limma(v.3.41.18), fastcluster(v.1.1.25), Biostrings(v.2.53.2), annotate(v.1.63.0), matrixStats(v.0.55.0), askpass(v.1.1), prettyunits(v.1.0.2), colorspace(v.1.4-1), blob(v.1.2.0), rappdirs(v.0.3.1), ggrepel(v.0.8.1), xfun(v.0.10), dplyr(v.0.8.3), crayon(v.1.3.4), RCurl(v.1.95-4.12), graph(v.1.63.0), genefilter(v.1.67.1), zeallot(v.0.1.0), survival(v.2.44-1.1), iterators(v.1.0.12), glue(v.1.3.1), gtable(v.0.3.0), zlibbioc(v.1.31.0), XVector(v.0.25.0), DelayedArray(v.0.11.8), DEoptimR(v.1.0-8), scales(v.1.0.0), DBI(v.1.0.0), edgeR(v.3.27.13), Rcpp(v.1.0.2), xtable(v.1.8-4), progress(v.1.2.2), htmlTable(v.1.13.2), xCell(v.1.1.0), foreign(v.0.8-72), bit(v.1.1-14), OrganismDbi(v.1.27.1), preprocessCore(v.1.47.1), Formula(v.1.2-3), stats4(v.3.6.1), GSVA(v.1.33.4), htmlwidgets(v.1.5.1), httr(v.1.4.1), gplots(v.3.0.1.1), RColorBrewer(v.1.1-2), acepack(v.1.4.1), pkgconfig(v.2.0.3), XML(v.3.98-1.20), nnet(v.7.3-12), dbplyr(v.1.4.2), locfit(v.1.5-9.1), tidyselect(v.0.2.5), labeling(v.0.3), rlang(v.0.4.1), reshape2(v.1.4.3), later(v.1.0.0), AnnotationDbi(v.1.47.1), munsell(v.0.5.0), tools(v.3.6.1), RSQLite(v.2.1.2), evaluate(v.0.14), stringr(v.1.4.0), fastmap(v.1.0.1), yaml(v.2.2.0), knitr(v.1.25), bit64(v.0.9-7), pander(v.0.6.3), zip(v.2.0.4), robustbase(v.0.93-5), caTools(v.1.17.1.2), Vennerable(v.3.1.0.9000), purrr(v.0.3.3), RBGL(v.1.61.0), nlme(v.3.1-141), mime(v.0.7), pracma(v.2.2.5), biomaRt(v.2.41.9), shinythemes(v.1.1.2), compiler(v.3.6.1), rstudioapi(v.0.10), curl(v.4.2), tibble(v.2.1.3), geneplotter(v.1.63.0), stringi(v.1.4.3), highr(v.0.8), GenomicFeatures(v.1.37.4), lattice(v.0.20-38), Matrix(v.1.2-17), vctrs(v.0.2.0), pillar(v.1.4.2), BiocManager(v.1.30.8), data.table(v.1.12.6), bitops(v.1.0-6), corpcor(v.1.6.9), httpuv(v.1.5.2), rtracklayer(v.1.45.6), GenomicRanges(v.1.37.17), R6(v.2.4.0), latticeExtra(v.0.6-28), directlabels(v.2018.05.22), promises(v.1.1.0), KernSmooth(v.2.23-16), gridExtra(v.2.3), IRanges(v.2.19.17), codetools(v.0.2-16), MASS(v.7.3-51.4), gtools(v.3.8.1), assertthat(v.0.2.1), SummarizedExperiment(v.1.15.9), gProfileR(v.0.6.8), openssl(v.1.4.1), DESeq2(v.1.25.17), GenomicAlignments(v.1.21.7), Rsamtools(v.2.1.7), S4Vectors(v.0.23.25), GenomeInfoDbData(v.1.2.1), mgcv(v.1.8-29), hms(v.0.5.1), doSNOW(v.1.0.18), quadprog(v.1.5-7), grid(v.3.6.1), rpart(v.4.1-15), rmarkdown(v.1.16), Rtsne(v.0.15), shiny(v.1.4.0) and base64enc(v.0.1-3)