1 Sample Estimation, PBMC Infections: 20190205

Start out by extracting the relevant data and querying it to see the general quality.

This first block sets the names of the samples and colors. It also makes separate data sets for:

All features with infected and uninfected samples.
All features with only the infected samples.
Only CDS features with infected and uninfected samples.
Only CDS features with only the infected samples.

## Reread the sample sheet because I am fiddling with other possible surrogates (like strain)
## In fact, copy it to a separate sheet because these samples are a mess
hs_infection <- subset_expt(hs_expt, subset="experimentname=='infection'")

## There were 42, now there are 15 samples.

chosen_colors <- c("#009900","#990000", "#000099")
names(chosen_colors) <- c("uninf","chr","sh")
hs_uninf <- set_expt_colors(hs_infection, colors=chosen_colors)

## The new colors are a character, changing according to condition.

hs_inf <- subset_expt(hs_uninf, subset="condition!='uninf'")

## There were 15, now there are 12 samples.

uninf_newnames <- paste0(pData(hs_uninf)$label, "_", pData(hs_uninf)$donor)
hs_uninf <- set_expt_samplenames(hs_uninf, newnames=uninf_newnames)
inf_newnames <- paste0(pData(hs_inf)$label, "_", pData(hs_inf)$donor)
hs_inf <- set_expt_samplenames(hs_inf, newnames=inf_newnames)

hs_cds_infection <- subset_expt(hs_cds_expt, subset="experimentname=='infection'")

## There were 42, now there are 15 samples.

hs_cds_uninf <- set_expt_colors(hs_cds_infection, colors=chosen_colors)

## The new colors are a character, changing according to condition.

hs_cds_inf <- subset_expt(hs_cds_uninf, subset="condition!='uninf'")

## There were 15, now there are 12 samples.

hs_cds_uninf <- set_expt_samplenames(hs_cds_uninf, newnames=uninf_newnames)
hs_cds_inf <- set_expt_samplenames(hs_cds_inf, newnames=inf_newnames)
##infection_model_test <- model_test(hs_cds_infection$design)

hs_cds_removeboth <- subset_expt(hs_cds_uninf, subset="pathogenstrain!='s2272'")

## There were 15, now there are 15 samples.

hs_cds_removeboth <- subset_expt(hs_cds_removeboth, subset="pathogenstrain!='s2504'")

## There were 15, now there are 15 samples.

1.1 Generate plots describing the data

The following creates metric plots of the raw data.

hs_uninf_met <- sm(graph_metrics(hs_uninf))

hs_inf_met <- sm(graph_metrics(hs_inf))

hs_cds_uninf_met <- sm(graph_metrics(hs_cds_uninf))

hs_cds_inf_met <- sm(graph_metrics(hs_cds_inf))

hs_cds_re_met <- sm(graph_metrics(hs_cds_removeboth))

Now let us visualize some of these metrics for the data set of all features including the uninfected samples.

Start with the relative library sizes. Note that this includes all feature types.

hs_uninf_met$libsize

The picture is slightly different if we only look at coding sequences.

hs_cds_uninf_met$libsize

hs_cds_re_met$libsize

Look at the density of counts / feature for all samples. Use density plots and boxplots to view this information.

hs_uninf_met$density

hs_uninf_met$boxplot

## Wow there is a pretty big range in counts observed in this data!!

hs_cds_re_met$boxplot

hs_cds_uninf_met$density

hs_cds_uninf_met$boxplot

Now let us look at how the samples relate to each other via pairwise correlation heatmaps. Once again, show this first for all features, then only cds features.

hs_uninf_met$corheat

hs_cds_uninf_met$corheat

2 Write the experiments

Having looked at these metrics, now let us write out the results in 4 excel workbooks, representing the same 4 data sets.

excel_file <- glue::glue("excel/{rundate}_hs_data-infection_with_uninf-v{ver}.xlsx")
hs_uninf_data <- sm(write_expt(
  hs_uninf, norm="quant", violin=FALSE, convert="cpm",
  transform="log2", batch="pca", filter=TRUE,
  excel=excel_file))

excel_file <- glue::glue("excel/{rundate}_hs_data-infection_no_uninf-v{ver}.xlsx")
hs_inf_data <- sm(write_expt(
  hs_inf, norm="quant", violin=FALSE, convert="cpm",
  transform="log2", batch="pca", filter=TRUE,
  excel=excel_file))

excel_file <- glue::glue("excel/{rundate}_hs_cds_data-infection_with_uninf-v{ver}.xlsx")
hs_cds_uninf_data <- sm(
  write_expt(hs_cds_uninf, norm="quant", violin=FALSE, convert="cpm",
             transform="log2", batch="pca", filter=TRUE,
             excel=excel_file))

excel_file <- glue::glue("excel/{rundate}_hs_cds_data-infection_no_uninf-v{ver}.xlsx")
hs_cds_inf_data <- sm(
  write_expt(hs_cds_inf, norm="quant", violin=FALSE, convert="cpm",
             transform="log2", batch="pca", filter=TRUE,
             excel=excel_file))

2.0.1 Default normalization

Now perform the ‘default’ normalization we use in the lab and look again.

hs_uninf_cqf <- sm(normalize_expt(hs_uninf, convert="cpm", filter=TRUE, norm="quant"))
hs_uninf_met <- sm(graph_metrics(hs_uninf))

hs_inf_cqf <- sm(normalize_expt(hs_inf, convert="cpm", filter=TRUE, norm="quant"))
hs_inf_cqf_met <- sm(graph_metrics(hs_inf))

hs_inf_re_cqf <- sm(normalize_expt(hs_cds_removeboth, convert="cpm", filter=TRUE, norm="quant"))
hs_inf_re_cqf_met <- sm(graph_metrics(hs_inf_re_cqf))

hs_inf_re_scqf <- sm(normalize_expt(hs_cds_removeboth, convert="cpm",
                                    filter=TRUE, transform="log2", batch="svaseq"))
hs_inf_re_scqf_met <- sm(graph_metrics(hs_inf_re_scqf))

3 Figure 4

Construct figure 4, this should include the following panels:

Library sizes of pbmc data
PCA of log2(quant(data)), with uninfected
PCA of log2(quant(data)), without uninfected
TSNE of b
TSNE of c

pp(file="images/figure_4a.pdf")

## Going to write the image to: images/figure_4a.pdf when dev.off() is called.

hs_uninf_data$raw_libsize
dev.off()

## png 
##   2

pp(file="images/figure_4b.pdf")

## Going to write the image to: images/figure_4b.pdf when dev.off() is called.

hs_uninf_data$raw_scaled_pca
dev.off()

## png 
##   2

write.csv(hs_uninf_data$raw_scaled_pca_table, file="images/figure_4b.csv")
pp(file="images/figure_4c.pdf")

## Going to write the image to: images/figure_4c.pdf when dev.off() is called.

hs_inf_data$raw_scaled_pca
dev.off()

## png 
##   2

write.csv(hs_inf_data$raw_scaled_pca_table, file="images/figure_4c.csv")
pp(file="images/figure_4d.pdf")

## Going to write the image to: images/figure_4d.pdf when dev.off() is called.

hs_uninf_data$raw_tsne
dev.off()

## png 
##   2

pp(file="images/figure_4e.pdf")

## Going to write the image to: images/figure_4e.pdf when dev.off() is called.

hs_inf_data$raw_tsne
dev.off()

## png 
##   2

pp(file="images/figure_4a_cds.pdf")

## Going to write the image to: images/figure_4a_cds.pdf when dev.off() is called.

hs_cds_uninf_data$raw_libsize
dev.off()

## png 
##   2

pp(file="images/figure_4b_cds.pdf")

## Going to write the image to: images/figure_4b_cds.pdf when dev.off() is called.

hs_cds_uninf_data$raw_scaled_pca
dev.off()

## png 
##   2

write.csv(hs_cds_uninf_data$raw_scaled_pca_table, file="images/figure_4b_cds.csv")
pp(file="images/figure_4c_cds.pdf")

## Going to write the image to: images/figure_4c_cds.pdf when dev.off() is called.

hs_cds_inf_data$raw_scaled_pca
dev.off()

## png 
##   2

write.csv(hs_cds_inf_data$raw_scaled_pca_table, file="images/figure_4c_cds.csv")
pp(file="images/figure_4d_cds.pdf")

## Going to write the image to: images/figure_4d_cds.pdf when dev.off() is called.

hs_cds_uninf_data$raw_tsne
dev.off()

## png 
##   2

pp(file="images/figure_4e_cds.pdf")

## Going to write the image to: images/figure_4e_cds.pdf when dev.off() is called.

hs_cds_inf_data$raw_tsne
dev.off()

## png 
##   2

3.1 Start without the uninfected: no, patient, strain

Now let us try a few different ways of dealing with the batch effects/surrogate variables. In each case, I will use a PCA plot to see how the method changes the sample clustering.

3.1.1 PCA: No Batch correction

In this first iteration, we will log2(cpm(quant(filter()))) the data and leave the experimental parameters as the default: condition == the 6 strains, 3 chronic 3 self-healing batch == the three patients p107

## Start with the non-uninfected, no batch correction
hs_inf_lqcf <- sm(normalize_expt(hs_inf, filter=TRUE, convert="cpm",
                                 transform="log2", norm="quant"))
hs_pca_inf_lqcf <- plot_pca(hs_inf_lqcf)
hs_pca_inf_lqcf$plot

## 3 three patients are super obvious I think
## The patients 107/108 are on the left while 110 is on the right.
knitr::kable(hs_pca_inf_lqcf$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
chr_5430_d108	HPGL0631	chr	d108	1	#990000	HPGL0631	-0.1946	-0.4136	-0.1946	-0.4136	0.0541	-0.3363	0.0612	-0.0650	0.6781	0.0233	-0.3162	-0.0772	-0.1322
chr_5397_d108	HPGL0632	chr	d108	1	#990000	HPGL0632	-0.1647	-0.3877	-0.1647	-0.3877	0.0073	-0.1022	0.2906	-0.3099	-0.6296	0.3302	-0.1880	-0.0769	-0.0406
sh_1022_d108	HPGL0635	sh	d108	1	#000099	HPGL0635	-0.0715	-0.3280	-0.0715	-0.3280	0.0733	0.0264	0.0369	0.7699	-0.2015	-0.3288	0.0797	-0.1455	0.1660
sh_2189_d108	HPGL0636	sh	d108	1	#000099	HPGL0636	-0.1185	-0.3566	-0.1185	-0.3566	0.2201	0.4665	-0.4034	-0.2463	0.0875	0.0162	0.4298	0.3056	0.0033
chr_5430_d110	HPGL0651	chr	d110	2	#990000	HPGL0651	0.3558	-0.0317	0.3558	-0.0317	-0.3547	-0.0975	-0.2049	-0.2931	0.0042	-0.2284	-0.1096	-0.1164	0.6694
chr_5397_d110	HPGL0652	chr	d110	2	#990000	HPGL0652	0.3688	-0.0053	0.3688	-0.0053	-0.5102	0.2696	0.0622	0.1699	0.0059	0.0303	-0.2632	0.4104	-0.4198
sh_1022_d110	HPGL0655	sh	d110	2	#000099	HPGL0655	0.4480	0.1721	0.4480	0.1721	0.3911	-0.2974	0.1033	0.2004	0.0777	0.5080	0.1950	0.2466	0.1765
sh_2189_d110	HPGL0656	sh	d110	2	#000099	HPGL0656	0.4163	0.1035	0.4163	0.1035	0.2617	0.0940	0.1395	-0.2234	-0.0010	-0.3137	0.1691	-0.5347	-0.4159
chr_5430_d107	HPGL0658	chr	d107	3	#990000	HPGL0658	-0.3063	0.2202	-0.3063	0.2202	-0.4010	-0.4792	0.0247	-0.0537	-0.0449	-0.1202	0.5672	0.0985	-0.1803
chr_5397_d107	HPGL0659	chr	d107	3	#990000	HPGL0659	-0.2846	0.3036	-0.2846	0.3036	-0.2250	0.4529	0.1458	0.1173	0.2127	0.4435	0.0478	-0.4246	0.1679
sh_1022_d107	HPGL0662	sh	d107	3	#000099	HPGL0662	-0.1810	0.3521	-0.1810	0.3521	0.1810	-0.1465	-0.6834	0.0734	-0.2050	0.0394	-0.3916	-0.0726	-0.1761
sh_2189_d107	HPGL0663	sh	d107	3	#000099	HPGL0663	-0.2677	0.3714	-0.2677	0.3714	0.3023	0.1498	0.4275	-0.1392	0.0157	-0.3998	-0.2199	0.3867	0.1819

hs_pca_inf_lqcf$variance

## NULL

write.csv(hs_pca_inf_lqcf$pcatable, file="csv/infection_nouninfected_no_batch.csv")
## This shows clean sehumantion by patient
## Therefore we will now add patient as a surrogate variable and minimize it

hscds_inf_lqcf <- sm(normalize_expt(hs_cds_inf, filter=TRUE, convert="cpm",
                                    transform="log2", norm="quant"))
hscds_pca_inf_lqcf <- plot_pca(hscds_inf_lqcf)
hscds_pca_inf_lqcf$plot

3.1.2 PCA: Repeat with combat adjustment

For the second iteration, use the same normalization, but add a combat correction in an attempt to minimize patient’s effect in the variance.

hs_inf_lqcf_cbdonor <- sm(normalize_expt(hs_inf_lqcf, batch="combat_scale"))

## Here the split is semi chronic/self-healing, but not quite
hs_pca_inf_lqcf_cbdonor <- plot_pca(hs_inf_lqcf_cbdonor)
hs_pca_inf_lqcf_cbdonor$plot

testing <- make_3d_pca(hs_pca_inf_lqcf_cbdonor)
testing$plot

## There are 2 sh and 1 chr on the right vs. 2 chr and 1 sh on the left
knitr::kable(hs_pca_inf_lqcf_cbdonor$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
chr_5430_d108	HPGL0631	chr	d108	1	#990000	HPGL0631	-0.2462	0.0257	-0.2462	0.0257	0.1865	-0.1959	0.2256	0.6612	0.1487	0.2002	0.4031	0.0680	-0.2548
chr_5397_d108	HPGL0632	chr	d108	1	#990000	HPGL0632	-0.3142	-0.0198	-0.3142	-0.0198	0.3953	0.3625	0.0358	-0.3706	-0.3146	-0.1907	0.0704	0.0579	-0.4976
sh_1022_d108	HPGL0635	sh	d108	1	#000099	HPGL0635	0.5957	0.1158	0.5957	0.1158	-0.1126	0.1306	-0.1458	-0.1759	0.4008	0.3148	0.0402	0.0302	-0.4520
sh_2189_d108	HPGL0636	sh	d108	1	#000099	HPGL0636	-0.1502	-0.1034	-0.1502	-0.1034	-0.5308	-0.4299	-0.2312	0.0854	-0.2249	-0.2088	-0.3310	0.1644	-0.3541
chr_5430_d110	HPGL0651	chr	d110	2	#990000	HPGL0651	-0.1561	0.4030	-0.1561	0.4030	0.1621	-0.3548	-0.4693	-0.3074	0.0570	0.0426	0.3254	0.2033	0.3328
chr_5397_d110	HPGL0652	chr	d110	2	#990000	HPGL0652	-0.0823	0.3519	-0.0823	0.3519	-0.2437	0.0900	0.6687	-0.1802	0.0272	0.1109	-0.1935	0.3764	0.2164
sh_1022_d110	HPGL0655	sh	d110	2	#000099	HPGL0655	0.1949	-0.4252	0.1949	-0.4252	0.1421	0.0286	0.0532	0.0336	0.3895	-0.6257	0.0529	0.2889	0.2085
sh_2189_d110	HPGL0656	sh	d110	2	#000099	HPGL0656	0.1502	-0.4115	0.1502	-0.4115	-0.0459	0.3036	-0.2129	0.1555	-0.4462	0.4199	0.0421	0.3014	0.3050
chr_5430_d107	HPGL0658	chr	d107	3	#990000	HPGL0658	0.0856	0.2708	0.0856	0.2708	0.4538	0.0315	-0.1486	0.3083	0.0120	-0.0070	-0.6816	-0.1784	0.1238
chr_5397_d107	HPGL0659	chr	d107	3	#990000	HPGL0659	0.1471	0.3391	0.1471	0.3391	-0.3582	0.3540	-0.0513	0.2147	-0.2142	-0.3696	0.2998	-0.4308	0.1401
sh_1022_d107	HPGL0662	sh	d107	3	#000099	HPGL0662	0.2863	-0.2508	0.2863	-0.2508	0.1643	-0.4946	0.3557	-0.2555	-0.2735	0.0754	0.0827	-0.4625	0.0847
sh_2189_d107	HPGL0663	sh	d107	3	#000099	HPGL0663	-0.5106	-0.2955	-0.5106	-0.2955	-0.2130	0.1745	-0.0798	-0.1692	0.4382	0.2379	-0.1104	-0.4188	0.1472

hs_pca_inf_lqcf_cbdonor$variance

## NULL

write.csv(hs_pca_inf_lqcf_cbdonor$pcatable, file="csv/infection_nouninfected_batch_patient.csv")

3.1.3 Look at correlations between experimental factors and variance

hs_inf_pcainfo <- pca_information(hs_inf, plot_pcas=TRUE,
                                  expt_factors=c("condition", "batch", "pathogenstrain",
                                                 "state", "donor", "rnangul"))

## More shallow curves in these plots suggest more genes in this principle component.

Look for significant correlations between the PCs and some factors in the experimental design.

hs_inf_pcainfo$anova_fstat_heatmap

hs_inf_pcainfo$pca_plots$PC2_PC6

3.1.4 PCA: Change batch to strain and condition to patient+state

Here we will set the batch to the humansite strains and condition to a combination of the patient and state state; then perform the pca again.

new_condition <- paste0(hs_inf$design$state, '_', hs_inf$design$donor)
hs_inf_strbatch <- set_expt_factors(hs_inf, batch="pathogenstrain", condition=new_condition)
hs_inf_lqcf_cbstr <- sm(normalize_expt(hs_inf_strbatch, transform="log2", convert="cpm",
                                       norm="quant", filter=TRUE, batch="combat_scale"))

hs_inf_lqcf_cbstr_pca <- plot_pca(hs_inf_lqcf_cbstr)
hs_inf_lqcf_cbstr_pca$plot

## Doing that kind of sucked the variance out of the data, but it did cause the
## samples to split by strain quite strongly
knitr::kable(hs_inf_lqcf_cbstr_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
chr_5430_d108	HPGL0631	chronic_d108	s5430	4	#1B9E77	HPGL0631	-0.2929	-0.4076	-0.2929	-0.4076	0.2051	-0.0961	0.4480	-0.2779	0.3533	-0.1624	-0.0594	0.2949	0.3063
chr_5397_d108	HPGL0632	chronic_d108	s5397	3	#1B9E77	HPGL0632	-0.2858	-0.3945	-0.2858	-0.3945	0.3930	0.4189	-0.3229	0.2126	-0.0661	-0.0291	-0.1571	-0.1147	-0.3960
sh_1022_d108	HPGL0635	self_heal_d108	s1022	1	#D95F02	HPGL0635	0.2191	-0.3901	0.2191	-0.3901	-0.1488	-0.5389	-0.1717	0.2854	-0.1851	-0.2584	0.3908	0.1482	-0.1315
sh_2189_d108	HPGL0636	self_heal_d108	s2189	2	#D95F02	HPGL0636	0.2207	-0.4311	0.2207	-0.4311	-0.4510	0.1529	0.0484	-0.1778	-0.1796	0.4539	-0.1637	-0.3303	0.2172
chr_5430_d110	HPGL0651	chronic_d110	s5430	4	#7570B3	HPGL0651	-0.0516	0.2144	-0.0516	0.2144	-0.3083	0.2699	0.0529	0.5827	-0.0493	-0.0029	-0.2433	0.4844	0.2480
chr_5397_d110	HPGL0652	chronic_d110	s5397	3	#7570B3	HPGL0652	-0.0545	0.1919	-0.0545	0.1919	-0.3222	-0.2961	-0.2213	-0.1994	0.4899	0.0741	-0.3700	0.0605	-0.4589
sh_1022_d110	HPGL0655	self_heal_d110	s1022	1	#E7298A	HPGL0655	0.4316	0.1721	0.4316	0.1721	0.4199	0.0164	0.1859	-0.1394	-0.1267	0.4900	0.1234	0.3771	-0.2381
sh_2189_d110	HPGL0656	self_heal_d110	s2189	2	#E7298A	HPGL0656	0.4457	0.1653	0.4457	0.1653	0.1626	0.0565	-0.0388	-0.2311	-0.2265	-0.5927	-0.4053	-0.1435	0.1366
chr_5430_d107	HPGL0658	chronic_d107	s5430	4	#66A61E	HPGL0658	-0.4045	0.2501	-0.4045	0.2501	0.0814	-0.1468	-0.5294	-0.3207	-0.2783	0.1437	0.1483	0.1007	0.3860
chr_5397_d107	HPGL0659	chronic_d107	s5397	3	#66A61E	HPGL0659	-0.4070	0.2677	-0.4070	0.2677	-0.1140	-0.1269	0.5427	0.0227	-0.4199	-0.0567	0.0534	-0.2820	-0.3055
sh_1022_d107	HPGL0662	self_heal_d107	s1022	1	#E6AB02	HPGL0662	0.0958	0.1625	0.0958	0.1625	-0.2358	0.5041	-0.0136	-0.1782	0.3004	-0.2265	0.6231	-0.0732	-0.0654
sh_2189_d107	HPGL0663	self_heal_d107	s2189	2	#E6AB02	HPGL0663	0.0833	0.1993	0.0833	0.1993	0.3181	-0.2140	0.0198	0.4210	0.3878	0.1670	0.0598	-0.5221	0.3014

hs_inf_lqcf_cbstr_pca$variance

## NULL

write.csv(hs_inf_lqcf_cbstr_pca$pcatable, file="csv/infection_nouninfected_batch_strain.csv")

3.1.5 PCA: Repeat but with just chronic/self-state

Now change only the condition to self/chronic and make super-explicit the split in the samples.

hs_inf_lqcf_cbstrv2 <- set_expt_conditions(hs_inf_lqcf_cbstr, fact="state")
hs_inf_lqcf_cbstrv2 <- set_expt_colors(hs_inf_lqcf_cbstrv2, colors=c("#880000","#000088"))

## The new colors are a character, changing according to condition.

hs_inf_lqcf_cbstrv2_pca <- plot_pca(hs_inf_lqcf_cbstrv2)
hs_inf_lqcf_cbstrv2_pca$plot

## Thus 3 runs of chronic on the right and self-state on the left
knitr::kable(hs_inf_lqcf_cbstrv2_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
chr_5430_d108	HPGL0631	chronic	s5430	4	#880000	HPGL0631	-0.2929	-0.4076	-0.2929	-0.4076	0.2051	-0.0961	0.4480	-0.2779	0.3533	-0.1624	-0.0594	0.2949	0.3063
chr_5397_d108	HPGL0632	chronic	s5397	3	#880000	HPGL0632	-0.2858	-0.3945	-0.2858	-0.3945	0.3930	0.4189	-0.3229	0.2126	-0.0661	-0.0291	-0.1571	-0.1147	-0.3960
sh_1022_d108	HPGL0635	self_heal	s1022	1	#000088	HPGL0635	0.2191	-0.3901	0.2191	-0.3901	-0.1488	-0.5389	-0.1717	0.2854	-0.1851	-0.2584	0.3908	0.1482	-0.1315
sh_2189_d108	HPGL0636	self_heal	s2189	2	#000088	HPGL0636	0.2207	-0.4311	0.2207	-0.4311	-0.4510	0.1529	0.0484	-0.1778	-0.1796	0.4539	-0.1637	-0.3303	0.2172
chr_5430_d110	HPGL0651	chronic	s5430	4	#880000	HPGL0651	-0.0516	0.2144	-0.0516	0.2144	-0.3083	0.2699	0.0529	0.5827	-0.0493	-0.0029	-0.2433	0.4844	0.2480
chr_5397_d110	HPGL0652	chronic	s5397	3	#880000	HPGL0652	-0.0545	0.1919	-0.0545	0.1919	-0.3222	-0.2961	-0.2213	-0.1994	0.4899	0.0741	-0.3700	0.0605	-0.4589
sh_1022_d110	HPGL0655	self_heal	s1022	1	#000088	HPGL0655	0.4316	0.1721	0.4316	0.1721	0.4199	0.0164	0.1859	-0.1394	-0.1267	0.4900	0.1234	0.3771	-0.2381
sh_2189_d110	HPGL0656	self_heal	s2189	2	#000088	HPGL0656	0.4457	0.1653	0.4457	0.1653	0.1626	0.0565	-0.0388	-0.2311	-0.2265	-0.5927	-0.4053	-0.1435	0.1366
chr_5430_d107	HPGL0658	chronic	s5430	4	#880000	HPGL0658	-0.4045	0.2501	-0.4045	0.2501	0.0814	-0.1468	-0.5294	-0.3207	-0.2783	0.1437	0.1483	0.1007	0.3860
chr_5397_d107	HPGL0659	chronic	s5397	3	#880000	HPGL0659	-0.4070	0.2677	-0.4070	0.2677	-0.1140	-0.1269	0.5427	0.0227	-0.4199	-0.0567	0.0534	-0.2820	-0.3055
sh_1022_d107	HPGL0662	self_heal	s1022	1	#000088	HPGL0662	0.0958	0.1625	0.0958	0.1625	-0.2358	0.5041	-0.0136	-0.1782	0.3004	-0.2265	0.6231	-0.0732	-0.0654
sh_2189_d107	HPGL0663	self_heal	s2189	2	#000088	HPGL0663	0.0833	0.1993	0.0833	0.1993	0.3181	-0.2140	0.0198	0.4210	0.3878	0.1670	0.0598	-0.5221	0.3014

3.2 Restart but include the uninfected samples

For the next few blocks we will just repeat what we did but include the uninfected samples. Ideally doing so will have ~0 effect on the positions of the sample types.

3.2.1 PCA: +uninfected: No Batch correction

In this first example, we see why the uninfected samples were initially removed from the analyses I think.

## Start with the non-uninfected, no batch correction
hs_uninf_lqcf <- sm(normalize_expt(hs_uninf, filter=TRUE, convert="cpm",
                                   transform="log2", norm="quant"))
hs_uninf_lqcf_pca <- plot_pca(hs_uninf_lqcf)
hs_uninf_lqcf_pca$plot

## In this case, the uninfected samples cause the p107/p108 samples to smoosh together
knitr::kable(hs_uninf_lqcf_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	pc_12	pc_13	pc_14
uninf_d108	HPGL0630	uninf	d108	1	#009900	HPGL0630	-0.4808	-0.0581	-0.4808	-0.0581	0.2830	-0.0822	0.0972	0.4322	0.0573	-0.2898	0.3568	-0.3745	0.0606	-0.0351	0.2364	-0.0068
chr_5430_d108	HPGL0631	chr	d108	1	#990000	HPGL0631	0.1268	-0.1719	0.1268	-0.1719	0.3658	-0.1102	-0.2859	-0.3985	-0.1112	0.2565	-0.1933	-0.4740	0.0317	-0.3750	0.1198	-0.0709
chr_5397_d108	HPGL0632	chr	d108	1	#990000	HPGL0632	0.1225	-0.1421	0.1225	-0.1421	0.3544	-0.0270	-0.0724	-0.2746	0.2023	-0.4254	0.2835	0.5003	-0.2771	-0.2076	-0.1358	-0.0188
sh_1022_d108	HPGL0635	sh	d108	1	#000099	HPGL0635	0.1005	-0.0565	0.1005	-0.0565	0.2971	-0.1041	0.0258	0.2465	-0.6886	-0.0083	-0.1080	0.2358	0.3126	0.1501	-0.3074	0.0573
sh_2189_d108	HPGL0636	sh	d108	1	#000099	HPGL0636	0.1783	-0.0981	0.1783	-0.0981	0.3118	-0.2882	0.3135	0.1240	0.3946	0.3075	-0.2697	0.0682	-0.1679	0.4715	0.1377	0.0294
uninf_d110	HPGL0650	uninf	d110	2	#009900	HPGL0650	-0.5435	0.1390	-0.5435	0.1390	0.1209	0.5233	0.1572	-0.2518	0.0373	0.4247	0.0179	0.2065	0.0513	0.0206	-0.1187	-0.0142
chr_5430_d110	HPGL0651	chr	d110	2	#990000	HPGL0651	0.1728	0.3580	0.1728	0.3580	-0.0067	0.1093	-0.3180	0.1165	0.3331	-0.0486	-0.0285	-0.0627	0.3014	-0.0272	-0.1382	0.6479
chr_5397_d110	HPGL0652	chr	d110	2	#990000	HPGL0652	0.1877	0.3739	0.1877	0.3739	-0.0144	0.3186	-0.0344	0.2669	-0.1068	-0.1563	-0.3637	0.1722	-0.1489	-0.2022	0.5024	-0.2666
sh_1022_d110	HPGL0655	sh	d110	2	#000099	HPGL0655	0.0426	0.4316	0.0426	0.4316	-0.2017	-0.2693	0.0019	-0.2458	-0.3528	0.1399	0.4062	-0.0744	-0.3944	0.2110	0.1706	0.1670
sh_2189_d110	HPGL0656	sh	d110	2	#000099	HPGL0656	0.0658	0.4049	0.0658	0.4049	-0.1323	-0.1789	0.2128	-0.1242	0.1989	-0.1803	-0.0356	-0.2178	0.2292	-0.0048	-0.4484	-0.5296
uninf_d107	HPGL0657	uninf	d107	3	#009900	HPGL0657	-0.5041	-0.1422	-0.5041	-0.1422	-0.3566	-0.3726	-0.2693	-0.0360	-0.0003	-0.1617	-0.4815	0.1429	-0.1671	-0.0184	-0.0709	0.0862
chr_5430_d107	HPGL0658	chr	d107	3	#990000	HPGL0658	0.1272	-0.3002	0.1272	-0.3002	-0.1712	0.3419	-0.4993	-0.0108	0.0299	-0.0769	0.1489	-0.1223	0.0134	0.5546	0.0025	-0.2823
chr_5397_d107	HPGL0659	chr	d107	3	#990000	HPGL0659	0.1868	-0.2754	0.1868	-0.2754	-0.2282	0.3199	0.3630	0.1632	-0.0839	-0.0618	-0.0887	-0.3077	-0.4432	-0.1728	-0.3479	0.2215
sh_1022_d107	HPGL0662	sh	d107	3	#000099	HPGL0662	0.1232	-0.1913	0.1232	-0.1913	-0.3135	-0.1911	-0.1024	0.3503	0.1279	0.4868	0.3290	0.2485	0.1181	-0.3814	0.0029	-0.1753
sh_2189_d107	HPGL0663	sh	d107	3	#000099	HPGL0663	0.0944	-0.2716	0.0944	-0.2716	-0.3084	0.0105	0.4103	-0.3578	-0.0378	-0.2063	0.0267	0.0591	0.4800	0.0168	0.3951	0.1552

hs_uninf_lqcf_pca$variance

## NULL

write.csv(hs_uninf_lqcf_pca$pcatable, file="csv/infection_withuninfected_with_batch.csv")

3.2.2 PCA: +uninfected Repeat with combat adjustment

For the second iteration, use the same normalization, but add a combat correction in an attempt to minimize patient’s effect in the variance.

hs_uninf_lqcf_cbdonor <- sm(normalize_expt(hs_uninf_lqcf, batch="combat_scale"))

## Here the split is semi chronic/self-state, but not quite
hs_uninf_lqcf_cbdonor_pca <- plot_pca(hs_uninf_lqcf_cbdonor)
hs_uninf_lqcf_cbdonor_pca$plot

## Now we have weak sehumantion between strains, I thought for a moment it might
## be few snps vs. many but that is not true.
## There are 2 sh and 1 chr on the right vs. 2 chr and 1 sh on the left
knitr::kable(hs_uninf_lqcf_cbdonor_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	pc_12	pc_13	pc_14
uninf_d108	HPGL0630	uninf	d108	1	#009900	HPGL0630	-0.4055	0.3147	-0.4055	0.3147	-0.2762	0.3615	0.4048	0.2448	0.1367	0.0012	-0.0270	0.1592	-0.2696	0.0276	-0.0815	0.3381
chr_5430_d108	HPGL0631	chr	d108	1	#990000	HPGL0631	0.0373	-0.2855	0.0373	-0.2855	0.1307	-0.3376	-0.1720	0.0654	-0.1626	0.0795	0.0987	0.3392	-0.6808	0.0207	0.1988	0.1566
chr_5397_d108	HPGL0632	chr	d108	1	#990000	HPGL0632	0.0357	-0.2956	0.0357	-0.2956	0.1509	-0.1810	0.3956	-0.2690	-0.1826	0.1314	-0.2963	-0.0154	0.2031	0.4518	-0.3284	0.2616
sh_1022_d108	HPGL0635	sh	d108	1	#000099	HPGL0635	0.2320	0.2811	0.2320	0.2811	-0.2131	-0.0786	-0.3089	-0.1918	-0.0024	-0.1067	0.5664	0.2220	0.2487	0.1796	-0.2064	0.3138
sh_2189_d108	HPGL0636	sh	d108	1	#000099	HPGL0636	0.0460	-0.2250	0.0460	-0.2250	0.1745	0.2832	-0.4177	0.4813	-0.1783	-0.2195	-0.1772	-0.1673	0.0534	-0.1394	-0.4245	0.1480
uninf_d110	HPGL0650	uninf	d110	2	#009900	HPGL0650	-0.5967	-0.1876	-0.5967	-0.1876	-0.3528	-0.1122	-0.2258	-0.0663	-0.1406	0.1834	0.0931	0.0257	0.1080	0.0864	-0.1530	-0.4944
chr_5430_d110	HPGL0651	chr	d110	2	#990000	HPGL0651	0.1846	-0.1379	0.1846	-0.1379	0.0403	-0.1131	0.3682	0.4529	0.1178	-0.4192	0.2550	0.0662	0.1445	0.2599	0.1765	-0.3828
chr_5397_d110	HPGL0652	chr	d110	2	#990000	HPGL0652	0.2104	-0.1496	0.2104	-0.1496	0.0771	0.0995	-0.0432	-0.1570	0.7703	0.2134	-0.0883	0.1705	-0.0861	-0.1493	-0.2763	-0.2062
sh_1022_d110	HPGL0655	sh	d110	2	#000099	HPGL0655	0.1474	0.3690	0.1474	0.3690	0.2176	0.0457	0.0792	-0.0813	-0.1376	0.2117	0.2250	-0.5998	-0.3671	0.1195	-0.1566	-0.2559
sh_2189_d110	HPGL0656	sh	d110	2	#000099	HPGL0656	0.1403	0.3207	0.1403	0.3207	0.1936	0.2314	0.0632	-0.2624	-0.3857	-0.1588	-0.2646	0.4844	0.0429	-0.2207	-0.0015	-0.3431
uninf_d107	HPGL0657	uninf	d107	3	#009900	HPGL0657	-0.4669	0.2231	-0.4669	0.2231	0.4933	-0.2653	-0.1629	-0.1522	0.2713	-0.3209	-0.0893	-0.1390	0.1478	-0.0328	0.2283	0.1523
chr_5430_d107	HPGL0658	chr	d107	3	#990000	HPGL0658	0.1330	-0.0232	0.1330	-0.0232	-0.3752	-0.4353	0.2401	-0.0628	-0.0646	-0.1636	-0.0829	-0.2441	0.0353	-0.6396	-0.0771	0.0977
chr_5397_d107	HPGL0659	chr	d107	3	#990000	HPGL0659	0.1876	-0.1059	0.1876	-0.1059	-0.4099	0.2759	-0.2346	-0.2685	0.0701	-0.2620	-0.3269	-0.2547	-0.1144	0.2846	0.4192	0.0248
sh_1022_d107	HPGL0662	sh	d107	3	#000099	HPGL0662	0.1663	0.2829	0.1663	0.2829	-0.0433	-0.1901	-0.1382	0.4078	-0.0008	0.5716	-0.2740	0.0422	0.3027	0.0502	0.3197	0.0481
sh_2189_d107	HPGL0663	sh	d107	3	#000099	HPGL0663	-0.0516	-0.3812	-0.0516	-0.3812	0.1925	0.4162	0.1522	-0.1410	-0.1110	0.2586	0.3881	-0.0892	0.2315	-0.2984	0.3630	0.1413

hs_uninf_lqcf_cbdonor_pca$variance

## NULL

write.csv(hs_uninf_lqcf_cbdonor_pca$pcatable,
          file="csv/infection_withuninfected_batch_patient.csv")

3.2.3 PCA: +uninfected, change the condition to chr/sh

Including the uninfected samples and changing the condition should not much matter

3.2.4 PCA: +uninfected, Change batch to strain and condition to patient+state

## Here we will set the batch to the humansite strains and condition to a
## combination of the patient and state state; then perform the pca.
new_condition <- paste0(hs_uninf$design$state, '_', hs_uninf$design$donor)
hs_uninfv2 <- set_expt_factors(hs_uninf, condition=new_condition, batch="pathogenstrain")

hs_uninfv2_lqcf_cbstr <- sm(normalize_expt(hs_uninfv2, transform="log2", convert="cpm",
                                           norm="quant", filter=TRUE, batch="combat_scale"))

hs_uninfv2_lqcf_cbstr_pca <- plot_pca(hs_uninfv2_lqcf_cbstr)
hs_uninfv2_lqcf_cbstr_pca$plot

## This is a surprise to me, I would have expected the uninfected to still push
## the other samples off to a side or somesuch.
knitr::kable(hs_uninfv2_lqcf_cbstr_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	pc_12	pc_13	pc_14
uninf_d108	HPGL0630	uninfected_d108	none	1	#1B9E77	HPGL0630	-0.3977	-0.2732	-0.3977	-0.2732	0.0594	0.2258	0.1614	-0.0158	-0.5708	0.0970	0.3152	0.2646	0.2362	-0.2071	0.1282	0.0006
chr_5430_d108	HPGL0631	chronic_d108	s5430	5	#C16610	HPGL0631	0.2767	-0.3763	0.2767	-0.3763	0.1145	-0.2778	-0.4681	-0.0498	0.1920	0.0731	0.3974	0.0295	0.1246	-0.1213	-0.1097	0.4000
chr_5397_d108	HPGL0632	chronic_d108	s5397	4	#C16610	HPGL0632	0.2734	-0.3742	0.2734	-0.3742	0.1964	-0.4194	0.4514	0.2620	-0.0190	-0.1366	-0.0976	-0.1095	0.0584	-0.0684	-0.0886	-0.4186
sh_1022_d108	HPGL0635	self_heal_d108	s1022	2	#8D6B86	HPGL0635	-0.0432	-0.3499	-0.0432	-0.3499	-0.1155	0.1463	-0.0384	-0.5311	-0.1196	-0.1886	-0.1741	-0.3595	-0.5260	-0.0620	0.0283	0.0017
sh_2189_d108	HPGL0636	self_heal_d108	s2189	3	#8D6B86	HPGL0636	-0.0424	-0.3784	-0.0424	-0.3784	0.1347	0.3986	-0.1055	0.1645	0.2615	0.1865	-0.4337	0.2548	0.0972	0.4529	0.0468	0.0143
uninf_d110	HPGL0650	uninfected_d110	none	1	#BC4399	HPGL0650	-0.4743	-0.0555	-0.4743	-0.0555	-0.6450	-0.1173	-0.0069	0.2411	0.3250	-0.0268	-0.0421	-0.1557	0.1342	-0.1698	-0.1949	-0.0103
chr_5430_d110	HPGL0651	chronic_d110	s5430	5	#A66753	HPGL0651	0.1216	0.2363	0.1216	0.2363	0.2013	0.2959	0.0426	0.2285	-0.1661	-0.4636	-0.2366	-0.1518	0.1540	-0.1936	-0.3715	0.3999
chr_5397_d110	HPGL0652	chronic_d110	s5397	4	#A66753	HPGL0652	0.1230	0.2186	0.1230	0.2186	0.0309	0.2709	0.0826	-0.4585	0.3342	-0.0778	0.2602	0.2489	0.1789	-0.0557	-0.3485	-0.4185
sh_1022_d110	HPGL0655	self_heal_d110	s1022	2	#96A713	HPGL0655	-0.1764	0.2426	-0.1764	0.2426	0.1340	-0.3530	-0.1543	0.0403	-0.1479	0.2059	-0.2395	0.4772	-0.4686	-0.1053	-0.3085	-0.0099
sh_2189_d110	HPGL0656	self_heal_d110	s2189	3	#96A713	HPGL0656	-0.1843	0.2217	-0.1843	0.2217	0.1698	-0.0989	0.0436	0.0025	-0.1623	0.2793	0.2598	-0.4807	0.0207	0.5857	-0.2513	0.0098
uninf_d107	HPGL0657	uninfected_d107	none	1	#D59D08	HPGL0657	-0.3127	0.2392	-0.3127	0.2392	0.4254	-0.2098	-0.0729	-0.2135	0.2548	-0.0023	-0.2297	-0.1782	0.2560	-0.2288	0.4849	-0.0014
chr_5430_d107	HPGL0658	chronic_d107	s5430	5	#9D7426	HPGL0658	0.3613	0.1646	0.3613	0.1646	-0.3089	-0.0443	0.4910	-0.2072	-0.0062	0.4147	-0.1336	0.0727	0.0781	-0.0375	0.1597	0.4075
chr_5397_d107	HPGL0659	chronic_d107	s5397	4	#9D7426	HPGL0659	0.3638	0.1841	0.3638	0.1841	-0.2330	0.1358	-0.4841	0.1654	-0.3187	0.2230	-0.1400	-0.1832	0.1213	-0.1518	0.1675	-0.4031
sh_1022_d107	HPGL0662	self_heal_d107	s1022	2	#666666	HPGL0662	0.0509	0.1493	0.0509	0.1493	0.0980	0.2892	0.1331	0.4228	0.2770	-0.0073	0.4084	-0.0190	-0.5072	-0.1039	0.3267	-0.0001
sh_2189_d107	HPGL0663	self_heal_d107	s2189	3	#666666	HPGL0663	0.0603	0.1511	0.0603	0.1511	-0.2620	-0.2420	-0.0752	-0.0512	-0.1339	-0.5765	0.0861	0.2899	0.0422	0.4665	0.3310	0.0279

hs_uninfv2_lqcf_cbstr_pca$variance

## NULL

write.csv(hs_uninfv2_lqcf_cbstr_pca$pcatable,
          file="csv/infection_withuninfected_batch_strain.csv")

3.2.5 PCA: +uninfected, Repeat but with just chronic/self-state

hs_uninfv2_lqcf_cbstr <- set_expt_conditions(hs_uninfv2_lqcf_cbstr, fact="state")
hs_uninfv2_lqcf_cbstr <- set_expt_colors(hs_uninfv2_lqcf_cbstr,
                                         colors=c("#880000","#000088","#008800"))

## The new colors are a character, changing according to condition.

hs_uninfv2_lqcf_cbstr_pca <- plot_pca(hs_uninfv2_lqcf_cbstr)
hs_uninfv2_lqcf_cbstr_pca$plot

knitr::kable(hs_uninfv2_lqcf_cbstr_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	pc_12	pc_13	pc_14
uninf_d108	HPGL0630	uninfected	none	1	#008800	HPGL0630	-0.3977	-0.2732	-0.3977	-0.2732	0.0594	0.2258	0.1614	-0.0158	-0.5708	0.0970	0.3152	0.2646	0.2362	-0.2071	0.1282	0.0006
chr_5430_d108	HPGL0631	chronic	s5430	5	#880000	HPGL0631	0.2767	-0.3763	0.2767	-0.3763	0.1145	-0.2778	-0.4681	-0.0498	0.1920	0.0731	0.3974	0.0295	0.1246	-0.1213	-0.1097	0.4000
chr_5397_d108	HPGL0632	chronic	s5397	4	#880000	HPGL0632	0.2734	-0.3742	0.2734	-0.3742	0.1964	-0.4194	0.4514	0.2620	-0.0190	-0.1366	-0.0976	-0.1095	0.0584	-0.0684	-0.0886	-0.4186
sh_1022_d108	HPGL0635	self_heal	s1022	2	#000088	HPGL0635	-0.0432	-0.3499	-0.0432	-0.3499	-0.1155	0.1463	-0.0384	-0.5311	-0.1196	-0.1886	-0.1741	-0.3595	-0.5260	-0.0620	0.0283	0.0017
sh_2189_d108	HPGL0636	self_heal	s2189	3	#000088	HPGL0636	-0.0424	-0.3784	-0.0424	-0.3784	0.1347	0.3986	-0.1055	0.1645	0.2615	0.1865	-0.4337	0.2548	0.0972	0.4529	0.0468	0.0143
uninf_d110	HPGL0650	uninfected	none	1	#008800	HPGL0650	-0.4743	-0.0555	-0.4743	-0.0555	-0.6450	-0.1173	-0.0069	0.2411	0.3250	-0.0268	-0.0421	-0.1557	0.1342	-0.1698	-0.1949	-0.0103
chr_5430_d110	HPGL0651	chronic	s5430	5	#880000	HPGL0651	0.1216	0.2363	0.1216	0.2363	0.2013	0.2959	0.0426	0.2285	-0.1661	-0.4636	-0.2366	-0.1518	0.1540	-0.1936	-0.3715	0.3999
chr_5397_d110	HPGL0652	chronic	s5397	4	#880000	HPGL0652	0.1230	0.2186	0.1230	0.2186	0.0309	0.2709	0.0826	-0.4585	0.3342	-0.0778	0.2602	0.2489	0.1789	-0.0557	-0.3485	-0.4185
sh_1022_d110	HPGL0655	self_heal	s1022	2	#000088	HPGL0655	-0.1764	0.2426	-0.1764	0.2426	0.1340	-0.3530	-0.1543	0.0403	-0.1479	0.2059	-0.2395	0.4772	-0.4686	-0.1053	-0.3085	-0.0099
sh_2189_d110	HPGL0656	self_heal	s2189	3	#000088	HPGL0656	-0.1843	0.2217	-0.1843	0.2217	0.1698	-0.0989	0.0436	0.0025	-0.1623	0.2793	0.2598	-0.4807	0.0207	0.5857	-0.2513	0.0098
uninf_d107	HPGL0657	uninfected	none	1	#008800	HPGL0657	-0.3127	0.2392	-0.3127	0.2392	0.4254	-0.2098	-0.0729	-0.2135	0.2548	-0.0023	-0.2297	-0.1782	0.2560	-0.2288	0.4849	-0.0014
chr_5430_d107	HPGL0658	chronic	s5430	5	#880000	HPGL0658	0.3613	0.1646	0.3613	0.1646	-0.3089	-0.0443	0.4910	-0.2072	-0.0062	0.4147	-0.1336	0.0727	0.0781	-0.0375	0.1597	0.4075
chr_5397_d107	HPGL0659	chronic	s5397	4	#880000	HPGL0659	0.3638	0.1841	0.3638	0.1841	-0.2330	0.1358	-0.4841	0.1654	-0.3187	0.2230	-0.1400	-0.1832	0.1213	-0.1518	0.1675	-0.4031
sh_1022_d107	HPGL0662	self_heal	s1022	2	#000088	HPGL0662	0.0509	0.1493	0.0509	0.1493	0.0980	0.2892	0.1331	0.4228	0.2770	-0.0073	0.4084	-0.0190	-0.5072	-0.1039	0.3267	-0.0001
sh_2189_d107	HPGL0663	self_heal	s2189	3	#000088	HPGL0663	0.0603	0.1511	0.0603	0.1511	-0.2620	-0.2420	-0.0752	-0.0512	-0.1339	-0.5765	0.0861	0.2899	0.0422	0.4665	0.3310	0.0279

3.2.6 PCA: Try only using samples for 1 patient

As per a conversation with Maria Adelaida on skype, lets remove all samples except those for one patient, then see if some aspect of the data jumps out (strain:strain variation, for example)

single_patient <- subset_expt(hs_inf, subset="donor=='d107'")

## There were 12, now there are 4 samples.

single_patient <- set_expt_batches(single_patient, fact="state")
single_norm <- sm(normalize_expt(single_patient, transform="log2", norm="quant",
                                 convert="cpm", filter=TRUE))
single_norm_pca <- plot_pca(single_norm)
single_norm_pca$plot

single_patient <- subset_expt(hs_inf, subset="donor=='d108'")

## There were 12, now there are 4 samples.

single_patient <- set_expt_batches(single_patient, fact="state")
single_norm <- sm(normalize_expt(single_patient, transform="log2", norm="quant",
                                 convert="cpm", filter=TRUE))
single_norm_pca <- plot_pca(single_norm)
single_norm_pca$plot

single_patient <- subset_expt(hs_inf, subset="donor=='d110'")

## There were 12, now there are 4 samples.

single_patient <- set_expt_batches(single_patient, fact="state")
single_norm <- sm(normalize_expt(single_patient, transform="log2", norm="quant",
                                 convert="cpm", filter=TRUE))
single_norm_pca <- plot_pca(single_norm)
single_norm_pca$plot

## hmmm so the answer is, no -- I think.
knitr::kable(single_norm_pca$table)

	sampleid	condition	batch	batch_int	colors	labels	PC1	PC2	pc_1	pc_2	pc_3
chr_5430_d110	HPGL0651	chr	chronic	1	#990000	HPGL0651	-0.4139	0.3802	-0.4139	0.3802	-0.6589
chr_5397_d110	HPGL0652	chr	chronic	1	#990000	HPGL0652	-0.5581	-0.4864	-0.5581	-0.4864	0.4493
sh_1022_d110	HPGL0655	sh	self_heal	2	#000099	HPGL0655	0.6357	-0.5006	0.6357	-0.5006	-0.3088
sh_2189_d110	HPGL0656	sh	self_heal	2	#000099	HPGL0656	0.3363	0.6068	0.3363	0.6068	0.5183

3.2.7 PCA: Re-label one sample

In our previous discussion, Hector suggested that sample ‘HPGL0635’ is sufficiently dis-similar to its cohort samples that it might actually be a member of strain ‘2504’ rather than ‘1022’. Let us look and see what happens if that is changed.

I am going to leave out the uninfected samples to avoid the confusion they generate.

hs_lqcf_noswitch <- sm(normalize_expt(hs_inf, transform="log2", convert="cpm",
                                      norm="quant", filter=TRUE))
plot_pca(hs_lqcf_noswitch)$plot

## This is just to note that the original color for sample 635 was orange to
## match strain '1022'
##switch_one <- set_expt_condition(no_uninfected, ids=c("sHPGL0635"), fact="ch2504")
switch_one <- set_expt_conditions(hs_inf, ids=c("sh_1022_d108"), fact="chr")
switcher <- list("sh_1022_d108" = "pink")
switch_one <- set_expt_colors(expt=switch_one, colors=switcher, change_by="sample")

## The new colors are a list, changing according to sampleID.

lqcf_switch <- sm(normalize_expt(switch_one, transform="log2", convert="cpm",
                                 norm="quant", filter=TRUE, batch="combat_scale"))
##plot_pca(lqcf_switch)$plot
## Note that now it is pink, matching 'ch2504'

I may be biased, but I think this suggests that the samples were not switched.

4 Testing out some ideas

One query was to see if there is a reversal of two samples.

combined_condition <- paste0(hs_inf$design$state, '_', hs_inf$design$donor)
## with_uninfected_combined <- set_expt_factors(with_uninfected,
##                                              batch="pathogenstrain",
##                                              condition=combined_condition)
hs_inf_combined <- set_expt_factors(hs_inf, batch="donor", condition="state")
head(exprs(normalize_expt(hs_inf, convert="cpm", filter=TRUE)))

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

## cpm(hpgl(data))

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

## Leaving the data in its current base format, keep in mind that
##  some metrics are easier to see when the data is log2 transformed, but
##  EdgeR/DESeq do not accept transformed data.

## Leaving the data unnormalized.  This is necessary for DESeq, but
##  EdgeR/limma might benefit from normalization.  Good choices include quantile,
##  size-factor, tmm, etc.

## Not correcting the count-data for batch effects.  If batch is
##  included in EdgerR/limma's model, then this is probably wise; but in extreme
##  batch effects this is a good parameter to play with.

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

## Removing 37656 low-count genes (13385 remaining).

## Step 2: not normalizing the data.

## Step 3: converting the data with cpm.

## Step 4: not transforming the data.

## Step 5: not doing batch correction.

##                 chr_5430_d108 chr_5397_d108 sh_1022_d108 sh_2189_d108
## ENSG00000000419         19.93        19.095       17.839       17.857
## ENSG00000000457         25.97        30.211       23.498       28.006
## ENSG00000000460         13.89        11.027        9.207        8.197
## ENSG00000000938        531.27       522.552      402.052      435.895
## ENSG00000000971          4.43         4.931        4.700        3.415
## ENSG00000001036         38.86        37.203       40.378       44.009
##                 chr_5430_d110 chr_5397_d110 sh_1022_d110 sh_2189_d110
## ENSG00000000419        16.117        17.215       12.681       15.250
## ENSG00000000457        18.314        20.265       17.558       18.094
## ENSG00000000460         7.265         7.117        4.633        4.459
## ENSG00000000938       618.721       552.993      477.564      452.209
## ENSG00000000971         2.564         1.762        1.544        2.973
## ENSG00000001036        46.030        39.311       35.604       35.929
##                 chr_5430_d107 chr_5397_d107 sh_1022_d107 sh_2189_d107
## ENSG00000000419        16.318        14.829       14.626       15.874
## ENSG00000000457        27.501        26.048       24.619       27.976
## ENSG00000000460         9.928         8.473        7.449        6.905
## ENSG00000000938       461.352       332.347      391.544      298.729
## ENSG00000000971         4.222         7.846        3.725        3.808
## ENSG00000001036        31.723        23.616       30.524       21.000

combined_pca1 <- sm(normalize_expt(hs_inf_combined, filter=TRUE, batch="pca", convert="cpm"))
combined_pca1 <- set_expt_colors(combined_pca1, colors=c("#880000", "#000088"))

## The new colors are a character, changing according to condition.

plot_pca(sm(normalize_expt(combined_pca1, filter=TRUE, transform="log2",
                           convert="cpm", norm="quant")))$plot

combined_pca2 <- set_expt_factors(combined_pca1, batch="pathogenstrain", condition="state")
combined_pca2 <- set_expt_colors(combined_pca2, colors=c("#880000", "#000088"))

## The new colors are a character, changing according to condition.

combined_pca3 <- sm(normalize_expt(combined_pca2, filter=TRUE, batch="pca"))
l2cq_combined_pca3 <- sm(normalize_expt(combined_pca3, filter=TRUE, transform="log2",
                                        convert="cpm", norm="quant"))
plot_pca(l2cq_combined_pca3)$plot

donor_strain_varpart <- sm(varpart(expt=hs_inf, predictor=NULL,
                                   factors=c("condition","pathogenstrain","donor")))
donor_strain_varpart$percent_plot

pp(file="images/varpart_donor_strain.png")

## Going to write the image to: images/varpart_donor_strain.png when dev.off() is called.

donor_strain_varpart$partition_plot
dev.off()

## png 
##   2

pp(file="images/varpart_donor_strain_pct.png")

## Going to write the image to: images/varpart_donor_strain_pct.png when dev.off() is called.

replot_varpart_percent(donor_strain_varpart, n=40)
dev.off()

## png 
##   2

sorted <- donor_strain_varpart$sorted_df

5 Try out some limma invocations with interaction models

The experimental design does not fully supprt interaction models, but I want to see how it looks.

test_data <- sm(normalize_expt(hs_inf_combined, convert="cpm", norm="quant", filter=TRUE))
query_model_string <- "~ condition:pathogenstrain + donor"
query_design <- hs_inf_combined[["design"]]
query_conditions <- as.factor(query_design[["condition"]])
##query_batches <- as.factor(query_design[["anotherbatch"]])
query_batches <- as.factor(x=c("a","a","a","a","a","a","b","b","b","b","b","b","c","c","c","c","c","c"))
query_strains <- as.factor(query_design[["pathogenstrain"]])
query_donors <- as.factor(query_design[["donor"]])
data_mtrx <- as.data.frame(exprs(test_data))
query_model <- model.matrix(~ 0 + query_conditions + query_donors + query_strains, data=query_design)
combined_voom <- limma::voom(counts=data_mtrx, design=query_model, normalize.method="quantile")
combined_fit <- limma::lmFit(combined_voom, query_model, robust=TRUE)
combined_contrast <- limma::makeContrasts(
                                chsh=query_conditionschronic-query_conditionsself_heal,
                                levels=query_model)
combined_cfit <- limma::contrasts.fit(combined_fit, combined_contrast)
combined_bayes <- limma::eBayes(combined_cfit, robust=TRUE)
combined_table <- limma::topTable(combined_bayes, number=nrow(combined_bayes), resort.by="logFC")
hist(combined_table$adj.P.Val)
min(combined_table$adj.P.Val)
test_ma <- plot_ma_de(table=combined_table, expr_col="AveExpr", fc_col="logFC", p_col="adj.P.Val", logfc_cutoff=0.6)
test_ma$plot
head(combined_table)

6 Switch to the parasite transcriptome data

7 Look during infection

“Changes during infection hpgl0630-0636 and hpgl0650-hpgl0663”

Start out by creating the expt and poking at it to see how well/badly behaved the data is.

## Reread the sample sheet because I am fiddling with other possible surrogates (like strain)
## In fact, copy it to a separate sheet because these samples are a mess
lp_inf <- subset_expt(parasite_expt, subset="experimentname=='infection'")

## There were 25, now there are 12 samples.

chosen_colors <- c("#990000", "#000099")
names(chosen_colors) <- c("chr","sh")
lp_inf <- set_expt_colors(lp_inf, colors=chosen_colors)

## The new colors are a character, changing according to condition.

##lp_inf <- expt_exclude_genes(lp_inf, column="type")

7.1 Generate plots describing the data

The following creates all the metric plots of the raw data.

lp_inf_metrics <- sm(graph_metrics(lp_inf))

Now visualize some relevant metrics.

## Repeat for the parasite
lp_inf_metrics$libsize

## Wow, the range of coverage is shockingly large
lp_inf_metrics$density

## But this looks much better I think
lp_inf_metrics$boxplot

excel_file <- glue::glue("excel/{rundate}_infection_parasite_data-v{ver}.xlsx")
lp_inf_written <- sm(write_expt(
  lp_inf,
  excel=excel_file,
  violin=TRUE))

7.1.1 Default normalization

Now perform the ‘default’ normalization we use in the lab and look again.

lp_inf_norm <- sm(normalize_expt(lp_inf, convert="cpm", filter=TRUE, norm="quant"))
lp_inf_norm_metrics <- sm(graph_metrics(lp_inf_norm))

7.2 PCA: Parasite edition

In this section, try out some normalizations/batch corrections and see the effect in PCA plots. Start out by taking the parasite data and doing the default normalization and see what there is to see.

lp_l2qcpm <- sm(normalize_expt(lp_inf, transform="log2", convert="cpm",
                               norm="quant", filter=TRUE))
lp_l2qcpm_pca <- sm(plot_pca(lp_l2qcpm))
lp_l2qcpm_pca$plot

## Though the colors don't show it well, the samples are actually split
## beautifully by strain, but clearly not by chronic/healing
knitr::kable(lp_l2qcpm_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
hpgl0631	HPGL0631	chr	d108	1	#990000	HPGL0631	-0.4834	-0.1571	-0.4834	-0.1571	-0.0722	-0.0559	0.0081	-0.0893	0.0068	-0.1928	0.2552	-0.0563	-0.7324
hpgl0632	HPGL0632	chr	d108	1	#990000	HPGL0632	0.2127	-0.1613	0.2127	-0.1613	-0.1133	-0.4873	0.2562	-0.2970	0.2275	0.5394	0.2876	-0.1083	0.0640
hpgl0635	HPGL0635	sh	d108	1	#000099	HPGL0635	0.0091	0.5149	0.0091	0.5149	-0.1514	-0.0644	-0.0006	-0.2383	0.6117	-0.3718	-0.2237	0.0021	0.0717
hpgl0636	HPGL0636	sh	d108	1	#000099	HPGL0636	0.2346	-0.1982	0.2346	-0.1982	-0.4736	0.6504	0.4162	0.0327	-0.0028	0.0064	0.0043	0.0251	-0.0030
hpgl0651	HPGL0651	chr	d110	2	#990000	HPGL0651	-0.4435	-0.1404	-0.4435	-0.1404	0.2364	0.1450	0.0070	0.2201	0.2264	0.4698	-0.5464	0.0213	0.0619
hpgl0652	HPGL0652	chr	d110	2	#990000	HPGL0652	0.2555	-0.1521	0.2555	-0.1521	0.5531	-0.0630	0.3166	0.3276	0.0498	-0.3492	0.0504	-0.4274	0.0345
hpgl0655	HPGL0655	sh	d110	2	#000099	HPGL0655	0.0465	0.4722	0.0465	0.4722	0.3609	0.1887	0.0624	0.1523	-0.0920	0.1990	0.4112	0.5292	-0.0367
hpgl0656	HPGL0656	sh	d110	2	#000099	HPGL0656	0.2658	-0.1949	0.2658	-0.1949	0.3006	0.3171	-0.5482	-0.5461	-0.0754	0.0283	-0.0398	-0.0980	-0.0277
hpgl0658	HPGL0658	chr	d107	3	#990000	HPGL0658	-0.4910	-0.1772	-0.4910	-0.1772	-0.0827	-0.0363	-0.0344	-0.1057	-0.1744	-0.2587	0.2901	0.0352	0.6640
hpgl0659	HPGL0659	chr	d107	3	#990000	HPGL0659	0.1896	-0.1541	0.1896	-0.1541	-0.0289	-0.3762	0.1575	-0.1017	-0.3967	-0.2285	-0.4631	0.4996	-0.0759
hpgl0662	HPGL0662	sh	d107	3	#000099	HPGL0662	-0.0270	0.5097	-0.0270	0.5097	-0.2156	-0.0618	-0.0703	0.0590	-0.5411	0.1763	-0.1379	-0.5042	-0.0205
hpgl0663	HPGL0663	sh	d107	3	#000099	HPGL0663	0.2311	-0.1617	0.2311	-0.1617	-0.3133	-0.1563	-0.5705	0.5864	0.1600	-0.0181	0.1122	0.0819	0.0000

lp_l2qcpm_tsne <- plot_tsne(lp_l2qcpm)
lp_l2qcpm_tsne$plot

##tt <- plot_tsne(lp_l2qcpm)
##ttt <- plot_tsne_genes(lp_l2qcpm)

Now repeat the same thing, but let sva minimize surrogate variables.

lp_l2qcpm_normbatch <- sm(normalize_expt(lp_inf, transform="log2", convert="cpm",
                                         norm="quant", filter=TRUE, batch="sva"))
lp_l2qcpm_normbatch_pca <- plot_pca(lp_l2qcpm_normbatch)

Now plot the result and see if things make more sense.

lp_l2qcpm_normbatch_pca$plot

Adding SVA to the normalization does not help much.

## That does nothing significant to clarify things.
knitr::kable(lp_l2qcpm_normbatch_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
hpgl0631	HPGL0631	chr	d108	1	#990000	HPGL0631	-0.0527	0.1296	-0.0527	0.1296	0.0128	-0.0996	0.0054	-0.1964	-0.2518	0.0458	-0.7325	0.3060	0.3908
hpgl0632	HPGL0632	chr	d108	1	#990000	HPGL0632	-0.1677	0.5102	-0.1677	0.5102	0.1896	-0.2647	-0.2426	0.5221	-0.3122	0.1044	0.0761	-0.2665	-0.0751
hpgl0635	HPGL0635	sh	d108	1	#000099	HPGL0635	0.2048	0.0813	0.2048	0.0813	-0.0165	-0.2151	-0.6266	-0.3626	0.1899	-0.0199	0.0840	0.3076	-0.3992
hpgl0636	HPGL0636	sh	d108	1	#000099	HPGL0636	0.6715	-0.2705	0.6715	-0.2705	0.5034	-0.0033	0.0216	0.0125	0.0030	-0.0065	-0.0155	-0.3237	0.1829
hpgl0651	HPGL0651	chr	d110	2	#990000	HPGL0651	-0.1976	-0.2089	-0.1976	-0.2089	0.0251	0.2322	-0.2492	0.4708	0.5229	0.0018	0.0682	0.2748	0.3770
hpgl0652	HPGL0652	chr	d110	2	#990000	HPGL0652	-0.5132	-0.2049	-0.5132	-0.2049	0.2930	0.3293	-0.0529	-0.3731	-0.0576	0.4210	0.0395	-0.2803	-0.1200
hpgl0655	HPGL0655	sh	d110	2	#000099	HPGL0655	-0.1283	-0.3735	-0.1283	-0.3735	0.0729	0.1816	0.1058	0.2083	-0.4016	-0.5505	-0.0375	0.2168	-0.3938
hpgl0656	HPGL0656	sh	d110	2	#000099	HPGL0656	-0.0954	-0.4679	-0.0954	-0.4679	-0.5002	-0.5689	0.0675	0.0442	0.0396	0.1077	-0.0311	-0.3065	0.0164
hpgl0658	HPGL0658	chr	d107	3	#990000	HPGL0658	-0.0216	0.1083	-0.0216	0.1083	-0.0269	-0.1194	0.2156	-0.2629	-0.2729	-0.0507	0.6593	0.2909	0.4213
hpgl0659	HPGL0659	chr	d107	3	#990000	HPGL0659	-0.1794	0.3524	-0.1794	0.3524	0.1061	-0.1025	0.3549	-0.2117	0.5055	-0.4896	-0.0802	-0.2479	-0.0683
hpgl0662	HPGL0662	sh	d107	3	#000099	HPGL0662	0.2234	0.1274	0.2234	0.1274	-0.0662	0.0588	0.5280	0.1886	0.1458	0.4985	-0.0260	0.3261	-0.3893
hpgl0663	HPGL0663	sh	d107	3	#000099	HPGL0663	0.2562	0.2165	0.2562	0.2165	-0.5932	0.5715	-0.1275	-0.0397	-0.1106	-0.0620	-0.0042	-0.2975	0.0574

No, not really, so lets change things by putting the ‘snp status’ as the “batch” factor and minimize it with sva/combat.

lp_infv2 <- set_expt_conditions(lp_inf, fact="state")
lp_infv2 <- set_expt_batches(lp_infv2, fact="snpclade")
lp_l2qcpm_snpbatch_straincond_sva <- sm(normalize_expt(lp_infv2, norm="quant",
                                                       transform="log2",
                                                       filter=TRUE,
                                                       batch="fsva"))

lp_l2qcpm_snpbatch_straincond_pca <- plot_pca(lp_l2qcpm_snpbatch_straincond_sva)
lp_l2qcpm_snpbatch_straincond_pca$plot

SNP status does not clarify things.

## Pulling strain 5430 away from the others makes a semi-split
knitr::kable(lp_l2qcpm_snpbatch_straincond_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
hpgl0631	HPGL0631	chronic	red	3	#1B9E77	HPGL0631	-0.2233	-0.1180	-0.2233	-0.1180	-0.1626	-0.0133	-0.1030	0.1025	0.2177	-0.1736	0.2353	0.7027	-0.4225
hpgl0632	HPGL0632	chronic	yellow	4	#1B9E77	HPGL0632	0.2409	-0.2099	0.2409	-0.2099	-0.4783	-0.1831	-0.1319	0.0033	-0.5194	-0.3886	0.2618	-0.1343	0.1659
hpgl0635	HPGL0635	self_heal	blue_self	1	#7570B3	HPGL0635	-0.3690	0.2797	-0.3690	0.2797	-0.1049	-0.0056	-0.6731	-0.3156	0.1194	0.1592	-0.0395	-0.0669	0.3049
hpgl0636	HPGL0636	self_heal	pink	2	#7570B3	HPGL0636	0.3640	0.6219	0.3640	0.6219	0.2171	-0.5507	0.0326	0.0468	0.0088	-0.0331	-0.0228	0.0129	-0.2046
hpgl0651	HPGL0651	chronic	red	3	#1B9E77	HPGL0651	-0.2033	-0.2060	-0.2033	-0.2060	0.2704	-0.0040	0.0978	-0.4131	-0.5420	0.2952	-0.1442	-0.0437	-0.4194
hpgl0652	HPGL0652	chronic	yellow	4	#1B9E77	HPGL0652	0.2441	-0.4701	0.2441	-0.4701	0.2249	-0.2550	0.0779	-0.1868	0.4005	0.3029	0.4120	-0.0866	0.2239
hpgl0655	HPGL0655	self_heal	blue_self	1	#7570B3	HPGL0655	-0.2868	-0.1165	-0.2868	-0.1165	0.4408	-0.0683	0.2135	-0.0201	0.0462	-0.6017	-0.2838	0.0532	0.3583
hpgl0656	HPGL0656	self_heal	pink	2	#7570B3	HPGL0656	0.3083	-0.0153	0.3083	-0.0153	0.4184	0.5021	-0.3521	0.4751	-0.1790	0.0751	0.0716	0.0292	0.0282
hpgl0658	HPGL0658	chronic	red	3	#1B9E77	HPGL0658	-0.2178	-0.0966	-0.2178	-0.0966	-0.1519	0.0273	-0.0103	0.2805	0.3243	-0.1313	-0.0699	-0.6577	-0.4443
hpgl0659	HPGL0659	chronic	yellow	4	#1B9E77	HPGL0659	0.2056	-0.2091	0.2056	-0.2091	-0.3343	-0.1000	0.0628	0.1968	0.0768	0.3108	-0.7093	0.1944	0.1512
hpgl0662	HPGL0662	self_heal	blue_self	1	#7570B3	HPGL0662	-0.3874	0.2922	-0.3874	0.2922	-0.1469	0.0817	0.4930	0.3180	-0.1611	0.3321	0.2996	0.0054	0.2877
hpgl0663	HPGL0663	self_heal	pink	2	#7570B3	HPGL0663	0.3247	0.2477	0.3247	0.2477	-0.1927	0.5689	0.2927	-0.4873	0.2079	-0.1470	-0.0107	-0.0086	-0.0295

Ok, so let us remove the healing state with combat and see if that allows us to see a split on some other factor.

lp_inf_strain <- set_expt_conditions(lp_inf, fact="pathogenstrain")
lp_inf_strain <- set_expt_batches(lp_inf_strain, fact="state")
lp_l2qcpm_strain <- sm(normalize_expt(lp_inf_strain, transform="log2", convert="cpm",
                                      norm="quant", filter=TRUE, batch="combat_scale"))

lp_l2qcpm_strain_pca <- plot_pca(lp_l2qcpm_strain)
lp_l2qcpm_strain_pca$plot

hmm ok, I think I quit for today.

plot_tsne(lp_l2qcpm_strain)$plot

hmm ok, I think I quit for today.

## wtf!?!?  how did this happen?
knitr::kable(lp_l2qcpm_strain_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
hpgl0631	HPGL0631	s5430	chronic	1	#1B9E77	HPGL0631	-0.4841	0.1044	-0.4841	0.1044	-0.0995	0.0189	-0.0847	0.0967	0.2492	0.1920	-0.0028	0.7299	-0.1137
hpgl0632	HPGL0632	s5397	chronic	1	#D95F02	HPGL0632	0.0172	-0.3172	0.0172	-0.3172	-0.3354	0.0868	-0.2829	-0.5622	-0.1517	0.3593	-0.0033	-0.0471	0.3811
hpgl0635	HPGL0635	s1022	self_heal	2	#7570B3	HPGL0635	0.2343	0.4341	0.2343	0.4341	-0.1534	0.0157	-0.1824	-0.2487	0.5802	-0.4389	-0.0304	-0.0938	0.1243
hpgl0636	HPGL0636	s2189	self_heal	2	#E7298A	HPGL0636	0.2099	-0.2127	0.2099	-0.2127	-0.2481	-0.7816	0.0074	0.0299	-0.0298	-0.0193	0.0149	-0.0067	-0.3903
hpgl0651	HPGL0651	s5430	chronic	1	#1B9E77	HPGL0651	-0.4547	0.0895	-0.4547	0.0895	0.2866	-0.0701	0.2007	-0.4370	-0.4048	-0.4468	-0.0750	-0.0666	-0.1000
hpgl0652	HPGL0652	s5397	chronic	1	#D95F02	HPGL0652	0.0498	-0.3352	0.0498	-0.3352	0.4981	-0.1558	0.3307	0.0988	0.3541	0.1199	-0.3471	-0.0296	0.3864
hpgl0655	HPGL0655	s1022	self_heal	2	#7570B3	HPGL0655	0.2551	0.3907	0.2551	0.3907	0.3821	-0.0974	0.0740	-0.0266	-0.1562	0.3296	0.6245	0.0516	0.1079
hpgl0656	HPGL0656	s2189	self_heal	2	#E7298A	HPGL0656	0.2370	-0.2186	0.2370	-0.2186	0.4112	0.2995	-0.5931	0.0924	-0.0808	-0.0534	-0.1483	0.0199	-0.4023
hpgl0658	HPGL0658	s5430	chronic	1	#1B9E77	HPGL0658	-0.4940	0.0937	-0.4940	0.0937	-0.1019	0.0411	-0.1086	0.2997	0.1651	0.2539	0.0853	-0.6625	-0.1106
hpgl0659	HPGL0659	s5397	chronic	1	#D95F02	HPGL0659	0.0009	-0.2999	0.0009	-0.2999	-0.2018	0.1018	-0.0402	0.4744	-0.1989	-0.4800	0.3474	0.0876	0.3882
hpgl0662	HPGL0662	s1022	self_heal	2	#7570B3	HPGL0662	0.2094	0.4507	0.2094	0.4507	-0.2060	0.0536	0.0859	0.2788	-0.4250	0.1354	-0.5677	0.0315	0.1306
hpgl0663	HPGL0663	s2189	self_heal	2	#E7298A	HPGL0663	0.2191	-0.1794	0.2191	-0.1794	-0.2319	0.4875	0.5932	-0.0961	0.0985	0.0483	0.1025	-0.0144	-0.4015

---
title: "L. panamensis 20190205: Sample Estiation of Infected PBMCs."
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
  html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    keep_md: false
    mode: selfcontained
    number_sections: true
    self_contained: true
    theme: readable
    toc: true
    toc_float:
      collapsed: false
      smooth_scroll: false
  rmdformats::readthedown:
    code_download: true
    code_folding: show
    df_print: paged
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    width: 300
    keep_md: false
    mode: selfcontained
    toc_float: true
  BiocStyle::html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    keep_md: false
    mode: selfcontained
    toc_float: true
---

<style type="text/css">
body, td {
  font-size: 16px;
}
code.r{
  font-size: 16px;
}
pre {
 font-size: 16px
}
</style>

```{r options, include=FALSE}
library("hpgltools")
tt <- devtools::load_all("~/hpgltools")
knitr::opts_knit$set(width=120,
                     progress=TRUE,
                     verbose=TRUE,
                     echo=TRUE)
knitr::opts_chunk$set(error=TRUE,
                      dpi=96)
old_options <- options(digits=4,
                       max.print=120,
                       stringsAsFactors=FALSE,
                       knitr.duplicate.label="allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size=10))
rundate <- format(Sys.Date(), format="%Y%m%d")
previous_file <- "01_annotation_20190205.Rmd"
ver <- "20190205"

tmp <- loadme(filename=paste0(gsub(pattern="\\.Rmd", replace="", x=previous_file), "-v", ver, ".rda.xz"))
rmd_file <- "02_estimation_infection_20190205.Rmd"
```

# Sample Estimation, PBMC Infections: `r ver`

Start out by extracting the relevant data and querying it to see the general quality.

This first block sets the names of the samples and colors.
It also makes separate data sets for:

* All features with infected and uninfected samples.
* All features with only the infected samples.
* Only CDS features with infected and uninfected samples.
* Only CDS features with only the infected samples.

```{r infection_expt}
## Reread the sample sheet because I am fiddling with other possible surrogates (like strain)
## In fact, copy it to a separate sheet because these samples are a mess
hs_infection <- subset_expt(hs_expt, subset="experimentname=='infection'")
chosen_colors <- c("#009900","#990000", "#000099")
names(chosen_colors) <- c("uninf","chr","sh")
hs_uninf <- set_expt_colors(hs_infection, colors=chosen_colors)
hs_inf <- subset_expt(hs_uninf, subset="condition!='uninf'")
uninf_newnames <- paste0(pData(hs_uninf)$label, "_", pData(hs_uninf)$donor)
hs_uninf <- set_expt_samplenames(hs_uninf, newnames=uninf_newnames)
inf_newnames <- paste0(pData(hs_inf)$label, "_", pData(hs_inf)$donor)
hs_inf <- set_expt_samplenames(hs_inf, newnames=inf_newnames)

hs_cds_infection <- subset_expt(hs_cds_expt, subset="experimentname=='infection'")
hs_cds_uninf <- set_expt_colors(hs_cds_infection, colors=chosen_colors)
hs_cds_inf <- subset_expt(hs_cds_uninf, subset="condition!='uninf'")
hs_cds_uninf <- set_expt_samplenames(hs_cds_uninf, newnames=uninf_newnames)
hs_cds_inf <- set_expt_samplenames(hs_cds_inf, newnames=inf_newnames)
##infection_model_test <- model_test(hs_cds_infection$design)

hs_cds_removeboth <- subset_expt(hs_cds_uninf, subset="pathogenstrain!='s2272'")
hs_cds_removeboth <- subset_expt(hs_cds_removeboth, subset="pathogenstrain!='s2504'")
```

## Generate plots describing the data

The following creates metric plots of the raw data.

```{r pbmc_plots, fig.show="hide"}
hs_uninf_met <- sm(graph_metrics(hs_uninf))
hs_inf_met <- sm(graph_metrics(hs_inf))
hs_cds_uninf_met <- sm(graph_metrics(hs_cds_uninf))
hs_cds_inf_met <- sm(graph_metrics(hs_cds_inf))

hs_cds_re_met <- sm(graph_metrics(hs_cds_removeboth))
```

Now let us visualize some of these metrics for the data set of all features
including the uninfected samples.

Start with the relative library sizes.  Note that this includes all feature types.

```{r pbmc_libsize_all}
hs_uninf_met$libsize
```

The picture is slightly different if we only look at coding sequences.

```{r pbmc_libsize_cds}
hs_cds_uninf_met$libsize

hs_cds_re_met$libsize
```

Look at the density of counts / feature for all samples.
Use density plots and boxplots to view this information.

```{r pbmc_density_all}
hs_uninf_met$density
hs_uninf_met$boxplot
## Wow there is a pretty big range in counts observed in this data!!

hs_cds_re_met$boxplot
```

```{r pbmc_density_cds}
hs_cds_uninf_met$density
hs_cds_uninf_met$boxplot
```

Now let us look at how the samples relate to each other via pairwise correlation heatmaps.
Once again, show this first for all features, then only cds features.

```{r pbmc_corheat_all}
hs_uninf_met$corheat
hs_cds_uninf_met$corheat
```

# Write the experiments

Having looked at these metrics, now let us write out the results in 4 excel workbooks,
representing the same 4 data sets.

```{r write_pbmc_excel, fig.show="hide"}
excel_file <- glue::glue("excel/{rundate}_hs_data-infection_with_uninf-v{ver}.xlsx")
hs_uninf_data <- sm(write_expt(
  hs_uninf, norm="quant", violin=FALSE, convert="cpm",
  transform="log2", batch="pca", filter=TRUE,
  excel=excel_file))
excel_file <- glue::glue("excel/{rundate}_hs_data-infection_no_uninf-v{ver}.xlsx")
hs_inf_data <- sm(write_expt(
  hs_inf, norm="quant", violin=FALSE, convert="cpm",
  transform="log2", batch="pca", filter=TRUE,
  excel=excel_file))

excel_file <- glue::glue("excel/{rundate}_hs_cds_data-infection_with_uninf-v{ver}.xlsx")
hs_cds_uninf_data <- sm(
  write_expt(hs_cds_uninf, norm="quant", violin=FALSE, convert="cpm",
             transform="log2", batch="pca", filter=TRUE,
             excel=excel_file))

excel_file <- glue::glue("excel/{rundate}_hs_cds_data-infection_no_uninf-v{ver}.xlsx")
hs_cds_inf_data <- sm(
  write_expt(hs_cds_inf, norm="quant", violin=FALSE, convert="cpm",
             transform="log2", batch="pca", filter=TRUE,
             excel=excel_file))
```

### Default normalization

Now perform the 'default' normalization we use in the lab and look again.

```{r normalize_infection, fig.show="hide"}
hs_uninf_cqf <- sm(normalize_expt(hs_uninf, convert="cpm", filter=TRUE, norm="quant"))
hs_uninf_met <- sm(graph_metrics(hs_uninf))
hs_inf_cqf <- sm(normalize_expt(hs_inf, convert="cpm", filter=TRUE, norm="quant"))
hs_inf_cqf_met <- sm(graph_metrics(hs_inf))

hs_inf_re_cqf <- sm(normalize_expt(hs_cds_removeboth, convert="cpm", filter=TRUE, norm="quant"))
hs_inf_re_cqf_met <- sm(graph_metrics(hs_inf_re_cqf))
hs_inf_re_scqf <- sm(normalize_expt(hs_cds_removeboth, convert="cpm",
                                    filter=TRUE, transform="log2", batch="svaseq"))
hs_inf_re_scqf_met <- sm(graph_metrics(hs_inf_re_scqf))
```

# Figure 4

Construct figure 4, this should include the following panels:

  a.  Library sizes of pbmc data
  b.  PCA of log2(quant(data)), with uninfected
  c.  PCA of log2(quant(data)), without uninfected
  d.  TSNE of b
  e.  TSNE of c

```{r figure_04}
pp(file="images/figure_4a.pdf")
hs_uninf_data$raw_libsize
dev.off()
pp(file="images/figure_4b.pdf")
hs_uninf_data$raw_scaled_pca
dev.off()
write.csv(hs_uninf_data$raw_scaled_pca_table, file="images/figure_4b.csv")
pp(file="images/figure_4c.pdf")
hs_inf_data$raw_scaled_pca
dev.off()
write.csv(hs_inf_data$raw_scaled_pca_table, file="images/figure_4c.csv")
pp(file="images/figure_4d.pdf")
hs_uninf_data$raw_tsne
dev.off()
pp(file="images/figure_4e.pdf")
hs_inf_data$raw_tsne
dev.off()

pp(file="images/figure_4a_cds.pdf")
hs_cds_uninf_data$raw_libsize
dev.off()
pp(file="images/figure_4b_cds.pdf")
hs_cds_uninf_data$raw_scaled_pca
dev.off()
write.csv(hs_cds_uninf_data$raw_scaled_pca_table, file="images/figure_4b_cds.csv")
pp(file="images/figure_4c_cds.pdf")
hs_cds_inf_data$raw_scaled_pca
dev.off()
write.csv(hs_cds_inf_data$raw_scaled_pca_table, file="images/figure_4c_cds.csv")
pp(file="images/figure_4d_cds.pdf")
hs_cds_uninf_data$raw_tsne
dev.off()
pp(file="images/figure_4e_cds.pdf")
hs_cds_inf_data$raw_tsne
dev.off()
```

## Start without the uninfected: no, patient, strain

Now let us try a few different ways of dealing with the batch effects/surrogate variables.
In each case, I will use a PCA plot to see how the method changes the sample clustering.

### PCA: No Batch correction

In this first iteration, we will log2(cpm(quant(filter()))) the data and leave the experimental
parameters as the default:
condition == the 6 strains, 3 chronic 3 self-healing
batch == the three patients p107

```{r no_uninfected_pca}
## Start with the non-uninfected, no batch correction
hs_inf_lqcf <- sm(normalize_expt(hs_inf, filter=TRUE, convert="cpm",
                                 transform="log2", norm="quant"))
hs_pca_inf_lqcf <- plot_pca(hs_inf_lqcf)
hs_pca_inf_lqcf$plot
## 3 three patients are super obvious I think
## The patients 107/108 are on the left while 110 is on the right.
knitr::kable(hs_pca_inf_lqcf$table)

hs_pca_inf_lqcf$variance
write.csv(hs_pca_inf_lqcf$pcatable, file="csv/infection_nouninfected_no_batch.csv")
## This shows clean sehumantion by patient
## Therefore we will now add patient as a surrogate variable and minimize it

hscds_inf_lqcf <- sm(normalize_expt(hs_cds_inf, filter=TRUE, convert="cpm",
                                    transform="log2", norm="quant"))
hscds_pca_inf_lqcf <- plot_pca(hscds_inf_lqcf)
hscds_pca_inf_lqcf$plot
```

### PCA: Repeat with combat adjustment

For the second iteration, use the same normalization, but add a combat correction in an attempt to
minimize patient's effect in the variance.

```{r patient_pca_no_uninfectedv1, fig.show="hide"}
hs_inf_lqcf_cbdonor <- sm(normalize_expt(hs_inf_lqcf, batch="combat_scale"))
```

```{r patient_pca_no_uninfectedv1pic}
## Here the split is semi chronic/self-healing, but not quite
hs_pca_inf_lqcf_cbdonor <- plot_pca(hs_inf_lqcf_cbdonor)
hs_pca_inf_lqcf_cbdonor$plot
testing <- make_3d_pca(hs_pca_inf_lqcf_cbdonor)
testing$plot
## There are 2 sh and 1 chr on the right vs. 2 chr and 1 sh on the left
knitr::kable(hs_pca_inf_lqcf_cbdonor$table)

hs_pca_inf_lqcf_cbdonor$variance
write.csv(hs_pca_inf_lqcf_cbdonor$pcatable, file="csv/infection_nouninfected_batch_patient.csv")
```

### Look at correlations between experimental factors and variance

```{r pca_information_v1, fig.show="hide"}
hs_inf_pcainfo <- pca_information(hs_inf, plot_pcas=TRUE,
                                  expt_factors=c("condition", "batch", "pathogenstrain",
                                                 "state", "donor", "rnangul"))
```

Look for significant correlations between the PCs and some factors in
the experimental design.

```{r pca_info_images}
hs_inf_pcainfo$anova_fstat_heatmap
hs_inf_pcainfo$pca_plots$PC2_PC6
```

### PCA: Change batch to strain and condition to patient+state

Here we will set the batch to the humansite strains and condition to a combination of the patient and
state state; then perform the pca again.

```{r change_condition_batch}
new_condition <- paste0(hs_inf$design$state, '_', hs_inf$design$donor)
hs_inf_strbatch <- set_expt_factors(hs_inf, batch="pathogenstrain", condition=new_condition)
hs_inf_lqcf_cbstr <- sm(normalize_expt(hs_inf_strbatch, transform="log2", convert="cpm",
                                       norm="quant", filter=TRUE, batch="combat_scale"))
```

```{r change_condition_batchpic}
hs_inf_lqcf_cbstr_pca <- plot_pca(hs_inf_lqcf_cbstr)
hs_inf_lqcf_cbstr_pca$plot
## Doing that kind of sucked the variance out of the data, but it did cause the
## samples to split by strain quite strongly
knitr::kable(hs_inf_lqcf_cbstr_pca$table)

hs_inf_lqcf_cbstr_pca$variance
write.csv(hs_inf_lqcf_cbstr_pca$pcatable, file="csv/infection_nouninfected_batch_strain.csv")
```

### PCA: Repeat but with just chronic/self-state

Now change only the condition to self/chronic and make super-explicit the split in the samples.

```{r change_condition_batch_chsh}
hs_inf_lqcf_cbstrv2 <- set_expt_conditions(hs_inf_lqcf_cbstr, fact="state")
hs_inf_lqcf_cbstrv2 <- set_expt_colors(hs_inf_lqcf_cbstrv2, colors=c("#880000","#000088"))
hs_inf_lqcf_cbstrv2_pca <- plot_pca(hs_inf_lqcf_cbstrv2)
hs_inf_lqcf_cbstrv2_pca$plot
## Thus 3 runs of chronic on the right and self-state on the left
knitr::kable(hs_inf_lqcf_cbstrv2_pca$table)
```

## Restart but include the uninfected samples

For the next few blocks we will just repeat what we did but include the
uninfected samples. Ideally doing so will have ~0 effect on the positions of the
sample types.

### PCA: +uninfected: No Batch correction

In this first example, we see why the uninfected samples were initially removed
from the analyses I think.

```{r with_uninfected_pca}
## Start with the non-uninfected, no batch correction
hs_uninf_lqcf <- sm(normalize_expt(hs_uninf, filter=TRUE, convert="cpm",
                                   transform="log2", norm="quant"))
hs_uninf_lqcf_pca <- plot_pca(hs_uninf_lqcf)
hs_uninf_lqcf_pca$plot
## In this case, the uninfected samples cause the p107/p108 samples to smoosh together
knitr::kable(hs_uninf_lqcf_pca$table)

hs_uninf_lqcf_pca$variance
write.csv(hs_uninf_lqcf_pca$pcatable, file="csv/infection_withuninfected_with_batch.csv")
```

### PCA: +uninfected Repeat with combat adjustment

For the second iteration, use the same normalization, but add a combat
correction in an attempt to minimize patient's effect in the variance.

```{r patient_pca_with_uninfected, fig.show="hide"}
hs_uninf_lqcf_cbdonor <- sm(normalize_expt(hs_uninf_lqcf, batch="combat_scale"))
```

```{r patient_pca_with_uninfectedpicx}
## Here the split is semi chronic/self-state, but not quite
hs_uninf_lqcf_cbdonor_pca <- plot_pca(hs_uninf_lqcf_cbdonor)
hs_uninf_lqcf_cbdonor_pca$plot
## Now we have weak sehumantion between strains, I thought for a moment it might
## be few snps vs. many but that is not true.
## There are 2 sh and 1 chr on the right vs. 2 chr and 1 sh on the left
knitr::kable(hs_uninf_lqcf_cbdonor_pca$table)

hs_uninf_lqcf_cbdonor_pca$variance
write.csv(hs_uninf_lqcf_cbdonor_pca$pcatable,
          file="csv/infection_withuninfected_batch_patient.csv")
```

### PCA: +uninfected, change the condition to chr/sh

Including the uninfected samples and changing the condition should not much matter

### PCA: +uninfected, Change batch to strain and condition to patient+state

```{r uninf_change_conditionbatch}
## Here we will set the batch to the humansite strains and condition to a
## combination of the patient and state state; then perform the pca.
new_condition <- paste0(hs_uninf$design$state, '_', hs_uninf$design$donor)
hs_uninfv2 <- set_expt_factors(hs_uninf, condition=new_condition, batch="pathogenstrain")
```

```{r uninf_change_conditionbatch1, fig.show="hide"}
hs_uninfv2_lqcf_cbstr <- sm(normalize_expt(hs_uninfv2, transform="log2", convert="cpm",
                                           norm="quant", filter=TRUE, batch="combat_scale"))
```

```{r uninf_change_condition_batch2}
hs_uninfv2_lqcf_cbstr_pca <- plot_pca(hs_uninfv2_lqcf_cbstr)
hs_uninfv2_lqcf_cbstr_pca$plot
## This is a surprise to me, I would have expected the uninfected to still push
## the other samples off to a side or somesuch.
knitr::kable(hs_uninfv2_lqcf_cbstr_pca$table)

hs_uninfv2_lqcf_cbstr_pca$variance
write.csv(hs_uninfv2_lqcf_cbstr_pca$pcatable,
          file="csv/infection_withuninfected_batch_strain.csv")
```

### PCA: +uninfected, Repeat but with just chronic/self-state

```{r uninf_change_condition_chsh}
hs_uninfv2_lqcf_cbstr <- set_expt_conditions(hs_uninfv2_lqcf_cbstr, fact="state")
hs_uninfv2_lqcf_cbstr <- set_expt_colors(hs_uninfv2_lqcf_cbstr,
                                         colors=c("#880000","#000088","#008800"))
hs_uninfv2_lqcf_cbstr_pca <- plot_pca(hs_uninfv2_lqcf_cbstr)
hs_uninfv2_lqcf_cbstr_pca$plot
knitr::kable(hs_uninfv2_lqcf_cbstr_pca$table)
```

### PCA: Try only using samples for 1 patient

As per a conversation with Maria Adelaida on skype, lets remove all samples except those for one
patient, then see if some aspect of the data jumps out (strain:strain variation, for example)

```{r single_patient}
single_patient <- subset_expt(hs_inf, subset="donor=='d107'")
single_patient <- set_expt_batches(single_patient, fact="state")
single_norm <- sm(normalize_expt(single_patient, transform="log2", norm="quant",
                                 convert="cpm", filter=TRUE))
single_norm_pca <- plot_pca(single_norm)
single_norm_pca$plot

single_patient <- subset_expt(hs_inf, subset="donor=='d108'")
single_patient <- set_expt_batches(single_patient, fact="state")
single_norm <- sm(normalize_expt(single_patient, transform="log2", norm="quant",
                                 convert="cpm", filter=TRUE))
single_norm_pca <- plot_pca(single_norm)
single_norm_pca$plot

single_patient <- subset_expt(hs_inf, subset="donor=='d110'")
single_patient <- set_expt_batches(single_patient, fact="state")
single_norm <- sm(normalize_expt(single_patient, transform="log2", norm="quant",
                                 convert="cpm", filter=TRUE))
single_norm_pca <- plot_pca(single_norm)
single_norm_pca$plot

## hmmm so the answer is, no -- I think.
knitr::kable(single_norm_pca$table)
```

### PCA: Re-label one sample

In our previous discussion, Hector suggested that sample 'HPGL0635' is
sufficiently dis-similar to its cohort samples that it might actually be a
member of strain '2504' rather than '1022'. Let us look and see what happens if
that is changed.

I am going to leave out the uninfected samples to avoid the confusion they
generate.

```{r change_singleton}
hs_lqcf_noswitch <- sm(normalize_expt(hs_inf, transform="log2", convert="cpm",
                                      norm="quant", filter=TRUE))
plot_pca(hs_lqcf_noswitch)$plot
## This is just to note that the original color for sample 635 was orange to
## match strain '1022'
##switch_one <- set_expt_condition(no_uninfected, ids=c("sHPGL0635"), fact="ch2504")
switch_one <- set_expt_conditions(hs_inf, ids=c("sh_1022_d108"), fact="chr")
switcher <- list("sh_1022_d108" = "pink")
switch_one <- set_expt_colors(expt=switch_one, colors=switcher, change_by="sample")
lqcf_switch <- sm(normalize_expt(switch_one, transform="log2", convert="cpm",
                                 norm="quant", filter=TRUE, batch="combat_scale"))
##plot_pca(lqcf_switch)$plot
## Note that now it is pink, matching 'ch2504'
```

I may be biased, but I think this suggests that the samples were not switched.

# Testing out some ideas

One query was to see if there is a reversal of two samples.

```{r testing_ideas}
combined_condition <- paste0(hs_inf$design$state, '_', hs_inf$design$donor)
## with_uninfected_combined <- set_expt_factors(with_uninfected,
##                                              batch="pathogenstrain",
##                                              condition=combined_condition)
hs_inf_combined <- set_expt_factors(hs_inf, batch="donor", condition="state")
head(exprs(normalize_expt(hs_inf, convert="cpm", filter=TRUE)))
combined_pca1 <- sm(normalize_expt(hs_inf_combined, filter=TRUE, batch="pca", convert="cpm"))
combined_pca1 <- set_expt_colors(combined_pca1, colors=c("#880000", "#000088"))
plot_pca(sm(normalize_expt(combined_pca1, filter=TRUE, transform="log2",
                           convert="cpm", norm="quant")))$plot
combined_pca2 <- set_expt_factors(combined_pca1, batch="pathogenstrain", condition="state")
combined_pca2 <- set_expt_colors(combined_pca2, colors=c("#880000", "#000088"))
combined_pca3 <- sm(normalize_expt(combined_pca2, filter=TRUE, batch="pca"))
l2cq_combined_pca3 <- sm(normalize_expt(combined_pca3, filter=TRUE, transform="log2",
                                        convert="cpm", norm="quant"))
plot_pca(l2cq_combined_pca3)$plot

donor_strain_varpart <- sm(varpart(expt=hs_inf, predictor=NULL,
                                   factors=c("condition","pathogenstrain","donor")))
donor_strain_varpart$percent_plot
pp(file="images/varpart_donor_strain.png")
donor_strain_varpart$partition_plot
dev.off()
pp(file="images/varpart_donor_strain_pct.png")
replot_varpart_percent(donor_strain_varpart, n=40)
dev.off()
sorted <- donor_strain_varpart$sorted_df
```

# Try out some limma invocations with interaction models

The experimental design does not fully supprt interaction models, but I want to see
how it looks.

```{r test_condition_strain_donor, eval=FALSE}
test_data <- sm(normalize_expt(hs_inf_combined, convert="cpm", norm="quant", filter=TRUE))
query_model_string <- "~ condition:pathogenstrain + donor"
query_design <- hs_inf_combined[["design"]]
query_conditions <- as.factor(query_design[["condition"]])
##query_batches <- as.factor(query_design[["anotherbatch"]])
query_batches <- as.factor(x=c("a","a","a","a","a","a","b","b","b","b","b","b","c","c","c","c","c","c"))
query_strains <- as.factor(query_design[["pathogenstrain"]])
query_donors <- as.factor(query_design[["donor"]])
data_mtrx <- as.data.frame(exprs(test_data))
query_model <- model.matrix(~ 0 + query_conditions + query_donors + query_strains, data=query_design)
combined_voom <- limma::voom(counts=data_mtrx, design=query_model, normalize.method="quantile")
combined_fit <- limma::lmFit(combined_voom, query_model, robust=TRUE)
combined_contrast <- limma::makeContrasts(
                                chsh=query_conditionschronic-query_conditionsself_heal,
                                levels=query_model)
combined_cfit <- limma::contrasts.fit(combined_fit, combined_contrast)
combined_bayes <- limma::eBayes(combined_cfit, robust=TRUE)
combined_table <- limma::topTable(combined_bayes, number=nrow(combined_bayes), resort.by="logFC")
hist(combined_table$adj.P.Val)
min(combined_table$adj.P.Val)
test_ma <- plot_ma_de(table=combined_table, expr_col="AveExpr", fc_col="logFC", p_col="adj.P.Val", logfc_cutoff=0.6)
test_ma$plot
head(combined_table)
```

Switch to the parasite transcriptome data
=========================================

# Look during infection

"Changes during infection hpgl0630-0636 and hpgl0650-hpgl0663"

Start out by creating the expt and poking at it to see how well/badly behaved the data is.

```{r infection_expt2}
## Reread the sample sheet because I am fiddling with other possible surrogates (like strain)
## In fact, copy it to a separate sheet because these samples are a mess
lp_inf <- subset_expt(parasite_expt, subset="experimentname=='infection'")
chosen_colors <- c("#990000", "#000099")
names(chosen_colors) <- c("chr","sh")
lp_inf <- set_expt_colors(lp_inf, colors=chosen_colors)
##lp_inf <- expt_exclude_genes(lp_inf, column="type")
```

## Generate plots describing the data

The following creates all the metric plots of the raw data.

```{r macrophage_plots, fig.show="hide"}
lp_inf_metrics <- sm(graph_metrics(lp_inf))
```

Now visualize some relevant metrics.

```{r macrophage_raw_metrics}
## Repeat for the parasite
lp_inf_metrics$libsize
## Wow, the range of coverage is shockingly large
lp_inf_metrics$density
## But this looks much better I think
lp_inf_metrics$boxplot
```

```{r macrophage_write_metrics, fig.show="hide"}
excel_file <- glue::glue("excel/{rundate}_infection_parasite_data-v{ver}.xlsx")
lp_inf_written <- sm(write_expt(
  lp_inf,
  excel=excel_file,
  violin=TRUE))
```

### Default normalization

Now perform the 'default' normalization we use in the lab and look again.

```{r normalize_infect, fig.show="hide"}
lp_inf_norm <- sm(normalize_expt(lp_inf, convert="cpm", filter=TRUE, norm="quant"))
lp_inf_norm_metrics <- sm(graph_metrics(lp_inf_norm))
```

## PCA: Parasite edition

In this section, try out some normalizations/batch corrections and see the
effect in PCA plots. Start out by taking the parasite data and doing the default
normalization and see what there is to see.

```{r pca_parasite}
lp_l2qcpm <- sm(normalize_expt(lp_inf, transform="log2", convert="cpm",
                               norm="quant", filter=TRUE))
lp_l2qcpm_pca <- sm(plot_pca(lp_l2qcpm))
lp_l2qcpm_pca$plot
## Though the colors don't show it well, the samples are actually split
## beautifully by strain, but clearly not by chronic/healing
knitr::kable(lp_l2qcpm_pca$table)

lp_l2qcpm_tsne <- plot_tsne(lp_l2qcpm)
lp_l2qcpm_tsne$plot

##tt <- plot_tsne(lp_l2qcpm)
##ttt <- plot_tsne_genes(lp_l2qcpm)
```

Now repeat the same thing, but let sva minimize surrogate variables.

```{r pca_sva, fig.show="hide"}
lp_l2qcpm_normbatch <- sm(normalize_expt(lp_inf, transform="log2", convert="cpm",
                                         norm="quant", filter=TRUE, batch="sva"))
lp_l2qcpm_normbatch_pca <- plot_pca(lp_l2qcpm_normbatch)
```

Now plot the result and see if things make more sense.

```{r pca_sva_plot, fig.cap="Adding SVA to the normalization does not help much."}
lp_l2qcpm_normbatch_pca$plot
## That does nothing significant to clarify things.
knitr::kable(lp_l2qcpm_normbatch_pca$table)
```

No, not really, so lets change things by putting the 'snp status' as the "batch"
factor and minimize it with sva/combat.

```{r pca_snp, fig.show="hide"}
lp_infv2 <- set_expt_conditions(lp_inf, fact="state")
lp_infv2 <- set_expt_batches(lp_infv2, fact="snpclade")
lp_l2qcpm_snpbatch_straincond_sva <- sm(normalize_expt(lp_infv2, norm="quant",
                                                       transform="log2",
                                                       filter=TRUE,
                                                       batch="fsva"))
```

```{r pca_snp_plot, fig.cap="SNP status does not clarify things."}
lp_l2qcpm_snpbatch_straincond_pca <- plot_pca(lp_l2qcpm_snpbatch_straincond_sva)
lp_l2qcpm_snpbatch_straincond_pca$plot
## Pulling strain 5430 away from the others makes a semi-split
knitr::kable(lp_l2qcpm_snpbatch_straincond_pca$table)
```

Ok, so let us remove the healing state with combat and see if that allows us to see
a split on some other factor.

```{r pca_heal, fig.show="hide"}
lp_inf_strain <- set_expt_conditions(lp_inf, fact="pathogenstrain")
lp_inf_strain <- set_expt_batches(lp_inf_strain, fact="state")
lp_l2qcpm_strain <- sm(normalize_expt(lp_inf_strain, transform="log2", convert="cpm",
                                      norm="quant", filter=TRUE, batch="combat_scale"))
```

```{r pca_heal_plot, fig.cap="hmm ok, I think I quit for today."}
lp_l2qcpm_strain_pca <- plot_pca(lp_l2qcpm_strain)
lp_l2qcpm_strain_pca$plot
plot_tsne(lp_l2qcpm_strain)$plot
## wtf!?!?  how did this happen?
knitr::kable(lp_l2qcpm_strain_pca$table)
```

```{r saveme, include=FALSE}
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))
pander::pander(sessionInfo())
```


L. panamensis 20190205: Sample Estiation of Infected PBMCs.

atb abelew@gmail.com

2019-02-08

1 Sample Estimation, PBMC Infections: 20190205

1.1 Generate plots describing the data

2 Write the experiments

2.0.1 Default normalization

3 Figure 4

3.1 Start without the uninfected: no, patient, strain

3.1.1 PCA: No Batch correction

3.1.2 PCA: Repeat with combat adjustment

3.1.3 Look at correlations between experimental factors and variance

3.1.4 PCA: Change batch to strain and condition to patient+state

3.1.5 PCA: Repeat but with just chronic/self-state

3.2 Restart but include the uninfected samples

3.2.1 PCA: +uninfected: No Batch correction

3.2.2 PCA: +uninfected Repeat with combat adjustment

3.2.3 PCA: +uninfected, change the condition to chr/sh

3.2.4 PCA: +uninfected, Change batch to strain and condition to patient+state

3.2.5 PCA: +uninfected, Repeat but with just chronic/self-state

3.2.6 PCA: Try only using samples for 1 patient

3.2.7 PCA: Re-label one sample

4 Testing out some ideas

5 Try out some limma invocations with interaction models

6 Switch to the parasite transcriptome data

7 Look during infection

7.1 Generate plots describing the data

7.1.1 Default normalization

7.2 PCA: Parasite edition