1 Differential Expression, Macrophage: 20180822

2 Differential expression analyses

It appears that it is possible though somewhat difficult to apply batch estimations generated by sva to the model given to DESeq/EdgeR/limma. In the case of limma it is fairly simple, but in the other two it is a bit more difficult. There is a nice discussion of this at: https://www.biostars.org/p/156186/ I am attempting to apply that logic to this data with limited success.

hs_contrasts <- list(
    "macro_chr-sh" = c("chr","sh"),
    "macro_chr-nil" = c("chr","uninf"),
    "macro_sh-nil" = c("sh", "uninf"))
## Set up the data used in the comparative contrast sets.

2.1 No batch in the model

2.1.1 Set up no batch

Print a reminder of what we can expect when doing this with no batch information.

hs_macr_lowfilt <- sm(normalize_expt(hs_cds_macr, filter=TRUE))
hs_lowfilt_pca <- sm(plot_pca(hs_cds_macr, transform="log2"))
hs_lowfilt_pca$plot

hs_macr_nobatch <- sm(all_pairwise(input=hs_cds_macr, model_batch=FALSE, parallel=FALSE,
                                   limma_method="robust"))

## wow, all tools including basic agree almost completely
medians_by_condition <- hs_macr_nobatch$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_nobatch_contr-v{ver}.xlsx")
hs_macr_nobatch_tables <- sm(combine_de_tables(hs_macr_nobatch,
                                               excel=excel_file,
                                               keepers=hs_contrasts,
                                               extra_annot=medians_by_condition))

excel_file <- glue::glue("excel/{rundate}_hs_macr_nobatch_sig-v{ver}.xlsx")
hs_macr_nobatch_sig <- sm(extract_significant_genes(hs_macr_nobatch_tables,
                                                    excel=excel_file,
                                                    according_to="all"))

2.2 Batch in the model

2.2.1 Batch setup

hs_lowfilt_batch_pca <- sm(plot_pca(hs_cds_macr, transform="log2", batch="limma"))
hs_lowfilt_batch_pca$plot

In this attempt, we add a batch factor in the experimental model and see how it does.

## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
hs_macr_batch <- sm(all_pairwise(input=hs_cds_macr, limma_method="robust", parallel=FALSE))

medians_by_condition <- hs_macr_batch$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_batchmodel_contr-v{ver}.xlsx")
hs_macr_batch_tables <- sm(combine_de_tables(
  hs_macr_batch,
  keepers=hs_contrasts,
  extra_annot=medians_by_condition,
  include_limma=FALSE, include_edger=FALSE, include_basic=FALSE, include_ebseq=FALSE,
  excel=excel_file))
excel_file <- glue::glue("excel/{rundate}_hs_macr_batchmodel_sig-v{ver}.xlsx")
hs_macr_batch_sig <- sm(extract_significant_genes(
  hs_macr_batch_tables, excel=excel_file,
  according_to="deseq"))
excel_file <- glue::glue("excel/{rundate}_hs_macr_batchmodel_abund-v{ver}.xlsx")
hs_macr_batch_abun <- sm(extract_abundant_genes(
  hs_macr_batch_tables, excel=excel_file,
  according_to="deseq"))

3 Table S2 and Figure 1b, Table S3

Table S2 is taking only the DESeq2 results.
Figure 1c is intended to be a volcano plot of the DESeq2 results.

s2_contrasts <- list(
  "macro_chr-sh" = c("chr","sh"))
excel_file <- glue::glue("excel/{rundate}_table-s2_hs_macr_batchmodel_contr-v{ver}.xlsx")
table_s2 <- sm(combine_de_tables(
  hs_macr_batch,
  excel=excel_file,
  keepers=s2_contrasts,
  include_basic=FALSE, include_limma=FALSE,
  include_ebseq=FALSE, include_edger=FALSE))
excel_file <- glue::glue("excel/{rundate}_table-s3_hs_macr_batchmodel_sig-v{ver}.xlsx")
table_s3 <- sm(extract_significant_genes(
  table_s2,
  excel=excel_file,
  according_to="deseq"))

chosen_table <- table_s2[["data"]][[1]]
head(chosen_table)

##                 ensembltranscriptid   ensemblgeneid hgncsymbol
## ENSG00000000003     ENST00000373020 ENSG00000000003     TSPAN6
## ENSG00000000005     ENST00000373031 ENSG00000000005       TNMD
## ENSG00000000419     ENST00000371582 ENSG00000000419       DPM1
## ENSG00000000457     ENST00000367770 ENSG00000000457      SCYL3
## ENSG00000000460     ENST00000286031 ENSG00000000460   C1orf112
## ENSG00000000938     ENST00000374003 ENSG00000000938        FGR
##                                                                                                    description
## ENSG00000000003                                              tetraspanin 6 [Source:HGNC Symbol;Acc:HGNC:11858]
## ENSG00000000005                                                tenomodulin [Source:HGNC Symbol;Acc:HGNC:17757]
## ENSG00000000419 dolichyl-phosphate mannosyltransferase subunit 1, catalytic [Source:HGNC Symbol;Acc:HGNC:3005]
## ENSG00000000457                                   SCY1 like pseudokinase 3 [Source:HGNC Symbol;Acc:HGNC:19285]
## ENSG00000000460                        chromosome 1 open reading frame 112 [Source:HGNC Symbol;Acc:HGNC:25565]
## ENSG00000000938              FGR proto-oncogene, Src family tyrosine kinase [Source:HGNC Symbol;Acc:HGNC:3697]
##                    genebiotype deseq_logfc deseq_adjp deseq_basemean
## ENSG00000000003 protein_coding    -0.98470     1.0000          1.444
## ENSG00000000005 protein_coding     0.00000     1.0000          0.000
## ENSG00000000419 protein_coding     0.08404     0.5659        417.900
## ENSG00000000457 protein_coding    -0.11690     0.6087        205.300
## ENSG00000000460 protein_coding    -0.20640     0.3532        176.700
## ENSG00000000938 protein_coding     0.43850     0.2213       5293.000
##                 deseq_lfcse deseq_stat deseq_p deseq_adjp_fdr
## ENSG00000000003     1.24100    -0.7937 0.42730      1.000e+00
## ENSG00000000005     0.00000     0.0000 1.00000      1.000e+00
## ENSG00000000419     0.08866     0.9479 0.34320      1.000e+00
## ENSG00000000457     0.13560    -0.8624 0.38850      1.000e+00
## ENSG00000000460     0.14610    -1.4130 0.15760      1.000e+00
## ENSG00000000938     0.24590     1.7830 0.07457      7.749e-01

vol <- plot_volcano_de(table=chosen_table,
                       color_by="state",
                       fc_col="deseq_logfc",
                       p_col="deseq_adjp",
                       shapes_by_state=FALSE,
                       line_position="top")

## The color list must have 4, setting it to the default.

pp(file="images/Figure_1c.pdf")

## Going to write the image to: images/Figure_1c.pdf when dev.off() is called.

vol$plot
dev.off()

## png 
##   2

3.1 Batch estimated with SVA

3.1.1 Set up sva

hs_lowfilt_svaseq_pca <- sm(plot_pca(hs_cds_macr, transform="log2", batch="svaseq", filter=TRUE))
hs_lowfilt_svaseq_pca$plot

hs_cds_macr_lowfilt <- sm(normalize_expt(hs_cds_macr, filter=TRUE))
## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
hs_macr_sva <- sm(all_pairwise(
  input=hs_cds_macr_lowfilt,
  model_batch="svaseq",
  limma_method="robust"))
medians_by_condition <- hs_macr_sva$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_sva_contr-v{ver}.xlsx")
hs_macr_sva_tables <- sm(combine_de_tables(
  hs_macr_sva,
  excel=excel_file,
  keepers=hs_contrasts,
  extra_annot=medians_by_condition))
excel_file <- glue::glue("excel/{rundate}_hs_macr_sva_sig-v{ver}.xlsx")
hs_macr_sva_sig <- sm(extract_significant_genes(
  hs_macr_sva_tables,
  excel=excel_file,
hs_macr_sva_ma_limma <- extract_de_plots(
  pairwise=hs_macr_sva,
  type="limma",
  table="sh_vs_chr")

## Error: <text>:22:0: unexpected end of input
## 20:   type="limma",
## 21:   table="sh_vs_chr")
##    ^

hs_macr_sva_ma_limma$ma$plot

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

3.2 Batch correction via ruv residuals

3.2.1 Set up ruvresiduals

## hmm I got the RUVr error again, but when I ran it manually did not.
## Even more strangely, if I just run the same thing again, no error...
testme <- try(get_model_adjust(input=hs_macr_lowfilt, estimate_type="ruv_residuals"), silent=TRUE)

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

## Attempting ruvseq residual surrogate estimation with 1 surrogates.

hs_lowfilt_ruvresid_pca <- sm(plot_pca(hs_macr_lowfilt, transform="log2", batch="ruv_residuals"))
hs_lowfilt_ruvresid_pca$plot

## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
## Bizarrely, sometimes if one runs this, it gives an error "Error in (function (classes, fdef, mtable) : unable to find an inherited method for function 'RUVr' for signature '"matrix", "logical", "numeric", "NULL"'"  -- however, if one then simply runs it again it works fine.
## I am going to assume that it is because I do not explicitly invoke the library.
## library(ruv)  ## hopefully a small code change made this not needed.
testme <- get_model_adjust(input=hs_macr_lowfilt, estimate_type="ruv_residuals")

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

## Attempting ruvseq residual surrogate estimation with 1 surrogates.

hs_macr_ruvres <- sm(all_pairwise(
  input=hs_macr_lowfilt,
  model_batch="ruv_residuals",
  limma_method="robust"))

medians_by_condition <- hs_macr_ruvres$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_ruvres_contr-v{ver}.xlsx")
hs_macr_ruvres_tables <- sm(combine_de_tables(
  hs_macr_ruvres,
  excel=excel_file,
  extra_annot=medians_by_condition,
  keepers=hs_contrasts))

excel_file <- glue::glue("excel/{rundate}_hs_macr_ruvres_sig-v{ver}.xlsx")
hs_macr_ruvres_sig <- sm(extract_significant_genes(
  hs_macr_ruvres_tables,
  excel=excel_file))

3.3 Batch correction with pca

3.3.1 Setup pca

hs_lowfilt_pca_pca <- sm(plot_pca(hs_macr_lowfilt, transform="log2", batch="pca"))
hs_lowfilt_pca_pca$plot

## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
hs_macr_pca <- sm(all_pairwise(
  input=hs_macr_lowfilt,
  model_batch="pca",
  limma_method="robust"))
medians_by_condition <- hs_macr_pca$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_pca_contr-v{ver}.xlsx")
hs_macr_pca_tables <- sm(combine_de_tables(
  hs_macr_pca,
  excel=excel_file
  keepers=hs_contrasts,
  extra_annot=medians_by_condition))
excel_file <- glue::glue("excel/{rundate}_hs_macr_pca_sig-v{ver}.xlsx")
hs_macr_pca_sig <- sm(extract_significant_genes(
  hs_macr_pca_tables,
  excel=excel_file))

## Error: <text>:11:3: unexpected symbol
## 10:   excel=excel_file
## 11:   keepers
##       ^

3.4 Batch correction with ruv empirical

3.4.1 Setup ruv empirical

hs_lowfilt_ruvemp_pca <- sm(plot_pca(hs_macr_lowfilt, transform="log2", batch="ruv_empirical"))
hs_lowfilt_ruvemp_pca$plot

hs_macr_ruvemp <- sm(all_pairwise(
  input=hs_macr_lowfilt,
  model_batch="ruv_empirical",
  limma_method="robust"))

medians_by_condition <- hs_macr_ruvemp$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_ruvemp_contr-v{ver}.xlsx")
hs_macr_ruvemp_tables <- sm(combine_de_tables(
  hs_macr_ruvemp,
  excel=excel_file,
  keepers=hs_contrasts,
  extra_annot=medians_by_condition))

excel_file <- glue::glue("excel/{rundate}_hs_macr_ruvemp_sig-v{ver}.xlsx")
hs_macr_ruvemp_sig <- sm(extract_significant_genes(
  hs_macr_ruvemp_tables,
  excel=excel_file))

3.5 Batch correction with combat

Then repeat with the batch-corrected data and see the differences.

3.5.1 Setup combat

hs_lowfilt_combat_pca <- sm(plot_pca(hs_macr_lowfilt, transform="log2", batch="combat_noprior"))
hs_lowfilt_combat_pca$plot

hs_macr_combat <- sm(all_pairwise(
  input=hs_macr_combat_norm,
  force=TRUE,
  limma_method="robust"))

## Error in normalize_expt(input, filter = TRUE, batch = FALSE, transform = "log2", : object 'hs_macr_combat_norm' not found

medians_by_condition <- hs_macr_combat$basic$medians

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

excel_file <- glue::glue("excel/{rundate}_hs_macr_combat_contr-v{ver}.xlsx")
hs_macr_combat_tables <- sm(combine_de_tables(
  hs_macr_combat,
  excel=excel_file,
  keepers=hs_contrasts,
  extra_annot=medians_by_condition))

## Error in combine_de_tables(hs_macr_combat, excel = excel_file, keepers = hs_contrasts, : object 'hs_macr_combat' not found

excel_file <- glue::glue("excel/{rundate}_hs_macr_combat_contr-v{ver}.xlsx")
hs_macr_combat_sig <- sm(extract_significant_genes(
  hs_macr_combat_tables,
  excel=excel_file))

## Error in extract_significant_genes(hs_macr_combat_tables, excel = excel_file): object 'hs_macr_combat_tables' not found

hs_macr_combat_ma_limma <- extract_de_plots(
  pairwise=hs_macr_combat,
  type="limma",
  table="sh_vs_chr")

## Error in extract_de_plots(pairwise = hs_macr_combat, type = "limma", table = "sh_vs_chr"): object 'hs_macr_combat' not found

hs_macr_combat_ma_limma$ma$plot

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

hs_macr_combat_ma_edger <- extract_de_plots(
  pairwise=hs_macr_combat,
  type="edger",
  table="sh_vs_chr")

## Error in extract_de_plots(pairwise = hs_macr_combat, type = "edger", table = "sh_vs_chr"): object 'hs_macr_combat' not found

hs_macr_combat_ma_edger$ma$plot

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

hs_macr_combat_ma_deseq <- extract_de_plots(
  pairwise=hs_macr_combat,
  type="deseq",
  table="sh_vs_chr")

## Error in extract_de_plots(pairwise = hs_macr_combat, type = "deseq", table = "sh_vs_chr"): object 'hs_macr_combat' not found

hs_macr_combat_ma_deseq$ma$plot

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

4 Figure out how to compare these results

I have 4 methods of performing this differential expression analysis. Each one comes with a set of metrics defining ‘significant’. Perhaps I can make a table of the # of genes defined as significant by contrast for each. In addition it may be worth while to do a scatter plots of the logFCs between these comparisons and see how well they agree?

5 Look first at the de counts

hs_macr_nobatch_sig$limma$counts

##               change_counts_up change_counts_down
## macro_chr-sh              1032                261
## macro_chr-nil              711                989
## macro_sh-nil               271               1222

##hs_macr_batch_tables$significant$limma$counts
##hs_macr_sva_tables$significant$limma$counts
##hs_macr_ruvres_tables$significant$limma$counts
##hs_macr_pca_tables$significant$limma$counts
##hs_macr_ruvemp_tables$significant$limma$counts
##hs_macr_combat_tables$significant$limma$counts

5.1 Compare DeSeq / Basic without batch in model

hs_macr_nobatch_basic <- merge(
  hs_macr_nobatch$deseq$all_tables$sh_vs_chr,
  hs_macr_batch$basic$all_tables$sh_vs_chr,
  by="row.names")
rownames(hs_macr_nobatch_basic) <- hs_macr_nobatch_basic[["Row.names"]]
hs_macr_nobatch_logfc <- hs_macr_nobatch_basic[, c("logFC.x", "logFC.y")]
colnames(hs_macr_nobatch_logfc) <- c("nobatch", "basic")
lfc_nb_b <- sm(plot_linear_scatter(hs_macr_nobatch_logfc, pretty_colors=FALSE))
lfc_nb_b$scatter

lfc_nb_b$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 280, df = 13000, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.9242 0.9290
## sample estimates:
##    cor 
## 0.9266

hs_macr_nobatch_p <- hs_macr_nobatch_basic[, c("P.Value","p")]
hs_macr_nobatch_p[[2]] <- as.numeric(hs_macr_nobatch_p[[2]])
colnames(hs_macr_nobatch_p) <- c("nobatch","basic")
hs_macr_nobatch_p <- -1 * log(hs_macr_nobatch_p)
hs_macr_p_nb_b <- sm(plot_linear_scatter(hs_macr_nobatch_p, pretty_colors=FALSE))
hs_macr_p_nb_b$scatter

hs_macr_p_nb_b$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 100, df = 13000, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6618 0.6806
## sample estimates:
##    cor 
## 0.6713

5.2 Compare SVA to batch in model, DESeq

hs_macr_sva_batch <- merge(
  hs_macr_sva$deseq$all_tables$sh_vs_chr,
  hs_macr_batch$deseq$all_tables$sh_vs_chr,
  by="row.names")

## Error in merge(hs_macr_sva$deseq$all_tables$sh_vs_chr, hs_macr_batch$deseq$all_tables$sh_vs_chr, : object 'hs_macr_sva' not found

rownames(hs_macr_sva_batch) <- hs_macr_sva_batch[["Row.names"]]

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

hs_macr_sva_logfc <- hs_macr_sva_batch[, c("logFC.x","logFC.y")]

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

colnames(hs_macr_sva_logfc) <- c("sva","batch")

## Error in colnames(hs_macr_sva_logfc) <- c("sva", "batch"): object 'hs_macr_sva_logfc' not found

hs_macr_lfc_b_s <- sm(plot_linear_scatter(hs_macr_sva_logfc, pretty_colors=FALSE))

## Error in data.frame(df[, c(1, 2)]): object 'hs_macr_sva_logfc' not found

hs_macr_lfc_b_s$scatter

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

hs_macr_lfc_b_s$correlation

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

5.2.1 Include p-value estimations

Try putting some information of the p-values with the comparative log2fc

lfc_b_s <- hs_macr_sva_batch[, c("logFC.x", "logFC.y", "P.Value.x", "P.Value.y")]

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

colnames(lfc_b_s) <- c("l2fcsva", "l2fcbatch", "psva", "pbatch")

## Error in colnames(lfc_b_s) <- c("l2fcsva", "l2fcbatch", "psva", "pbatch"): object 'lfc_b_s' not found

hs_macr_lfc_b_s$scatter

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

cutoff <- 0.1
lfc_b_s$state <- ifelse(lfc_b_s$psva > cutoff & lfc_b_s$pbatch > cutoff, "bothinsig",
                 ifelse(lfc_b_s$psva <= cutoff & lfc_b_s$pbatch <= cutoff, "bothsig",
                 ifelse(lfc_b_s$psva <= cutoff, "svasig", "batchsig")))

## Error in ifelse(lfc_b_s$psva > cutoff & lfc_b_s$pbatch > cutoff, "bothinsig", : object 'lfc_b_s' not found

##lfcp_b_s$lfcstate <- ifelse(lfcp_b_s$l2fcsva >= 0.75 & lfcp_b_s$l2fcbatch, "", "")
num_bothinsig <- sum(lfc_b_s$state == "bothinsig")

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

num_bothsig <- sum(lfc_b_s$state == "bothsig")

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

num_svasig <- sum(lfc_b_s$state == "svasig")

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

num_batchsig <- sum(lfc_b_s$state == "batchsig")

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

library(ggplot2)
aes_color = "(l2fcsva >= 0.75 | l2fcsva <= -0.75 | l2fcbatch >= 0.75 | l2fcbatch <= -0.75)"
plt <- ggplot2::ggplot(lfc_b_s, aes_string(x="l2fcsva", y="l2fcbatch")) +
    ## ggplot2::geom_point(stat="identity", size=2, alpha=0.2, aes_string(shape="as.factor(aes_color)", colour="as.factor(state)", fill="as.factor(state)")) +
    ggplot2::geom_abline(colour="blue", slope=1, intercept=0, size=0.5) +
    ggplot2::geom_hline(yintercept=c(-0.75, 0.75), color="red", size=0.5) +
    ggplot2::geom_vline(xintercept=c(-0.75, 0.75), color="red", size=0.5) +
    ggplot2::geom_point(stat="identity", size=2, alpha=0.2, aes_string(colour="as.factor(state)", fill="as.factor(state)")) +
    ggplot2::scale_color_manual(name="state", values=c("bothinsig"="grey", "bothsig"="forestgreen", "svasig"="darkred", "batchsig"="darkblue")) +
    ggplot2::scale_fill_manual(name="state", values=c("bothinsig"="grey", "bothsig"="forestgreen", "svasig"="darkred", "batchsig"="darkblue"),
                               labels=c(
                                   paste0("Both InSig.: ", num_bothinsig),
                                   paste0("Both Sig.: ", num_bothsig),
                                   paste0("Sva Sig.: ", num_svasig),
                                   paste0("Batch Sig.: ", num_batchsig)),
                               guide=ggplot2::guide_legend(override.aes=aes(size=3, fill="grey"))) +
    ggplot2::guides(fill=ggplot2::guide_legend(override.aes=list(size=3))) +
    ggplot2::theme_bw()

## Error in ggplot2::ggplot(lfc_b_s, aes_string(x = "l2fcsva", y = "l2fcbatch")): object 'lfc_b_s' not found

plt

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

5.3 Compare ruvresid to batch in model, DESeq

hs_macr_batch_ruvresid_deseq <- merge(
  hs_macr_ruvres$deseq$all_tables$sh_vs_chr,
  hs_macr_batch$basic$all_tables$sh_vs_chr,
  by="row.names")
rownames(hs_macr_batch_ruvresid_deseq) <- hs_macr_batch_ruvresid_deseq[["Row.names"]]
hs_macr_batch_ruvresid_logfc <- hs_macr_batch_ruvresid_deseq[, c("logFC.x","logFC.y")]
colnames(hs_macr_batch_ruvresid_logfc) <- c("nobatch","basic")
lfc_ruv_bat <- plot_linear_scatter(hs_macr_batch_ruvresid_logfc, pretty_colors=FALSE)

## Warning in plot_multihistogram(df): NAs introduced by coercion

## Used Bon Ferroni corrected t test(s) between columns.

lfc_ruv_bat$scatter

lfc_ruv_bat$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 440, df = 13000, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.9663 0.9685
## sample estimates:
##    cor 
## 0.9674

5.4 Compare no batch to batch in model, limma

hs_macr_nobatch_batch <- merge(
  hs_macr_nobatch$limma$all_tables$sh_vs_chr,
  hs_macr_batch$limma$all_tables$sh_vs_chr,
  by="row.names")
rownames(hs_macr_nobatch_batch) <- hs_macr_nobatch_batch[["Row.names"]]
hs_macr_nobatch_batch <- hs_macr_nobatch_batch[, c("logFC.x","logFC.y")]
colnames(hs_macr_nobatch_batch) <- c("nobatch","batch")
nb_b <- plot_linear_scatter(hs_macr_nobatch_batch, pretty_colors=FALSE)

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in lmrob.S(x, y, control = control): find_scale() did not converge
## in 'maxit.scale' (= 200) iterations with tol=1e-10, last rel.diff=0

## Warning in outlierStats(ret, x, control): Detected possible local breakdown of SMDM-estimate in 2 coefficients 'Overall', ''.
## Use lmrob argument 'setting="KS2014"' to avoid this problem.

## Warning in plot_multihistogram(df): NAs introduced by coercion

## Used Bon Ferroni corrected t test(s) between columns.

nb_b$scatter

nb_b$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 130, df = 51000, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4918 0.5048
## sample estimates:
##    cor 
## 0.4983

5.5 Batch in model vs. SVA, limma

hs_macr_batch_sva <- merge(
  hs_macr_batch$limma$all_tables$sh_vs_chr,
  hs_macr_sva$limma$all_tables$sh_vs_chr,
  by="row.names")

## Error in as.data.frame(y): object 'hs_macr_sva' not found

rownames(hs_macr_batch_sva) <- hs_macr_batch_sva[["Row.names"]]

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

hs_macr_batch_sva <- hs_macr_batch_sva[, c("logFC.x","logFC.y")]

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

colnames(hs_macr_batch_sva) <- c("batch","sva")

## Error in colnames(hs_macr_batch_sva) <- c("batch", "sva"): object 'hs_macr_batch_sva' not found

b_s <- plot_linear_scatter(hs_macr_batch_sva, pretty_colors=FALSE)

## Error in data.frame(df[, c(1, 2)]): object 'hs_macr_batch_sva' not found

b_s$scatter

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

b_s$correlation

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

5.6 Nobatch vs. batch in model, edger

hs_macr_nobatch_batch <- merge(
  hs_macr_nobatch$edger$all_tables$sh_vs_chr,
  hs_macr_batch$edger$all_tables$sh_vs_chr,
  by="row.names")
rownames(hs_macr_nobatch_batch) <- hs_macr_nobatch_batch[["Row.names"]]
hs_macr_nobatch_batch <- hs_macr_nobatch_batch[, c("logFC.x","logFC.y")]
colnames(hs_macr_nobatch_batch) <- c("nobatch","batch")
nb_b <- sm(plot_linear_scatter(hs_macr_nobatch_batch, pretty_colors=FALSE))
nb_b$scatter

nb_b$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 320, df = 51000, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.8135 0.8193
## sample estimates:
##    cor 
## 0.8164

5.7 Batch in model vs. SVA, edger

hs_macr_batch_sva <- merge(
  hs_macr_batch$edger$all_tables$sh_vs_chr,
  hs_macr_sva$edger$all_tables$sh_vs_chr,
  by="row.names")

## Error in as.data.frame(y): object 'hs_macr_sva' not found

rownames(hs_macr_batch_sva) <- hs_macr_batch_sva[["Row.names"]]

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

hs_macr_batch_sva <- hs_macr_batch_sva[, c("logFC.x","logFC.y")]

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

colnames(hs_macr_batch_sva) <- c("batch","sva")

## Error in colnames(hs_macr_batch_sva) <- c("batch", "sva"): object 'hs_macr_batch_sva' not found

b_s <- plot_linear_scatter(hs_macr_batch_sva, pretty_colors=FALSE)

## Error in data.frame(df[, c(1, 2)]): object 'hs_macr_batch_sva' not found

b_s$scatter

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

b_s$correlation

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

5.8 Compare nobatch vs. batch, deseq

hs_macr_nobatch_batch <- merge(
  hs_macr_nobatch$deseq$all_tables$sh_vs_chr,
  hs_macr_batch$deseq$all_tables$sh_vs_chr,
  by="row.names")
rownames(hs_macr_nobatch_batch) <- hs_macr_nobatch_batch[["Row.names"]]
hs_macr_nobatch_batch <- hs_macr_nobatch_batch[, c("logFC.x","logFC.y")]
colnames(hs_macr_nobatch_batch) <- c("nobatch","batch")
nb_b <- sm(plot_linear_scatter(hs_macr_nobatch_batch, pretty_colors=FALSE))
nb_b$scatter

nb_b$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 190, df = 51000, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6286 0.6390
## sample estimates:
##    cor 
## 0.6338

5.9 Compare batch vs. SVA, deseq

hs_macr_batch_sva <- merge(
  hs_macr_batch$deseq$all_tables$sh_vs_chr,
  hs_macr_sva$deseq$all_tables$sh_vs_chr,
  by="row.names")

## Error in as.data.frame(y): object 'hs_macr_sva' not found

rownames(hs_macr_batch_sva) <- hs_macr_batch_sva[["Row.names"]]

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

hs_macr_batch_sva <- hs_macr_batch_sva[, c("logFC.x", "logFC.y")]

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

colnames(hs_macr_batch_sva) <- c("batch", "sva")

## Error in colnames(hs_macr_batch_sva) <- c("batch", "sva"): object 'hs_macr_batch_sva' not found

b_s <- sm(plot_linear_scatter(hs_macr_batch_sva, pretty_colors=FALSE))

## Error in data.frame(df[, c(1, 2)]): object 'hs_macr_batch_sva' not found

b_s$scatter

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

b_s$correlation

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

6 Repeat using the parasite data

In ‘macrophage_estimation’, we did a series of analyses to try to pick out some of the surrogate variables in the data. Now we will perform a set of differential expression analyses using the results from that. Since the ‘batch’ element of the data is reasonably well explained, we will not abuse the data with sva/combat, but instead include batch in the experimental model.

lp_contrasts <- list(
    "macro_chr-sh" = c("chr", "sh"))
lp_macr_norm <- sm(normalize_expt(lp_macr, filter=TRUE, convert="cpm", norm="quant"))
lp_macr_combat_norm <- sm(normalize_expt(lp_macr, filter=TRUE, norm="quant",
                                         low_to_zero=TRUE, batch="combat"))
lp_macr_lowfilt <- sm(normalize_expt(lp_macr, filter=TRUE))
## Set up the data used in the 3 comparative contrast sets.

6.1 No batch in the model

lp_macr_nobatch <- sm(all_pairwise(lp_macr_lowfilt, limma_method="robust", model_batch=FALSE))
## wow, all tools including basic agree almost completely
medians_by_condition <- lp_macr_nobatch$basic$medians
lp_macr_nobatch_tables <- sm(combine_de_tables(
  lp_macr_nobatch,
  excel=paste0("excel/lp_macr_nobatch-v", ver, ".xlsx"),
  keepers=lp_contrasts,
  extra_annot=medians_by_condition))

lp_macr_nobatch_sig <- sm(extract_significant_genes(
  lp_macr_nobatch_tables,
  excel=paste0("excel/lp_macr_nobatch_significant-v", ver, ".xlsx")))

6.2 Batch in the model

In this attempt, we add a batch factor in the experimental model and see how it does.

## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
lp_macr_batch <- sm(all_pairwise(lp_macr_lowfilt, limma_method="robust"))
medians_by_condition <- lp_macr_batch$basic$medians
lp_macr_batch_tables <- sm(combine_de_tables(
  lp_macr_batch,
  excel=paste0("excel/lp_macr_batchmodel-v", ver, ".xlsx"),
  keepers=lp_contrasts,
  extra_annot=medians_by_condition))

lp_macr_batch_sig <- sm(extract_significant_genes(
  lp_macr_batch_tables,
  excel=paste0("excel/lp_macr_batchmodel_significant-v", ver, ".xlsx")))

6.3 Batch estimated with SVA

## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
lp_macr_sva <- sm(all_pairwise(lp_macr_lowfilt, limma_method="robust", model_batch="sva"))
medians_by_condition <- lp_macr_sva$basic$medians
lp_macr_sva_tables <- sm(combine_de_tables(
  lp_macr_sva,
  excel=paste0("excel/lp_macr_sva-v", ver, ".xlsx"),
  keepers=lp_contrasts,
  extra_annot=medians_by_condition))

lp_macr_sva_sig <- sm(extract_significant_genes(
  lp_macr_sva_tables,
  excel=paste0("excel/lp_macr_sva_significant-v", ver, ".xlsx")))

7 Figure out how to compare these results

7.1 Compare DeSeq / Basic without batch in model

lp_macr_nobatch_basic <- merge(
  lp_macr_nobatch$deseq$all_tables$sh_vs_chr,
  lp_macr_batch$basic$all_tables$sh_vs_chr,
  by="row.names")
rownames(lp_macr_nobatch_basic) <- lp_macr_nobatch_basic[["Row.names"]]
lp_macr_nobatch_logfc <- lp_macr_nobatch_basic[, c("logFC.x", "logFC.y")]
colnames(lp_macr_nobatch_logfc) <- c("nobatch","basic")
lfc_nb_b <- sm(plot_linear_scatter(lp_macr_nobatch_logfc, pretty_colors=FALSE))
lfc_nb_b$scatter

lfc_nb_b$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 89, df = 8700, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6805 0.7025
## sample estimates:
##    cor 
## 0.6917

lp_macr_nobatch_p <- lp_macr_nobatch_basic[, c("P.Value","p")]
lp_macr_nobatch_p[[2]] <- as.numeric(lp_macr_nobatch_p[[2]])
colnames(lp_macr_nobatch_p) <- c("nobatch","basic")
lp_macr_nobatch_p <- -1 * log(lp_macr_nobatch_p)
p_nb_b <- sm(plot_linear_scatter(lp_macr_nobatch_p, pretty_colors=FALSE))
p_nb_b$scatter

p_nb_b$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 120, df = 8700, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7760 0.7922
## sample estimates:
##    cor 
## 0.7842

7.2 Compare SVA to batch in model, DESeq

lp_macr_sva_batch <- merge(
  lp_macr_sva$deseq$all_tables$sh_vs_chr,
  lp_macr_batch$deseq$all_tables$sh_vs_chr,
  by="row.names")
rownames(lp_macr_sva_batch) <- lp_macr_sva_batch[["Row.names"]]
lp_macr_sva_logfc <- lp_macr_sva_batch[, c("logFC.x","logFC.y")]
colnames(lp_macr_sva_logfc) <- c("sva","batch")
lfc_b_s <- sm(plot_linear_scatter(lp_macr_sva_logfc, pretty_colors=FALSE))
lfc_b_s$scatter

lfc_b_s$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 55, df = 8700, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4939 0.5251
## sample estimates:
##    cor 
## 0.5097

lp_macr_sva_p <- lp_macr_sva_batch[, c("P.Value.x","P.Value.y")]
lp_macr_sva_p[[2]] <- as.numeric(lp_macr_sva_p[[2]])
colnames(lp_macr_sva_p) <- c("sva","batch")
lp_macr_sva_p <- -1 * log(lp_macr_sva_p)
p_b_s <- sm(plot_linear_scatter(lp_macr_sva_p, pretty_colors=FALSE))
p_b_s$scatter

p_b_s$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 25, df = 8700, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2381 0.2774
## sample estimates:
##    cor 
## 0.2579

7.2.1 Include p-value estimations

Try putting some information of the p-values with the comparative log2fc

lfcp_b_s <- lp_macr_sva_batch[, c("logFC.x", "logFC.y", "P.Value.x", "P.Value.y")]
colnames(lfcp_b_s) <- c("l2fcsva", "l2fcbatch", "psva", "pbatch")
lfc_b_s$scatter

cutoff <- 0.1
lfcp_b_s$state <- ifelse(lfcp_b_s$psva > cutoff & lfcp_b_s$pbatch > cutoff, "bothinsig",
                  ifelse(lfcp_b_s$psva <= cutoff & lfcp_b_s$pbatch <= cutoff, "bothsig",
                  ifelse(lfcp_b_s$psva <= cutoff, "svasig", "batchsig")))
##lfcp_b_s$lfcstate <- ifelse(lfcp_b_s$l2fcsva >= 0.75 & lfcp_b_s$l2fcbatch, "", "")
num_bothinsig <- sum(lfcp_b_s$state == "bothinsig")
num_bothsig <- sum(lfcp_b_s$state == "bothsig")
num_svasig <- sum(lfcp_b_s$state == "svasig")
num_batchsig <- sum(lfcp_b_s$state == "batchsig")

library(ggplot2)
aes_color = "(l2fcsva >= 0.75 | l2fcsva <= -0.75 | l2fcbatch >= 0.75 | l2fcbatch <= -0.75)"
plt <- ggplot2::ggplot(lfcp_b_s, aes_string(x="l2fcsva", y="l2fcbatch")) +
    ## ggplot2::geom_point(stat="identity", size=2, alpha=0.2, aes_string(shape="as.factor(aes_color)", colour="as.factor(state)", fill="as.factor(state)")) +
    ggplot2::geom_abline(colour="blue", slope=1, intercept=0, size=0.5) +
    ggplot2::geom_hline(yintercept=c(-0.75, 0.75), color="red", size=0.5) +
    ggplot2::geom_vline(xintercept=c(-0.75, 0.75), color="red", size=0.5) +
    ggplot2::geom_point(stat="identity", size=2, alpha=0.2, aes_string(colour="as.factor(state)", fill="as.factor(state)")) +
    ggplot2::scale_color_manual(name="state", values=c("bothinsig"="grey", "bothsig"="forestgreen", "svasig"="darkred", "batchsig"="darkblue")) +
    ggplot2::scale_fill_manual(name="state", values=c("bothinsig"="grey", "bothsig"="forestgreen", "svasig"="darkred", "batchsig"="darkblue"),
                               labels=c(
                                   paste0("Both InSig.: ", num_bothinsig),
                                   paste0("Both Sig.: ", num_bothsig),
                                   paste0("Sva Sig.: ", num_svasig),
                                   paste0("Batch Sig.: ", num_batchsig)),
                               guide=ggplot2::guide_legend(override.aes=aes(size=3, fill="grey"))) +
    ggplot2::guides(fill=ggplot2::guide_legend(override.aes=list(size=3))) +
    ggplot2::theme_bw()
plt

7.3 Compare no batch to batch in model, limma

lp_macr_nobatch_batch <- merge(
  lp_macr_nobatch$limma$all_tables$sh_vs_chr,
  lp_macr_batch$limma$all_tables$sh_vs_chr,
  by="row.names")
rownames(lp_macr_nobatch_batch) <- lp_macr_nobatch_batch[["Row.names"]]
lp_macr_nobatch_batch <- lp_macr_nobatch_batch[, c("logFC.x","logFC.y")]
colnames(lp_macr_nobatch_batch) <- c("nobatch","batch")
nb_b <- plot_linear_scatter(lp_macr_nobatch_batch, pretty_colors=FALSE)

## Warning in plot_multihistogram(df): NAs introduced by coercion

## Used Bon Ferroni corrected t test(s) between columns.

nb_b$scatter

nb_b$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 150, df = 8700, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.8441 0.8558
## sample estimates:
##    cor 
## 0.8501

7.4 Batch in model vs. SVA, limma

lp_macr_batch_sva <- merge(
  lp_macr_batch$limma$all_tables$sh_vs_chr,
  lp_macr_sva$limma$all_tables$sh_vs_chr,
  by="row.names")
rownames(lp_macr_batch_sva) <- lp_macr_batch_sva[["Row.names"]]
lp_macr_batch_sva <- lp_macr_batch_sva[, c("logFC.x","logFC.y")]
colnames(lp_macr_batch_sva) <- c("batch","sva")
b_s <- plot_linear_scatter(lp_macr_batch_sva, pretty_colors=FALSE)

## Warning in plot_multihistogram(df): NAs introduced by coercion

## Used Bon Ferroni corrected t test(s) between columns.

b_s$scatter

b_s$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 45, df = 8700, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4216 0.4556
## sample estimates:
##    cor 
## 0.4387

7.5 Nobatch vs. batch in model, edger

lp_macr_nobatch_batch <- merge(
  lp_macr_nobatch$edger$all_tables$sh_vs_chr,
  lp_macr_batch$edger$all_tables$sh_vs_chr,
  by="row.names")
rownames(lp_macr_nobatch_batch) <- lp_macr_nobatch_batch[["Row.names"]]
lp_macr_nobatch_batch <- lp_macr_nobatch_batch[, c("logFC.x","logFC.y")]
colnames(lp_macr_nobatch_batch) <- c("nobatch","batch")
nb_b <- sm(plot_linear_scatter(lp_macr_nobatch_batch, pretty_colors=FALSE))
nb_b$scatter

nb_b$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 100, df = 8700, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7371 0.7558
## sample estimates:
##    cor 
## 0.7466

7.6 Batch in model vs. SVA, edger

lp_macr_batch_sva <- merge(
  lp_macr_batch$edger$all_tables$sh_vs_chr,
  lp_macr_sva$edger$all_tables$sh_vs_chr,
  by="row.names")
rownames(lp_macr_batch_sva) <- lp_macr_batch_sva[["Row.names"]]
lp_macr_batch_sva <- lp_macr_batch_sva[, c("logFC.x","logFC.y")]
colnames(lp_macr_batch_sva) <- c("batch","sva")
b_s <- plot_linear_scatter(lp_macr_batch_sva, pretty_colors=FALSE)

## Warning in plot_multihistogram(df): NAs introduced by coercion

## Used Bon Ferroni corrected t test(s) between columns.

b_s$scatter

b_s$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 56, df = 8700, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4966 0.5277
## sample estimates:
##    cor 
## 0.5123

7.7 Compare nobatch vs. batch, deseq

lp_macr_nobatch_batch <- merge(
  lp_macr_nobatch$deseq$all_tables$sh_vs_chr,
  lp_macr_batch$deseq$all_tables$sh_vs_chr,
  by="row.names")
rownames(lp_macr_nobatch_batch) <- lp_macr_nobatch_batch[["Row.names"]]
lp_macr_nobatch_batch <- lp_macr_nobatch_batch[, c("logFC.x","logFC.y")]
colnames(lp_macr_nobatch_batch) <- c("nobatch","batch")
nb_b <- sm(plot_linear_scatter(lp_macr_nobatch_batch, pretty_colors=FALSE))
nb_b$scatter

nb_b$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 110, df = 8700, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7400 0.7585
## sample estimates:
##    cor 
## 0.7494

7.8 Compare batch vs. SVA, deseq

lp_macr_batch_sva <- merge(
  lp_macr_batch$deseq$all_tables$sh_vs_chr,
  lp_macr_sva$deseq$all_tables$sh_vs_chr,
  by="row.names")
rownames(lp_macr_batch_sva) <- lp_macr_batch_sva[["Row.names"]]
lp_macr_batch_sva <- lp_macr_batch_sva[, c("logFC.x","logFC.y")]
colnames(lp_macr_batch_sva) <- c("batch","sva")
b_s <- sm(plot_linear_scatter(lp_macr_batch_sva, pretty_colors=FALSE))
b_s$scatter

b_s$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 55, df = 8700, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4939 0.5251
## sample estimates:
##    cor 
## 0.5097

pander::pander(sessionInfo())

R version 3.5.1 (2018-07-02)

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

locale: LC_CTYPE=en_US.utf8, LC_NUMERIC=C, LC_TIME=en_US.utf8, LC_COLLATE=en_US.utf8, LC_MONETARY=en_US.utf8, LC_MESSAGES=en_US.utf8, LC_PAPER=en_US.utf8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.utf8 and LC_IDENTIFICATION=C

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

other attached packages: ggplot2(v.3.1.0), ruv(v.0.9.7), foreach(v.1.4.4), edgeR(v.3.22.5), bindrcpp(v.0.2.2), hpgltools(v.2018.11), Biobase(v.2.40.0) and BiocGenerics(v.0.26.0)

loaded via a namespace (and not attached): R.utils(v.2.7.0), tidyselect(v.0.2.5), lme4(v.1.1-19), RSQLite(v.2.1.1), AnnotationDbi(v.1.42.1), htmlwidgets(v.1.3), grid(v.3.5.1), BiocParallel(v.1.14.2), DESeq(v.1.32.0), devtools(v.2.0.1), munsell(v.0.5.0), codetools(v.0.2-15), preprocessCore(v.1.42.0), units(v.0.6-1), statmod(v.1.4.30), withr(v.2.1.2), colorspace(v.1.3-2), GOSemSim(v.2.6.2), BiocInstaller(v.1.30.0), OrganismDbi(v.1.22.0), knitr(v.1.20), rstudioapi(v.0.8), stats4(v.3.5.1), Vennerable(v.3.1.0.9000), robustbase(v.0.93-3), DOSE(v.3.6.1), labeling(v.0.3), GenomeInfoDbData(v.1.1.0), hwriter(v.1.3.2), bit64(v.0.9-7), farver(v.1.0), rprojroot(v.1.3-2), EDASeq(v.2.14.1), R6(v.2.3.0), doParallel(v.1.0.14), GenomeInfoDb(v.1.16.0), locfit(v.1.5-9.1), bitops(v.1.0-6), fgsea(v.1.6.0), DelayedArray(v.0.6.6), assertthat(v.0.2.0), scales(v.1.0.0), ggraph(v.1.0.2), nnet(v.7.3-12), enrichplot(v.1.0.2), gtable(v.0.2.0), RUVSeq(v.1.14.0), sva(v.3.28.0), processx(v.3.2.0), rlang(v.0.3.0.1), genefilter(v.1.62.0), splines(v.3.5.1), rtracklayer(v.1.40.6), lazyeval(v.0.2.1), acepack(v.1.4.1), checkmate(v.1.8.5), yaml(v.2.2.0), reshape2(v.1.4.3), GenomicFeatures(v.1.32.3), backports(v.1.1.2), qvalue(v.2.12.0), Hmisc(v.4.1-1), clusterProfiler(v.3.8.1), RBGL(v.1.56.0), tools(v.3.5.1), usethis(v.1.4.0), gplots(v.3.0.1), RColorBrewer(v.1.1-2), blockmodeling(v.0.3.1), sessioninfo(v.1.1.1), ggridges(v.0.5.1), Rcpp(v.1.0.0), plyr(v.1.8.4), base64enc(v.0.1-3), progress(v.1.2.0), zlibbioc(v.1.26.0), purrr(v.0.2.5), RCurl(v.1.95-4.11), ps(v.1.2.1), prettyunits(v.1.0.2), rpart(v.4.1-13), viridis(v.0.5.1), cowplot(v.0.9.3), S4Vectors(v.0.18.3), SummarizedExperiment(v.1.10.1), ggrepel(v.0.8.0), cluster(v.2.0.7-1), colorRamps(v.2.3), fs(v.1.2.6), variancePartition(v.1.10.4), magrittr(v.1.5), data.table(v.1.11.8), openxlsx(v.4.1.0), DO.db(v.2.9), packrat(v.0.4.9-3), matrixStats(v.0.54.0), aroma.light(v.3.10.0), pkgload(v.1.0.2), hms(v.0.4.2), evaluate(v.0.12), xtable(v.1.8-3), pbkrtest(v.0.4-7), XML(v.3.98-1.16), IRanges(v.2.14.12), gridExtra(v.2.3), testthat(v.2.0.1), compiler(v.3.5.1), biomaRt(v.2.36.1), tibble(v.1.4.2), KernSmooth(v.2.23-15), crayon(v.1.3.4), R.oo(v.1.22.0), minqa(v.1.2.4), htmltools(v.0.3.6), mgcv(v.1.8-25), corpcor(v.1.6.9), snow(v.0.4-3), Formula(v.1.2-3), tidyr(v.0.8.2), geneplotter(v.1.58.0), DBI(v.1.0.0), tweenr(v.1.0.0), MASS(v.7.3-51.1), ShortRead(v.1.38.0), Matrix(v.1.2-15), cli(v.1.0.1), R.methodsS3(v.1.7.1), quadprog(v.1.5-5), gdata(v.2.18.0), bindr(v.0.1.1), igraph(v.1.2.2), GenomicRanges(v.1.32.7), pkgconfig(v.2.0.2), registry(v.0.5), rvcheck(v.0.1.1), GenomicAlignments(v.1.16.0), foreign(v.0.8-71), annotate(v.1.58.0), rngtools(v.1.3.1), pkgmaker(v.0.27), XVector(v.0.20.0), bibtex(v.0.4.2), doRNG(v.1.7.1), EBSeq(v.1.20.0), stringr(v.1.3.1), callr(v.3.0.0), digest(v.0.6.18), graph(v.1.58.2), Biostrings(v.2.48.0), rmarkdown(v.1.10), fastmatch(v.1.1-0), htmlTable(v.1.12), directlabels(v.2018.05.22), Rsamtools(v.1.32.3), gtools(v.3.8.1), nloptr(v.1.2.1), nlme(v.3.1-137), desc(v.1.2.0), viridisLite(v.0.3.0), limma(v.3.36.5), pillar(v.1.3.0), lattice(v.0.20-38), DEoptimR(v.1.0-8), httr(v.1.3.1), pkgbuild(v.1.0.2), survival(v.2.43-1), GO.db(v.3.6.0), glue(v.1.3.0), remotes(v.2.0.2), zip(v.1.0.0), UpSetR(v.1.3.3), iterators(v.1.0.10), pander(v.0.6.3), bit(v.1.1-14), ggforce(v.0.1.3), stringi(v.1.2.4), blob(v.1.1.1), DESeq2(v.1.20.0), doSNOW(v.1.0.16), latticeExtra(v.0.6-28), caTools(v.1.17.1.1), memoise(v.1.1.0) and dplyr(v.0.7.8)

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 355f1065fb30b65f999b2e2a1aee18fdf8e6ebce

## This is hpgltools commit: Sun Nov 25 19:27:18 2018 -0500: 355f1065fb30b65f999b2e2a1aee18fdf8e6ebce

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

## Saving to 03_expression_macrophage_20180822-v20180822.rda.xz

tmp <- sm(saveme(filename=this_save))

---
title: "L.panamensis 2016: Differential Expression in human macrophages."
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
  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
  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
---

<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$set(progress=TRUE,
                     verbose=TRUE,
                     width=90,
                     echo=TRUE)
knitr::opts_chunk$set(error=TRUE,
                      fig.width=8,
                      fig.height=8,
                      dpi=96)
old_options <- options(digits=4,
                       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 <- "02_estimation_macrophage_20180822.Rmd"
ver <- "20180822"

tmp <- sm(loadme(filename=paste0(gsub(pattern="\\.Rmd", replace="", x=previous_file), "-v", ver, ".rda.xz")))
rmd_file <- "03_expression_macrophage_20180822.Rmd"
```

# Differential Expression, Macrophage: `r ver`

# Differential expression analyses

It appears that it is possible though somewhat difficult to apply batch estimations generated by sva
to the model given to DESeq/EdgeR/limma.  In the case of limma it is fairly simple, but in the other
two it is a bit more difficult.  There is a nice discussion of this at: https://www.biostars.org/p/156186/
I am attempting to apply that logic to this data with limited success.

```{r setup_de_norm, fig.show="hide"}
hs_contrasts <- list(
    "macro_chr-sh" = c("chr","sh"),
    "macro_chr-nil" = c("chr","uninf"),
    "macro_sh-nil" = c("sh", "uninf"))
## Set up the data used in the comparative contrast sets.
```

## No batch in the model

### Set up no batch

Print a reminder of what we can expect when doing this with no batch information.

```{r nobatch_setup}
hs_macr_lowfilt <- sm(normalize_expt(hs_cds_macr, filter=TRUE))
hs_lowfilt_pca <- sm(plot_pca(hs_cds_macr, transform="log2"))
hs_lowfilt_pca$plot
```

```{r macro_nobatch1, fig.show="hide"}
hs_macr_nobatch <- sm(all_pairwise(input=hs_cds_macr, model_batch=FALSE, parallel=FALSE,
                                   limma_method="robust"))
## wow, all tools including basic agree almost completely
medians_by_condition <- hs_macr_nobatch$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_nobatch_contr-v{ver}.xlsx")
hs_macr_nobatch_tables <- sm(combine_de_tables(hs_macr_nobatch,
                                               excel=excel_file,
                                               keepers=hs_contrasts,
                                               extra_annot=medians_by_condition))
excel_file <- glue::glue("excel/{rundate}_hs_macr_nobatch_sig-v{ver}.xlsx")
hs_macr_nobatch_sig <- sm(extract_significant_genes(hs_macr_nobatch_tables,
                                                    excel=excel_file,
                                                    according_to="all"))
```

## Batch in the model

### Batch setup

```{r batch_setup}
hs_lowfilt_batch_pca <- sm(plot_pca(hs_cds_macr, transform="log2", batch="limma"))
hs_lowfilt_batch_pca$plot
```

In this  attempt, we add a batch factor in the experimental model and see how it does.

```{r macro_batch1, fig.show="hide"}
## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
hs_macr_batch <- sm(all_pairwise(input=hs_cds_macr, limma_method="robust", parallel=FALSE))
medians_by_condition <- hs_macr_batch$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_batchmodel_contr-v{ver}.xlsx")
hs_macr_batch_tables <- sm(combine_de_tables(
  hs_macr_batch,
  keepers=hs_contrasts,
  extra_annot=medians_by_condition,
  include_limma=FALSE, include_edger=FALSE, include_basic=FALSE, include_ebseq=FALSE,
  excel=excel_file))
excel_file <- glue::glue("excel/{rundate}_hs_macr_batchmodel_sig-v{ver}.xlsx")
hs_macr_batch_sig <- sm(extract_significant_genes(
  hs_macr_batch_tables, excel=excel_file,
  according_to="deseq"))
excel_file <- glue::glue("excel/{rundate}_hs_macr_batchmodel_abund-v{ver}.xlsx")
hs_macr_batch_abun <- sm(extract_abundant_genes(
  hs_macr_batch_tables, excel=excel_file,
  according_to="deseq"))
```

# Table S2 and Figure 1b, Table S3

 * Table S2 is taking only the DESeq2 results.
 * Figure 1c is intended to be a volcano plot of the DESeq2 results.

```{r table_s2}
s2_contrasts <- list(
  "macro_chr-sh" = c("chr","sh"))
excel_file <- glue::glue("excel/{rundate}_table-s2_hs_macr_batchmodel_contr-v{ver}.xlsx")
table_s2 <- sm(combine_de_tables(
  hs_macr_batch,
  excel=excel_file,
  keepers=s2_contrasts,
  include_basic=FALSE, include_limma=FALSE,
  include_ebseq=FALSE, include_edger=FALSE))
excel_file <- glue::glue("excel/{rundate}_table-s3_hs_macr_batchmodel_sig-v{ver}.xlsx")
table_s3 <- sm(extract_significant_genes(
  table_s2,
  excel=excel_file,
  according_to="deseq"))

chosen_table <- table_s2[["data"]][[1]]
head(chosen_table)
vol <- plot_volcano_de(table=chosen_table,
                       color_by="state",
                       fc_col="deseq_logfc",
                       p_col="deseq_adjp",
                       shapes_by_state=FALSE,
                       line_position="top")
pp(file="images/Figure_1c.pdf")
vol$plot
dev.off()
```

## Batch estimated with SVA

### Set up sva

```{r setup_sva}
hs_lowfilt_svaseq_pca <- sm(plot_pca(hs_cds_macr, transform="log2", batch="svaseq", filter=TRUE))
hs_lowfilt_svaseq_pca$plot
```

```{r macro_sva1, fig.show="hide"}
hs_cds_macr_lowfilt <- sm(normalize_expt(hs_cds_macr, filter=TRUE))
## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
hs_macr_sva <- sm(all_pairwise(
  input=hs_cds_macr_lowfilt,
  model_batch="svaseq",
  limma_method="robust"))
medians_by_condition <- hs_macr_sva$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_sva_contr-v{ver}.xlsx")
hs_macr_sva_tables <- sm(combine_de_tables(
  hs_macr_sva,
  excel=excel_file,
  keepers=hs_contrasts,
  extra_annot=medians_by_condition))
excel_file <- glue::glue("excel/{rundate}_hs_macr_sva_sig-v{ver}.xlsx")
hs_macr_sva_sig <- sm(extract_significant_genes(
  hs_macr_sva_tables,
  excel=excel_file,
hs_macr_sva_ma_limma <- extract_de_plots(
  pairwise=hs_macr_sva,
  type="limma",
  table="sh_vs_chr")
```

```{r sva_ma_plot}
hs_macr_sva_ma_limma$ma$plot
```

## Batch correction via ruv residuals

### Set up ruvresiduals

```{r setup_ruvresid}
## hmm I got the RUVr error again, but when I ran it manually did not.
## Even more strangely, if I just run the same thing again, no error...
testme <- try(get_model_adjust(input=hs_macr_lowfilt, estimate_type="ruv_residuals"), silent=TRUE)

hs_lowfilt_ruvresid_pca <- sm(plot_pca(hs_macr_lowfilt, transform="log2", batch="ruv_residuals"))
hs_lowfilt_ruvresid_pca$plot
```

```{r macro_ruvresid1, fig.show="hide"}
## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
## Bizarrely, sometimes if one runs this, it gives an error "Error in (function (classes, fdef, mtable) : unable to find an inherited method for function 'RUVr' for signature '"matrix", "logical", "numeric", "NULL"'"  -- however, if one then simply runs it again it works fine.
## I am going to assume that it is because I do not explicitly invoke the library.
## library(ruv)  ## hopefully a small code change made this not needed.
testme <- get_model_adjust(input=hs_macr_lowfilt, estimate_type="ruv_residuals")
hs_macr_ruvres <- sm(all_pairwise(
  input=hs_macr_lowfilt,
  model_batch="ruv_residuals",
  limma_method="robust"))
medians_by_condition <- hs_macr_ruvres$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_ruvres_contr-v{ver}.xlsx")
hs_macr_ruvres_tables <- sm(combine_de_tables(
  hs_macr_ruvres,
  excel=excel_file,
  extra_annot=medians_by_condition,
  keepers=hs_contrasts))
excel_file <- glue::glue("excel/{rundate}_hs_macr_ruvres_sig-v{ver}.xlsx")
hs_macr_ruvres_sig <- sm(extract_significant_genes(
  hs_macr_ruvres_tables,
  excel=excel_file))
```

## Batch correction with pca

### Setup pca

```{r setup_pca01}
hs_lowfilt_pca_pca <- sm(plot_pca(hs_macr_lowfilt, transform="log2", batch="pca"))
hs_lowfilt_pca_pca$plot
```

```{r macro_pca1, fig.show="hide"}
## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
hs_macr_pca <- sm(all_pairwise(
  input=hs_macr_lowfilt,
  model_batch="pca",
  limma_method="robust"))
medians_by_condition <- hs_macr_pca$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_pca_contr-v{ver}.xlsx")
hs_macr_pca_tables <- sm(combine_de_tables(
  hs_macr_pca,
  excel=excel_file
  keepers=hs_contrasts,
  extra_annot=medians_by_condition))
excel_file <- glue::glue("excel/{rundate}_hs_macr_pca_sig-v{ver}.xlsx")
hs_macr_pca_sig <- sm(extract_significant_genes(
  hs_macr_pca_tables,
  excel=excel_file))
```

## Batch correction with ruv empirical

### Setup ruv empirical

```{r setup_pca02}
hs_lowfilt_ruvemp_pca <- sm(plot_pca(hs_macr_lowfilt, transform="log2", batch="ruv_empirical"))
hs_lowfilt_ruvemp_pca$plot
```

```{r macro_ruvemp1, fig.show="hide"}
hs_macr_ruvemp <- sm(all_pairwise(
  input=hs_macr_lowfilt,
  model_batch="ruv_empirical",
  limma_method="robust"))
medians_by_condition <- hs_macr_ruvemp$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_ruvemp_contr-v{ver}.xlsx")
hs_macr_ruvemp_tables <- sm(combine_de_tables(
  hs_macr_ruvemp,
  excel=excel_file,
  keepers=hs_contrasts,
  extra_annot=medians_by_condition))
excel_file <- glue::glue("excel/{rundate}_hs_macr_ruvemp_sig-v{ver}.xlsx")
hs_macr_ruvemp_sig <- sm(extract_significant_genes(
  hs_macr_ruvemp_tables,
  excel=excel_file))
```

## Batch correction with combat

Then repeat with the batch-corrected data and see the differences.

### Setup combat

```{r setup_combat}
hs_lowfilt_combat_pca <- sm(plot_pca(hs_macr_lowfilt, transform="log2", batch="combat_noprior"))
hs_lowfilt_combat_pca$plot
```

```{r repeat_pairwise_batch1, fig.show="hide"}
hs_macr_combat <- sm(all_pairwise(
  input=hs_macr_combat_norm,
  force=TRUE,
  limma_method="robust"))
medians_by_condition <- hs_macr_combat$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_combat_contr-v{ver}.xlsx")
hs_macr_combat_tables <- sm(combine_de_tables(
  hs_macr_combat,
  excel=excel_file,
  keepers=hs_contrasts,
  extra_annot=medians_by_condition))
excel_file <- glue::glue("excel/{rundate}_hs_macr_combat_contr-v{ver}.xlsx")
hs_macr_combat_sig <- sm(extract_significant_genes(
  hs_macr_combat_tables,
  excel=excel_file))

```

```{r finished_ma_plots}
hs_macr_combat_ma_limma <- extract_de_plots(
  pairwise=hs_macr_combat,
  type="limma",
  table="sh_vs_chr")
hs_macr_combat_ma_limma$ma$plot

hs_macr_combat_ma_edger <- extract_de_plots(
  pairwise=hs_macr_combat,
  type="edger",
  table="sh_vs_chr")
hs_macr_combat_ma_edger$ma$plot

hs_macr_combat_ma_deseq <- extract_de_plots(
  pairwise=hs_macr_combat,
  type="deseq",
  table="sh_vs_chr")
hs_macr_combat_ma_deseq$ma$plot
```

# Figure out how to compare these results

I have 4 methods of performing this differential expression analysis.  Each one comes with a set of
metrics defining 'significant'.  Perhaps I can make a table of the # of genes defined as significant
by contrast for each.  In addition it may be worth while to do a scatter plots of the logFCs between
these comparisons and see how well they agree?

# Look first at the de counts

```{r compare_de_setup1}
hs_macr_nobatch_sig$limma$counts
##hs_macr_batch_tables$significant$limma$counts
##hs_macr_sva_tables$significant$limma$counts
##hs_macr_ruvres_tables$significant$limma$counts
##hs_macr_pca_tables$significant$limma$counts
##hs_macr_ruvemp_tables$significant$limma$counts
##hs_macr_combat_tables$significant$limma$counts
```

## Compare DeSeq / Basic without batch in model

```{r basic_deseq_nobatch1}
hs_macr_nobatch_basic <- merge(
  hs_macr_nobatch$deseq$all_tables$sh_vs_chr,
  hs_macr_batch$basic$all_tables$sh_vs_chr,
  by="row.names")
rownames(hs_macr_nobatch_basic) <- hs_macr_nobatch_basic[["Row.names"]]
hs_macr_nobatch_logfc <- hs_macr_nobatch_basic[, c("logFC.x", "logFC.y")]
colnames(hs_macr_nobatch_logfc) <- c("nobatch", "basic")
lfc_nb_b <- sm(plot_linear_scatter(hs_macr_nobatch_logfc, pretty_colors=FALSE))
lfc_nb_b$scatter
lfc_nb_b$correlation
hs_macr_nobatch_p <- hs_macr_nobatch_basic[, c("P.Value","p")]
hs_macr_nobatch_p[[2]] <- as.numeric(hs_macr_nobatch_p[[2]])
colnames(hs_macr_nobatch_p) <- c("nobatch","basic")
hs_macr_nobatch_p <- -1 * log(hs_macr_nobatch_p)
hs_macr_p_nb_b <- sm(plot_linear_scatter(hs_macr_nobatch_p, pretty_colors=FALSE))
hs_macr_p_nb_b$scatter
hs_macr_p_nb_b$correlation
```

## Compare SVA to batch in model, DESeq

```{r deseq_sva_batch1}
hs_macr_sva_batch <- merge(
  hs_macr_sva$deseq$all_tables$sh_vs_chr,
  hs_macr_batch$deseq$all_tables$sh_vs_chr,
  by="row.names")
rownames(hs_macr_sva_batch) <- hs_macr_sva_batch[["Row.names"]]
hs_macr_sva_logfc <- hs_macr_sva_batch[, c("logFC.x","logFC.y")]
colnames(hs_macr_sva_logfc) <- c("sva","batch")
hs_macr_lfc_b_s <- sm(plot_linear_scatter(hs_macr_sva_logfc, pretty_colors=FALSE))
hs_macr_lfc_b_s$scatter
hs_macr_lfc_b_s$correlation
```

### Include p-value estimations

Try putting some information of the p-values with the comparative log2fc

```{r l2fs_pvals1}
lfc_b_s <- hs_macr_sva_batch[, c("logFC.x", "logFC.y", "P.Value.x", "P.Value.y")]
colnames(lfc_b_s) <- c("l2fcsva", "l2fcbatch", "psva", "pbatch")
hs_macr_lfc_b_s$scatter
cutoff <- 0.1
lfc_b_s$state <- ifelse(lfc_b_s$psva > cutoff & lfc_b_s$pbatch > cutoff, "bothinsig",
                 ifelse(lfc_b_s$psva <= cutoff & lfc_b_s$pbatch <= cutoff, "bothsig",
                 ifelse(lfc_b_s$psva <= cutoff, "svasig", "batchsig")))
##lfcp_b_s$lfcstate <- ifelse(lfcp_b_s$l2fcsva >= 0.75 & lfcp_b_s$l2fcbatch, "", "")
num_bothinsig <- sum(lfc_b_s$state == "bothinsig")
num_bothsig <- sum(lfc_b_s$state == "bothsig")
num_svasig <- sum(lfc_b_s$state == "svasig")
num_batchsig <- sum(lfc_b_s$state == "batchsig")

library(ggplot2)
aes_color = "(l2fcsva >= 0.75 | l2fcsva <= -0.75 | l2fcbatch >= 0.75 | l2fcbatch <= -0.75)"
plt <- ggplot2::ggplot(lfc_b_s, aes_string(x="l2fcsva", y="l2fcbatch")) +
    ## ggplot2::geom_point(stat="identity", size=2, alpha=0.2, aes_string(shape="as.factor(aes_color)", colour="as.factor(state)", fill="as.factor(state)")) +
    ggplot2::geom_abline(colour="blue", slope=1, intercept=0, size=0.5) +
    ggplot2::geom_hline(yintercept=c(-0.75, 0.75), color="red", size=0.5) +
    ggplot2::geom_vline(xintercept=c(-0.75, 0.75), color="red", size=0.5) +
    ggplot2::geom_point(stat="identity", size=2, alpha=0.2, aes_string(colour="as.factor(state)", fill="as.factor(state)")) +
    ggplot2::scale_color_manual(name="state", values=c("bothinsig"="grey", "bothsig"="forestgreen", "svasig"="darkred", "batchsig"="darkblue")) +
    ggplot2::scale_fill_manual(name="state", values=c("bothinsig"="grey", "bothsig"="forestgreen", "svasig"="darkred", "batchsig"="darkblue"),
                               labels=c(
                                   paste0("Both InSig.: ", num_bothinsig),
                                   paste0("Both Sig.: ", num_bothsig),
                                   paste0("Sva Sig.: ", num_svasig),
                                   paste0("Batch Sig.: ", num_batchsig)),
                               guide=ggplot2::guide_legend(override.aes=aes(size=3, fill="grey"))) +
    ggplot2::guides(fill=ggplot2::guide_legend(override.aes=list(size=3))) +
    ggplot2::theme_bw()
plt
```

## Compare ruvresid to batch in model, DESeq

```{r batch_ruvresid_deseq1}
hs_macr_batch_ruvresid_deseq <- merge(
  hs_macr_ruvres$deseq$all_tables$sh_vs_chr,
  hs_macr_batch$basic$all_tables$sh_vs_chr,
  by="row.names")
rownames(hs_macr_batch_ruvresid_deseq) <- hs_macr_batch_ruvresid_deseq[["Row.names"]]
hs_macr_batch_ruvresid_logfc <- hs_macr_batch_ruvresid_deseq[, c("logFC.x","logFC.y")]
colnames(hs_macr_batch_ruvresid_logfc) <- c("nobatch","basic")
lfc_ruv_bat <- plot_linear_scatter(hs_macr_batch_ruvresid_logfc, pretty_colors=FALSE)
lfc_ruv_bat$scatter
lfc_ruv_bat$correlation
```

## Compare no batch to batch in model, limma

```{r compare_batch_nobatch_limma1}
hs_macr_nobatch_batch <- merge(
  hs_macr_nobatch$limma$all_tables$sh_vs_chr,
  hs_macr_batch$limma$all_tables$sh_vs_chr,
  by="row.names")
rownames(hs_macr_nobatch_batch) <- hs_macr_nobatch_batch[["Row.names"]]
hs_macr_nobatch_batch <- hs_macr_nobatch_batch[, c("logFC.x","logFC.y")]
colnames(hs_macr_nobatch_batch) <- c("nobatch","batch")
nb_b <- plot_linear_scatter(hs_macr_nobatch_batch, pretty_colors=FALSE)
nb_b$scatter
nb_b$correlation
```

## Batch in model vs. SVA, limma

```{r compare_batch_sva_limma1}
hs_macr_batch_sva <- merge(
  hs_macr_batch$limma$all_tables$sh_vs_chr,
  hs_macr_sva$limma$all_tables$sh_vs_chr,
  by="row.names")
rownames(hs_macr_batch_sva) <- hs_macr_batch_sva[["Row.names"]]
hs_macr_batch_sva <- hs_macr_batch_sva[, c("logFC.x","logFC.y")]
colnames(hs_macr_batch_sva) <- c("batch","sva")
b_s <- plot_linear_scatter(hs_macr_batch_sva, pretty_colors=FALSE)
b_s$scatter
b_s$correlation
```

## Nobatch vs. batch in model, edger

```{r compare_nobatch_batch_edger1}
hs_macr_nobatch_batch <- merge(
  hs_macr_nobatch$edger$all_tables$sh_vs_chr,
  hs_macr_batch$edger$all_tables$sh_vs_chr,
  by="row.names")
rownames(hs_macr_nobatch_batch) <- hs_macr_nobatch_batch[["Row.names"]]
hs_macr_nobatch_batch <- hs_macr_nobatch_batch[, c("logFC.x","logFC.y")]
colnames(hs_macr_nobatch_batch) <- c("nobatch","batch")
nb_b <- sm(plot_linear_scatter(hs_macr_nobatch_batch, pretty_colors=FALSE))
nb_b$scatter
nb_b$correlation
```

## Batch in model vs. SVA, edger

```{r compare_batch_sva_edger1}
hs_macr_batch_sva <- merge(
  hs_macr_batch$edger$all_tables$sh_vs_chr,
  hs_macr_sva$edger$all_tables$sh_vs_chr,
  by="row.names")
rownames(hs_macr_batch_sva) <- hs_macr_batch_sva[["Row.names"]]
hs_macr_batch_sva <- hs_macr_batch_sva[, c("logFC.x","logFC.y")]
colnames(hs_macr_batch_sva) <- c("batch","sva")
b_s <- plot_linear_scatter(hs_macr_batch_sva, pretty_colors=FALSE)
b_s$scatter
b_s$correlation
```

## Compare nobatch vs. batch, deseq

```{r compare_nobatch_batch_deseq1}
hs_macr_nobatch_batch <- merge(
  hs_macr_nobatch$deseq$all_tables$sh_vs_chr,
  hs_macr_batch$deseq$all_tables$sh_vs_chr,
  by="row.names")
rownames(hs_macr_nobatch_batch) <- hs_macr_nobatch_batch[["Row.names"]]
hs_macr_nobatch_batch <- hs_macr_nobatch_batch[, c("logFC.x","logFC.y")]
colnames(hs_macr_nobatch_batch) <- c("nobatch","batch")
nb_b <- sm(plot_linear_scatter(hs_macr_nobatch_batch, pretty_colors=FALSE))
nb_b$scatter
nb_b$correlation
```

## Compare batch vs. SVA, deseq

```{r compare_batch_sva_deseq1}
hs_macr_batch_sva <- merge(
  hs_macr_batch$deseq$all_tables$sh_vs_chr,
  hs_macr_sva$deseq$all_tables$sh_vs_chr,
  by="row.names")
rownames(hs_macr_batch_sva) <- hs_macr_batch_sva[["Row.names"]]
hs_macr_batch_sva <- hs_macr_batch_sva[, c("logFC.x", "logFC.y")]
colnames(hs_macr_batch_sva) <- c("batch", "sva")
b_s <- sm(plot_linear_scatter(hs_macr_batch_sva, pretty_colors=FALSE))
b_s$scatter
b_s$correlation
```

# Repeat using the parasite data

In 'macrophage_estimation', we did a series of analyses to try to pick out some of the surrogate
variables in the data.  Now we will perform a set of differential expression analyses using the
results from that.  Since the 'batch' element of the data is reasonably well explained, we will not
abuse the data with sva/combat, but instead include batch in the experimental model.

It appears that it is possible though somewhat difficult to apply batch estimations generated by sva
to the model given to DESeq/EdgeR/limma.  In the case of limma it is fairly simple, but in the other
two it is a bit more difficult.  There is a nice discussion of this at: https://www.biostars.org/p/156186/
I am attempting to apply that logic to this data with limited success.

```{r setup_de, fig.show="hide"}
lp_contrasts <- list(
    "macro_chr-sh" = c("chr", "sh"))
lp_macr_norm <- sm(normalize_expt(lp_macr, filter=TRUE, convert="cpm", norm="quant"))
lp_macr_combat_norm <- sm(normalize_expt(lp_macr, filter=TRUE, norm="quant",
                                         low_to_zero=TRUE, batch="combat"))
lp_macr_lowfilt <- sm(normalize_expt(lp_macr, filter=TRUE))
## Set up the data used in the 3 comparative contrast sets.
```

## No batch in the model

```{r macro_nobatch, fig.show="hide"}
lp_macr_nobatch <- sm(all_pairwise(lp_macr_lowfilt, limma_method="robust", model_batch=FALSE))
## wow, all tools including basic agree almost completely
medians_by_condition <- lp_macr_nobatch$basic$medians
lp_macr_nobatch_tables <- sm(combine_de_tables(
  lp_macr_nobatch,
  excel=paste0("excel/lp_macr_nobatch-v", ver, ".xlsx"),
  keepers=lp_contrasts,
  extra_annot=medians_by_condition))
lp_macr_nobatch_sig <- sm(extract_significant_genes(
  lp_macr_nobatch_tables,
  excel=paste0("excel/lp_macr_nobatch_significant-v", ver, ".xlsx")))
```

## Batch in the model

In this  attempt, we add a batch factor in the experimental model and see how it does.

```{r macro_batch, fig.show="hide"}
## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
lp_macr_batch <- sm(all_pairwise(lp_macr_lowfilt, limma_method="robust"))
medians_by_condition <- lp_macr_batch$basic$medians
lp_macr_batch_tables <- sm(combine_de_tables(
  lp_macr_batch,
  excel=paste0("excel/lp_macr_batchmodel-v", ver, ".xlsx"),
  keepers=lp_contrasts,
  extra_annot=medians_by_condition))
lp_macr_batch_sig <- sm(extract_significant_genes(
  lp_macr_batch_tables,
  excel=paste0("excel/lp_macr_batchmodel_significant-v", ver, ".xlsx")))
```

## Batch estimated with SVA

```{r macro_sva, fig.show="hide"}
## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
lp_macr_sva <- sm(all_pairwise(lp_macr_lowfilt, limma_method="robust", model_batch="sva"))
medians_by_condition <- lp_macr_sva$basic$medians
lp_macr_sva_tables <- sm(combine_de_tables(
  lp_macr_sva,
  excel=paste0("excel/lp_macr_sva-v", ver, ".xlsx"),
  keepers=lp_contrasts,
  extra_annot=medians_by_condition))
lp_macr_sva_sig <- sm(extract_significant_genes(
  lp_macr_sva_tables,
  excel=paste0("excel/lp_macr_sva_significant-v", ver, ".xlsx")))
```

# Figure out how to compare these results

I have 4 methods of performing this differential expression analysis.  Each one comes with a set of
metrics defining 'significant'.  Perhaps I can make a table of the # of genes defined as significant
by contrast for each.  In addition it may be worth while to do a scatter plots of the logFCs between
these comparisons and see how well they agree?

## Compare DeSeq / Basic without batch in model

```{r basic_deseq_nobatch}
lp_macr_nobatch_basic <- merge(
  lp_macr_nobatch$deseq$all_tables$sh_vs_chr,
  lp_macr_batch$basic$all_tables$sh_vs_chr,
  by="row.names")
rownames(lp_macr_nobatch_basic) <- lp_macr_nobatch_basic[["Row.names"]]
lp_macr_nobatch_logfc <- lp_macr_nobatch_basic[, c("logFC.x", "logFC.y")]
colnames(lp_macr_nobatch_logfc) <- c("nobatch","basic")
lfc_nb_b <- sm(plot_linear_scatter(lp_macr_nobatch_logfc, pretty_colors=FALSE))
lfc_nb_b$scatter
lfc_nb_b$correlation

lp_macr_nobatch_p <- lp_macr_nobatch_basic[, c("P.Value","p")]
lp_macr_nobatch_p[[2]] <- as.numeric(lp_macr_nobatch_p[[2]])
colnames(lp_macr_nobatch_p) <- c("nobatch","basic")
lp_macr_nobatch_p <- -1 * log(lp_macr_nobatch_p)
p_nb_b <- sm(plot_linear_scatter(lp_macr_nobatch_p, pretty_colors=FALSE))
p_nb_b$scatter
p_nb_b$correlation
```

## Compare SVA to batch in model, DESeq

```{r deseq_sva_batch}
lp_macr_sva_batch <- merge(
  lp_macr_sva$deseq$all_tables$sh_vs_chr,
  lp_macr_batch$deseq$all_tables$sh_vs_chr,
  by="row.names")
rownames(lp_macr_sva_batch) <- lp_macr_sva_batch[["Row.names"]]
lp_macr_sva_logfc <- lp_macr_sva_batch[, c("logFC.x","logFC.y")]
colnames(lp_macr_sva_logfc) <- c("sva","batch")
lfc_b_s <- sm(plot_linear_scatter(lp_macr_sva_logfc, pretty_colors=FALSE))
lfc_b_s$scatter
lfc_b_s$correlation

lp_macr_sva_p <- lp_macr_sva_batch[, c("P.Value.x","P.Value.y")]
lp_macr_sva_p[[2]] <- as.numeric(lp_macr_sva_p[[2]])
colnames(lp_macr_sva_p) <- c("sva","batch")
lp_macr_sva_p <- -1 * log(lp_macr_sva_p)
p_b_s <- sm(plot_linear_scatter(lp_macr_sva_p, pretty_colors=FALSE))
p_b_s$scatter
p_b_s$correlation
```

### Include p-value estimations

Try putting some information of the p-values with the comparative log2fc

```{r l2fs_pvals}
lfcp_b_s <- lp_macr_sva_batch[, c("logFC.x", "logFC.y", "P.Value.x", "P.Value.y")]
colnames(lfcp_b_s) <- c("l2fcsva", "l2fcbatch", "psva", "pbatch")
lfc_b_s$scatter
cutoff <- 0.1
lfcp_b_s$state <- ifelse(lfcp_b_s$psva > cutoff & lfcp_b_s$pbatch > cutoff, "bothinsig",
                  ifelse(lfcp_b_s$psva <= cutoff & lfcp_b_s$pbatch <= cutoff, "bothsig",
                  ifelse(lfcp_b_s$psva <= cutoff, "svasig", "batchsig")))
##lfcp_b_s$lfcstate <- ifelse(lfcp_b_s$l2fcsva >= 0.75 & lfcp_b_s$l2fcbatch, "", "")
num_bothinsig <- sum(lfcp_b_s$state == "bothinsig")
num_bothsig <- sum(lfcp_b_s$state == "bothsig")
num_svasig <- sum(lfcp_b_s$state == "svasig")
num_batchsig <- sum(lfcp_b_s$state == "batchsig")

library(ggplot2)
aes_color = "(l2fcsva >= 0.75 | l2fcsva <= -0.75 | l2fcbatch >= 0.75 | l2fcbatch <= -0.75)"
plt <- ggplot2::ggplot(lfcp_b_s, aes_string(x="l2fcsva", y="l2fcbatch")) +
    ## ggplot2::geom_point(stat="identity", size=2, alpha=0.2, aes_string(shape="as.factor(aes_color)", colour="as.factor(state)", fill="as.factor(state)")) +
    ggplot2::geom_abline(colour="blue", slope=1, intercept=0, size=0.5) +
    ggplot2::geom_hline(yintercept=c(-0.75, 0.75), color="red", size=0.5) +
    ggplot2::geom_vline(xintercept=c(-0.75, 0.75), color="red", size=0.5) +
    ggplot2::geom_point(stat="identity", size=2, alpha=0.2, aes_string(colour="as.factor(state)", fill="as.factor(state)")) +
    ggplot2::scale_color_manual(name="state", values=c("bothinsig"="grey", "bothsig"="forestgreen", "svasig"="darkred", "batchsig"="darkblue")) +
    ggplot2::scale_fill_manual(name="state", values=c("bothinsig"="grey", "bothsig"="forestgreen", "svasig"="darkred", "batchsig"="darkblue"),
                               labels=c(
                                   paste0("Both InSig.: ", num_bothinsig),
                                   paste0("Both Sig.: ", num_bothsig),
                                   paste0("Sva Sig.: ", num_svasig),
                                   paste0("Batch Sig.: ", num_batchsig)),
                               guide=ggplot2::guide_legend(override.aes=aes(size=3, fill="grey"))) +
    ggplot2::guides(fill=ggplot2::guide_legend(override.aes=list(size=3))) +
    ggplot2::theme_bw()
plt
```

## Compare no batch to batch in model, limma

```{r compare_batch_nobatch_limma}
lp_macr_nobatch_batch <- merge(
  lp_macr_nobatch$limma$all_tables$sh_vs_chr,
  lp_macr_batch$limma$all_tables$sh_vs_chr,
  by="row.names")
rownames(lp_macr_nobatch_batch) <- lp_macr_nobatch_batch[["Row.names"]]
lp_macr_nobatch_batch <- lp_macr_nobatch_batch[, c("logFC.x","logFC.y")]
colnames(lp_macr_nobatch_batch) <- c("nobatch","batch")
nb_b <- plot_linear_scatter(lp_macr_nobatch_batch, pretty_colors=FALSE)
nb_b$scatter
nb_b$correlation
```

## Batch in model vs. SVA, limma

```{r compare_batch_sva_limma2}
lp_macr_batch_sva <- merge(
  lp_macr_batch$limma$all_tables$sh_vs_chr,
  lp_macr_sva$limma$all_tables$sh_vs_chr,
  by="row.names")
rownames(lp_macr_batch_sva) <- lp_macr_batch_sva[["Row.names"]]
lp_macr_batch_sva <- lp_macr_batch_sva[, c("logFC.x","logFC.y")]
colnames(lp_macr_batch_sva) <- c("batch","sva")
b_s <- plot_linear_scatter(lp_macr_batch_sva, pretty_colors=FALSE)
b_s$scatter
b_s$correlation
```

## Nobatch vs. batch in model, edger

```{r compare_nobatch_batch_edger}
lp_macr_nobatch_batch <- merge(
  lp_macr_nobatch$edger$all_tables$sh_vs_chr,
  lp_macr_batch$edger$all_tables$sh_vs_chr,
  by="row.names")
rownames(lp_macr_nobatch_batch) <- lp_macr_nobatch_batch[["Row.names"]]
lp_macr_nobatch_batch <- lp_macr_nobatch_batch[, c("logFC.x","logFC.y")]
colnames(lp_macr_nobatch_batch) <- c("nobatch","batch")
nb_b <- sm(plot_linear_scatter(lp_macr_nobatch_batch, pretty_colors=FALSE))
nb_b$scatter
nb_b$correlation
```

## Batch in model vs. SVA, edger

```{r compare_batch_sva_edger}
lp_macr_batch_sva <- merge(
  lp_macr_batch$edger$all_tables$sh_vs_chr,
  lp_macr_sva$edger$all_tables$sh_vs_chr,
  by="row.names")
rownames(lp_macr_batch_sva) <- lp_macr_batch_sva[["Row.names"]]
lp_macr_batch_sva <- lp_macr_batch_sva[, c("logFC.x","logFC.y")]
colnames(lp_macr_batch_sva) <- c("batch","sva")
b_s <- plot_linear_scatter(lp_macr_batch_sva, pretty_colors=FALSE)
b_s$scatter
b_s$correlation
```

## Compare nobatch vs. batch, deseq

```{r compare_nobatch_batch_deseq}
lp_macr_nobatch_batch <- merge(
  lp_macr_nobatch$deseq$all_tables$sh_vs_chr,
  lp_macr_batch$deseq$all_tables$sh_vs_chr,
  by="row.names")
rownames(lp_macr_nobatch_batch) <- lp_macr_nobatch_batch[["Row.names"]]
lp_macr_nobatch_batch <- lp_macr_nobatch_batch[, c("logFC.x","logFC.y")]
colnames(lp_macr_nobatch_batch) <- c("nobatch","batch")
nb_b <- sm(plot_linear_scatter(lp_macr_nobatch_batch, pretty_colors=FALSE))
nb_b$scatter
nb_b$correlation
```

## Compare batch vs. SVA, deseq

```{r compare_batch_sva_deseq}
lp_macr_batch_sva <- merge(
  lp_macr_batch$deseq$all_tables$sh_vs_chr,
  lp_macr_sva$deseq$all_tables$sh_vs_chr,
  by="row.names")
rownames(lp_macr_batch_sva) <- lp_macr_batch_sva[["Row.names"]]
lp_macr_batch_sva <- lp_macr_batch_sva[, c("logFC.x","logFC.y")]
colnames(lp_macr_batch_sva) <- c("batch","sva")
b_s <- sm(plot_linear_scatter(lp_macr_batch_sva, pretty_colors=FALSE))
b_s$scatter
b_s$correlation
```

```{r saveme}
pander::pander(sessionInfo())
message(paste0("This is hpgltools commit: ", get_git_commit()))
this_save <- paste0(gsub(pattern="\\.Rmd", replace="", x=rmd_file), "-v", ver, ".rda.xz")
message(paste0("Saving to ", this_save))
tmp <- sm(saveme(filename=this_save))
```


L.panamensis 2016: Differential Expression in human macrophages.

atb abelew@gmail.com

2018-11-28

1 Differential Expression, Macrophage: 20180822

2 Differential expression analyses

2.1 No batch in the model

2.1.1 Set up no batch

2.2 Batch in the model

2.2.1 Batch setup

3 Table S2 and Figure 1b, Table S3

3.1 Batch estimated with SVA

3.1.1 Set up sva

3.2 Batch correction via ruv residuals

3.2.1 Set up ruvresiduals

3.3 Batch correction with pca

3.3.1 Setup pca

3.4 Batch correction with ruv empirical

3.4.1 Setup ruv empirical

3.5 Batch correction with combat

3.5.1 Setup combat

4 Figure out how to compare these results

5 Look first at the de counts

5.1 Compare DeSeq / Basic without batch in model

5.2 Compare SVA to batch in model, DESeq

5.2.1 Include p-value estimations

5.3 Compare ruvresid to batch in model, DESeq

5.4 Compare no batch to batch in model, limma

5.5 Batch in model vs. SVA, limma

5.6 Nobatch vs. batch in model, edger

5.7 Batch in model vs. SVA, edger

5.8 Compare nobatch vs. batch, deseq

5.9 Compare batch vs. SVA, deseq

6 Repeat using the parasite data

6.1 No batch in the model

6.2 Batch in the model

6.3 Batch estimated with SVA

7 Figure out how to compare these results

7.1 Compare DeSeq / Basic without batch in model

7.2 Compare SVA to batch in model, DESeq

7.2.1 Include p-value estimations

7.3 Compare no batch to batch in model, limma

7.4 Batch in model vs. SVA, limma

7.5 Nobatch vs. batch in model, edger

7.6 Batch in model vs. SVA, edger

7.7 Compare nobatch vs. batch, deseq

7.8 Compare batch vs. SVA, deseq