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1 RNA sequencing of Leishmania panamensis during infection of human macrophages: Differential Expression

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.

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.

my_contrasts <- list(
    "macro_chr-sh" = c("macro_ch","macro_sh"),
    "macro_chr-nil" = c("macro_ch","macro_nil"),
    "macro_sh-nil" = c("macro_sh", "macro_nil"))
macro_norm <- sm(normalize_expt(macrophage_parasite, filter=TRUE, convert="cpm", norm="quant"))
## Error in normalize_expt(macrophage_parasite, filter = TRUE, convert = "cpm", : object 'macrophage_parasite' not found
macro_combat_norm <- sm(normalize_expt(macrophage_parasite, filter=TRUE, norm="quant", batch="combat_scale"))
## Error in normalize_expt(macrophage_parasite, filter = TRUE, norm = "quant", : object 'macrophage_parasite' not found
macro_lowfilt <- sm(normalize_expt(macrophage_parasite, filter=TRUE))
## Error in normalize_expt(macrophage_parasite, filter = TRUE): object 'macrophage_parasite' not found
## Set up the data used in the 3 comparative contrast sets.

2.1 No batch in the model

macro_nobatch <- sm(all_pairwise(macro_lowfilt, model_batch=FALSE))
## Error in exists(sym, env, mode = "function", inherits = FALSE): object 'macro_lowfilt' not found
## wow, all tools including basic agree almost completely
medians_by_condition <- macro_nobatch$basic$medians
## Error in eval(expr, envir, enclos): object 'macro_nobatch' not found
macro_nobatch_tables <- sm(combine_de_tables(macro_nobatch,
                                             excel=paste0("excel/macrophage_nobatch-v", ver, ".xlsx"),
                                             keepers=my_contrasts,
                                             extra_annot=medians_by_condition))
## Error in combine_de_tables(macro_nobatch, excel = paste0("excel/macrophage_nobatch-v", : object 'macro_nobatch' not found
macro_nobatch_sig <- sm(extract_significant_genes(macro_nobatch_tables,
                                                  excel=paste0("excel/macrophage_nobatch_significant-v", ver, ".xlsx"),
                                                  p_type="unadjusted",
                                                  according_to="all"))
## Error in extract_significant_genes(macro_nobatch_tables, excel = paste0("excel/macrophage_nobatch_significant-v", : object 'macro_nobatch_tables' not found

2.2 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
macro_batch <- sm(all_pairwise(macro_lowfilt))
## Error in exists(sym, env, mode = "function", inherits = FALSE): object 'macro_lowfilt' not found
medians_by_condition <- macro_batch$basic$medians
## Error in eval(expr, envir, enclos): object 'macro_batch' not found
macro_batch_tables <- sm(combine_de_tables(macro_batch, excel=paste0("excel/macrophage_batchmodel-v", ver, ".xlsx"),
                                           keepers=my_contrasts,
                                           extra_annot=medians_by_condition))
## Error in combine_de_tables(macro_batch, excel = paste0("excel/macrophage_batchmodel-v", : object 'macro_batch' not found
macro_batch_sig <- sm(extract_significant_genes(macro_batch_tables,
                                                excel=paste0("excel/macrophage_batchmodel_significant-v", ver, ".xlsx"),
                                                p_type="unadjusted",
                                                according_to="all"))
## Error in extract_significant_genes(macro_batch_tables, excel = paste0("excel/macrophage_batchmodel_significant-v", : object 'macro_batch_tables' not found

2.3 Batch estimated with SVA

## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
macro_sva <- sm(all_pairwise(macro_lowfilt, model_batch="sva"))
## Error in get_model_adjust(input, estimate_type = model_batch, surrogates = surrogates): object 'macro_lowfilt' not found
medians_by_condition <- macro_sva$basic$medians
## Error in eval(expr, envir, enclos): object 'macro_sva' not found
macro_sva_tables <- sm(combine_de_tables(macro_sva, excel=paste0("excel/macrophage_sva-v", ver, ".xlsx"),
                                         keepers=my_contrasts,
                                         extra_annot=medians_by_condition))
## Error in combine_de_tables(macro_sva, excel = paste0("excel/macrophage_sva-v", : object 'macro_sva' not found
macro_sva_sig <- sm(extract_significant_genes(macro_sva_tables,
                                              excel=paste0("excel/macrophage_sva_significant-v", ver, ".xlsx"),
                                              p_type="unadjusted",
                                              according_to="all"))
## Error in extract_significant_genes(macro_sva_tables, excel = paste0("excel/macrophage_sva_significant-v", : object 'macro_sva_tables' not found

2.4 Batch correction via ruv residuals

## 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)
macro_ruvres <- try(sm(all_pairwise(macro_lowfilt, model_batch="ruv_residuals")))
if (class(macro_ruvres) == "try-error") {
    macro_ruvres <- sm(all_pairwise(macro_lowfilt, model_batch="ruv_residuals"))
}
## Error in get_model_adjust(input, estimate_type = model_batch, surrogates = surrogates): object 'macro_lowfilt' not found
medians_by_condition <- macro_ruvres$basic$medians
## Error in macro_ruvres$basic: $ operator is invalid for atomic vectors
macro_ruvres_tables <- sm(combine_de_tables(macro_ruvres, excel=paste0("excel/macrophage_ruvres-v", ver, ".xlsx"),
                                            keepers=my_contrasts,
                                            extra_annot=medians_by_condition))
## Error in all_pairwise_result[["limma"]]: subscript out of bounds
macro_ruvres_sig <- sm(extract_significant_genes(macro_ruvres_tables,
                                                 excel=paste0("excel/macrophage_ruvres_significant-v", ver, ".xlsx"),
                                                 p_type="unadjusted",
                                                 according_to="all"))
## Error in extract_significant_genes(macro_ruvres_tables, excel = paste0("excel/macrophage_ruvres_significant-v", : object 'macro_ruvres_tables' not found

2.5 Batch correction with pca

## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
macro_pca <- sm(all_pairwise(macro_lowfilt, model_batch="pca"))
## Error in get_model_adjust(input, estimate_type = model_batch, surrogates = surrogates): object 'macro_lowfilt' not found
medians_by_condition <- macro_pca$basic$medians
## Error in eval(expr, envir, enclos): object 'macro_pca' not found
macro_pca_tables <- sm(combine_de_tables(macro_pca, excel=paste0("excel/macrophage_pca-v", ver, ".xlsx"),
                                         keepers=my_contrasts,
                                         extra_annot=medians_by_condition))
## Error in combine_de_tables(macro_pca, excel = paste0("excel/macrophage_pca-v", : object 'macro_pca' not found
macro_pca_sig <- sm(extract_significant_genes(macro_pca_tables,
                                              excel=paste0("excel/macrophage_pca_significant-v", ver, ".xlsx"),
                                              p_type="unadjusted",
                                              according_to="all"))
## Error in extract_significant_genes(macro_pca_tables, excel = paste0("excel/macrophage_pca_significant-v", : object 'macro_pca_tables' not found

2.6 Batch correction with ruv empirical

## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
macro_ruvemp <- sm(all_pairwise(macro_lowfilt, model_batch="ruv_empirical"))
## Error in get_model_adjust(input, estimate_type = model_batch, surrogates = surrogates): object 'macro_lowfilt' not found
medians_by_condition <- macro_ruvemp$basic$medians
## Error in eval(expr, envir, enclos): object 'macro_ruvemp' not found
macro_ruvemp_tables <- sm(combine_de_tables(macro_ruvemp, excel=paste0("excel/macrophage_ruvemp-v", ver, ".xlsx"),
                                            keepers=my_contrasts,
                                            extra_annot=medians_by_condition))
## Error in combine_de_tables(macro_ruvemp, excel = paste0("excel/macrophage_ruvemp-v", : object 'macro_ruvemp' not found
macro_ruvemp_sig <- sm(extract_significant_genes(macro_ruvemp_tables,
                                                 excel=paste0("excel/macrophage_ruvemp_significant-v", ver, ".xlsx"),
                                                 p_type="unadjusted",
                                                 according_to="all"))
## Error in extract_significant_genes(macro_ruvemp_tables, excel = paste0("excel/macrophage_ruvemp_significant-v", : object 'macro_ruvemp_tables' not found

2.7 Batch correction with combat

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

macro_combat <- sm(all_pairwise(macro_combat_norm, force=TRUE))
## Error in exists(sym, env, mode = "function", inherits = FALSE): object 'macro_combat_norm' not found
medians_by_condition <- macro_combat$basic$medians
## Error in eval(expr, envir, enclos): object 'macro_combat' not found
macro_combat_tables <- sm(combine_de_tables(macro_combat,
                                            excel=paste0("excel/macrophage_combat-v", ver, ".xlsx"),
                                            keepers=my_contrasts,
                                            extra_annot=medians_by_condition))
## Error in combine_de_tables(macro_combat, excel = paste0("excel/macrophage_combat-v", : object 'macro_combat' not found
macro_combat_sig <- sm(extract_significant_genes(macro_combat_tables,
                                                 excel=paste0("excel/macrophage_combat_significant-v", ver, ".xlsx"),
                                                 p_type="unadjusted",
                                                 according_to="all"))
## Error in extract_significant_genes(macro_combat_tables, excel = paste0("excel/macrophage_combat_significant-v", : object 'macro_combat_tables' not found

3 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?

4 Look first at the de counts

macro_nobatch_sig$limma$counts
## Error in eval(expr, envir, enclos): object 'macro_nobatch_sig' not found
macro_batch_sig$limma$counts
## Error in eval(expr, envir, enclos): object 'macro_batch_sig' not found
macro_sva_sig$limma$counts
## Error in eval(expr, envir, enclos): object 'macro_sva_sig' not found
macro_ruvres_sig$limma$counts
## Error in eval(expr, envir, enclos): object 'macro_ruvres_sig' not found
macro_pca_sig$limma$counts
## Error in eval(expr, envir, enclos): object 'macro_pca_sig' not found
macro_ruvemp_sig$limma$counts
## Error in eval(expr, envir, enclos): object 'macro_ruvemp_sig' not found
macro_combat_sig$limma$counts
## Error in eval(expr, envir, enclos): object 'macro_combat_sig' not found

4.1 Compare DeSeq / Basic without batch in model

nobatch_basic <- merge(macro_nobatch$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$basic$all_tables$macro_sh_vs_macro_ch, by="row.names")
## Error in merge(macro_nobatch$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$basic$all_tables$macro_sh_vs_macro_ch, : object 'macro_nobatch' not found
rownames(nobatch_basic) <- nobatch_basic[["Row.names"]]
## Error in eval(expr, envir, enclos): object 'nobatch_basic' not found
nobatch_logfc <- nobatch_basic[, c("logFC.x","logFC.y")]
## Error in eval(expr, envir, enclos): object 'nobatch_basic' not found
colnames(nobatch_logfc) <- c("nobatch","basic")
## Error in colnames(nobatch_logfc) <- c("nobatch", "basic"): object 'nobatch_logfc' not found
lfc_nb_b <- sm(plot_linear_scatter(nobatch_logfc, pretty_colors=FALSE))
## Error in data.frame(df[, c(1, 2)]): object 'nobatch_logfc' not found
lfc_nb_b$scatter
## Error in eval(expr, envir, enclos): object 'lfc_nb_b' not found
lfc_nb_b$correlation
## Error in eval(expr, envir, enclos): object 'lfc_nb_b' not found
nobatch_p <- nobatch_basic[, c("P.Value","p")]
## Error in eval(expr, envir, enclos): object 'nobatch_basic' not found
nobatch_p[[2]] <- as.numeric(nobatch_p[[2]])
## Error in eval(expr, envir, enclos): object 'nobatch_p' not found
colnames(nobatch_p) <- c("nobatch","basic")
## Error in colnames(nobatch_p) <- c("nobatch", "basic"): object 'nobatch_p' not found
nobatch_p <- -1 * log(nobatch_p)
## Error in eval(expr, envir, enclos): object 'nobatch_p' not found
p_nb_b <- sm(plot_linear_scatter(nobatch_p, pretty_colors=FALSE))
## Error in data.frame(df[, c(1, 2)]): object 'nobatch_p' not found
p_nb_b$scatter
## Error in eval(expr, envir, enclos): object 'p_nb_b' not found
p_nb_b$correlation
## Error in eval(expr, envir, enclos): object 'p_nb_b' not found

4.2 Compare SVA to batch in model, DESeq

sva_batch <- merge(macro_sva$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$deseq$all_tables$macro_sh_vs_macro_ch, by="row.names")
## Error in merge(macro_sva$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$deseq$all_tables$macro_sh_vs_macro_ch, : object 'macro_sva' not found
rownames(sva_batch) <- sva_batch[["Row.names"]]
## Error in eval(expr, envir, enclos): object 'sva_batch' not found
sva_logfc <- sva_batch[, c("logFC.x","logFC.y")]
## Error in eval(expr, envir, enclos): object 'sva_batch' not found
colnames(sva_logfc) <- c("sva","batch")
## Error in colnames(sva_logfc) <- c("sva", "batch"): object 'sva_logfc' not found
lfc_b_s <- sm(plot_linear_scatter(sva_logfc, pretty_colors=FALSE))
## Error in data.frame(df[, c(1, 2)]): object 'sva_logfc' not found
lfc_b_s$scatter
## Error in eval(expr, envir, enclos): object 'lfc_b_s' not found
lfc_b_s$correlation
## Error in eval(expr, envir, enclos): object 'lfc_b_s' not found
sva_p <- sva_batch[, c("P.Value.x","P.Value.y")]
## Error in eval(expr, envir, enclos): object 'sva_batch' not found
sva_p[[2]] <- as.numeric(sva_p[[2]])
## Error in eval(expr, envir, enclos): object 'sva_p' not found
colnames(sva_p) <- c("sva","batch")
## Error in colnames(sva_p) <- c("sva", "batch"): object 'sva_p' not found
sva_p <- -1 * log(sva_p)
## Error in eval(expr, envir, enclos): object 'sva_p' not found
p_b_s <- sm(plot_linear_scatter(sva_p, pretty_colors=FALSE))
## Error in data.frame(df[, c(1, 2)]): object 'sva_p' not found
p_b_s$scatter
## Error in eval(expr, envir, enclos): object 'p_b_s' not found
p_b_s$correlation
## Error in eval(expr, envir, enclos): object 'p_b_s' not found

4.2.1 Include p-value estimations

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

lfcp_b_s <- sva_batch[, c("logFC.x", "logFC.y", "P.Value.x", "P.Value.y")]
## Error in eval(expr, envir, enclos): object 'sva_batch' not found
colnames(lfcp_b_s) <- c("l2fcsva", "l2fcbatch", "psva", "pbatch")
## Error in colnames(lfcp_b_s) <- c("l2fcsva", "l2fcbatch", "psva", "pbatch"): object 'lfcp_b_s' not found
lfc_b_s$scatter
## Error in eval(expr, envir, enclos): object 'lfc_b_s' not found
cutoff <- 0.1
lfcp_b_s$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$psva <= cutoff, "svasig", "batchsig")))
## Error in ifelse(lfcp_b_s$psva > cutoff & lfcp_b_s$pbatch > cutoff, "bothinsig", : object 'lfcp_b_s' not found
##lfcp_b_s$lfcstate <- ifelse(lfcp_b_s$l2fcsva >= 0.75 & lfcp_b_s$l2fcbatch, "", "")
num_bothinsig <- sum(lfcp_b_s$state == "bothinsig")
## Error in eval(expr, envir, enclos): object 'lfcp_b_s' not found
num_bothsig <- sum(lfcp_b_s$state == "bothsig")
## Error in eval(expr, envir, enclos): object 'lfcp_b_s' not found
num_svasig <- sum(lfcp_b_s$state == "svasig")
## Error in eval(expr, envir, enclos): object 'lfcp_b_s' not found
num_batchsig <- sum(lfcp_b_s$state == "batchsig")
## Error in eval(expr, envir, enclos): object 'lfcp_b_s' not found
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()
## Error in ggplot2::ggplot(lfcp_b_s, aes_string(x = "l2fcsva", y = "l2fcbatch")): object 'lfcp_b_s' not found
plt
## Error in eval(expr, envir, enclos): object 'plt' not found

4.3 Compare ruvresid to batch in model, DESeq

batch_ruvresid_deseq <- merge(macro_ruvres$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$basic$all_tables$macro_sh_vs_macro_ch, by="row.names")
## Error in macro_ruvres$deseq: $ operator is invalid for atomic vectors
rownames(batch_ruvresid_deseq) <- batch_ruvresid_deseq[["Row.names"]]
## Error in eval(expr, envir, enclos): object 'batch_ruvresid_deseq' not found
batch_ruvresid_logfc <- batch_ruvresid_deseq[, c("logFC.x","logFC.y")]
## Error in eval(expr, envir, enclos): object 'batch_ruvresid_deseq' not found
colnames(batch_ruvresid_logfc) <- c("nobatch","basic")
## Error in colnames(batch_ruvresid_logfc) <- c("nobatch", "basic"): object 'batch_ruvresid_logfc' not found
lfc_ruv_bat <- plot_linear_scatter(batch_ruvresid_logfc, pretty_colors=FALSE)
## Error in data.frame(df[, c(1, 2)]): object 'batch_ruvresid_logfc' not found
lfc_ruv_bat$scatter
## Error in eval(expr, envir, enclos): object 'lfc_ruv_bat' not found
lfc_ruv_bat$correlation
## Error in eval(expr, envir, enclos): object 'lfc_ruv_bat' not found

4.4 Compare pca to batch in model, DESeq

batch_pca_deseq <- merge(macro_pca$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$basic$all_tables$macro_sh_vs_macro_ch, by="row.names")
## Error in merge(macro_pca$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$basic$all_tables$macro_sh_vs_macro_ch, : object 'macro_pca' not found
rownames(batch_pca_deseq) <- batch_pca_deseq[["Row.names"]]
## Error in eval(expr, envir, enclos): object 'batch_pca_deseq' not found
batch_pca_logfc <- batch_pca_deseq[, c("logFC.x","logFC.y")]
## Error in eval(expr, envir, enclos): object 'batch_pca_deseq' not found
colnames(batch_pca_logfc) <- c("nobatch","basic")
## Error in colnames(batch_pca_logfc) <- c("nobatch", "basic"): object 'batch_pca_logfc' not found
lfc_pca_bat <- plot_linear_scatter(batch_pca_logfc, pretty_colors=FALSE)
## Error in data.frame(df[, c(1, 2)]): object 'batch_pca_logfc' not found
lfc_pca_bat$scatter
## Error in eval(expr, envir, enclos): object 'lfc_pca_bat' not found
lfc_pca_bat$correlation
## Error in eval(expr, envir, enclos): object 'lfc_pca_bat' not found

4.5 Compare ruv empirical to batch in model, DESeq

batch_ruvemp_deseq <- merge(macro_ruvemp$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$basic$all_tables$macro_sh_vs_macro_ch, by="row.names")
## Error in merge(macro_ruvemp$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$basic$all_tables$macro_sh_vs_macro_ch, : object 'macro_ruvemp' not found
rownames(batch_ruvemp_deseq) <- batch_ruvemp_deseq[["Row.names"]]
## Error in eval(expr, envir, enclos): object 'batch_ruvemp_deseq' not found
batch_ruvemp_logfc <- batch_ruvemp_deseq[, c("logFC.x","logFC.y")]
## Error in eval(expr, envir, enclos): object 'batch_ruvemp_deseq' not found
colnames(batch_ruvemp_logfc) <- c("nobatch","basic")
## Error in colnames(batch_ruvemp_logfc) <- c("nobatch", "basic"): object 'batch_ruvemp_logfc' not found
lfc_ruvemp_bat <- sm(plot_linear_scatter(batch_ruvemp_logfc, pretty_colors=FALSE))
## Error in data.frame(df[, c(1, 2)]): object 'batch_ruvemp_logfc' not found
lfc_ruvemp_bat$scatter
## Error in eval(expr, envir, enclos): object 'lfc_ruvemp_bat' not found
lfc_ruvemp_bat$correlation
## Error in eval(expr, envir, enclos): object 'lfc_ruvemp_bat' not found

4.6 Compare combat to batch in model, DESeq

combat_batch <- merge(macro_combat$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$deseq$all_tables$macro_sh_vs_macro_ch, by="row.names")
## Error in merge(macro_combat$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$deseq$all_tables$macro_sh_vs_macro_ch, : object 'macro_combat' not found
rownames(combat_batch) <- combat_batch[["Row.names"]]
## Error in eval(expr, envir, enclos): object 'combat_batch' not found
combat_batch <- combat_batch[, c("logFC.x","logFC.y")]
## Error in eval(expr, envir, enclos): object 'combat_batch' not found
colnames(combat_batch) <- c("batch","combat")
## Error in colnames(combat_batch) <- c("batch", "combat"): object 'combat_batch' not found
b_c <- plot_linear_scatter(combat_batch, pretty_colors=FALSE)
## Error in data.frame(df[, c(1, 2)]): object 'combat_batch' not found
b_c$scatter
## Error in eval(expr, envir, enclos): object 'b_c' not found
b_c$correlation
## Error in eval(expr, envir, enclos): object 'b_c' not found

4.7 Compare no batch to batch in model, limma

nobatch_batch <- merge(macro_nobatch$limma$all_tables$macro_sh_vs_macro_ch, macro_batch$limma$all_tables$macro_sh_vs_macro_ch, by="row.names")
## Error in merge(macro_nobatch$limma$all_tables$macro_sh_vs_macro_ch, macro_batch$limma$all_tables$macro_sh_vs_macro_ch, : object 'macro_nobatch' not found
rownames(nobatch_batch) <- nobatch_batch[["Row.names"]]
## Error in eval(expr, envir, enclos): object 'nobatch_batch' not found
nobatch_batch <- nobatch_batch[, c("logFC.x","logFC.y")]
## Error in eval(expr, envir, enclos): object 'nobatch_batch' not found
colnames(nobatch_batch) <- c("nobatch","batch")
## Error in colnames(nobatch_batch) <- c("nobatch", "batch"): object 'nobatch_batch' not found
nb_b <- plot_linear_scatter(nobatch_batch, pretty_colors=FALSE)
## Error in data.frame(df[, c(1, 2)]): object 'nobatch_batch' not found
nb_b$scatter
## Error in eval(expr, envir, enclos): object 'nb_b' not found
nb_b$correlation
## Error in eval(expr, envir, enclos): object 'nb_b' not found

4.8 Batch in model vs. SVA, limma

batch_sva <- merge(macro_batch$limma$all_tables$macro_sh_vs_macro_ch, macro_sva$limma$all_tables$macro_sh_vs_macro_ch, by="row.names")
## Error in merge(macro_batch$limma$all_tables$macro_sh_vs_macro_ch, macro_sva$limma$all_tables$macro_sh_vs_macro_ch, : object 'macro_batch' not found
rownames(batch_sva) <- batch_sva[["Row.names"]]
## Error in eval(expr, envir, enclos): object 'batch_sva' not found
batch_sva <- batch_sva[, c("logFC.x","logFC.y")]
## Error in eval(expr, envir, enclos): object 'batch_sva' not found
colnames(batch_sva) <- c("batch","sva")
## Error in colnames(batch_sva) <- c("batch", "sva"): object 'batch_sva' not found
b_s <- plot_linear_scatter(batch_sva, pretty_colors=FALSE)
## Error in data.frame(df[, c(1, 2)]): object '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

4.9 Batch in model vs. combat, limma

batch_combat <- merge(macro_batch$limma$all_tables$macro_sh_vs_macro_ch, macro_combat$limma$all_tables$macro_sh_vs_macro_ch, by="row.names")
## Error in merge(macro_batch$limma$all_tables$macro_sh_vs_macro_ch, macro_combat$limma$all_tables$macro_sh_vs_macro_ch, : object 'macro_batch' not found
rownames(batch_combat) <- batch_combat[["Row.names"]]
## Error in eval(expr, envir, enclos): object 'batch_combat' not found
batch_combat <- batch_combat[, c("logFC.x","logFC.y")]
## Error in eval(expr, envir, enclos): object 'batch_combat' not found
colnames(batch_combat) <- c("batch","combat")
## Error in colnames(batch_combat) <- c("batch", "combat"): object 'batch_combat' not found
b_c <- plot_linear_scatter(batch_combat, pretty_colors=FALSE)
## Error in data.frame(df[, c(1, 2)]): object 'batch_combat' not found
b_c$scatter
## Error in eval(expr, envir, enclos): object 'b_c' not found
b_c$correlation
## Error in eval(expr, envir, enclos): object 'b_c' not found

4.10 Nobatch vs. batch in model, edger

nobatch_batch <- merge(macro_nobatch$edger$all_tables$macro_sh_vs_macro_ch, macro_batch$edger$all_tables$macro_sh_vs_macro_ch, by="row.names")
## Error in merge(macro_nobatch$edger$all_tables$macro_sh_vs_macro_ch, macro_batch$edger$all_tables$macro_sh_vs_macro_ch, : object 'macro_nobatch' not found
rownames(nobatch_batch) <- nobatch_batch[["Row.names"]]
## Error in eval(expr, envir, enclos): object 'nobatch_batch' not found
nobatch_batch <- nobatch_batch[, c("logFC.x","logFC.y")]
## Error in eval(expr, envir, enclos): object 'nobatch_batch' not found
colnames(nobatch_batch) <- c("nobatch","batch")
## Error in colnames(nobatch_batch) <- c("nobatch", "batch"): object 'nobatch_batch' not found
nb_b <- sm(plot_linear_scatter(nobatch_batch, pretty_colors=FALSE))
## Error in data.frame(df[, c(1, 2)]): object 'nobatch_batch' not found
nb_b$scatter
## Error in eval(expr, envir, enclos): object 'nb_b' not found
nb_b$correlation
## Error in eval(expr, envir, enclos): object 'nb_b' not found

4.11 Batch in model vs. SVA, edger

batch_sva <- merge(macro_batch$edger$all_tables$macro_sh_vs_macro_ch, macro_sva$edger$all_tables$macro_sh_vs_macro_ch, by="row.names")
## Error in merge(macro_batch$edger$all_tables$macro_sh_vs_macro_ch, macro_sva$edger$all_tables$macro_sh_vs_macro_ch, : object 'macro_batch' not found
rownames(batch_sva) <- batch_sva[["Row.names"]]
## Error in eval(expr, envir, enclos): object 'batch_sva' not found
batch_sva <- batch_sva[, c("logFC.x","logFC.y")]
## Error in eval(expr, envir, enclos): object 'batch_sva' not found
colnames(batch_sva) <- c("batch","sva")
## Error in colnames(batch_sva) <- c("batch", "sva"): object 'batch_sva' not found
b_s <- plot_linear_scatter(batch_sva, pretty_colors=FALSE)
## Error in data.frame(df[, c(1, 2)]): object '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

4.12 Batch in model vs. combat, edger

batch_combat <- merge(macro_batch$edger$all_tables$macro_sh_vs_macro_ch, macro_combat$edger$all_tables$macro_sh_vs_macro_ch, by="row.names")
## Error in merge(macro_batch$edger$all_tables$macro_sh_vs_macro_ch, macro_combat$edger$all_tables$macro_sh_vs_macro_ch, : object 'macro_batch' not found
rownames(batch_combat) <- batch_combat[["Row.names"]]
## Error in eval(expr, envir, enclos): object 'batch_combat' not found
batch_combat <- batch_combat[, c("logFC.x","logFC.y")]
## Error in eval(expr, envir, enclos): object 'batch_combat' not found
colnames(batch_combat) <- c("batch","combat")
## Error in colnames(batch_combat) <- c("batch", "combat"): object 'batch_combat' not found
b_c <- plot_linear_scatter(batch_combat, pretty_colors=FALSE)
## Error in data.frame(df[, c(1, 2)]): object 'batch_combat' not found
b_c$scatter
## Error in eval(expr, envir, enclos): object 'b_c' not found
b_c$correlation
## Error in eval(expr, envir, enclos): object 'b_c' not found

4.13 Compare nobatch vs. batch, deseq

nobatch_batch <- merge(macro_nobatch$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$deseq$all_tables$macro_sh_vs_macro_ch, by="row.names")
## Error in merge(macro_nobatch$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$deseq$all_tables$macro_sh_vs_macro_ch, : object 'macro_nobatch' not found
rownames(nobatch_batch) <- nobatch_batch[["Row.names"]]
## Error in eval(expr, envir, enclos): object 'nobatch_batch' not found
nobatch_batch <- nobatch_batch[, c("logFC.x","logFC.y")]
## Error in eval(expr, envir, enclos): object 'nobatch_batch' not found
colnames(nobatch_batch) <- c("nobatch","batch")
## Error in colnames(nobatch_batch) <- c("nobatch", "batch"): object 'nobatch_batch' not found
nb_b <- sm(plot_linear_scatter(nobatch_batch, pretty_colors=FALSE))
## Error in data.frame(df[, c(1, 2)]): object 'nobatch_batch' not found
nb_b$scatter
## Error in eval(expr, envir, enclos): object 'nb_b' not found
nb_b$correlation
## Error in eval(expr, envir, enclos): object 'nb_b' not found

4.14 Compare batch vs. SVA, deseq

batch_sva <- merge(macro_batch$deseq$all_tables$macro_sh_vs_macro_ch, macro_sva$deseq$all_tables$macro_sh_vs_macro_ch, by="row.names")
## Error in merge(macro_batch$deseq$all_tables$macro_sh_vs_macro_ch, macro_sva$deseq$all_tables$macro_sh_vs_macro_ch, : object 'macro_batch' not found
rownames(batch_sva) <- batch_sva[["Row.names"]]
## Error in eval(expr, envir, enclos): object 'batch_sva' not found
batch_sva <- batch_sva[, c("logFC.x","logFC.y")]
## Error in eval(expr, envir, enclos): object 'batch_sva' not found
colnames(batch_sva) <- c("batch","sva")
## Error in colnames(batch_sva) <- c("batch", "sva"): object 'batch_sva' not found
b_s <- sm(plot_linear_scatter(batch_sva, pretty_colors=FALSE))
## Error in data.frame(df[, c(1, 2)]): object '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

4.15 Batch in model vs. combat, deseq

batch_combat <- merge(macro_batch$deseq$all_tables$macro_sh_vs_macro_ch, macro_combat$deseq$all_tables$macro_sh_vs_macro_ch, by="row.names")
## Error in merge(macro_batch$deseq$all_tables$macro_sh_vs_macro_ch, macro_combat$deseq$all_tables$macro_sh_vs_macro_ch, : object 'macro_batch' not found
rownames(batch_combat) <- batch_combat[["Row.names"]]
## Error in eval(expr, envir, enclos): object 'batch_combat' not found
batch_combat <- batch_combat[, c("logFC.x","logFC.y")]
## Error in eval(expr, envir, enclos): object 'batch_combat' not found
colnames(batch_combat) <- c("batch","combat")
## Error in colnames(batch_combat) <- c("batch", "combat"): object 'batch_combat' not found
b_c <- sm(plot_linear_scatter(batch_combat, pretty_colors=FALSE))
## Error in data.frame(df[, c(1, 2)]): object 'batch_combat' not found
b_c$scatter
## Error in eval(expr, envir, enclos): object 'b_c' not found
b_c$correlation
## Error in eval(expr, envir, enclos): object 'b_c' not found
tmp <- sm(saveme(filename="macrophage_expression_parasite.rda.xz"))

index.html macrophage_estimation.html

---
title: "RNAseq of L.panamensis: Human Macrophage Differential Expression."
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: cosmo
  toc: true
  toc_float:
    collapsed: false
    smooth_scroll: false
---

<style>
  <!-- Document prelude revision 2016-10 -->
  body .main-container {
    max-width: 1600px;
}
</style>

```{r options, include=FALSE}
## These are the options I tend to favor
library("hpgltools")
tt <- sm(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)
options(
    digits = 4,
    stringsAsFactors = FALSE,
    knitr.duplicate.label = "allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size=10))
set.seed(1)
rmd_file <- "macrophage_expression_parasite.Rmd"
ver <- "20170202"
```

[index.html](index.html) [macrophage_estimation.html](macrophage_estimation.html)

```{r rendering, include=FALSE, eval=FALSE}
## This block is used to render a document from within it.
rmarkdown::render(rmd_file)

## An extra renderer for pdf output
rmarkdown::render(rmd_file, output_format="pdf_document", output_options=c("skip_html"))
## Or to save/load large Rdata files.
hpgltools:::saveme()
hpgltools:::loadme()
rm(list=ls())
```

RNA sequencing of Leishmania panamensis during infection of human macrophages: Differential Expression
======================================================================================================

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.

```{r loadme, include=FALSE}
tmp <- sm(loadme(filename="macrophage_estimation_parasite.rda.xz"))
```

# 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, fig.show="hide"}
my_contrasts <- list(
    "macro_chr-sh" = c("macro_ch","macro_sh"),
    "macro_chr-nil" = c("macro_ch","macro_nil"),
    "macro_sh-nil" = c("macro_sh", "macro_nil"))
macro_norm <- sm(normalize_expt(macrophage_parasite, filter=TRUE, convert="cpm", norm="quant"))
macro_combat_norm <- sm(normalize_expt(macrophage_parasite, filter=TRUE, norm="quant", batch="combat_scale"))
macro_lowfilt <- sm(normalize_expt(macrophage_parasite, filter=TRUE))
## Set up the data used in the 3 comparative contrast sets.
```

## No batch in the model

```{r macro_nobatch, fig.show="hide"}
macro_nobatch <- sm(all_pairwise(macro_lowfilt, model_batch=FALSE))
## wow, all tools including basic agree almost completely
medians_by_condition <- macro_nobatch$basic$medians
macro_nobatch_tables <- sm(combine_de_tables(macro_nobatch,
                                             excel=paste0("excel/macrophage_nobatch-v", ver, ".xlsx"),
                                             keepers=my_contrasts,
                                             extra_annot=medians_by_condition))
macro_nobatch_sig <- sm(extract_significant_genes(macro_nobatch_tables,
                                                  excel=paste0("excel/macrophage_nobatch_significant-v", ver, ".xlsx"),
                                                  p_type="unadjusted",
                                                  according_to="all"))
```

## 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
macro_batch <- sm(all_pairwise(macro_lowfilt))
medians_by_condition <- macro_batch$basic$medians
macro_batch_tables <- sm(combine_de_tables(macro_batch, excel=paste0("excel/macrophage_batchmodel-v", ver, ".xlsx"),
                                           keepers=my_contrasts,
                                           extra_annot=medians_by_condition))
macro_batch_sig <- sm(extract_significant_genes(macro_batch_tables,
                                                excel=paste0("excel/macrophage_batchmodel_significant-v", ver, ".xlsx"),
                                                p_type="unadjusted",
                                                according_to="all"))
```

## 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
macro_sva <- sm(all_pairwise(macro_lowfilt, model_batch="sva"))
medians_by_condition <- macro_sva$basic$medians
macro_sva_tables <- sm(combine_de_tables(macro_sva, excel=paste0("excel/macrophage_sva-v", ver, ".xlsx"),
                                         keepers=my_contrasts,
                                         extra_annot=medians_by_condition))
macro_sva_sig <- sm(extract_significant_genes(macro_sva_tables,
                                              excel=paste0("excel/macrophage_sva_significant-v", ver, ".xlsx"),
                                              p_type="unadjusted",
                                              according_to="all"))
```

## Batch correction via ruv residuals

```{r macro_ruvresid, 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)
macro_ruvres <- try(sm(all_pairwise(macro_lowfilt, model_batch="ruv_residuals")))
if (class(macro_ruvres) == "try-error") {
    macro_ruvres <- sm(all_pairwise(macro_lowfilt, model_batch="ruv_residuals"))
}
medians_by_condition <- macro_ruvres$basic$medians
macro_ruvres_tables <- sm(combine_de_tables(macro_ruvres, excel=paste0("excel/macrophage_ruvres-v", ver, ".xlsx"),
                                            keepers=my_contrasts,
                                            extra_annot=medians_by_condition))
macro_ruvres_sig <- sm(extract_significant_genes(macro_ruvres_tables,
                                                 excel=paste0("excel/macrophage_ruvres_significant-v", ver, ".xlsx"),
                                                 p_type="unadjusted",
                                                 according_to="all"))
```

## Batch correction with pca

```{r macro_pca, fig.show="hide"}
## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
macro_pca <- sm(all_pairwise(macro_lowfilt, model_batch="pca"))
medians_by_condition <- macro_pca$basic$medians
macro_pca_tables <- sm(combine_de_tables(macro_pca, excel=paste0("excel/macrophage_pca-v", ver, ".xlsx"),
                                         keepers=my_contrasts,
                                         extra_annot=medians_by_condition))
macro_pca_sig <- sm(extract_significant_genes(macro_pca_tables,
                                              excel=paste0("excel/macrophage_pca_significant-v", ver, ".xlsx"),
                                              p_type="unadjusted",
                                              according_to="all"))
```

## Batch correction with ruv empirical

```{r macro_ruvemp, fig.show="hide"}
## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
macro_ruvemp <- sm(all_pairwise(macro_lowfilt, model_batch="ruv_empirical"))
medians_by_condition <- macro_ruvemp$basic$medians
macro_ruvemp_tables <- sm(combine_de_tables(macro_ruvemp, excel=paste0("excel/macrophage_ruvemp-v", ver, ".xlsx"),
                                            keepers=my_contrasts,
                                            extra_annot=medians_by_condition))
macro_ruvemp_sig <- sm(extract_significant_genes(macro_ruvemp_tables,
                                                 excel=paste0("excel/macrophage_ruvemp_significant-v", ver, ".xlsx"),
                                                 p_type="unadjusted",
                                                 according_to="all"))
```

## Batch correction with combat

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

```{r repeat_pairwise_batch, fig.show="hide"}
macro_combat <- sm(all_pairwise(macro_combat_norm, force=TRUE))
medians_by_condition <- macro_combat$basic$medians
macro_combat_tables <- sm(combine_de_tables(macro_combat,
                                            excel=paste0("excel/macrophage_combat-v", ver, ".xlsx"),
                                            keepers=my_contrasts,
                                            extra_annot=medians_by_condition))
macro_combat_sig <- sm(extract_significant_genes(macro_combat_tables,
                                                 excel=paste0("excel/macrophage_combat_significant-v", ver, ".xlsx"),
                                                 p_type="unadjusted",
                                                 according_to="all"))
```

# 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_setup}
macro_nobatch_sig$limma$counts
macro_batch_sig$limma$counts
macro_sva_sig$limma$counts
macro_ruvres_sig$limma$counts
macro_pca_sig$limma$counts
macro_ruvemp_sig$limma$counts
macro_combat_sig$limma$counts
```

## Compare DeSeq / Basic without batch in model

```{r basic_deseq_nobatch}
nobatch_basic <- merge(macro_nobatch$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$basic$all_tables$macro_sh_vs_macro_ch, by="row.names")
rownames(nobatch_basic) <- nobatch_basic[["Row.names"]]
nobatch_logfc <- nobatch_basic[, c("logFC.x","logFC.y")]
colnames(nobatch_logfc) <- c("nobatch","basic")
lfc_nb_b <- sm(plot_linear_scatter(nobatch_logfc, pretty_colors=FALSE))
lfc_nb_b$scatter
lfc_nb_b$correlation
nobatch_p <- nobatch_basic[, c("P.Value","p")]
nobatch_p[[2]] <- as.numeric(nobatch_p[[2]])
colnames(nobatch_p) <- c("nobatch","basic")
nobatch_p <- -1 * log(nobatch_p)
p_nb_b <- sm(plot_linear_scatter(nobatch_p, pretty_colors=FALSE))
p_nb_b$scatter
p_nb_b$correlation
```

## Compare SVA to batch in model, DESeq

```{r deseq_sva_batch}
sva_batch <- merge(macro_sva$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$deseq$all_tables$macro_sh_vs_macro_ch, by="row.names")
rownames(sva_batch) <- sva_batch[["Row.names"]]
sva_logfc <- sva_batch[, c("logFC.x","logFC.y")]
colnames(sva_logfc) <- c("sva","batch")
lfc_b_s <- sm(plot_linear_scatter(sva_logfc, pretty_colors=FALSE))
lfc_b_s$scatter
lfc_b_s$correlation
sva_p <- sva_batch[, c("P.Value.x","P.Value.y")]
sva_p[[2]] <- as.numeric(sva_p[[2]])
colnames(sva_p) <- c("sva","batch")
sva_p <- -1 * log(sva_p)
p_b_s <- sm(plot_linear_scatter(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 <- 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 ruvresid to batch in model, DESeq

```{r batch_ruvresid_deseq}
batch_ruvresid_deseq <- merge(macro_ruvres$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$basic$all_tables$macro_sh_vs_macro_ch, by="row.names")
rownames(batch_ruvresid_deseq) <- batch_ruvresid_deseq[["Row.names"]]
batch_ruvresid_logfc <- batch_ruvresid_deseq[, c("logFC.x","logFC.y")]
colnames(batch_ruvresid_logfc) <- c("nobatch","basic")
lfc_ruv_bat <- plot_linear_scatter(batch_ruvresid_logfc, pretty_colors=FALSE)
lfc_ruv_bat$scatter
lfc_ruv_bat$correlation
```

## Compare pca to batch in model, DESeq

```{r batch_pca_deseq}
batch_pca_deseq <- merge(macro_pca$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$basic$all_tables$macro_sh_vs_macro_ch, by="row.names")
rownames(batch_pca_deseq) <- batch_pca_deseq[["Row.names"]]
batch_pca_logfc <- batch_pca_deseq[, c("logFC.x","logFC.y")]
colnames(batch_pca_logfc) <- c("nobatch","basic")
lfc_pca_bat <- plot_linear_scatter(batch_pca_logfc, pretty_colors=FALSE)
lfc_pca_bat$scatter
lfc_pca_bat$correlation
```

## Compare ruv empirical to batch in model, DESeq

```{r batch_ruvemp_deseq}
batch_ruvemp_deseq <- merge(macro_ruvemp$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$basic$all_tables$macro_sh_vs_macro_ch, by="row.names")
rownames(batch_ruvemp_deseq) <- batch_ruvemp_deseq[["Row.names"]]
batch_ruvemp_logfc <- batch_ruvemp_deseq[, c("logFC.x","logFC.y")]
colnames(batch_ruvemp_logfc) <- c("nobatch","basic")
lfc_ruvemp_bat <- sm(plot_linear_scatter(batch_ruvemp_logfc, pretty_colors=FALSE))
lfc_ruvemp_bat$scatter
lfc_ruvemp_bat$correlation
```

## Compare combat to batch in model, DESeq

```{r compare_batch_combat}
combat_batch <- merge(macro_combat$deseq$all_tables$macro_sh_vs_macro_ch, macro_batch$deseq$all_tables$macro_sh_vs_macro_ch, by="row.names")
rownames(combat_batch) <- combat_batch[["Row.names"]]
combat_batch <- combat_batch[, c("logFC.x","logFC.y")]
colnames(combat_batch) <- c("batch","combat")
b_c <- plot_linear_scatter(combat_batch, pretty_colors=FALSE)
b_c$scatter
b_c$correlation
```

## Compare no batch to batch in model, limma

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

## Batch in model vs. SVA, limma

```{r compare_batch_sva_limma}
batch_sva <- merge(macro_batch$limma$all_tables$macro_sh_vs_macro_ch, macro_sva$limma$all_tables$macro_sh_vs_macro_ch, by="row.names")
rownames(batch_sva) <- batch_sva[["Row.names"]]
batch_sva <- batch_sva[, c("logFC.x","logFC.y")]
colnames(batch_sva) <- c("batch","sva")
b_s <- plot_linear_scatter(batch_sva, pretty_colors=FALSE)
b_s$scatter
b_s$correlation
```

## Batch in model vs. combat, limma

```{r compare_batch_combat_limma}
batch_combat <- merge(macro_batch$limma$all_tables$macro_sh_vs_macro_ch, macro_combat$limma$all_tables$macro_sh_vs_macro_ch, by="row.names")
rownames(batch_combat) <- batch_combat[["Row.names"]]
batch_combat <- batch_combat[, c("logFC.x","logFC.y")]
colnames(batch_combat) <- c("batch","combat")
b_c <- plot_linear_scatter(batch_combat, pretty_colors=FALSE)
b_c$scatter
b_c$correlation
```

## Nobatch vs. batch in model, edger

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

## Batch in model vs. SVA, edger

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

## Batch in model vs. combat, edger

```{r compare_batch_combat_edger}
batch_combat <- merge(macro_batch$edger$all_tables$macro_sh_vs_macro_ch, macro_combat$edger$all_tables$macro_sh_vs_macro_ch, by="row.names")
rownames(batch_combat) <- batch_combat[["Row.names"]]
batch_combat <- batch_combat[, c("logFC.x","logFC.y")]
colnames(batch_combat) <- c("batch","combat")
b_c <- plot_linear_scatter(batch_combat, pretty_colors=FALSE)
b_c$scatter
b_c$correlation
```

## Compare nobatch vs. batch, deseq

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

## Compare batch vs. SVA, deseq

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

## Batch in model vs. combat, deseq

```{r batch_combat_deseq}
batch_combat <- merge(macro_batch$deseq$all_tables$macro_sh_vs_macro_ch, macro_combat$deseq$all_tables$macro_sh_vs_macro_ch, by="row.names")
rownames(batch_combat) <- batch_combat[["Row.names"]]
batch_combat <- batch_combat[, c("logFC.x","logFC.y")]
colnames(batch_combat) <- c("batch","combat")
b_c <- sm(plot_linear_scatter(batch_combat, pretty_colors=FALSE))
b_c$scatter
b_c$correlation
```

```{r save_macro_expression}
tmp <- sm(saveme(filename="macrophage_expression_parasite.rda.xz"))
```

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