L.panamensis 20190205: Differential Expression in human macrophages.

1 Differential Expression, Macrophage: 20190205

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"))

## Error in density.default(x, adjust = adj) : 'x' contains missing values

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))

## Error in write_combined_summary(wb, excel_basename, apr, extracted, compare_plots, : object 'xl_result' not found

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"))

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

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"))

## Error in extract_abundant_genes(hs_macr_batch_tables, excel = excel_file, : object 'hs_macr_batch_tables' not found

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))

## Error in write_combined_summary(wb, excel_basename, apr, extracted, compare_plots, : object 'xl_result' not found

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"))

## Error in extract_significant_genes(table_s2, excel = excel_file, according_to = "deseq"): object 'table_s2' not found

chosen_table <- table_s2[["data"]][[1]]

## Error in eval(expr, envir, enclos): object 'table_s2' not found

head(chosen_table)

## Error in head(chosen_table): object 'chosen_table' not found

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")

## Error in data.frame(xaxis = as.numeric(table[[fc_col]]), yaxis = as.numeric(table[[p_col]]), : object 'chosen_table' not found

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

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

vol$plot

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

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")

hs_macr_sva_ma_limma$ma$plot

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(all_adjusters(input=hs_macr_lowfilt, estimate_type="ruv_residuals"), silent=TRUE)

## batch_counts: Before batch/surrogate estimation, 117505 entries are x>1: 100%.

## batch_counts: Before batch/surrogate estimation, 93 entries are x==0: 0%.

## The be method chose 1 surrogate variable.

## Attempting ruvseq residual surrogate estimation with 1 surrogate.

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 <- all_adjusters(input=hs_macr_lowfilt, estimate_type="ruv_residuals")

## batch_counts: Before batch/surrogate estimation, 117505 entries are x>1: 100%.

## batch_counts: Before batch/surrogate estimation, 93 entries are x==0: 0%.

## The be method chose 1 surrogate variable.

## Attempting ruvseq residual surrogate estimation with 1 surrogate.

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))

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_norm <- sm(normalize_expt(hs_macr_lowfilt, batch="combat_noscale"))
hs_macr_combat <- all_pairwise(
  input=hs_macr_combat_norm,
  force=TRUE, parallel=FALSE,
  limma_method="robust")

## Plotting a PCA before surrogate/batch inclusion.

## Not putting labels on the PC plot.

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

## Not putting labels on the PC plot.

## Starting basic_pairwise().

## Starting basic pairwise comparison.

## Leaving the data alone, regardless of normalization state.

## Basic step 0/3: Transforming data.

## Basic step 1/3: Creating mean and variance tables.

## Basic step 2/3: Performing 6 comparisons.

## Basic step 3/3: Creating faux DE Tables.

## Basic: Returning tables.

## Starting deseq_pairwise().

## Starting DESeq2 pairwise comparisons.

## About to round the data, this is a pretty terrible thing to do. But if you, like me, want to see what happens when you put non-standard data into deseq, then here you go.

## Warning in choose_binom_dataset(input, force = force): This data was
## inappropriately forced into integers.

## Choosing the non-intercept containing model.

## DESeq2 step 1/5: Including batch and condition in the deseq model.

## converting counts to integer mode

## DESeq2 step 2/5: Estimate size factors.

## DESeq2 step 3/5: Estimate dispersions.

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## Using a parametric fitting seems to have worked.

## DESeq2 step 4/5: nbinomWaldTest.

## Starting ebseq_pairwise().

## About to round the data, this is a pretty terrible thing to do. But if you, like me, want to see what happens when you put non-standard data into deseq, then here you go.

## Warning in choose_binom_dataset(input, force = force): This data was
## inappropriately forced into integers.

## Starting EBSeq pairwise subset.

## Choosing the non-intercept containing model.

## Starting EBTest of chr vs. sh.

## Forcing out NA values by putting in the mean of all data.

## Copying ppee values as ajusted p-values until I figure out how to deal with them.

## Starting EBTest of chr vs. uninf.

## Forcing out NA values by putting in the mean of all data.

## Copying ppee values as ajusted p-values until I figure out how to deal with them.

## Starting EBTest of sh vs. uninf.

## Forcing out NA values by putting in the mean of all data.

## Copying ppee values as ajusted p-values until I figure out how to deal with them.

## Starting edger_pairwise().

## Starting edgeR pairwise comparisons.

## About to round the data, this is a pretty terrible thing to do. But if you, like me, want to see what happens when you put non-standard data into deseq, then here you go.

## Warning in choose_binom_dataset(input, force = force): This data was
## inappropriately forced into integers.

## Choosing the non-intercept containing model.

## EdgeR step 1/9: Importing and normalizing data.

## EdgeR step 2/9: Estimating the common dispersion.

## EdgeR step 3/9: Estimating dispersion across genes.

## EdgeR step 4/9: Estimating GLM Common dispersion.

## EdgeR step 5/9: Estimating GLM Trended dispersion.

## EdgeR step 6/9: Estimating GLM Tagged dispersion.

## EdgeR step 7/9: Running glmFit, switch to glmQLFit by changing the argument 'edger_test'.

## EdgeR step 8/9: Making pairwise contrasts.

## Starting limma_pairwise().

## Starting limma pairwise comparison.

## Leaving the data alone, regardless of normalization state.

## libsize was not specified, this parameter has profound effects on limma's result.

## Using the libsize from expt$libsize.

## Limma step 1/6: choosing model.

## Choosing the non-intercept containing model.

## Limma step 2/6: running limma::voom(), switch with the argument 'which_voom'.

## Using normalize.method=quantile for voom.

## Limma step 3/6: running lmFit with method: robust.

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
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## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

## Warning in rlm.default(x = X, y = y, weights = w, ...): 'rlm' failed to converge
## in 20 steps

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## in 20 steps

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## in 20 steps

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## in 20 steps

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## in 20 steps

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## in 20 steps

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## in 20 steps

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## in 20 steps

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## in 20 steps

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## in 20 steps

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## in 20 steps

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## in 20 steps

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## in 20 steps

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## in 20 steps

## Limma step 4/6: making and fitting contrasts with no intercept. (~ 0 + factors)

## Limma step 5/6: Running eBayes with robust=FALSE and trend=FALSE.

## Limma step 6/6: Writing limma outputs.

## Limma step 6/6: 1/3: Creating table: sh_vs_chr.  Adjust=BH

## Limma step 6/6: 2/3: Creating table: uninf_vs_chr.  Adjust=BH

## Limma step 6/6: 3/3: Creating table: uninf_vs_sh.  Adjust=BH

## Limma step 6/6: 1/3: Creating table: chr.  Adjust=BH

## Limma step 6/6: 2/3: Creating table: sh.  Adjust=BH

## Limma step 6/6: 3/3: Creating table: uninf.  Adjust=BH

## Comparing analyses.

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 <- extract_significant_genes(
  hs_macr_combat_tables,
  excel=excel_file)

## Writing a legend of columns.

## Printing significant genes to the file: excel/20210113_hs_macr_combat_contr-v20190205.xlsx

## 1/3: Creating significant table up_limma_macro_chr-sh

## 2/3: Creating significant table up_limma_macro_chr-nil

## 3/3: Creating significant table up_limma_macro_sh-nil

## Printing significant genes to the file: excel/20210113_hs_macr_combat_contr-v20190205.xlsx

## 1/3: Creating significant table up_edger_macro_chr-sh

## 2/3: Creating significant table up_edger_macro_chr-nil

## 3/3: Creating significant table up_edger_macro_sh-nil

## Printing significant genes to the file: excel/20210113_hs_macr_combat_contr-v20190205.xlsx

## 1/3: Creating significant table up_deseq_macro_chr-sh

## 2/3: Creating significant table up_deseq_macro_chr-nil

## 3/3: Creating significant table up_deseq_macro_sh-nil

## Printing significant genes to the file: excel/20210113_hs_macr_combat_contr-v20190205.xlsx

## 1/3: Creating significant table up_ebseq_macro_chr-sh

## The down table macro_chr-sh is empty.

## 2/3: Creating significant table up_ebseq_macro_chr-nil

## 3/3: Creating significant table up_ebseq_macro_sh-nil

## Printing significant genes to the file: excel/20210113_hs_macr_combat_contr-v20190205.xlsx

## The up table macro_chr-sh is empty.

## The down table macro_chr-sh is empty.

## The up table macro_chr-nil is empty.

## The up table macro_sh-nil is empty.

## The down table macro_sh-nil is empty.

## Adding significance bar 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

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              2036                560
## macro_chr-nil              949               1176
## macro_sh-nil               459               1792

##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 = 404, df = 13079, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.9608 0.9634
## sample estimates:
##    cor 
## 0.9621

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 = 82, df = 13079, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5694 0.5921
## sample estimates:
##    cor 
## 0.5808

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")
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

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 64, df = 13079, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4769 0.5030
## sample estimates:
##    cor 
## 0.4901

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")]
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

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

lfc_ruv_bat$scatter

lfc_ruv_bat$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 701, df = 13079, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.9865 0.9874
## sample estimates:
##   cor 
## 0.987

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 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): S refinements did not converge (to
## refine.tol=1e-07) in 200 (= k.max) steps

## Warning in lmrob.S(x, y, control = control): S refinements did not converge (to
## refine.tol=1e-07) in 200 (= k.max) steps

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

nb_b$scatter

nb_b$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 107, df = 51039, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4200 0.4342
## sample estimates:
##    cor 
## 0.4271

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")
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)

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

b_s$scatter

b_s$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 62, df = 13079, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4647 0.4911
## sample estimates:
##   cor 
## 0.478

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 = 265, df = 51039, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7567 0.7640
## sample estimates:
##    cor 
## 0.7604

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")
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)

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

b_s$scatter

b_s$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 64, df = 13079, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4781 0.5041
## sample estimates:
##    cor 
## 0.4912

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 = 149, df = 51039, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5443 0.5564
## sample estimates:
##    cor 
## 0.5503

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")
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

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 64, df = 13079, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4769 0.5030
## sample estimates:
##    cor 
## 0.4901

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.

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.

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

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?

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 = 189, df = 8660, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.8927 0.9009
## sample estimates:
##    cor 
## 0.8969

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 = 105, df = 8660, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7387 0.7573
## sample estimates:
##    cor 
## 0.7481

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 = 80, df = 8660, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6378 0.6622
## sample estimates:
##    cor 
## 0.6502

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 = 43, df = 8660, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4051 0.4397
## sample estimates:
##    cor 
## 0.4225

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

nb_b$scatter

nb_b$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 77, df = 8660, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6235 0.6486
## sample estimates:
##    cor 
## 0.6362

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

b_s$scatter

b_s$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 85, df = 8660, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6608 0.6839
## sample estimates:
##    cor 
## 0.6725

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 = 65, df = 8660, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5599 0.5881
## sample estimates:
##    cor 
## 0.5741

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

b_s$scatter

b_s$correlation

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = 80, df = 8660, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6391 0.6634
## sample estimates:
##    cor 
## 0.6514

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 = 66, df = 8660, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5634 0.5915
## sample estimates:
##    cor 
## 0.5776

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 = 80, df = 8660, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6378 0.6622
## sample estimates:
##    cor 
## 0.6502

pander::pander(sessionInfo())

R version 4.0.3 (2020-10-10)

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

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

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

other attached packages: ggplot2(v.3.3.3), ruv(v.0.9.7.1), edgeR(v.3.32.0), hpgltools(v.1.0), testthat(v.3.0.1), R6(v.2.5.0), Biobase(v.2.50.0) and BiocGenerics(v.0.36.0)

loaded via a namespace (and not attached): R.utils(v.2.10.1), tidyselect(v.1.1.0), lme4(v.1.1-26), RSQLite(v.2.2.1), AnnotationDbi(v.1.52.0), grid(v.4.0.3), BiocParallel(v.1.24.1), IHW(v.1.18.0), devtools(v.2.3.2), scatterpie(v.0.1.5), munsell(v.0.5.0), codetools(v.0.2-18), preprocessCore(v.1.52.0), statmod(v.1.4.35), withr(v.2.3.0), colorspace(v.2.0-0), GOSemSim(v.2.16.1), OrganismDbi(v.1.32.0), knitr(v.1.30), rstudioapi(v.0.13), stats4(v.4.0.3), Vennerable(v.3.1.0.9000), robustbase(v.0.93-6), DOSE(v.3.16.0), MatrixGenerics(v.1.2.0), labeling(v.0.4.2), slam(v.0.1-48), lpsymphony(v.1.18.0), GenomeInfoDbData(v.1.2.4), hwriter(v.1.3.2), polyclip(v.1.10-0), bit64(v.4.0.5), farver(v.2.0.3), rprojroot(v.2.0.2), downloader(v.0.4), vctrs(v.0.3.6), generics(v.0.1.0), xfun(v.0.19), BiocFileCache(v.1.14.0), EDASeq(v.2.24.0), doParallel(v.1.0.16), GenomeInfoDb(v.1.26.2), graphlayouts(v.0.7.1), locfit(v.1.5-9.4), bitops(v.1.0-6), fgsea(v.1.16.0), DelayedArray(v.0.16.0), assertthat(v.0.2.1), scales(v.1.1.1), ggraph(v.2.0.4), enrichplot(v.1.10.1), gtable(v.0.3.0), RUVSeq(v.1.24.0), sva(v.3.38.0), processx(v.3.4.5), tidygraph(v.1.2.0), rlang(v.0.4.10), genefilter(v.1.72.0), splines(v.4.0.3), rtracklayer(v.1.50.0), broom(v.0.7.3), BiocManager(v.1.30.10), yaml(v.2.2.1), reshape2(v.1.4.4), GenomicFeatures(v.1.42.1), backports(v.1.2.1), qvalue(v.2.22.0), clusterProfiler(v.3.18.0), RBGL(v.1.66.0), tools(v.4.0.3), usethis(v.1.6.3), ellipsis(v.0.3.1), gplots(v.3.1.1), RColorBrewer(v.1.1-2), blockmodeling(v.1.0.0), sessioninfo(v.1.1.1), Rcpp(v.1.0.5), plyr(v.1.8.6), progress(v.1.2.2), zlibbioc(v.1.36.0), purrr(v.0.3.4), RCurl(v.1.98-1.2), ps(v.1.5.0), prettyunits(v.1.1.1), openssl(v.1.4.3), viridis(v.0.5.1), cowplot(v.1.1.0), S4Vectors(v.0.28.1), SummarizedExperiment(v.1.20.0), ggrepel(v.0.9.0), colorRamps(v.2.3), fs(v.1.5.0), variancePartition(v.1.20.0), magrittr(v.2.0.1), data.table(v.1.13.6), openxlsx(v.4.2.3), DO.db(v.2.9), matrixStats(v.0.57.0), aroma.light(v.3.20.0), pkgload(v.1.1.0), hms(v.0.5.3), evaluate(v.0.14), xtable(v.1.8-4), pbkrtest(v.0.5-0.1), XML(v.3.99-0.5), jpeg(v.0.1-8.1), IRanges(v.2.24.1), gridExtra(v.2.3), compiler(v.4.0.3), biomaRt(v.2.46.0), tibble(v.3.0.4), KernSmooth(v.2.23-18), crayon(v.1.3.4), shadowtext(v.0.0.7), R.oo(v.1.24.0), minqa(v.1.2.4), htmltools(v.0.5.0), mgcv(v.1.8-33), corpcor(v.1.6.9), geneplotter(v.1.68.0), tidyr(v.1.1.2), DBI(v.1.1.0), tweenr(v.1.0.1), dbplyr(v.2.0.0), MASS(v.7.3-53), rappdirs(v.0.3.1), boot(v.1.3-25), ShortRead(v.1.48.0), Matrix(v.1.3-0), cli(v.2.2.0), R.methodsS3(v.1.8.1), quadprog(v.1.5-8), igraph(v.1.2.6), GenomicRanges(v.1.42.0), pkgconfig(v.2.0.3), rvcheck(v.0.1.8), GenomicAlignments(v.1.26.0), xml2(v.1.3.2), foreach(v.1.5.1), annotate(v.1.68.0), XVector(v.0.30.0), EBSeq(v.1.30.0), stringr(v.1.4.0), callr(v.3.5.1), digest(v.0.6.27), graph(v.1.68.0), Biostrings(v.2.58.0), rmarkdown(v.2.6), fastmatch(v.1.1-0), PROPER(v.1.22.0), directlabels(v.2020.6.17), curl(v.4.3), Rsamtools(v.2.6.0), gtools(v.3.8.2), nloptr(v.1.2.2.2), lifecycle(v.0.2.0), nlme(v.3.1-151), desc(v.1.2.0), viridisLite(v.0.3.0), askpass(v.1.1), limma(v.3.46.0), fansi(v.0.4.1), pillar(v.1.4.7), lattice(v.0.20-41), DEoptimR(v.1.0-8), httr(v.1.4.2), pkgbuild(v.1.2.0), survival(v.3.2-7), GO.db(v.3.12.1), glue(v.1.4.2), remotes(v.2.2.0), fdrtool(v.1.2.15), zip(v.2.1.1), png(v.0.1-7), iterators(v.1.0.13), pander(v.0.6.3), bit(v.4.0.4), ggforce(v.0.3.2), stringi(v.1.5.3), blob(v.1.2.1), DESeq2(v.1.30.0), latticeExtra(v.0.6-29), caTools(v.1.18.0), memoise(v.1.1.0) and dplyr(v.1.0.2)

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 052640a0b091e9a740e487957e087f265d0c74b5

## This is hpgltools commit: Thu Dec 3 13:42:02 2020 -0500: 052640a0b091e9a740e487957e087f265d0c74b5

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_20190205-v20190205.rda.xz

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