In this version of the worksheet, I am hoping to perform basically the same analyses, but do it in a fashion which is more in my own style.
Collect the human annotation data using biomaRt.
## The biomart annotations file already exists, loading from it.
I am going to use Kajal’s sample sheet without modification.
design <- read.table("sample_sheets/MetaData only 4 hour.txt", header=TRUE, sep='\t')
design[["Patient"]] <- as.factor(design[["Patient"]])
design[["Stimulation"]] <- as.factor(design[["Stimulation"]])
design[["Batch"]] <- as.factor(design[["Batch"]])
design[["Growth"]] <- as.factor(design[["Growth"]])
files <- file.path("kallisto abundance files/", design$HPGL.Identifier, "abundance.tsv")
names(files) <- paste0("HPGL09", c(12:31, 42:60))
rownames(design) <- design[[1]]
design[["condition"]] <- design[["Stimulation"]]
design[["file"]] <- glue::glue("preprocessing/{rownames(design)}/abundance.tsv")
colnames(design) <- tolower(colnames(design))
design[["gp"]] <- as.factor(glue("{design[['growth']]}_{design[['stimulation']]}"))
## Set up a column called stim_pred which is a predicate of stimulated vs. unstimulated samples.
design[["stim_pred"]] <- "stimulated"
ns_idx <- design[["stimulation"]] == "NS"
design[ns_idx, "stim_pred"] <- "unstimulated"
We have some annotation data and experimental metadata.
## Reading the sample metadata.
## The sample definitions comprises: 39 rows(samples) and 10 columns(metadata fields).
## Reading count tables.
## Using the transcript to gene mapping.
## Reading kallisto data with tximport.
## Finished reading count data.
## Matched 34431 annotations and counts.
## Bringing together the count matrix and gene information.
## The mapped IDs are not the rownames of your gene information, changing them now.
## Some annotations were lost in merging, setting them to 'undefined'.
## Saving the expressionset to 'expt.rda'.
## The final expressionset has 34431 rows and 39 columns.
hs_expt <- set_expt_batches(hs_expt, fact="growth")
stim_expt <- subset_expt(hs_expt, subset="stimulation!='NS'")
## Using a subset expression.
## There were 39, now there are 30 samples.
We can write out the data to an excel file in the hopes that it will prove useful.
## Writing the first sheet, containing a legend and some summary data.
## Writing the raw reads.
## Graphing the raw reads.
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## Attempting mixed linear model with: ~ (1|condition) + (1|batch)
## Fitting the expressionset to the model, this is slow.
## Dividing work into 100 chunks...
##
## Total:163 s
## Placing factor: condition at the beginning of the model.
## Writing the normalized reads.
## Graphing the normalized reads.
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## Attempting mixed linear model with: ~ (1|condition) + (1|batch)
## Fitting the expressionset to the model, this is slow.
## Dividing work into 100 chunks...
##
## Total:154 s
## Placing factor: condition at the beginning of the model.
## Writing the median reads by factor.
There is an important caveat, this is not taking into account the patient effects.
We may wish to lower the variance from the patients and/or the GM/M effects.
hs_batch <- normalize_expt(hs_expt, transform="log2", convert="cpm",
norm="quant", filter=TRUE, batch="svaseq")
## This function will replace the expt$expressionset slot with:
## log2(svaseq(cpm(quant(cbcb(data)))))
## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize
## Warning in normalize_expt(hs_expt, transform = "log2", convert = "cpm", :
## Quantile normalization and sva do not always play well together.
## Step 1: performing count filter with option: cbcb
## Removing 21885 low-count genes (12546 remaining).
## Step 2: normalizing the data with quant.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 841 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with svaseq.
## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.
## Passing off to all_adjusters.
## batch_counts: Before batch/surrogate estimation, 459917 entries are x>1: 94%.
## batch_counts: Before batch/surrogate estimation, 841 entries are x==0: 0%.
## batch_counts: Before batch/surrogate estimation, 28536 entries are 0<x<1: 6%.
## The be method chose 6 surrogate variable(s).
## Attempting svaseq estimation with 6 surrogates.
## There are 466 (0%) elements which are < 0 after batch correction.
## Setting low elements to zero.
Because we are explicitly removing the effect of GM/M, the patient effect really becomes apparent.
hs_batch <- normalize_expt(hs_expt, transform="log2", convert="cpm",
norm="quant", filter=TRUE, batch="limmaresid")
## This function will replace the expt$expressionset slot with:
## log2(limmaresid(cpm(quant(cbcb(data)))))
## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize
## Step 1: performing count filter with option: cbcb
## Removing 21885 low-count genes (12546 remaining).
## Step 2: normalizing the data with quant.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 841 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with limmaresid.
## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.
## Passing off to all_adjusters.
## batch_counts: Before batch/surrogate estimation, 459917 entries are x>1: 94%.
## batch_counts: Before batch/surrogate estimation, 841 entries are x==0: 0%.
## batch_counts: Before batch/surrogate estimation, 28536 entries are 0<x<1: 6%.
## The be method chose 6 surrogate variable(s).
## batch_counts: Using residuals of limma's lmfit to remove batch effect.
## There are 236935 (48%) elements which are < 0 after batch correction.
## Setting low elements to zero.
The picture with sva should be the same as the first plot, just with 6 shapes instead of two.
hs_pat <- set_expt_batches(hs_expt, fact="patient")
hs_batch <- normalize_expt(hs_pat, transform="log2", convert="cpm",
norm="quant", filter=TRUE, batch="svaseq")
## This function will replace the expt$expressionset slot with:
## log2(svaseq(cpm(quant(cbcb(data)))))
## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize
## Warning in normalize_expt(hs_pat, transform = "log2", convert = "cpm", norm =
## "quant", : Quantile normalization and sva do not always play well together.
## Step 1: performing count filter with option: cbcb
## Removing 21885 low-count genes (12546 remaining).
## Step 2: normalizing the data with quant.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 841 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with svaseq.
## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.
## Passing off to all_adjusters.
## batch_counts: Before batch/surrogate estimation, 459917 entries are x>1: 94%.
## batch_counts: Before batch/surrogate estimation, 841 entries are x==0: 0%.
## batch_counts: Before batch/surrogate estimation, 28536 entries are 0<x<1: 6%.
## The be method chose 6 surrogate variable(s).
## Attempting svaseq estimation with 6 surrogates.
## There are 466 (0%) elements which are < 0 after batch correction.
## Setting low elements to zero.
This picture should be different, and should show us the M/GM effect as opposed to the patient effect.
hs_batch <- normalize_expt(hs_pat, transform="log2", convert="cpm",
norm="quant", filter=TRUE, batch="limmaresid")
## This function will replace the expt$expressionset slot with:
## log2(limmaresid(cpm(quant(cbcb(data)))))
## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize
## Step 1: performing count filter with option: cbcb
## Removing 21885 low-count genes (12546 remaining).
## Step 2: normalizing the data with quant.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 841 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with limmaresid.
## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.
## Passing off to all_adjusters.
## batch_counts: Before batch/surrogate estimation, 459917 entries are x>1: 94%.
## batch_counts: Before batch/surrogate estimation, 841 entries are x==0: 0%.
## batch_counts: Before batch/surrogate estimation, 28536 entries are 0<x<1: 6%.
## The be method chose 6 surrogate variable(s).
## batch_counts: Using residuals of limma's lmfit to remove batch effect.
## There are 239301 (49%) elements which are < 0 after batch correction.
## Setting low elements to zero.
I am not sure what this will look like. Since two aspects of the data are in the condition portion of the model matrix, I think it should look different. When I created the design matrix, I made a column for this purpose; I called it ‘gp’, but honestly I don’t remember why…
hs_gs <- set_expt_conditions(hs_pat, fact="gp")
hs_batch <- normalize_expt(hs_gs, transform="log2", convert="cpm",
norm="quant", filter=TRUE, batch="svaseq")
## This function will replace the expt$expressionset slot with:
## log2(svaseq(cpm(quant(cbcb(data)))))
## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize
## Warning in normalize_expt(hs_gs, transform = "log2", convert = "cpm", norm =
## "quant", : Quantile normalization and sva do not always play well together.
## Step 1: performing count filter with option: cbcb
## Removing 21885 low-count genes (12546 remaining).
## Step 2: normalizing the data with quant.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 841 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with svaseq.
## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.
## Passing off to all_adjusters.
## batch_counts: Before batch/surrogate estimation, 459917 entries are x>1: 94%.
## batch_counts: Before batch/surrogate estimation, 841 entries are x==0: 0%.
## batch_counts: Before batch/surrogate estimation, 28536 entries are 0<x<1: 6%.
## The be method chose 5 surrogate variable(s).
## Attempting svaseq estimation with 5 surrogates.
## There are 640 (0%) elements which are < 0 after batch correction.
## Setting low elements to zero.
It seems to me that is primarily showing us differences between M/GM.
What about limma?
hs_batch <- normalize_expt(hs_gs, transform="log2", convert="cpm",
norm="quant", filter=TRUE, batch="limma")
## This function will replace the expt$expressionset slot with:
## log2(limma(cpm(quant(cbcb(data)))))
## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize
## Step 1: performing count filter with option: cbcb
## Removing 21885 low-count genes (12546 remaining).
## Step 2: normalizing the data with quant.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 841 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with limma.
## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.
## Passing off to all_adjusters.
## batch_counts: Before batch/surrogate estimation, 459917 entries are x>1: 94%.
## batch_counts: Before batch/surrogate estimation, 841 entries are x==0: 0%.
## batch_counts: Before batch/surrogate estimation, 28536 entries are 0<x<1: 6%.
## The be method chose 5 surrogate variable(s).
## batch_counts: Using limma's removeBatchEffect to remove batch effect.
## If you receive a warning: 'NANs produced', one potential reason is that the data was quantile normalized.
## There are 762 (0%) elements which are < 0 after batch correction.
## Setting low elements to zero.
Same deal, just more. This might be the moment to reconsider the fact that Kajal’s work focused only on the M/GM samples and AFAICT ignored the non-stimulated samples.
## Using a subset expression.
## There were 39, now there are 30 samples.
hs_batch <- normalize_expt(tmp, transform="log2", convert="cpm",
norm="quant", filter=TRUE, batch="sva")
## This function will replace the expt$expressionset slot with:
## log2(sva(cpm(quant(cbcb(data)))))
## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize
## Warning in normalize_expt(tmp, transform = "log2", convert = "cpm", norm =
## "quant", : Quantile normalization and sva do not always play well together.
## Step 1: performing count filter with option: cbcb
## Removing 22066 low-count genes (12365 remaining).
## Step 2: normalizing the data with quant.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 644 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with sva.
## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.
## Passing off to all_adjusters.
## batch_counts: Before batch/surrogate estimation, 351625 entries are x>1: 95%.
## batch_counts: Before batch/surrogate estimation, 644 entries are x==0: 0%.
## batch_counts: Before batch/surrogate estimation, 18681 entries are 0<x<1: 5%.
## The be method chose 5 surrogate variable(s).
## Estimate type 'sva' is shorthand for 'sva_unsupervised'.
## Other sva options include: sva_supervised and svaseq.
## Attempting sva unsupervised surrogate estimation with 5 surrogates.
## There are 338 (0%) elements which are < 0 after batch correction.
## Setting low elements to zero.
## Using a subset expression.
## There were 39, now there are 30 samples.
hs_batch <- normalize_expt(tmp, transform="log2", convert="cpm",
norm="quant", filter=TRUE, batch="ruv_empirical")
## This function will replace the expt$expressionset slot with:
## log2(ruv_empirical(cpm(quant(cbcb(data)))))
## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize
## Step 1: performing count filter with option: cbcb
## Removing 22066 low-count genes (12365 remaining).
## Step 2: normalizing the data with quant.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 644 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with ruv_empirical.
## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.
## Passing off to all_adjusters.
## batch_counts: Before batch/surrogate estimation, 351625 entries are x>1: 95%.
## batch_counts: Before batch/surrogate estimation, 644 entries are x==0: 0%.
## batch_counts: Before batch/surrogate estimation, 18681 entries are 0<x<1: 5%.
## The be method chose 5 surrogate variable(s).
## Attempting ruvseq empirical surrogate estimation with 5 surrogates.
## There are 219 (0%) elements which are < 0 after batch correction.
## Setting low elements to zero.
Interesting, I did not put them all into the test block above, but I tried out a bunch of small changes to the model and adjusters. I think I learned one primary lesson: patient number 3 is a bit weird. I think I might suggest removing this person from the data.
The next lesson I learned is that LPS is way different than LA/LP, something which I kind of knew from other work, but worth remembering.
There are a few ways to consider differential expression for this data. In all cases I think it is safe to assume that we wish to use patient as the batch factor/surrogate variable.
With that in mind, here are the factors of the data to which we have usable variance/experimental design:
Before I run these, lets look at the variance in the data and make sure I am not full of crap.
## This function will replace the expt$expressionset slot with:
## cpm(cbcb(data))
## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize
## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.
## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.
## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.
## Step 1: performing count filter with option: cbcb
## Removing 21885 low-count genes (12546 remaining).
## Step 2: not normalizing the data.
## Step 3: converting the data with cpm.
## Step 4: not transforming the data.
## Step 5: not doing batch correction.
hs_varpart <- simple_varpart(hs_vpin, factors=c("stimulation", "growth", "patient"),
chosen_factor="patient", do_fit=TRUE)
## Attempting mixed linear model with: ~ (1|stimulation) + (1|growth) + (1|patient)
## Fitting the expressionset to the model, this is slow.
## Dividing work into 100 chunks...
##
## Total:132 s
## Placing factor: stimulation at the beginning of the model.
## Memory usage to store result: >450.6 Mb
## Dividing work into 100 chunks...
##
## Total:341 s
top_40_stimulation <- hs_varpart$percent_plot
## Now show a variance boxplot for the chosen batch factor (patient)
hs_varpart$stratify_batch_plot
## Perform some DE
## This function will replace the expt$expressionset slot with:
## cbcb(data)
## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize
## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.
## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).
## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.
## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.
## Step 1: performing count filter with option: cbcb
## Removing 21885 low-count genes (12546 remaining).
## Step 2: not normalizing the data.
## Step 3: not converting the data.
## Step 4: not transforming the data.
## Step 5: not doing batch correction.
pat_gs_sva <- set_expt_conditions(hs_filt, fact="gp")
pat_gs_sva <- set_expt_batches(pat_gs_sva, fact="patient")
pat_gs_sva_de <- all_pairwise(pat_gs_sva, model_batch="sva")
## batch_counts: Before batch/surrogate estimation, 485493 entries are x>1: 99%.
## batch_counts: Before batch/surrogate estimation, 2575 entries are x==0: 1%.
## batch_counts: Before batch/surrogate estimation, 413 entries are 0<x<1: 0%.
## The be method chose 6 surrogate variable(s).
## Estimate type 'sva' is shorthand for 'sva_unsupervised'.
## Other sva options include: sva_supervised and svaseq.
## Attempting sva unsupervised surrogate estimation with 6 surrogates.
## Plotting a PCA before surrogates/batch inclusion.
## Not putting labels on the plot.
## Using sva to visualize before/after batch inclusion.
## Performing a test normalization with: raw
## This function will replace the expt$expressionset slot with:
## log2(sva(cpm(cbcb(data))))
## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize
## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.
## Step 1: performing count filter with option: cbcb
## Removing 0 low-count genes (12546 remaining).
## Step 2: not normalizing the data.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 2575 values equal to 0, adding 1 to the matrix.
## Step 5: doing batch correction with sva.
## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.
## Passing off to all_adjusters.
## batch_counts: Before batch/surrogate estimation, 458520 entries are x>1: 94%.
## batch_counts: Before batch/surrogate estimation, 2575 entries are x==0: 1%.
## batch_counts: Before batch/surrogate estimation, 28199 entries are 0<x<1: 6%.
## The be method chose 5 surrogate variable(s).
## Estimate type 'sva' is shorthand for 'sva_unsupervised'.
## Other sva options include: sva_supervised and svaseq.
## Attempting sva unsupervised surrogate estimation with 5 surrogates.
## There are 765 (0%) elements which are < 0 after batch correction.
## Setting low elements to zero.
## Not putting labels on the plot.
## Finished running DE analyses, collecting outputs.
## Comparing analyses.
keepers <- list(
## GM against unstimulated
"GM_LPS_vs_GM_NS" = c("GM_LPS", "GM_NS"),
"GM_LP_vs_GM_NS" = c("GM_LP", "GM_NS"),
"GM_LA_vs_GM_NS" = c("GM_LA", "GM_NS"),
## M against unstimulated
"M_LPS_vs_M_NS" = c("M_LPS", "M_NS"),
"M_LP_vs_M_NS" = c("M_LP", "M_NS"),
"M_LA_vs_M_NS" = c("M_LA", "M_NS"),
## GM against LPS
"GM_LP_vs_GM_LPS" = c("GM_LP", "GM_LPS"),
"GM_LA_vs_GM_LPS" = c("GM_LA", "GM_LPS"),
## M against LPS
"M_LP_vs_M_LPS" = c("M_LP", "M_LPS"),
"M_LA_vs_M_LPS" = c("M_LA", "M_LPS"),
## GM, LA vs LP
"GM_LP_vs_GM_LA" = c("GM_LP", "GM_LA"),
## M, LA vs LP
"M_LP_vs_M_LA" = c("M_LP", "M_LA"),
## Last, each M vs GM
"GM_NS_vs_M_NS" = c("GM_NS", "M_NS"),
"GM_LPS_vs_M_LPS" = c("GM_LPS", "M_LPS"),
"GM_LP_vs_M_LP" = c("GM_LP", "M_LP"),
"GM_LA_vs_M_LA" = c("GM_LA", "M_LA"))
pat_gs_sva_tables <- combine_de_tables(
pat_gs_sva_de, keepers=keepers,
excel=glue::glue("excel/pat_gs_sva_tables-v{ver}.xlsx"))
## Deleting the file excel/pat_gs_sva_tables-v20200330.xlsx before writing the tables.
## Writing a legend of columns.
## Printing a pca plot before/after surrogates/batch estimation.
## Working on 1/16: GM_LPS_vs_GM_NS which is: GM_LPS/GM_NS.
## Found inverse table with GM_NS_vs_GM_LPS
## The ebseq table is null.
## Working on 2/16: GM_LP_vs_GM_NS which is: GM_LP/GM_NS.
## Found inverse table with GM_NS_vs_GM_LP
## The ebseq table is null.
## Working on 3/16: GM_LA_vs_GM_NS which is: GM_LA/GM_NS.
## Found inverse table with GM_NS_vs_GM_LA
## The ebseq table is null.
## Working on 4/16: M_LPS_vs_M_NS which is: M_LPS/M_NS.
## Found inverse table with M_NS_vs_M_LPS
## The ebseq table is null.
## Working on 5/16: M_LP_vs_M_NS which is: M_LP/M_NS.
## Found inverse table with M_NS_vs_M_LP
## The ebseq table is null.
## Working on 6/16: M_LA_vs_M_NS which is: M_LA/M_NS.
## Found inverse table with M_NS_vs_M_LA
## The ebseq table is null.
## Working on 7/16: GM_LP_vs_GM_LPS which is: GM_LP/GM_LPS.
## Found inverse table with GM_LPS_vs_GM_LP
## The ebseq table is null.
## Working on 8/16: GM_LA_vs_GM_LPS which is: GM_LA/GM_LPS.
## Found inverse table with GM_LPS_vs_GM_LA
## The ebseq table is null.
## Working on 9/16: M_LP_vs_M_LPS which is: M_LP/M_LPS.
## Found inverse table with M_LPS_vs_M_LP
## The ebseq table is null.
## Working on 10/16: M_LA_vs_M_LPS which is: M_LA/M_LPS.
## Found inverse table with M_LPS_vs_M_LA
## The ebseq table is null.
## Working on 11/16: GM_LP_vs_GM_LA which is: GM_LP/GM_LA.
## Found table with GM_LP_vs_GM_LA
## The ebseq table is null.
## Working on 12/16: M_LP_vs_M_LA which is: M_LP/M_LA.
## Found table with M_LP_vs_M_LA
## The ebseq table is null.
## Working on 13/16: GM_NS_vs_M_NS which is: GM_NS/M_NS.
## Found inverse table with M_NS_vs_GM_NS
## The ebseq table is null.
## Working on 14/16: GM_LPS_vs_M_LPS which is: GM_LPS/M_LPS.
## Found inverse table with M_LPS_vs_GM_LPS
## The ebseq table is null.
## Working on 15/16: GM_LP_vs_M_LP which is: GM_LP/M_LP.
## Found inverse table with M_LP_vs_GM_LP
## The ebseq table is null.
## Working on 16/16: GM_LA_vs_M_LA which is: GM_LA/M_LA.
## Found inverse table with M_LA_vs_GM_LA
## The ebseq table is null.
## Adding venn plots for GM_LPS_vs_GM_NS.
## Limma expression coefficients for GM_LPS_vs_GM_NS; R^2: 0.998; equation: y = 0.998x - 0.00439
## Deseq expression coefficients for GM_LPS_vs_GM_NS; R^2: 0.992; equation: y = 0.982x + 0.172
## Edger expression coefficients for GM_LPS_vs_GM_NS; R^2: 0.996; equation: y = 0.997x + 0.0247
## Adding venn plots for GM_LP_vs_GM_NS.
## Limma expression coefficients for GM_LP_vs_GM_NS; R^2: 0.998; equation: y = 0.998x - 0.00439
## Deseq expression coefficients for GM_LP_vs_GM_NS; R^2: 0.992; equation: y = 0.982x + 0.172
## Edger expression coefficients for GM_LP_vs_GM_NS; R^2: 0.996; equation: y = 0.997x + 0.0247
## Adding venn plots for GM_LA_vs_GM_NS.
## Limma expression coefficients for GM_LA_vs_GM_NS; R^2: 0.998; equation: y = 0.998x - 0.00439
## Deseq expression coefficients for GM_LA_vs_GM_NS; R^2: 0.992; equation: y = 0.982x + 0.172
## Edger expression coefficients for GM_LA_vs_GM_NS; R^2: 0.996; equation: y = 0.997x + 0.0247
## Adding venn plots for M_LPS_vs_M_NS.
## Limma expression coefficients for M_LPS_vs_M_NS; R^2: 0.998; equation: y = 0.998x - 0.00439
## Deseq expression coefficients for M_LPS_vs_M_NS; R^2: 0.992; equation: y = 0.982x + 0.172
## Edger expression coefficients for M_LPS_vs_M_NS; R^2: 0.996; equation: y = 0.997x + 0.0247
## Adding venn plots for M_LP_vs_M_NS.
## Limma expression coefficients for M_LP_vs_M_NS; R^2: 0.998; equation: y = 0.998x - 0.00439
## Deseq expression coefficients for M_LP_vs_M_NS; R^2: 0.992; equation: y = 0.982x + 0.172
## Edger expression coefficients for M_LP_vs_M_NS; R^2: 0.996; equation: y = 0.997x + 0.0247
## Adding venn plots for M_LA_vs_M_NS.
## Limma expression coefficients for M_LA_vs_M_NS; R^2: 0.998; equation: y = 0.998x - 0.00439
## Deseq expression coefficients for M_LA_vs_M_NS; R^2: 0.992; equation: y = 0.982x + 0.172
## Edger expression coefficients for M_LA_vs_M_NS; R^2: 0.996; equation: y = 0.997x + 0.0247
## Adding venn plots for GM_LP_vs_GM_LPS.
## Limma expression coefficients for GM_LP_vs_GM_LPS; R^2: 0.998; equation: y = 0.998x - 0.00439
## Deseq expression coefficients for GM_LP_vs_GM_LPS; R^2: 0.992; equation: y = 0.982x + 0.172
## Edger expression coefficients for GM_LP_vs_GM_LPS; R^2: 0.996; equation: y = 0.997x + 0.0247
## Adding venn plots for GM_LA_vs_GM_LPS.
## Limma expression coefficients for GM_LA_vs_GM_LPS; R^2: 0.998; equation: y = 0.998x - 0.00439
## Deseq expression coefficients for GM_LA_vs_GM_LPS; R^2: 0.992; equation: y = 0.982x + 0.172
## Edger expression coefficients for GM_LA_vs_GM_LPS; R^2: 0.996; equation: y = 0.997x + 0.0247
## Adding venn plots for M_LP_vs_M_LPS.
## Limma expression coefficients for M_LP_vs_M_LPS; R^2: 0.998; equation: y = 0.998x - 0.00439
## Deseq expression coefficients for M_LP_vs_M_LPS; R^2: 0.992; equation: y = 0.982x + 0.172
## Edger expression coefficients for M_LP_vs_M_LPS; R^2: 0.996; equation: y = 0.997x + 0.0247
## Adding venn plots for M_LA_vs_M_LPS.
## Limma expression coefficients for M_LA_vs_M_LPS; R^2: 0.998; equation: y = 0.998x - 0.00439
## Deseq expression coefficients for M_LA_vs_M_LPS; R^2: 0.992; equation: y = 0.982x + 0.172
## Edger expression coefficients for M_LA_vs_M_LPS; R^2: 0.996; equation: y = 0.997x + 0.0247
## Adding venn plots for GM_LP_vs_GM_LA.
## Limma expression coefficients for GM_LP_vs_GM_LA; R^2: 0.998; equation: y = 0.998x - 0.00439
## Deseq expression coefficients for GM_LP_vs_GM_LA; R^2: 0.992; equation: y = 0.982x + 0.172
## Edger expression coefficients for GM_LP_vs_GM_LA; R^2: 0.996; equation: y = 0.997x + 0.0247
## Adding venn plots for M_LP_vs_M_LA.
## Limma expression coefficients for M_LP_vs_M_LA; R^2: 0.998; equation: y = 0.998x - 0.00439
## Deseq expression coefficients for M_LP_vs_M_LA; R^2: 0.992; equation: y = 0.982x + 0.172
## Edger expression coefficients for M_LP_vs_M_LA; R^2: 0.996; equation: y = 0.997x + 0.0247
## Adding venn plots for GM_NS_vs_M_NS.
## Limma expression coefficients for GM_NS_vs_M_NS; R^2: 0.998; equation: y = 0.998x - 0.00439
## Deseq expression coefficients for GM_NS_vs_M_NS; R^2: 0.992; equation: y = 0.982x + 0.172
## Edger expression coefficients for GM_NS_vs_M_NS; R^2: 0.996; equation: y = 0.997x + 0.0247
## Adding venn plots for GM_LPS_vs_M_LPS.
## Limma expression coefficients for GM_LPS_vs_M_LPS; R^2: 0.998; equation: y = 0.998x - 0.00439
## Deseq expression coefficients for GM_LPS_vs_M_LPS; R^2: 0.992; equation: y = 0.982x + 0.172
## Edger expression coefficients for GM_LPS_vs_M_LPS; R^2: 0.996; equation: y = 0.997x + 0.0247
## Adding venn plots for GM_LP_vs_M_LP.
## Limma expression coefficients for GM_LP_vs_M_LP; R^2: 0.998; equation: y = 0.998x - 0.00439
## Deseq expression coefficients for GM_LP_vs_M_LP; R^2: 0.992; equation: y = 0.982x + 0.172
## Edger expression coefficients for GM_LP_vs_M_LP; R^2: 0.996; equation: y = 0.997x + 0.0247
## Adding venn plots for GM_LA_vs_M_LA.
## Limma expression coefficients for GM_LA_vs_M_LA; R^2: 0.998; equation: y = 0.998x - 0.00439
## Deseq expression coefficients for GM_LA_vs_M_LA; R^2: 0.992; equation: y = 0.982x + 0.172
## Edger expression coefficients for GM_LA_vs_M_LA; R^2: 0.996; equation: y = 0.997x + 0.0247
## Writing summary information, compare_plot is: TRUE.
## Performing save of excel/pat_gs_sva_tables-v20200330.xlsx.
pat_gs_sva_sig <- extract_significant_genes(
pat_gs_sva_tables,
excel=glue::glue("excel/pat_gs_sva_sig-v{ver}.xlsx"))
## Writing a legend of columns.
## Did not find the ebseq_logfc, skipping ebseq.
## Writing excel data according to limma for GM_LPS_vs_GM_NS: 1/64.
## After (adj)p filter, the up genes table has 313 genes.
## After (adj)p filter, the down genes table has 162 genes.
## After fold change filter, the up genes table has 190 genes.
## After fold change filter, the down genes table has 27 genes.
## Writing excel data according to limma for GM_LP_vs_GM_NS: 2/64.
## After (adj)p filter, the up genes table has 457 genes.
## After (adj)p filter, the down genes table has 379 genes.
## After fold change filter, the up genes table has 304 genes.
## After fold change filter, the down genes table has 80 genes.
## Writing excel data according to limma for GM_LA_vs_GM_NS: 3/64.
## After (adj)p filter, the up genes table has 522 genes.
## After (adj)p filter, the down genes table has 480 genes.
## After fold change filter, the up genes table has 283 genes.
## After fold change filter, the down genes table has 91 genes.
## Writing excel data according to limma for M_LPS_vs_M_NS: 4/64.
## After (adj)p filter, the up genes table has 2033 genes.
## After (adj)p filter, the down genes table has 2942 genes.
## After fold change filter, the up genes table has 993 genes.
## After fold change filter, the down genes table has 560 genes.
## Writing excel data according to limma for M_LP_vs_M_NS: 5/64.
## After (adj)p filter, the up genes table has 2195 genes.
## After (adj)p filter, the down genes table has 2904 genes.
## After fold change filter, the up genes table has 1036 genes.
## After fold change filter, the down genes table has 712 genes.
## Writing excel data according to limma for M_LA_vs_M_NS: 6/64.
## After (adj)p filter, the up genes table has 2336 genes.
## After (adj)p filter, the down genes table has 2909 genes.
## After fold change filter, the up genes table has 1038 genes.
## After fold change filter, the down genes table has 765 genes.
## Writing excel data according to limma for GM_LP_vs_GM_LPS: 7/64.
## After (adj)p filter, the up genes table has 122 genes.
## After (adj)p filter, the down genes table has 88 genes.
## After fold change filter, the up genes table has 73 genes.
## After fold change filter, the down genes table has 11 genes.
## Writing excel data according to limma for GM_LA_vs_GM_LPS: 8/64.
## After (adj)p filter, the up genes table has 5 genes.
## After (adj)p filter, the down genes table has 2 genes.
## After fold change filter, the up genes table has 5 genes.
## After fold change filter, the down genes table has 0 genes.
## Writing excel data according to limma for M_LP_vs_M_LPS: 9/64.
## After (adj)p filter, the up genes table has 1034 genes.
## After (adj)p filter, the down genes table has 1032 genes.
## After fold change filter, the up genes table has 253 genes.
## After fold change filter, the down genes table has 255 genes.
## Writing excel data according to limma for M_LA_vs_M_LPS: 10/64.
## After (adj)p filter, the up genes table has 621 genes.
## After (adj)p filter, the down genes table has 513 genes.
## After fold change filter, the up genes table has 145 genes.
## After fold change filter, the down genes table has 132 genes.
## Writing excel data according to limma for GM_LP_vs_GM_LA: 11/64.
## After (adj)p filter, the up genes table has 0 genes.
## After (adj)p filter, the down genes table has 0 genes.
## After fold change filter, the up genes table has 0 genes.
## After fold change filter, the down genes table has 0 genes.
## Writing excel data according to limma for M_LP_vs_M_LA: 12/64.
## After (adj)p filter, the up genes table has 143 genes.
## After (adj)p filter, the down genes table has 162 genes.
## After fold change filter, the up genes table has 55 genes.
## After fold change filter, the down genes table has 28 genes.
## Writing excel data according to limma for GM_NS_vs_M_NS: 13/64.
## After (adj)p filter, the up genes table has 1072 genes.
## After (adj)p filter, the down genes table has 1161 genes.
## After fold change filter, the up genes table has 482 genes.
## After fold change filter, the down genes table has 388 genes.
## Writing excel data according to limma for GM_LPS_vs_M_LPS: 14/64.
## After (adj)p filter, the up genes table has 2105 genes.
## After (adj)p filter, the down genes table has 1894 genes.
## After fold change filter, the up genes table has 687 genes.
## After fold change filter, the down genes table has 757 genes.
## Writing excel data according to limma for GM_LP_vs_M_LP: 15/64.
## After (adj)p filter, the up genes table has 1740 genes.
## After (adj)p filter, the down genes table has 1764 genes.
## After fold change filter, the up genes table has 712 genes.
## After fold change filter, the down genes table has 657 genes.
## Writing excel data according to limma for GM_LA_vs_M_LA: 16/64.
## After (adj)p filter, the up genes table has 1933 genes.
## After (adj)p filter, the down genes table has 2036 genes.
## After fold change filter, the up genes table has 717 genes.
## After fold change filter, the down genes table has 701 genes.
## Printing significant genes to the file: excel/pat_gs_sva_sig-v20200330.xlsx
## 1/16: Creating significant table up_limma_GM_LPS_vs_GM_NS
## 2/16: Creating significant table up_limma_GM_LP_vs_GM_NS
## 3/16: Creating significant table up_limma_GM_LA_vs_GM_NS
## 4/16: Creating significant table up_limma_M_LPS_vs_M_NS
## 5/16: Creating significant table up_limma_M_LP_vs_M_NS
## 6/16: Creating significant table up_limma_M_LA_vs_M_NS
## 7/16: Creating significant table up_limma_GM_LP_vs_GM_LPS
## 8/16: Creating significant table up_limma_GM_LA_vs_GM_LPS
## The down table GM_LA_vs_GM_LPS is empty.
## 9/16: Creating significant table up_limma_M_LP_vs_M_LPS
## 10/16: Creating significant table up_limma_M_LA_vs_M_LPS
## The up table GM_LP_vs_GM_LA is empty.
## The down table GM_LP_vs_GM_LA is empty.
## 12/16: Creating significant table up_limma_M_LP_vs_M_LA
## 13/16: Creating significant table up_limma_GM_NS_vs_M_NS
## 14/16: Creating significant table up_limma_GM_LPS_vs_M_LPS
## 15/16: Creating significant table up_limma_GM_LP_vs_M_LP
## 16/16: Creating significant table up_limma_GM_LA_vs_M_LA
## Writing excel data according to edger for GM_LPS_vs_GM_NS: 1/64.
## After (adj)p filter, the up genes table has 513 genes.
## After (adj)p filter, the down genes table has 155 genes.
## After fold change filter, the up genes table has 281 genes.
## After fold change filter, the down genes table has 34 genes.
## Writing excel data according to edger for GM_LP_vs_GM_NS: 2/64.
## After (adj)p filter, the up genes table has 672 genes.
## After (adj)p filter, the down genes table has 415 genes.
## After fold change filter, the up genes table has 420 genes.
## After fold change filter, the down genes table has 113 genes.
## Writing excel data according to edger for GM_LA_vs_GM_NS: 3/64.
## After (adj)p filter, the up genes table has 702 genes.
## After (adj)p filter, the down genes table has 447 genes.
## After fold change filter, the up genes table has 390 genes.
## After fold change filter, the down genes table has 127 genes.
## Writing excel data according to edger for M_LPS_vs_M_NS: 4/64.
## After (adj)p filter, the up genes table has 2003 genes.
## After (adj)p filter, the down genes table has 1889 genes.
## After fold change filter, the up genes table has 1124 genes.
## After fold change filter, the down genes table has 583 genes.
## Writing excel data according to edger for M_LP_vs_M_NS: 5/64.
## After (adj)p filter, the up genes table has 2033 genes.
## After (adj)p filter, the down genes table has 2035 genes.
## After fold change filter, the up genes table has 1160 genes.
## After fold change filter, the down genes table has 675 genes.
## Writing excel data according to edger for M_LA_vs_M_NS: 6/64.
## After (adj)p filter, the up genes table has 2127 genes.
## After (adj)p filter, the down genes table has 2066 genes.
## After fold change filter, the up genes table has 1185 genes.
## After fold change filter, the down genes table has 741 genes.
## Writing excel data according to edger for GM_LP_vs_GM_LPS: 7/64.
## After (adj)p filter, the up genes table has 219 genes.
## After (adj)p filter, the down genes table has 190 genes.
## After fold change filter, the up genes table has 136 genes.
## After fold change filter, the down genes table has 29 genes.
## Writing excel data according to edger for GM_LA_vs_GM_LPS: 8/64.
## After (adj)p filter, the up genes table has 20 genes.
## After (adj)p filter, the down genes table has 12 genes.
## After fold change filter, the up genes table has 17 genes.
## After fold change filter, the down genes table has 7 genes.
## Writing excel data according to edger for M_LP_vs_M_LPS: 9/64.
## After (adj)p filter, the up genes table has 837 genes.
## After (adj)p filter, the down genes table has 1134 genes.
## After fold change filter, the up genes table has 304 genes.
## After fold change filter, the down genes table has 299 genes.
## Writing excel data according to edger for M_LA_vs_M_LPS: 10/64.
## After (adj)p filter, the up genes table has 513 genes.
## After (adj)p filter, the down genes table has 674 genes.
## After fold change filter, the up genes table has 201 genes.
## After fold change filter, the down genes table has 173 genes.
## Writing excel data according to edger for GM_LP_vs_GM_LA: 11/64.
## After (adj)p filter, the up genes table has 40 genes.
## After (adj)p filter, the down genes table has 8 genes.
## After fold change filter, the up genes table has 25 genes.
## After fold change filter, the down genes table has 2 genes.
## Writing excel data according to edger for M_LP_vs_M_LA: 12/64.
## After (adj)p filter, the up genes table has 136 genes.
## After (adj)p filter, the down genes table has 141 genes.
## After fold change filter, the up genes table has 52 genes.
## After fold change filter, the down genes table has 36 genes.
## Writing excel data according to edger for GM_NS_vs_M_NS: 13/64.
## After (adj)p filter, the up genes table has 1192 genes.
## After (adj)p filter, the down genes table has 1013 genes.
## After fold change filter, the up genes table has 613 genes.
## After fold change filter, the down genes table has 419 genes.
## Writing excel data according to edger for GM_LPS_vs_M_LPS: 14/64.
## After (adj)p filter, the up genes table has 1709 genes.
## After (adj)p filter, the down genes table has 1733 genes.
## After fold change filter, the up genes table has 741 genes.
## After fold change filter, the down genes table has 816 genes.
## Writing excel data according to edger for GM_LP_vs_M_LP: 15/64.
## After (adj)p filter, the up genes table has 1543 genes.
## After (adj)p filter, the down genes table has 1487 genes.
## After fold change filter, the up genes table has 790 genes.
## After fold change filter, the down genes table has 698 genes.
## Writing excel data according to edger for GM_LA_vs_M_LA: 16/64.
## After (adj)p filter, the up genes table has 1674 genes.
## After (adj)p filter, the down genes table has 1755 genes.
## After fold change filter, the up genes table has 793 genes.
## After fold change filter, the down genes table has 808 genes.
## Printing significant genes to the file: excel/pat_gs_sva_sig-v20200330.xlsx
## 1/16: Creating significant table up_edger_GM_LPS_vs_GM_NS
## 2/16: Creating significant table up_edger_GM_LP_vs_GM_NS
## 3/16: Creating significant table up_edger_GM_LA_vs_GM_NS
## 4/16: Creating significant table up_edger_M_LPS_vs_M_NS
## 5/16: Creating significant table up_edger_M_LP_vs_M_NS
## 6/16: Creating significant table up_edger_M_LA_vs_M_NS
## 7/16: Creating significant table up_edger_GM_LP_vs_GM_LPS
## 8/16: Creating significant table up_edger_GM_LA_vs_GM_LPS
## 9/16: Creating significant table up_edger_M_LP_vs_M_LPS
## 10/16: Creating significant table up_edger_M_LA_vs_M_LPS
## 11/16: Creating significant table up_edger_GM_LP_vs_GM_LA
## 12/16: Creating significant table up_edger_M_LP_vs_M_LA
## 13/16: Creating significant table up_edger_GM_NS_vs_M_NS
## 14/16: Creating significant table up_edger_GM_LPS_vs_M_LPS
## 15/16: Creating significant table up_edger_GM_LP_vs_M_LP
## 16/16: Creating significant table up_edger_GM_LA_vs_M_LA
## Writing excel data according to deseq for GM_LPS_vs_GM_NS: 1/64.
## After (adj)p filter, the up genes table has 758 genes.
## After (adj)p filter, the down genes table has 333 genes.
## After fold change filter, the up genes table has 283 genes.
## After fold change filter, the down genes table has 26 genes.
## Writing excel data according to deseq for GM_LP_vs_GM_NS: 2/64.
## After (adj)p filter, the up genes table has 986 genes.
## After (adj)p filter, the down genes table has 598 genes.
## After fold change filter, the up genes table has 437 genes.
## After fold change filter, the down genes table has 101 genes.
## Writing excel data according to deseq for GM_LA_vs_GM_NS: 3/64.
## After (adj)p filter, the up genes table has 1043 genes.
## After (adj)p filter, the down genes table has 693 genes.
## After fold change filter, the up genes table has 360 genes.
## After fold change filter, the down genes table has 106 genes.
## Writing excel data according to deseq for M_LPS_vs_M_NS: 4/64.
## After (adj)p filter, the up genes table has 2627 genes.
## After (adj)p filter, the down genes table has 2685 genes.
## After fold change filter, the up genes table has 931 genes.
## After fold change filter, the down genes table has 455 genes.
## Writing excel data according to deseq for M_LP_vs_M_NS: 5/64.
## After (adj)p filter, the up genes table has 2664 genes.
## After (adj)p filter, the down genes table has 2712 genes.
## After fold change filter, the up genes table has 939 genes.
## After fold change filter, the down genes table has 530 genes.
## Writing excel data according to deseq for M_LA_vs_M_NS: 6/64.
## After (adj)p filter, the up genes table has 2782 genes.
## After (adj)p filter, the down genes table has 2811 genes.
## After fold change filter, the up genes table has 913 genes.
## After fold change filter, the down genes table has 587 genes.
## Writing excel data according to deseq for GM_LP_vs_GM_LPS: 7/64.
## After (adj)p filter, the up genes table has 350 genes.
## After (adj)p filter, the down genes table has 306 genes.
## After fold change filter, the up genes table has 140 genes.
## After fold change filter, the down genes table has 31 genes.
## Writing excel data according to deseq for GM_LA_vs_GM_LPS: 8/64.
## After (adj)p filter, the up genes table has 24 genes.
## After (adj)p filter, the down genes table has 23 genes.
## After fold change filter, the up genes table has 14 genes.
## After fold change filter, the down genes table has 2 genes.
## Writing excel data according to deseq for M_LP_vs_M_LPS: 9/64.
## After (adj)p filter, the up genes table has 1274 genes.
## After (adj)p filter, the down genes table has 1494 genes.
## After fold change filter, the up genes table has 254 genes.
## After fold change filter, the down genes table has 250 genes.
## Writing excel data according to deseq for M_LA_vs_M_LPS: 10/64.
## After (adj)p filter, the up genes table has 778 genes.
## After (adj)p filter, the down genes table has 1006 genes.
## After fold change filter, the up genes table has 145 genes.
## After fold change filter, the down genes table has 155 genes.
## Writing excel data according to deseq for GM_LP_vs_GM_LA: 11/64.
## After (adj)p filter, the up genes table has 55 genes.
## After (adj)p filter, the down genes table has 14 genes.
## After fold change filter, the up genes table has 34 genes.
## After fold change filter, the down genes table has 2 genes.
## Writing excel data according to deseq for M_LP_vs_M_LA: 12/64.
## After (adj)p filter, the up genes table has 309 genes.
## After (adj)p filter, the down genes table has 303 genes.
## After fold change filter, the up genes table has 57 genes.
## After fold change filter, the down genes table has 28 genes.
## Writing excel data according to deseq for GM_NS_vs_M_NS: 13/64.
## After (adj)p filter, the up genes table has 1581 genes.
## After (adj)p filter, the down genes table has 1354 genes.
## After fold change filter, the up genes table has 519 genes.
## After fold change filter, the down genes table has 361 genes.
## Writing excel data according to deseq for GM_LPS_vs_M_LPS: 14/64.
## After (adj)p filter, the up genes table has 2450 genes.
## After (adj)p filter, the down genes table has 2355 genes.
## After fold change filter, the up genes table has 615 genes.
## After fold change filter, the down genes table has 689 genes.
## Writing excel data according to deseq for GM_LP_vs_M_LP: 15/64.
## After (adj)p filter, the up genes table has 2243 genes.
## After (adj)p filter, the down genes table has 2004 genes.
## After fold change filter, the up genes table has 665 genes.
## After fold change filter, the down genes table has 587 genes.
## Writing excel data according to deseq for GM_LA_vs_M_LA: 16/64.
## After (adj)p filter, the up genes table has 2468 genes.
## After (adj)p filter, the down genes table has 2322 genes.
## After fold change filter, the up genes table has 677 genes.
## After fold change filter, the down genes table has 638 genes.
## Printing significant genes to the file: excel/pat_gs_sva_sig-v20200330.xlsx
## 1/16: Creating significant table up_deseq_GM_LPS_vs_GM_NS
## 2/16: Creating significant table up_deseq_GM_LP_vs_GM_NS
## 3/16: Creating significant table up_deseq_GM_LA_vs_GM_NS
## 4/16: Creating significant table up_deseq_M_LPS_vs_M_NS
## 5/16: Creating significant table up_deseq_M_LP_vs_M_NS
## 6/16: Creating significant table up_deseq_M_LA_vs_M_NS
## 7/16: Creating significant table up_deseq_GM_LP_vs_GM_LPS
## 8/16: Creating significant table up_deseq_GM_LA_vs_GM_LPS
## 9/16: Creating significant table up_deseq_M_LP_vs_M_LPS
## 10/16: Creating significant table up_deseq_M_LA_vs_M_LPS
## 11/16: Creating significant table up_deseq_GM_LP_vs_GM_LA
## 12/16: Creating significant table up_deseq_M_LP_vs_M_LA
## 13/16: Creating significant table up_deseq_GM_NS_vs_M_NS
## 14/16: Creating significant table up_deseq_GM_LPS_vs_M_LPS
## 15/16: Creating significant table up_deseq_GM_LP_vs_M_LP
## 16/16: Creating significant table up_deseq_GM_LA_vs_M_LA
## Writing excel data according to basic for GM_LPS_vs_GM_NS: 1/64.
## After (adj)p filter, the up genes table has 1 genes.
## After (adj)p filter, the down genes table has 0 genes.
## After fold change filter, the up genes table has 1 genes.
## After fold change filter, the down genes table has 0 genes.
## Writing excel data according to basic for GM_LP_vs_GM_NS: 2/64.
## After (adj)p filter, the up genes table has 0 genes.
## After (adj)p filter, the down genes table has 0 genes.
## After fold change filter, the up genes table has 0 genes.
## After fold change filter, the down genes table has 0 genes.
## Writing excel data according to basic for GM_LA_vs_GM_NS: 3/64.
## After (adj)p filter, the up genes table has 0 genes.
## After (adj)p filter, the down genes table has 0 genes.
## After fold change filter, the up genes table has 0 genes.
## After fold change filter, the down genes table has 0 genes.
## Writing excel data according to basic for M_LPS_vs_M_NS: 4/64.
## After (adj)p filter, the up genes table has 1056 genes.
## After (adj)p filter, the down genes table has 1023 genes.
## After fold change filter, the up genes table has 623 genes.
## After fold change filter, the down genes table has 268 genes.
## Writing excel data according to basic for M_LP_vs_M_NS: 5/64.
## After (adj)p filter, the up genes table has 1090 genes.
## After (adj)p filter, the down genes table has 1024 genes.
## After fold change filter, the up genes table has 636 genes.
## After fold change filter, the down genes table has 357 genes.
## Writing excel data according to basic for M_LA_vs_M_NS: 6/64.
## After (adj)p filter, the up genes table has 1446 genes.
## After (adj)p filter, the down genes table has 1500 genes.
## After fold change filter, the up genes table has 695 genes.
## After fold change filter, the down genes table has 452 genes.
## Writing excel data according to basic for GM_LP_vs_GM_LPS: 7/64.
## After (adj)p filter, the up genes table has 0 genes.
## After (adj)p filter, the down genes table has 0 genes.
## After fold change filter, the up genes table has 0 genes.
## After fold change filter, the down genes table has 0 genes.
## Writing excel data according to basic for GM_LA_vs_GM_LPS: 8/64.
## After (adj)p filter, the up genes table has 0 genes.
## After (adj)p filter, the down genes table has 0 genes.
## After fold change filter, the up genes table has 0 genes.
## After fold change filter, the down genes table has 0 genes.
## Writing excel data according to basic for M_LP_vs_M_LPS: 9/64.
## After (adj)p filter, the up genes table has 233 genes.
## After (adj)p filter, the down genes table has 222 genes.
## After fold change filter, the up genes table has 104 genes.
## After fold change filter, the down genes table has 85 genes.
## Writing excel data according to basic for M_LA_vs_M_LPS: 10/64.
## After (adj)p filter, the up genes table has 0 genes.
## After (adj)p filter, the down genes table has 2 genes.
## After fold change filter, the up genes table has 0 genes.
## After fold change filter, the down genes table has 1 genes.
## Writing excel data according to basic for GM_LP_vs_GM_LA: 11/64.
## After (adj)p filter, the up genes table has 0 genes.
## After (adj)p filter, the down genes table has 0 genes.
## After fold change filter, the up genes table has 0 genes.
## After fold change filter, the down genes table has 0 genes.
## Writing excel data according to basic for M_LP_vs_M_LA: 12/64.
## After (adj)p filter, the up genes table has 1 genes.
## After (adj)p filter, the down genes table has 1 genes.
## After fold change filter, the up genes table has 1 genes.
## After fold change filter, the down genes table has 0 genes.
## Writing excel data according to basic for GM_NS_vs_M_NS: 13/64.
## After (adj)p filter, the up genes table has 36 genes.
## After (adj)p filter, the down genes table has 34 genes.
## After fold change filter, the up genes table has 26 genes.
## After fold change filter, the down genes table has 16 genes.
## Writing excel data according to basic for GM_LPS_vs_M_LPS: 14/64.
## After (adj)p filter, the up genes table has 163 genes.
## After (adj)p filter, the down genes table has 262 genes.
## After fold change filter, the up genes table has 98 genes.
## After fold change filter, the down genes table has 188 genes.
## Writing excel data according to basic for GM_LP_vs_M_LP: 15/64.
## After (adj)p filter, the up genes table has 230 genes.
## After (adj)p filter, the down genes table has 311 genes.
## After fold change filter, the up genes table has 141 genes.
## After fold change filter, the down genes table has 198 genes.
## Writing excel data according to basic for GM_LA_vs_M_LA: 16/64.
## After (adj)p filter, the up genes table has 289 genes.
## After (adj)p filter, the down genes table has 378 genes.
## After fold change filter, the up genes table has 174 genes.
## After fold change filter, the down genes table has 235 genes.
## Printing significant genes to the file: excel/pat_gs_sva_sig-v20200330.xlsx
## 1/16: Creating significant table up_basic_GM_LPS_vs_GM_NS
## The down table GM_LPS_vs_GM_NS is empty.
## The up table GM_LP_vs_GM_NS is empty.
## The down table GM_LP_vs_GM_NS is empty.
## The up table GM_LA_vs_GM_NS is empty.
## The down table GM_LA_vs_GM_NS is empty.
## 4/16: Creating significant table up_basic_M_LPS_vs_M_NS
## 5/16: Creating significant table up_basic_M_LP_vs_M_NS
## 6/16: Creating significant table up_basic_M_LA_vs_M_NS
## The up table GM_LP_vs_GM_LPS is empty.
## The down table GM_LP_vs_GM_LPS is empty.
## The up table GM_LA_vs_GM_LPS is empty.
## The down table GM_LA_vs_GM_LPS is empty.
## 9/16: Creating significant table up_basic_M_LP_vs_M_LPS
## The up table M_LA_vs_M_LPS is empty.
## The up table GM_LP_vs_GM_LA is empty.
## The down table GM_LP_vs_GM_LA is empty.
## 12/16: Creating significant table up_basic_M_LP_vs_M_LA
## The down table M_LP_vs_M_LA is empty.
## 13/16: Creating significant table up_basic_GM_NS_vs_M_NS
## 14/16: Creating significant table up_basic_GM_LPS_vs_M_LPS
## 15/16: Creating significant table up_basic_GM_LP_vs_M_LP
## 16/16: Creating significant table up_basic_GM_LA_vs_M_LA
## Adding significance bar plots.
pat_gs_batch <- set_expt_conditions(hs_filt, fact="gp")
pat_gs_batch <- set_expt_batches(pat_gs_batch, fact="patient")
pat_gs_batch_de <- all_pairwise(pat_gs_batch, model_batch=TRUE)
## Plotting a PCA before surrogates/batch inclusion.
## Not putting labels on the plot.
## Using limma's removeBatchEffect to visualize with(out) batch inclusion.
## Not putting labels on the plot.
## Finished running DE analyses, collecting outputs.
## Comparing analyses.
pat_gs_batch_tables <- combine_de_tables(
pat_gs_batch_de, keepers=keepers,
excel=glue::glue("excel/pat_gs_batch_tables-v{ver}.xlsx"))
## Deleting the file excel/pat_gs_batch_tables-v20200330.xlsx before writing the tables.
## Writing a legend of columns.
## Printing a pca plot before/after surrogates/batch estimation.
## Working on 1/16: GM_LPS_vs_GM_NS which is: GM_LPS/GM_NS.
## Found inverse table with GM_NS_vs_GM_LPS
## The ebseq table is null.
## Working on 2/16: GM_LP_vs_GM_NS which is: GM_LP/GM_NS.
## Found inverse table with GM_NS_vs_GM_LP
## The ebseq table is null.
## Working on 3/16: GM_LA_vs_GM_NS which is: GM_LA/GM_NS.
## Found inverse table with GM_NS_vs_GM_LA
## The ebseq table is null.
## Working on 4/16: M_LPS_vs_M_NS which is: M_LPS/M_NS.
## Found inverse table with M_NS_vs_M_LPS
## The ebseq table is null.
## Working on 5/16: M_LP_vs_M_NS which is: M_LP/M_NS.
## Found inverse table with M_NS_vs_M_LP
## The ebseq table is null.
## Working on 6/16: M_LA_vs_M_NS which is: M_LA/M_NS.
## Found inverse table with M_NS_vs_M_LA
## The ebseq table is null.
## Working on 7/16: GM_LP_vs_GM_LPS which is: GM_LP/GM_LPS.
## Found inverse table with GM_LPS_vs_GM_LP
## The ebseq table is null.
## Working on 8/16: GM_LA_vs_GM_LPS which is: GM_LA/GM_LPS.
## Found inverse table with GM_LPS_vs_GM_LA
## The ebseq table is null.
## Working on 9/16: M_LP_vs_M_LPS which is: M_LP/M_LPS.
## Found inverse table with M_LPS_vs_M_LP
## The ebseq table is null.
## Working on 10/16: M_LA_vs_M_LPS which is: M_LA/M_LPS.
## Found inverse table with M_LPS_vs_M_LA
## The ebseq table is null.
## Working on 11/16: GM_LP_vs_GM_LA which is: GM_LP/GM_LA.
## Found table with GM_LP_vs_GM_LA
## The ebseq table is null.
## Working on 12/16: M_LP_vs_M_LA which is: M_LP/M_LA.
## Found table with M_LP_vs_M_LA
## The ebseq table is null.
## Working on 13/16: GM_NS_vs_M_NS which is: GM_NS/M_NS.
## Found inverse table with M_NS_vs_GM_NS
## The ebseq table is null.
## Working on 14/16: GM_LPS_vs_M_LPS which is: GM_LPS/M_LPS.
## Found inverse table with M_LPS_vs_GM_LPS
## The ebseq table is null.
## Working on 15/16: GM_LP_vs_M_LP which is: GM_LP/M_LP.
## Found inverse table with M_LP_vs_GM_LP
## The ebseq table is null.
## Working on 16/16: GM_LA_vs_M_LA which is: GM_LA/M_LA.
## Found inverse table with M_LA_vs_GM_LA
## The ebseq table is null.
## Adding venn plots for GM_LPS_vs_GM_NS.
## Limma expression coefficients for GM_LPS_vs_GM_NS; R^2: 0.998; equation: y = 0.999x - 0.00812
## Deseq expression coefficients for GM_LPS_vs_GM_NS; R^2: 0.99; equation: y = 1x - 0.0224
## Edger expression coefficients for GM_LPS_vs_GM_NS; R^2: 0.997; equation: y = 1x - 0.0147
## Adding venn plots for GM_LP_vs_GM_NS.
## Limma expression coefficients for GM_LP_vs_GM_NS; R^2: 0.998; equation: y = 0.999x - 0.00812
## Deseq expression coefficients for GM_LP_vs_GM_NS; R^2: 0.99; equation: y = 1x - 0.0224
## Edger expression coefficients for GM_LP_vs_GM_NS; R^2: 0.997; equation: y = 1x - 0.0147
## Adding venn plots for GM_LA_vs_GM_NS.
## Limma expression coefficients for GM_LA_vs_GM_NS; R^2: 0.998; equation: y = 0.999x - 0.00812
## Deseq expression coefficients for GM_LA_vs_GM_NS; R^2: 0.99; equation: y = 1x - 0.0224
## Edger expression coefficients for GM_LA_vs_GM_NS; R^2: 0.997; equation: y = 1x - 0.0147
## Adding venn plots for M_LPS_vs_M_NS.
## Limma expression coefficients for M_LPS_vs_M_NS; R^2: 0.998; equation: y = 0.999x - 0.00812
## Deseq expression coefficients for M_LPS_vs_M_NS; R^2: 0.99; equation: y = 1x - 0.0224
## Edger expression coefficients for M_LPS_vs_M_NS; R^2: 0.997; equation: y = 1x - 0.0147
## Adding venn plots for M_LP_vs_M_NS.
## Limma expression coefficients for M_LP_vs_M_NS; R^2: 0.998; equation: y = 0.999x - 0.00812
## Deseq expression coefficients for M_LP_vs_M_NS; R^2: 0.99; equation: y = 1x - 0.0224
## Edger expression coefficients for M_LP_vs_M_NS; R^2: 0.997; equation: y = 1x - 0.0147
## Adding venn plots for M_LA_vs_M_NS.
## Limma expression coefficients for M_LA_vs_M_NS; R^2: 0.998; equation: y = 0.999x - 0.00812
## Deseq expression coefficients for M_LA_vs_M_NS; R^2: 0.99; equation: y = 1x - 0.0224
## Edger expression coefficients for M_LA_vs_M_NS; R^2: 0.997; equation: y = 1x - 0.0147
## Adding venn plots for GM_LP_vs_GM_LPS.
## Limma expression coefficients for GM_LP_vs_GM_LPS; R^2: 0.998; equation: y = 0.999x - 0.00812
## Deseq expression coefficients for GM_LP_vs_GM_LPS; R^2: 0.99; equation: y = 1x - 0.0224
## Edger expression coefficients for GM_LP_vs_GM_LPS; R^2: 0.997; equation: y = 1x - 0.0147
## Adding venn plots for GM_LA_vs_GM_LPS.
## Limma expression coefficients for GM_LA_vs_GM_LPS; R^2: 0.998; equation: y = 0.999x - 0.00812
## Deseq expression coefficients for GM_LA_vs_GM_LPS; R^2: 0.99; equation: y = 1x - 0.0224
## Edger expression coefficients for GM_LA_vs_GM_LPS; R^2: 0.997; equation: y = 1x - 0.0147
## Adding venn plots for M_LP_vs_M_LPS.
## Limma expression coefficients for M_LP_vs_M_LPS; R^2: 0.998; equation: y = 0.999x - 0.00812
## Deseq expression coefficients for M_LP_vs_M_LPS; R^2: 0.99; equation: y = 1x - 0.0224
## Edger expression coefficients for M_LP_vs_M_LPS; R^2: 0.997; equation: y = 1x - 0.0147
## Adding venn plots for M_LA_vs_M_LPS.
## Limma expression coefficients for M_LA_vs_M_LPS; R^2: 0.998; equation: y = 0.999x - 0.00812
## Deseq expression coefficients for M_LA_vs_M_LPS; R^2: 0.99; equation: y = 1x - 0.0224
## Edger expression coefficients for M_LA_vs_M_LPS; R^2: 0.997; equation: y = 1x - 0.0147
## Adding venn plots for GM_LP_vs_GM_LA.
## Limma expression coefficients for GM_LP_vs_GM_LA; R^2: 0.998; equation: y = 0.999x - 0.00812
## Deseq expression coefficients for GM_LP_vs_GM_LA; R^2: 0.99; equation: y = 1x - 0.0224
## Edger expression coefficients for GM_LP_vs_GM_LA; R^2: 0.997; equation: y = 1x - 0.0147
## Adding venn plots for M_LP_vs_M_LA.
## Limma expression coefficients for M_LP_vs_M_LA; R^2: 0.998; equation: y = 0.999x - 0.00812
## Deseq expression coefficients for M_LP_vs_M_LA; R^2: 0.99; equation: y = 1x - 0.0224
## Edger expression coefficients for M_LP_vs_M_LA; R^2: 0.997; equation: y = 1x - 0.0147
## Adding venn plots for GM_NS_vs_M_NS.
## Limma expression coefficients for GM_NS_vs_M_NS; R^2: 0.998; equation: y = 0.999x - 0.00812
## Deseq expression coefficients for GM_NS_vs_M_NS; R^2: 0.99; equation: y = 1x - 0.0224
## Edger expression coefficients for GM_NS_vs_M_NS; R^2: 0.997; equation: y = 1x - 0.0147
## Adding venn plots for GM_LPS_vs_M_LPS.
## Limma expression coefficients for GM_LPS_vs_M_LPS; R^2: 0.998; equation: y = 0.999x - 0.00812
## Deseq expression coefficients for GM_LPS_vs_M_LPS; R^2: 0.99; equation: y = 1x - 0.0224
## Edger expression coefficients for GM_LPS_vs_M_LPS; R^2: 0.997; equation: y = 1x - 0.0147
## Adding venn plots for GM_LP_vs_M_LP.
## Limma expression coefficients for GM_LP_vs_M_LP; R^2: 0.998; equation: y = 0.999x - 0.00812
## Deseq expression coefficients for GM_LP_vs_M_LP; R^2: 0.99; equation: y = 1x - 0.0224
## Edger expression coefficients for GM_LP_vs_M_LP; R^2: 0.997; equation: y = 1x - 0.0147
## Adding venn plots for GM_LA_vs_M_LA.
## Limma expression coefficients for GM_LA_vs_M_LA; R^2: 0.998; equation: y = 0.999x - 0.00812
## Deseq expression coefficients for GM_LA_vs_M_LA; R^2: 0.99; equation: y = 1x - 0.0224
## Edger expression coefficients for GM_LA_vs_M_LA; R^2: 0.997; equation: y = 1x - 0.0147
## Writing summary information, compare_plot is: TRUE.
## Performing save of excel/pat_gs_batch_tables-v20200330.xlsx.
pat_gs_batch_sig <- extract_significant_genes(
pat_gs_batch_tables,
excel=glue::glue("excel/pat_gs_batch_sig-v{ver}.xlsx"))
## Writing a legend of columns.
## Did not find the ebseq_logfc, skipping ebseq.
## Writing excel data according to limma for GM_LPS_vs_GM_NS: 1/64.
## After (adj)p filter, the up genes table has 300 genes.
## After (adj)p filter, the down genes table has 123 genes.
## After fold change filter, the up genes table has 199 genes.
## After fold change filter, the down genes table has 27 genes.
## Writing excel data according to limma for GM_LP_vs_GM_NS: 2/64.
## After (adj)p filter, the up genes table has 565 genes.
## After (adj)p filter, the down genes table has 538 genes.
## After fold change filter, the up genes table has 365 genes.
## After fold change filter, the down genes table has 143 genes.
## Writing excel data according to limma for GM_LA_vs_GM_NS: 3/64.
## After (adj)p filter, the up genes table has 472 genes.
## After (adj)p filter, the down genes table has 313 genes.
## After fold change filter, the up genes table has 285 genes.
## After fold change filter, the down genes table has 80 genes.
## Writing excel data according to limma for M_LPS_vs_M_NS: 4/64.
## After (adj)p filter, the up genes table has 1867 genes.
## After (adj)p filter, the down genes table has 2465 genes.
## After fold change filter, the up genes table has 992 genes.
## After fold change filter, the down genes table has 563 genes.
## Writing excel data according to limma for M_LP_vs_M_NS: 5/64.
## After (adj)p filter, the up genes table has 1935 genes.
## After (adj)p filter, the down genes table has 2362 genes.
## After fold change filter, the up genes table has 1017 genes.
## After fold change filter, the down genes table has 682 genes.
## Writing excel data according to limma for M_LA_vs_M_NS: 6/64.
## After (adj)p filter, the up genes table has 2073 genes.
## After (adj)p filter, the down genes table has 2511 genes.
## After fold change filter, the up genes table has 1030 genes.
## After fold change filter, the down genes table has 778 genes.
## Writing excel data according to limma for GM_LP_vs_GM_LPS: 7/64.
## After (adj)p filter, the up genes table has 116 genes.
## After (adj)p filter, the down genes table has 71 genes.
## After fold change filter, the up genes table has 76 genes.
## After fold change filter, the down genes table has 14 genes.
## Writing excel data according to limma for GM_LA_vs_GM_LPS: 8/64.
## After (adj)p filter, the up genes table has 3 genes.
## After (adj)p filter, the down genes table has 1 genes.
## After fold change filter, the up genes table has 3 genes.
## After fold change filter, the down genes table has 1 genes.
## Writing excel data according to limma for M_LP_vs_M_LPS: 9/64.
## After (adj)p filter, the up genes table has 817 genes.
## After (adj)p filter, the down genes table has 837 genes.
## After fold change filter, the up genes table has 261 genes.
## After fold change filter, the down genes table has 249 genes.
## Writing excel data according to limma for M_LA_vs_M_LPS: 10/64.
## After (adj)p filter, the up genes table has 385 genes.
## After (adj)p filter, the down genes table has 335 genes.
## After fold change filter, the up genes table has 129 genes.
## After fold change filter, the down genes table has 117 genes.
## Writing excel data according to limma for GM_LP_vs_GM_LA: 11/64.
## After (adj)p filter, the up genes table has 2 genes.
## After (adj)p filter, the down genes table has 0 genes.
## After fold change filter, the up genes table has 2 genes.
## After fold change filter, the down genes table has 0 genes.
## Writing excel data according to limma for M_LP_vs_M_LA: 12/64.
## After (adj)p filter, the up genes table has 95 genes.
## After (adj)p filter, the down genes table has 91 genes.
## After fold change filter, the up genes table has 38 genes.
## After fold change filter, the down genes table has 25 genes.
## Writing excel data according to limma for GM_NS_vs_M_NS: 13/64.
## After (adj)p filter, the up genes table has 864 genes.
## After (adj)p filter, the down genes table has 901 genes.
## After fold change filter, the up genes table has 440 genes.
## After fold change filter, the down genes table has 365 genes.
## Writing excel data according to limma for GM_LPS_vs_M_LPS: 14/64.
## After (adj)p filter, the up genes table has 1817 genes.
## After (adj)p filter, the down genes table has 1670 genes.
## After fold change filter, the up genes table has 709 genes.
## After fold change filter, the down genes table has 779 genes.
## Writing excel data according to limma for GM_LP_vs_M_LP: 15/64.
## After (adj)p filter, the up genes table has 2050 genes.
## After (adj)p filter, the down genes table has 1963 genes.
## After fold change filter, the up genes table has 847 genes.
## After fold change filter, the down genes table has 818 genes.
## Writing excel data according to limma for GM_LA_vs_M_LA: 16/64.
## After (adj)p filter, the up genes table has 1887 genes.
## After (adj)p filter, the down genes table has 1817 genes.
## After fold change filter, the up genes table has 809 genes.
## After fold change filter, the down genes table has 742 genes.
## Printing significant genes to the file: excel/pat_gs_batch_sig-v20200330.xlsx
## 1/16: Creating significant table up_limma_GM_LPS_vs_GM_NS
## 2/16: Creating significant table up_limma_GM_LP_vs_GM_NS
## 3/16: Creating significant table up_limma_GM_LA_vs_GM_NS
## 4/16: Creating significant table up_limma_M_LPS_vs_M_NS
## 5/16: Creating significant table up_limma_M_LP_vs_M_NS
## 6/16: Creating significant table up_limma_M_LA_vs_M_NS
## 7/16: Creating significant table up_limma_GM_LP_vs_GM_LPS
## 8/16: Creating significant table up_limma_GM_LA_vs_GM_LPS
## 9/16: Creating significant table up_limma_M_LP_vs_M_LPS
## 10/16: Creating significant table up_limma_M_LA_vs_M_LPS
## 11/16: Creating significant table up_limma_GM_LP_vs_GM_LA
## The down table GM_LP_vs_GM_LA is empty.
## 12/16: Creating significant table up_limma_M_LP_vs_M_LA
## 13/16: Creating significant table up_limma_GM_NS_vs_M_NS
## 14/16: Creating significant table up_limma_GM_LPS_vs_M_LPS
## 15/16: Creating significant table up_limma_GM_LP_vs_M_LP
## 16/16: Creating significant table up_limma_GM_LA_vs_M_LA
## Writing excel data according to edger for GM_LPS_vs_GM_NS: 1/64.
## After (adj)p filter, the up genes table has 414 genes.
## After (adj)p filter, the down genes table has 102 genes.
## After fold change filter, the up genes table has 267 genes.
## After fold change filter, the down genes table has 31 genes.
## Writing excel data according to edger for GM_LP_vs_GM_NS: 2/64.
## After (adj)p filter, the up genes table has 700 genes.
## After (adj)p filter, the down genes table has 484 genes.
## After fold change filter, the up genes table has 431 genes.
## After fold change filter, the down genes table has 168 genes.
## Writing excel data according to edger for GM_LA_vs_GM_NS: 3/64.
## After (adj)p filter, the up genes table has 600 genes.
## After (adj)p filter, the down genes table has 291 genes.
## After fold change filter, the up genes table has 377 genes.
## After fold change filter, the down genes table has 106 genes.
## Writing excel data according to edger for M_LPS_vs_M_NS: 4/64.
## After (adj)p filter, the up genes table has 1969 genes.
## After (adj)p filter, the down genes table has 1647 genes.
## After fold change filter, the up genes table has 1153 genes.
## After fold change filter, the down genes table has 533 genes.
## Writing excel data according to edger for M_LP_vs_M_NS: 5/64.
## After (adj)p filter, the up genes table has 1909 genes.
## After (adj)p filter, the down genes table has 1688 genes.
## After fold change filter, the up genes table has 1164 genes.
## After fold change filter, the down genes table has 608 genes.
## Writing excel data according to edger for M_LA_vs_M_NS: 6/64.
## After (adj)p filter, the up genes table has 1973 genes.
## After (adj)p filter, the down genes table has 1896 genes.
## After fold change filter, the up genes table has 1170 genes.
## After fold change filter, the down genes table has 723 genes.
## Writing excel data according to edger for GM_LP_vs_GM_LPS: 7/64.
## After (adj)p filter, the up genes table has 189 genes.
## After (adj)p filter, the down genes table has 133 genes.
## After fold change filter, the up genes table has 126 genes.
## After fold change filter, the down genes table has 37 genes.
## Writing excel data according to edger for GM_LA_vs_GM_LPS: 8/64.
## After (adj)p filter, the up genes table has 18 genes.
## After (adj)p filter, the down genes table has 4 genes.
## After fold change filter, the up genes table has 15 genes.
## After fold change filter, the down genes table has 2 genes.
## Writing excel data according to edger for M_LP_vs_M_LPS: 9/64.
## After (adj)p filter, the up genes table has 667 genes.
## After (adj)p filter, the down genes table has 855 genes.
## After fold change filter, the up genes table has 283 genes.
## After fold change filter, the down genes table has 284 genes.
## Writing excel data according to edger for M_LA_vs_M_LPS: 10/64.
## After (adj)p filter, the up genes table has 313 genes.
## After (adj)p filter, the down genes table has 443 genes.
## After fold change filter, the up genes table has 164 genes.
## After fold change filter, the down genes table has 151 genes.
## Writing excel data according to edger for GM_LP_vs_GM_LA: 11/64.
## After (adj)p filter, the up genes table has 48 genes.
## After (adj)p filter, the down genes table has 14 genes.
## After fold change filter, the up genes table has 36 genes.
## After fold change filter, the down genes table has 7 genes.
## Writing excel data according to edger for M_LP_vs_M_LA: 12/64.
## After (adj)p filter, the up genes table has 91 genes.
## After (adj)p filter, the down genes table has 69 genes.
## After fold change filter, the up genes table has 45 genes.
## After fold change filter, the down genes table has 23 genes.
## Writing excel data according to edger for GM_NS_vs_M_NS: 13/64.
## After (adj)p filter, the up genes table has 900 genes.
## After (adj)p filter, the down genes table has 831 genes.
## After fold change filter, the up genes table has 558 genes.
## After fold change filter, the down genes table has 406 genes.
## Writing excel data according to edger for GM_LPS_vs_M_LPS: 14/64.
## After (adj)p filter, the up genes table has 1470 genes.
## After (adj)p filter, the down genes table has 1702 genes.
## After fold change filter, the up genes table has 720 genes.
## After fold change filter, the down genes table has 890 genes.
## Writing excel data according to edger for GM_LP_vs_M_LP: 15/64.
## After (adj)p filter, the up genes table has 1722 genes.
## After (adj)p filter, the down genes table has 1849 genes.
## After fold change filter, the up genes table has 865 genes.
## After fold change filter, the down genes table has 935 genes.
## Writing excel data according to edger for GM_LA_vs_M_LA: 16/64.
## After (adj)p filter, the up genes table has 1682 genes.
## After (adj)p filter, the down genes table has 1612 genes.
## After fold change filter, the up genes table has 835 genes.
## After fold change filter, the down genes table has 827 genes.
## Printing significant genes to the file: excel/pat_gs_batch_sig-v20200330.xlsx
## 1/16: Creating significant table up_edger_GM_LPS_vs_GM_NS
## 2/16: Creating significant table up_edger_GM_LP_vs_GM_NS
## 3/16: Creating significant table up_edger_GM_LA_vs_GM_NS
## 4/16: Creating significant table up_edger_M_LPS_vs_M_NS
## 5/16: Creating significant table up_edger_M_LP_vs_M_NS
## 6/16: Creating significant table up_edger_M_LA_vs_M_NS
## 7/16: Creating significant table up_edger_GM_LP_vs_GM_LPS
## 8/16: Creating significant table up_edger_GM_LA_vs_GM_LPS
## 9/16: Creating significant table up_edger_M_LP_vs_M_LPS
## 10/16: Creating significant table up_edger_M_LA_vs_M_LPS
## 11/16: Creating significant table up_edger_GM_LP_vs_GM_LA
## 12/16: Creating significant table up_edger_M_LP_vs_M_LA
## 13/16: Creating significant table up_edger_GM_NS_vs_M_NS
## 14/16: Creating significant table up_edger_GM_LPS_vs_M_LPS
## 15/16: Creating significant table up_edger_GM_LP_vs_M_LP
## 16/16: Creating significant table up_edger_GM_LA_vs_M_LA
## Writing excel data according to deseq for GM_LPS_vs_GM_NS: 1/64.
## After (adj)p filter, the up genes table has 625 genes.
## After (adj)p filter, the down genes table has 269 genes.
## After fold change filter, the up genes table has 283 genes.
## After fold change filter, the down genes table has 31 genes.
## Writing excel data according to deseq for GM_LP_vs_GM_NS: 2/64.
## After (adj)p filter, the up genes table has 1305 genes.
## After (adj)p filter, the down genes table has 870 genes.
## After fold change filter, the up genes table has 442 genes.
## After fold change filter, the down genes table has 182 genes.
## Writing excel data according to deseq for GM_LA_vs_GM_NS: 3/64.
## After (adj)p filter, the up genes table has 906 genes.
## After (adj)p filter, the down genes table has 543 genes.
## After fold change filter, the up genes table has 367 genes.
## After fold change filter, the down genes table has 107 genes.
## Writing excel data according to deseq for M_LPS_vs_M_NS: 4/64.
## After (adj)p filter, the up genes table has 2396 genes.
## After (adj)p filter, the down genes table has 2147 genes.
## After fold change filter, the up genes table has 976 genes.
## After fold change filter, the down genes table has 419 genes.
## Writing excel data according to deseq for M_LP_vs_M_NS: 5/64.
## After (adj)p filter, the up genes table has 2308 genes.
## After (adj)p filter, the down genes table has 2184 genes.
## After fold change filter, the up genes table has 950 genes.
## After fold change filter, the down genes table has 507 genes.
## Writing excel data according to deseq for M_LA_vs_M_NS: 6/64.
## After (adj)p filter, the up genes table has 2396 genes.
## After (adj)p filter, the down genes table has 2402 genes.
## After fold change filter, the up genes table has 931 genes.
## After fold change filter, the down genes table has 589 genes.
## Writing excel data according to deseq for GM_LP_vs_GM_LPS: 7/64.
## After (adj)p filter, the up genes table has 275 genes.
## After (adj)p filter, the down genes table has 228 genes.
## After fold change filter, the up genes table has 125 genes.
## After fold change filter, the down genes table has 31 genes.
## Writing excel data according to deseq for GM_LA_vs_GM_LPS: 8/64.
## After (adj)p filter, the up genes table has 16 genes.
## After (adj)p filter, the down genes table has 14 genes.
## After fold change filter, the up genes table has 10 genes.
## After fold change filter, the down genes table has 4 genes.
## Writing excel data according to deseq for M_LP_vs_M_LPS: 9/64.
## After (adj)p filter, the up genes table has 984 genes.
## After (adj)p filter, the down genes table has 1280 genes.
## After fold change filter, the up genes table has 237 genes.
## After fold change filter, the down genes table has 269 genes.
## Writing excel data according to deseq for M_LA_vs_M_LPS: 10/64.
## After (adj)p filter, the up genes table has 491 genes.
## After (adj)p filter, the down genes table has 738 genes.
## After fold change filter, the up genes table has 131 genes.
## After fold change filter, the down genes table has 153 genes.
## Writing excel data according to deseq for GM_LP_vs_GM_LA: 11/64.
## After (adj)p filter, the up genes table has 72 genes.
## After (adj)p filter, the down genes table has 17 genes.
## After fold change filter, the up genes table has 41 genes.
## After fold change filter, the down genes table has 2 genes.
## Writing excel data according to deseq for M_LP_vs_M_LA: 12/64.
## After (adj)p filter, the up genes table has 191 genes.
## After (adj)p filter, the down genes table has 161 genes.
## After fold change filter, the up genes table has 51 genes.
## After fold change filter, the down genes table has 24 genes.
## Writing excel data according to deseq for GM_NS_vs_M_NS: 13/64.
## After (adj)p filter, the up genes table has 1208 genes.
## After (adj)p filter, the down genes table has 1073 genes.
## After fold change filter, the up genes table has 509 genes.
## After fold change filter, the down genes table has 362 genes.
## Writing excel data according to deseq for GM_LPS_vs_M_LPS: 14/64.
## After (adj)p filter, the up genes table has 2075 genes.
## After (adj)p filter, the down genes table has 2112 genes.
## After fold change filter, the up genes table has 644 genes.
## After fold change filter, the down genes table has 749 genes.
## Writing excel data according to deseq for GM_LP_vs_M_LP: 15/64.
## After (adj)p filter, the up genes table has 2477 genes.
## After (adj)p filter, the down genes table has 2236 genes.
## After fold change filter, the up genes table has 742 genes.
## After fold change filter, the down genes table has 731 genes.
## Writing excel data according to deseq for GM_LA_vs_M_LA: 16/64.
## After (adj)p filter, the up genes table has 2232 genes.
## After (adj)p filter, the down genes table has 2014 genes.
## After fold change filter, the up genes table has 725 genes.
## After fold change filter, the down genes table has 687 genes.
## Printing significant genes to the file: excel/pat_gs_batch_sig-v20200330.xlsx
## 1/16: Creating significant table up_deseq_GM_LPS_vs_GM_NS
## 2/16: Creating significant table up_deseq_GM_LP_vs_GM_NS
## 3/16: Creating significant table up_deseq_GM_LA_vs_GM_NS
## 4/16: Creating significant table up_deseq_M_LPS_vs_M_NS
## 5/16: Creating significant table up_deseq_M_LP_vs_M_NS
## 6/16: Creating significant table up_deseq_M_LA_vs_M_NS
## 7/16: Creating significant table up_deseq_GM_LP_vs_GM_LPS
## 8/16: Creating significant table up_deseq_GM_LA_vs_GM_LPS
## 9/16: Creating significant table up_deseq_M_LP_vs_M_LPS
## 10/16: Creating significant table up_deseq_M_LA_vs_M_LPS
## 11/16: Creating significant table up_deseq_GM_LP_vs_GM_LA
## 12/16: Creating significant table up_deseq_M_LP_vs_M_LA
## 13/16: Creating significant table up_deseq_GM_NS_vs_M_NS
## 14/16: Creating significant table up_deseq_GM_LPS_vs_M_LPS
## 15/16: Creating significant table up_deseq_GM_LP_vs_M_LP
## 16/16: Creating significant table up_deseq_GM_LA_vs_M_LA
## Writing excel data according to basic for GM_LPS_vs_GM_NS: 1/64.
## After (adj)p filter, the up genes table has 1 genes.
## After (adj)p filter, the down genes table has 0 genes.
## After fold change filter, the up genes table has 1 genes.
## After fold change filter, the down genes table has 0 genes.
## Writing excel data according to basic for GM_LP_vs_GM_NS: 2/64.
## After (adj)p filter, the up genes table has 0 genes.
## After (adj)p filter, the down genes table has 0 genes.
## After fold change filter, the up genes table has 0 genes.
## After fold change filter, the down genes table has 0 genes.
## Writing excel data according to basic for GM_LA_vs_GM_NS: 3/64.
## After (adj)p filter, the up genes table has 0 genes.
## After (adj)p filter, the down genes table has 0 genes.
## After fold change filter, the up genes table has 0 genes.
## After fold change filter, the down genes table has 0 genes.
## Writing excel data according to basic for M_LPS_vs_M_NS: 4/64.
## After (adj)p filter, the up genes table has 1056 genes.
## After (adj)p filter, the down genes table has 1023 genes.
## After fold change filter, the up genes table has 623 genes.
## After fold change filter, the down genes table has 268 genes.
## Writing excel data according to basic for M_LP_vs_M_NS: 5/64.
## After (adj)p filter, the up genes table has 1090 genes.
## After (adj)p filter, the down genes table has 1024 genes.
## After fold change filter, the up genes table has 636 genes.
## After fold change filter, the down genes table has 357 genes.
## Writing excel data according to basic for M_LA_vs_M_NS: 6/64.
## After (adj)p filter, the up genes table has 1446 genes.
## After (adj)p filter, the down genes table has 1500 genes.
## After fold change filter, the up genes table has 695 genes.
## After fold change filter, the down genes table has 452 genes.
## Writing excel data according to basic for GM_LP_vs_GM_LPS: 7/64.
## After (adj)p filter, the up genes table has 0 genes.
## After (adj)p filter, the down genes table has 0 genes.
## After fold change filter, the up genes table has 0 genes.
## After fold change filter, the down genes table has 0 genes.
## Writing excel data according to basic for GM_LA_vs_GM_LPS: 8/64.
## After (adj)p filter, the up genes table has 0 genes.
## After (adj)p filter, the down genes table has 0 genes.
## After fold change filter, the up genes table has 0 genes.
## After fold change filter, the down genes table has 0 genes.
## Writing excel data according to basic for M_LP_vs_M_LPS: 9/64.
## After (adj)p filter, the up genes table has 233 genes.
## After (adj)p filter, the down genes table has 222 genes.
## After fold change filter, the up genes table has 104 genes.
## After fold change filter, the down genes table has 85 genes.
## Writing excel data according to basic for M_LA_vs_M_LPS: 10/64.
## After (adj)p filter, the up genes table has 0 genes.
## After (adj)p filter, the down genes table has 2 genes.
## After fold change filter, the up genes table has 0 genes.
## After fold change filter, the down genes table has 1 genes.
## Writing excel data according to basic for GM_LP_vs_GM_LA: 11/64.
## After (adj)p filter, the up genes table has 0 genes.
## After (adj)p filter, the down genes table has 0 genes.
## After fold change filter, the up genes table has 0 genes.
## After fold change filter, the down genes table has 0 genes.
## Writing excel data according to basic for M_LP_vs_M_LA: 12/64.
## After (adj)p filter, the up genes table has 1 genes.
## After (adj)p filter, the down genes table has 1 genes.
## After fold change filter, the up genes table has 1 genes.
## After fold change filter, the down genes table has 0 genes.
## Writing excel data according to basic for GM_NS_vs_M_NS: 13/64.
## After (adj)p filter, the up genes table has 36 genes.
## After (adj)p filter, the down genes table has 34 genes.
## After fold change filter, the up genes table has 26 genes.
## After fold change filter, the down genes table has 16 genes.
## Writing excel data according to basic for GM_LPS_vs_M_LPS: 14/64.
## After (adj)p filter, the up genes table has 163 genes.
## After (adj)p filter, the down genes table has 262 genes.
## After fold change filter, the up genes table has 98 genes.
## After fold change filter, the down genes table has 188 genes.
## Writing excel data according to basic for GM_LP_vs_M_LP: 15/64.
## After (adj)p filter, the up genes table has 230 genes.
## After (adj)p filter, the down genes table has 311 genes.
## After fold change filter, the up genes table has 141 genes.
## After fold change filter, the down genes table has 198 genes.
## Writing excel data according to basic for GM_LA_vs_M_LA: 16/64.
## After (adj)p filter, the up genes table has 289 genes.
## After (adj)p filter, the down genes table has 378 genes.
## After fold change filter, the up genes table has 174 genes.
## After fold change filter, the down genes table has 235 genes.
## Printing significant genes to the file: excel/pat_gs_batch_sig-v20200330.xlsx
## 1/16: Creating significant table up_basic_GM_LPS_vs_GM_NS
## The down table GM_LPS_vs_GM_NS is empty.
## The up table GM_LP_vs_GM_NS is empty.
## The down table GM_LP_vs_GM_NS is empty.
## The up table GM_LA_vs_GM_NS is empty.
## The down table GM_LA_vs_GM_NS is empty.
## 4/16: Creating significant table up_basic_M_LPS_vs_M_NS
## 5/16: Creating significant table up_basic_M_LP_vs_M_NS
## 6/16: Creating significant table up_basic_M_LA_vs_M_NS
## The up table GM_LP_vs_GM_LPS is empty.
## The down table GM_LP_vs_GM_LPS is empty.
## The up table GM_LA_vs_GM_LPS is empty.
## The down table GM_LA_vs_GM_LPS is empty.
## 9/16: Creating significant table up_basic_M_LP_vs_M_LPS
## The up table M_LA_vs_M_LPS is empty.
## The up table GM_LP_vs_GM_LA is empty.
## The down table GM_LP_vs_GM_LA is empty.
## 12/16: Creating significant table up_basic_M_LP_vs_M_LA
## The down table M_LP_vs_M_LA is empty.
## 13/16: Creating significant table up_basic_GM_NS_vs_M_NS
## 14/16: Creating significant table up_basic_GM_LPS_vs_M_LPS
## 15/16: Creating significant table up_basic_GM_LP_vs_M_LP
## 16/16: Creating significant table up_basic_GM_LA_vs_M_LA
## Adding significance bar plots.
## Testing method: limma.
## Adding method: limma to the set.
## Testing method: deseq.
## Adding method: deseq to the set.
## Testing method: edger.
## Adding method: edger to the set.
## Testing method: ebseq.
## The first datum is missing method: ebseq.
## Testing method: basic.
## Adding method: basic to the set.
## Starting method limma, table GM_LPS_vs_GM_NS.
## Starting method limma, table GM_LP_vs_GM_NS.
## Starting method limma, table GM_LA_vs_GM_NS.
## Starting method limma, table M_LPS_vs_M_NS.
## Starting method limma, table M_LP_vs_M_NS.
## Starting method limma, table M_LA_vs_M_NS.
## Starting method limma, table GM_LP_vs_GM_LPS.
## Starting method limma, table GM_LA_vs_GM_LPS.
## Starting method limma, table M_LP_vs_M_LPS.
## Starting method limma, table M_LA_vs_M_LPS.
## Starting method limma, table GM_LP_vs_GM_LA.
## Starting method limma, table M_LP_vs_M_LA.
## Starting method limma, table GM_NS_vs_M_NS.
## Starting method limma, table GM_LPS_vs_M_LPS.
## Starting method limma, table GM_LP_vs_M_LP.
## Starting method limma, table GM_LA_vs_M_LA.
## Starting method deseq, table GM_LPS_vs_GM_NS.
## Starting method deseq, table GM_LP_vs_GM_NS.
## Starting method deseq, table GM_LA_vs_GM_NS.
## Starting method deseq, table M_LPS_vs_M_NS.
## Starting method deseq, table M_LP_vs_M_NS.
## Starting method deseq, table M_LA_vs_M_NS.
## Starting method deseq, table GM_LP_vs_GM_LPS.
## Starting method deseq, table GM_LA_vs_GM_LPS.
## Starting method deseq, table M_LP_vs_M_LPS.
## Starting method deseq, table M_LA_vs_M_LPS.
## Starting method deseq, table GM_LP_vs_GM_LA.
## Starting method deseq, table M_LP_vs_M_LA.
## Starting method deseq, table GM_NS_vs_M_NS.
## Starting method deseq, table GM_LPS_vs_M_LPS.
## Starting method deseq, table GM_LP_vs_M_LP.
## Starting method deseq, table GM_LA_vs_M_LA.
## Starting method edger, table GM_LPS_vs_GM_NS.
## Starting method edger, table GM_LP_vs_GM_NS.
## Starting method edger, table GM_LA_vs_GM_NS.
## Starting method edger, table M_LPS_vs_M_NS.
## Starting method edger, table M_LP_vs_M_NS.
## Starting method edger, table M_LA_vs_M_NS.
## Starting method edger, table GM_LP_vs_GM_LPS.
## Starting method edger, table GM_LA_vs_GM_LPS.
## Starting method edger, table M_LP_vs_M_LPS.
## Starting method edger, table M_LA_vs_M_LPS.
## Starting method edger, table GM_LP_vs_GM_LA.
## Starting method edger, table M_LP_vs_M_LA.
## Starting method edger, table GM_NS_vs_M_NS.
## Starting method edger, table GM_LPS_vs_M_LPS.
## Starting method edger, table GM_LP_vs_M_LP.
## Starting method edger, table GM_LA_vs_M_LA.
## Starting method basic, table GM_LPS_vs_GM_NS.
## Starting method basic, table GM_LP_vs_GM_NS.
## Starting method basic, table GM_LA_vs_GM_NS.
## Starting method basic, table M_LPS_vs_M_NS.
## Starting method basic, table M_LP_vs_M_NS.
## Starting method basic, table M_LA_vs_M_NS.
## Starting method basic, table GM_LP_vs_GM_LPS.
## Starting method basic, table GM_LA_vs_GM_LPS.
## Starting method basic, table M_LP_vs_M_LPS.
## Starting method basic, table M_LA_vs_M_LPS.
## Starting method basic, table GM_LP_vs_GM_LA.
## Starting method basic, table M_LP_vs_M_LA.
## Starting method basic, table GM_NS_vs_M_NS.
## Starting method basic, table GM_LPS_vs_M_LPS.
## Starting method basic, table GM_LP_vs_M_LP.
## Starting method basic, table GM_LA_vs_M_LA.
It appears that I crashed the gProfiler web server by sending in my various searches. So I will leave these off for the moment and replace them with some clusterProfiler searches.
table <- "GM_LPS_vs_GM_NS"
up <- pat_gs_sva_sig[["deseq"]][["ups"]][[table]]
down <- pat_gs_sa_sig[["deseq"]][["downs"]][[table]]
## Error in eval(expr, envir, enclos): object 'pat_gs_sa_sig' not found
ont_up <- simple_gprofiler(up)
ont_up[["pvalue_plots"]][["mfp_plot_over"]]
ont_up[["pvalue_plots"]][["bpp_plot_over"]]
ont_up[["pvalue_plots"]][["kegg_plot_over"]]
ont_up[["pvalue_plots"]][["reactome_plot_over"]]
ont_up[["pvalue_plots"]][["hp_plot_over"]]
ont_down <- simple_gprofiler(down)
ont_down[["pvalue_plots"]][["mfp_plot_over"]]
ont_down[["pvalue_plots"]][["bpp_plot_over"]]
ont_down[["pvalue_plots"]][["kegg_plot_over"]]
ont_down[["pvalue_plots"]][["reactome_plot_over"]]
ont_down[["pvalue_plots"]][["hp_plot_over"]]
ont_up <- simple_clusterprofiler(sig_genes=up, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Testing available OrgDb keytypes for the best mapping to entrez.
## Chose keytype: ENSEMBL for all genes because it had 20051 out of 34431 genes.
## Chose keytype: ENSEMBL for sig genes because it had 272 out of 283 genes.
## Calculating GO groups.
## Found 146 MF, 558 BP, and 755 CC hits.
## Calculating enriched GO groups.
## Found 26 MF, 1076 BP, and 2 CC enriched hits.
## Performing GSE analyses of gene lists (this is slow).
## Found 565 enriched hits.
## Found 268 matches between the significant gene list and kegg universe.
## Performing KEGG analyses.
## Found 19329 matches between the gene list and kegg universe.
## Found 59 KEGG enriched hits.
## Attempting DAVID search.
## Loading required package: RDAVIDWebService
## Loading required package: graph
## Loading required package: GOstats
## Loading required package: Category
## Loading required package: stats4
## Loading required package: AnnotationDbi
## Loading required package: IRanges
## Loading required package: S4Vectors
##
## Attaching package: 'S4Vectors'
## The following object is masked from 'package:base':
##
## expand.grid
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
## The following object is masked from 'package:S4Vectors':
##
## expand
##
## Attaching package: 'GOstats'
## The following object is masked from 'package:AnnotationDbi':
##
## makeGOGraph
## Loading required package: ggplot2
## Need help? Try Stackoverflow: https://stackoverflow.com/tags/ggplot2
##
## Attaching package: 'RDAVIDWebService'
## The following object is masked from 'package:AnnotationDbi':
##
## species
## The following object is masked from 'package:IRanges':
##
## members
## The following objects are masked from 'package:BiocGenerics':
##
## counts, species, type
## Found 550 DAVID hits.
## Plotting results.
ont_down <- simple_clusterprofiler(sig_genes=down, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Error in rownames(sig_genes): object 'down' not found
## Error in eval(expr, envir, enclos): object 'ont_down' not found
## Error in eval(expr, envir, enclos): object 'ont_down' not found
## Error in eval(expr, envir, enclos): object 'ont_down' not found
## Error in eval(expr, envir, enclos): object 'ont_down' not found
## Error in eval(expr, envir, enclos): object 'ont_down' not found
## Error in eval(expr, envir, enclos): object 'ont_down' not found
table <- "GM_LP_vs_GM_NS"
up <- pat_gs_sva_sig[["deseq"]][["ups"]][[table]]
down <- pat_gs_sva_sig[["deseq"]][["downs"]][[table]]
de <- pat_gs_sva_tables[["data"]][[table]]
ont_up <- simple_gprofiler(up)
ont_up[["pvalue_plots"]][["mfp_plot_over"]]
ont_up[["pvalue_plots"]][["bpp_plot_over"]]
ont_up[["pvalue_plots"]][["kegg_plot_over"]]
ont_up[["pvalue_plots"]][["reactome_plot_over"]]
ont_up[["pvalue_plots"]][["hp_plot_over"]]
ont_down <- simple_gprofiler(down)
ont_down[["pvalue_plots"]][["mfp_plot_over"]]
ont_down[["pvalue_plots"]][["bpp_plot_over"]]
ont_down[["pvalue_plots"]][["kegg_plot_over"]]
ont_down[["pvalue_plots"]][["reactome_plot_over"]]
ont_down[["pvalue_plots"]][["hp_plot_over"]]
ont_up <- simple_clusterprofiler(sig_genes=up, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Testing available OrgDb keytypes for the best mapping to entrez.
## Chose keytype: ENSEMBL for all genes because it had 20051 out of 34431 genes.
## Chose keytype: ENSEMBL for sig genes because it had 423 out of 437 genes.
## Calculating GO groups.
## Found 146 MF, 558 BP, and 755 CC hits.
## Calculating enriched GO groups.
## Found 22 MF, 1070 BP, and 1 CC enriched hits.
## Performing GSE analyses of gene lists (this is slow).
## Found 524 enriched hits.
## Found 414 matches between the significant gene list and kegg universe.
## Performing KEGG analyses.
## Found 19329 matches between the gene list and kegg universe.
## Found 63 KEGG enriched hits.
## Attempting DAVID search.
## Found 566 DAVID hits.
## Plotting results.
## Using `stress` as default layout
ont_down <- simple_clusterprofiler(sig_genes=down, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Testing available OrgDb keytypes for the best mapping to entrez.
## Chose keytype: ENSEMBL for all genes because it had 20051 out of 34431 genes.
## Chose keytype: ENSEMBL for sig genes because it had 98 out of 101 genes.
## Calculating GO groups.
## Found 146 MF, 558 BP, and 755 CC hits.
## Calculating enriched GO groups.
## Found 0 MF, 0 BP, and 0 CC enriched hits.
## Performing GSE analyses of gene lists (this is slow).
## Found 746 enriched hits.
## Found 98 matches between the significant gene list and kegg universe.
## Performing KEGG analyses.
## Found 19329 matches between the gene list and kegg universe.
## Found 0 KEGG enriched hits.
## Attempting DAVID search.
## Found 0 DAVID hits.
## Plotting results.
## NULL
## NULL
table <- "GM_LA_vs_GM_NS"
up <- pat_gs_sva_sig[["deseq"]][["ups"]][[table]]
down <- pat_gs_sva_sig[["deseq"]][["downs"]][[table]]
de <- pat_gs_sva_tables[["data"]][[table]]
ont_up <- simple_gprofiler(up)
ont_up[["pvalue_plots"]][["mfp_plot_over"]]
ont_up[["pvalue_plots"]][["bpp_plot_over"]]
ont_up[["pvalue_plots"]][["kegg_plot_over"]]
ont_up[["pvalue_plots"]][["reactome_plot_over"]]
ont_up[["pvalue_plots"]][["hp_plot_over"]]
ont_down <- simple_gprofiler(down)
ont_down[["pvalue_plots"]][["mfp_plot_over"]]
ont_down[["pvalue_plots"]][["bpp_plot_over"]]
ont_down[["pvalue_plots"]][["kegg_plot_over"]]
ont_down[["pvalue_plots"]][["reactome_plot_over"]]
ont_down[["pvalue_plots"]][["hp_plot_over"]]
ont_up <- simple_clusterprofiler(sig_genes=up, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Testing available OrgDb keytypes for the best mapping to entrez.
## Chose keytype: ENSEMBL for all genes because it had 20051 out of 34431 genes.
## Chose keytype: ENSEMBL for sig genes because it had 341 out of 360 genes.
## Calculating GO groups.
## Found 146 MF, 558 BP, and 755 CC hits.
## Calculating enriched GO groups.
## Found 20 MF, 977 BP, and 1 CC enriched hits.
## Performing GSE analyses of gene lists (this is slow).
## Found 489 enriched hits.
## Found 334 matches between the significant gene list and kegg universe.
## Performing KEGG analyses.
## Found 19329 matches between the gene list and kegg universe.
## Found 63 KEGG enriched hits.
## Attempting DAVID search.
## Found 503 DAVID hits.
## Plotting results.
## Using `stress` as default layout
ont_down <- simple_clusterprofiler(sig_genes=down, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Testing available OrgDb keytypes for the best mapping to entrez.
## Chose keytype: ENSEMBL for all genes because it had 20051 out of 34431 genes.
## Chose keytype: ENSEMBL for sig genes because it had 103 out of 106 genes.
## Calculating GO groups.
## Found 146 MF, 558 BP, and 755 CC hits.
## Calculating enriched GO groups.
## Found 2 MF, 0 BP, and 0 CC enriched hits.
## Performing GSE analyses of gene lists (this is slow).
## Found 824 enriched hits.
## Found 102 matches between the significant gene list and kegg universe.
## Performing KEGG analyses.
## Found 19329 matches between the gene list and kegg universe.
## Found 0 KEGG enriched hits.
## Attempting DAVID search.
## Found 0 DAVID hits.
## Plotting results.
## NULL
## NULL
table <- "GM_LP_vs_GM_LPS"
up <- pat_gs_sva_sig[["deseq"]][["ups"]][[table]]
down <- pat_gs_sva_sig[["deseq"]][["downs"]][[table]]
de <- pat_gs_sva_tables[["data"]][[table]]
ont_up <- simple_gprofiler(up)
ont_up[["pvalue_plots"]][["mfp_plot_over"]]
ont_up[["pvalue_plots"]][["bpp_plot_over"]]
ont_up[["pvalue_plots"]][["kegg_plot_over"]]
ont_up[["pvalue_plots"]][["reactome_plot_over"]]
ont_up[["pvalue_plots"]][["hp_plot_over"]]
ont_down <- simple_gprofiler(down)
ont_down[["pvalue_plots"]][["mfp_plot_over"]]
ont_down[["pvalue_plots"]][["bpp_plot_over"]]
ont_down[["pvalue_plots"]][["kegg_plot_over"]]
ont_down[["pvalue_plots"]][["reactome_plot_over"]]
ont_down[["pvalue_plots"]][["hp_plot_over"]]
ont_up <- simple_clusterprofiler(sig_genes=up, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Testing available OrgDb keytypes for the best mapping to entrez.
## Chose keytype: ENSEMBL for all genes because it had 20051 out of 34431 genes.
## Chose keytype: ENSEMBL for sig genes because it had 135 out of 140 genes.
## Calculating GO groups.
## Found 146 MF, 558 BP, and 755 CC hits.
## Calculating enriched GO groups.
## Found 9 MF, 73 BP, and 1 CC enriched hits.
## Performing GSE analyses of gene lists (this is slow).
## Found 0 enriched hits.
## Found 133 matches between the significant gene list and kegg universe.
## Performing KEGG analyses.
## Found 19329 matches between the gene list and kegg universe.
## Found 1 KEGG enriched hits.
## Attempting DAVID search.
## Found 2 DAVID hits.
## Plotting results.
## Using `stress` as default layout
ont_down <- simple_clusterprofiler(sig_genes=down, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Testing available OrgDb keytypes for the best mapping to entrez.
## Chose keytype: ENSEMBL for all genes because it had 20051 out of 34431 genes.
## Chose keytype: ENSEMBL for sig genes because it had 29 out of 31 genes.
## Calculating GO groups.
## Found 146 MF, 558 BP, and 755 CC hits.
## Calculating enriched GO groups.
## Found 12 MF, 76 BP, and 2 CC enriched hits.
## Performing GSE analyses of gene lists (this is slow).
## Found 0 enriched hits.
## Found 28 matches between the significant gene list and kegg universe.
## Performing KEGG analyses.
## Found 19329 matches between the gene list and kegg universe.
## Found 1 KEGG enriched hits.
## Attempting DAVID search.
## Found 0 DAVID hits.
## Plotting results.
## NULL
## NULL
up <- pat_gs_sva_sig[["deseq"]][["ups"]][["GM_LP_vs_GM_LA"]]
down <- pat_gs_sva_sig[["deseq"]][["downs"]][["GM_LP_vs_GM_LA"]]
ont_up <- simple_gprofiler(up)
## Performing gProfiler GO search of 34 genes against hsapiens.
## Performing gProfiler KEGG search of 34 genes against hsapiens.
## Performing gProfiler REAC search of 34 genes against hsapiens.
## Performing gProfiler MI search of 34 genes against hsapiens.
## Performing gProfiler TF search of 34 genes against hsapiens.
## Performing gProfiler CORUM search of 34 genes against hsapiens.
## Performing gProfiler HP search of 34 genes against hsapiens.
## Error in ont_up[["pvalue_plots"]][["mfp_plot_over"]]: subscript out of bounds
## Error in ont_up[["pvalue_plots"]][["bpp_plot_over"]]: subscript out of bounds
## Error in ont_up[["pvalue_plots"]][["kegg_plot_over"]]: subscript out of bounds
## Error in ont_up[["pvalue_plots"]][["reactome_plot_over"]]: subscript out of bounds
## Error in ont_up[["pvalue_plots"]][["hp_plot_over"]]: subscript out of bounds
ont_up <- simple_clusterprofiler(sig_genes=up, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Testing available OrgDb keytypes for the best mapping to entrez.
## Chose keytype: ENSEMBL for all genes because it had 20051 out of 34431 genes.
## Chose keytype: ENSEMBL for sig genes because it had 31 out of 34 genes.
## Calculating GO groups.
## Found 146 MF, 558 BP, and 755 CC hits.
## Calculating enriched GO groups.
## Found 20 MF, 41 BP, and 0 CC enriched hits.
## Performing GSE analyses of gene lists (this is slow).
## Found 0 enriched hits.
## Found 31 matches between the significant gene list and kegg universe.
## Performing KEGG analyses.
## Found 19329 matches between the gene list and kegg universe.
## Found 0 KEGG enriched hits.
## Attempting DAVID search.
## Found 1 DAVID hits.
## Plotting results.
ont_down <- simple_clusterprofiler(sig_genes=down, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Testing available OrgDb keytypes for the best mapping to entrez.
## Chose keytype: ENSEMBL for all genes because it had 20051 out of 34431 genes.
## Chose keytype: ENSEMBL for sig genes because it had 1 out of 2 genes.
## Calculating GO groups.
## Found 146 MF, 558 BP, and 755 CC hits.
## Calculating enriched GO groups.
## Found 6 MF, 144 BP, and 1 CC enriched hits.
## Performing GSE analyses of gene lists (this is slow).
## Found 0 enriched hits.
## Found 1 matches between the significant gene list and kegg universe.
## Performing KEGG analyses.
## Found 19329 matches between the gene list and kegg universe.
## Found 32 KEGG enriched hits.
## Attempting DAVID search.
## Warning in clusterProfiler::enrichDAVID(gene = sig_gene_list, minGSSize =
## min_groupsize, : No significant enrichment found...
## Found 0 DAVID hits.
## Plotting results.
## Using `stress` as default layout
## NULL
## NULL
This search crashed the gProfiler server, so I will stop it at least for the moment.
table <- "M_LPS_vs_M_NS"
up <- pat_gs_sva_sig[["deseq"]][["ups"]][[table]]
down <- pat_gs_sva_sig[["deseq"]][["downs"]][[table]]
de <- pat_gs_sva_tables[["data"]][[table]]
ont_up <- simple_gprofiler(up)
ont_up[["pvalue_plots"]][["mfp_plot_over"]]
ont_up[["pvalue_plots"]][["bpp_plot_over"]]
ont_up[["pvalue_plots"]][["kegg_plot_over"]]
ont_up[["pvalue_plots"]][["reactome_plot_over"]]
ont_up[["pvalue_plots"]][["hp_plot_over"]]
ont_down <- simple_gprofiler(down)
ont_down[["pvalue_plots"]][["mfp_plot_over"]]
ont_down[["pvalue_plots"]][["bpp_plot_over"]]
ont_down[["pvalue_plots"]][["kegg_plot_over"]]
ont_down[["pvalue_plots"]][["reactome_plot_over"]]
ont_down[["pvalue_plots"]][["hp_plot_over"]]
ont_up <- simple_clusterprofiler(sig_genes=up, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Testing available OrgDb keytypes for the best mapping to entrez.
## Chose keytype: ENSEMBL for all genes because it had 20051 out of 34431 genes.
## Chose keytype: ENSEMBL for sig genes because it had 908 out of 931 genes.
## Calculating GO groups.
## Found 146 MF, 558 BP, and 755 CC hits.
## Calculating enriched GO groups.
## Found 41 MF, 1455 BP, and 12 CC enriched hits.
## Performing GSE analyses of gene lists (this is slow).
## Found 834 enriched hits.
## Found 891 matches between the significant gene list and kegg universe.
## Performing KEGG analyses.
## Found 19329 matches between the gene list and kegg universe.
## Found 83 KEGG enriched hits.
## Attempting DAVID search.
## Found 824 DAVID hits.
## Plotting results.
ont_down <- simple_clusterprofiler(sig_genes=down, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Testing available OrgDb keytypes for the best mapping to entrez.
## Chose keytype: ENSEMBL for all genes because it had 20051 out of 34431 genes.
## Chose keytype: ENSEMBL for sig genes because it had 449 out of 455 genes.
## Calculating GO groups.
## Found 146 MF, 558 BP, and 755 CC hits.
## Calculating enriched GO groups.
## Found 4 MF, 5 BP, and 0 CC enriched hits.
## Performing GSE analyses of gene lists (this is slow).
## Found 861 enriched hits.
## Found 441 matches between the significant gene list and kegg universe.
## Performing KEGG analyses.
## Found 19329 matches between the gene list and kegg universe.
## Found 0 KEGG enriched hits.
## Attempting DAVID search.
## Found 0 DAVID hits.
## Plotting results.
## NULL
## NULL
table <- "M_LP_vs_M_NS"
up <- pat_gs_sva_sig[["deseq"]][["ups"]][[table]]
down <- pat_gs_sva_sig[["deseq"]][["downs"]][[table]]
de <- pat_gs_sva_tables[["data"]][[table]]
ont_up <- simple_gprofiler(up)
ont_up[["pvalue_plots"]][["mfp_plot_over"]]
ont_up[["pvalue_plots"]][["bpp_plot_over"]]
ont_up[["pvalue_plots"]][["kegg_plot_over"]]
ont_up[["pvalue_plots"]][["reactome_plot_over"]]
ont_up[["pvalue_plots"]][["hp_plot_over"]]
ont_down <- simple_gprofiler(down)
ont_down[["pvalue_plots"]][["mfp_plot_over"]]
ont_down[["pvalue_plots"]][["bpp_plot_over"]]
ont_down[["pvalue_plots"]][["kegg_plot_over"]]
ont_down[["pvalue_plots"]][["reactome_plot_over"]]
ont_down[["pvalue_plots"]][["hp_plot_over"]]
ont_up <- simple_clusterprofiler(sig_genes=up, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Testing available OrgDb keytypes for the best mapping to entrez.
## Chose keytype: ENSEMBL for all genes because it had 20051 out of 34431 genes.
## Chose keytype: ENSEMBL for sig genes because it had 913 out of 939 genes.
## Calculating GO groups.
## Found 146 MF, 558 BP, and 755 CC hits.
## Calculating enriched GO groups.
## Found 38 MF, 1267 BP, and 5 CC enriched hits.
## Performing GSE analyses of gene lists (this is slow).
## Found 507 enriched hits.
## Found 898 matches between the significant gene list and kegg universe.
## Performing KEGG analyses.
## Found 19329 matches between the gene list and kegg universe.
## Found 74 KEGG enriched hits.
## Attempting DAVID search.
## Found 720 DAVID hits.
## Plotting results.
ont_down <- simple_clusterprofiler(sig_genes=down, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Testing available OrgDb keytypes for the best mapping to entrez.
## Chose keytype: ENSEMBL for all genes because it had 20051 out of 34431 genes.
## Chose keytype: ENSEMBL for sig genes because it had 524 out of 530 genes.
## Calculating GO groups.
## Found 146 MF, 558 BP, and 755 CC hits.
## Calculating enriched GO groups.
## Found 16 MF, 88 BP, and 8 CC enriched hits.
## Performing GSE analyses of gene lists (this is slow).
## Found 0 enriched hits.
## Found 517 matches between the significant gene list and kegg universe.
## Performing KEGG analyses.
## Found 19329 matches between the gene list and kegg universe.
## Found 0 KEGG enriched hits.
## Attempting DAVID search.
## Found 10 DAVID hits.
## Plotting results.
## NULL
## NULL
table <- "M_LA_vs_M_NS"
up <- pat_gs_sva_sig[["deseq"]][["ups"]][[table]]
down <- pat_gs_sva_sig[["deseq"]][["downs"]][[table]]
de <- pat_gs_sva_tables[["data"]][[table]]
ont_up <- simple_gprofiler(up)
ont_up[["pvalue_plots"]][["mfp_plot_over"]]
ont_up[["pvalue_plots"]][["bpp_plot_over"]]
ont_up[["pvalue_plots"]][["kegg_plot_over"]]
ont_up[["pvalue_plots"]][["reactome_plot_over"]]
ont_up[["pvalue_plots"]][["hp_plot_over"]]
ont_down <- simple_gprofiler(down)
ont_down[["pvalue_plots"]][["mfp_plot_over"]]
ont_down[["pvalue_plots"]][["bpp_plot_over"]]
ont_down[["pvalue_plots"]][["kegg_plot_over"]]
ont_down[["pvalue_plots"]][["reactome_plot_over"]]
ont_down[["pvalue_plots"]][["hp_plot_over"]]
ont_up <- simple_clusterprofiler(sig_genes=up, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Testing available OrgDb keytypes for the best mapping to entrez.
## Chose keytype: ENSEMBL for all genes because it had 20051 out of 34431 genes.
## Chose keytype: ENSEMBL for sig genes because it had 892 out of 913 genes.
## Calculating GO groups.
## Found 146 MF, 558 BP, and 755 CC hits.
## Calculating enriched GO groups.
## Found 25 MF, 1394 BP, and 4 CC enriched hits.
## Performing GSE analyses of gene lists (this is slow).
## Found 592 enriched hits.
## Found 873 matches between the significant gene list and kegg universe.
## Performing KEGG analyses.
## Found 19329 matches between the gene list and kegg universe.
## Found 80 KEGG enriched hits.
## Attempting DAVID search.
## Found 763 DAVID hits.
## Plotting results.
ont_down <- simple_clusterprofiler(sig_genes=down, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Testing available OrgDb keytypes for the best mapping to entrez.
## Chose keytype: ENSEMBL for all genes because it had 20051 out of 34431 genes.
## Chose keytype: ENSEMBL for sig genes because it had 584 out of 587 genes.
## Calculating GO groups.
## Found 146 MF, 558 BP, and 755 CC hits.
## Calculating enriched GO groups.
## Found 18 MF, 51 BP, and 10 CC enriched hits.
## Performing GSE analyses of gene lists (this is slow).
## Found 360 enriched hits.
## Found 575 matches between the significant gene list and kegg universe.
## Performing KEGG analyses.
## Found 19329 matches between the gene list and kegg universe.
## Found 0 KEGG enriched hits.
## Attempting DAVID search.
## Found 15 DAVID hits.
## Plotting results.
## NULL
## NULL
table <- "GM_NS_vs_M_NS"
up <- pat_gs_sva_sig[["deseq"]][["ups"]][[table]]
down <- pat_gs_sva_sig[["deseq"]][["downs"]][[table]]
de <- pat_gs_sva_tables[["data"]][[table]]
ont_up <- simple_clusterprofiler(sig_genes=up, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Testing available OrgDb keytypes for the best mapping to entrez.
## Chose keytype: ENSEMBL for all genes because it had 20051 out of 34431 genes.
## Chose keytype: ENSEMBL for sig genes because it had 501 out of 519 genes.
## Calculating GO groups.
## Found 146 MF, 558 BP, and 755 CC hits.
## Calculating enriched GO groups.
## Found 26 MF, 374 BP, and 10 CC enriched hits.
## Performing GSE analyses of gene lists (this is slow).
## Found 0 enriched hits.
## Found 488 matches between the significant gene list and kegg universe.
## Performing KEGG analyses.
## Found 19329 matches between the gene list and kegg universe.
## Found 8 KEGG enriched hits.
## Attempting DAVID search.
## Found 133 DAVID hits.
## Plotting results.
ont_down <- simple_clusterprofiler(sig_genes=down, de_table=de,
do_david=TRUE, david_user="abelew@umd.edu",
fc_column="deseq_logfc", orgdb="org.Hs.eg.db")
## Testing available OrgDb keytypes for the best mapping to entrez.
## Chose keytype: ENSEMBL for all genes because it had 20051 out of 34431 genes.
## Chose keytype: ENSEMBL for sig genes because it had 338 out of 361 genes.
## Calculating GO groups.
## Found 146 MF, 558 BP, and 755 CC hits.
## Calculating enriched GO groups.
## Found 9 MF, 92 BP, and 2 CC enriched hits.
## Performing GSE analyses of gene lists (this is slow).
## Found 0 enriched hits.
## Found 332 matches between the significant gene list and kegg universe.
## Performing KEGG analyses.
## Found 19329 matches between the gene list and kegg universe.
## Found 0 KEGG enriched hits.
## Attempting DAVID search.
## Found 47 DAVID hits.
## Plotting results.
## NULL
## NULL