I downloaded a new version of cellranger along with the various reference files provided by 10x for the VD(J) references etc. I got a bit distracted by the pipeline language implemented by 10x called ‘martian’. I have the feeling that it might prove a good thing to play with.
Here are the commands I ran to separate the samples and perform the alignments. There are 4 sample names and each was done with one run of the ‘normal’ GEX scRNASeq method and one of the (new to me) V(D)J library.
April kindly sent some information from 10x which shows that I should have used the multi pipline when preprocessing the data.
Intra-Muscular vs. Nasal
I wrote 4 separate configuration csv files using the templates I downloaded and following a little reading. It seemed to me that I should be able to process them all as a single csv file, but when I attempted that, cellranger did not react well. It also took a few tries before I got the various reference/library options correct.
Note that once cellranger successfully ran for the samples I moved them to the multi/ directory so that I can compare the outputs to when I simply did the ‘count’ operation.
The following invocations of cellranger all appear to work without any problems. Ideally I would like them to be done in a single run, though.
My attempts so far to use the csv configuration to concatenate multiple vdj libraries have not worked, so I chose to do it the stupid way, which is what I should have just done to begin with. Caveat, it works fine for the gex libraries to do it the way the documentation suggests.
cd preprocessing
for i in R1 R2; do
for j in Control Mock_Mex09 IM_Mex09 IN_Mex09; do
A_file=$(/bin/ls A_${j}_VDJ*_${i}_001.fastq.gz)
B_file=$(/bin/ls B_${j}_VDJ*_${i}_001.fastq.gz)
out_file="Concat_${j}_VDJ_${i}.fastq.gz"
cp_cmd="cp ${A_file} ${out_file}"
echo "Running: ${cp_cmd}."
eval $cp_cmd
cat_cmd="cat ${B_file} >> ${out_file}"
echo "Running: ${cat_cmd}."
eval $cat_cmd
done
donemodule add cellranger
cellranger multi --id control --csv sample_sheets/multi_config_try05_control.csv
cellranger multi --id mock --csv sample_sheets/multi_config_try05_mock.csv
cellranger multi --id m --csv sample_sheets/multi_config_try05_m.csv
cellranger multi --id n --csv sample_sheets/multi_config_try05_n.csv
mv control mock m n 01multi_combined/annotations <- load_biomart_annotations()$annotation## The biomart annotations file already exists, loading from it.brief <- unique(annotations[, c("hgnc_symbol", "description")])prefix <- "multi"
# prefix <- "01multi_combined"Here is a snippet of code copy-pasted from:
which, if I read it correctly, will read in the vdj output and add it as a set of annotations to the seurat object. Note, I made some changes to it because I simply cannot help myself.
I think some variant of this function might be useful for the hpgltools if I think I will use it again…
add_clonotype <- function(seurat_obj, name="control", type="t", prefix=prefix) {
vdj_directory <- glue::glue("{prefix}/{name}/outs/per_sample_outs/{name}/vdj_t")
vdj_csv <- glue::glue("{vdj_directory}/filtered_contig_annotations.csv")
reference_csv <- glue::glue("{vdj_directory}/clonotypes.csv")
tcr <- readr::read_csv(vdj_csv, show_col_types = FALSE)
ref <- readr::read_csv(reference_csv, show_col_types = FALSE)
tcr_duplicate_barcode_idx <- duplicated(tcr[["barcode"]])
tcr_nodup <- tcr[!tcr_duplicate_barcode_idx, ]
both <- as.data.frame(merge(tcr_nodup, ref, by.x="raw_clonotype_id", by.y="clonotype_id"))
rownames(both) <- both[["barcode"]]
both[["barcode"]] <- NULL
new <- Seurat::AddMetaData(object=seurat_obj, metadata=both)
return(new)
}The following block is mostly a cut/paste of itself where I set the (over)simplified name of each sample. This then becomes the template for the path and parameters used to read the data, create a seurat object, and add the clonotype data from the vdj run.
name <- "control"
path <- glue::glue("{prefix}/{name}/outs/per_sample_outs/{name}/count/sample_filtered_feature_bc_matrix")
a_control <- Seurat::Read10X(path) %>%
CreateSeuratObject(project = name) %>%
add_clonotype(name = name)## Error in eval(parse(text = text, keep.source = FALSE), envir): promise already under evaluation: recursive default argument reference or earlier problems?name <- "mock"
path <- glue::glue("{prefix}/{name}/outs/per_sample_outs/{name}/count/sample_filtered_feature_bc_matrix")
a_mock <- Seurat::Read10X(path) %>%
CreateSeuratObject(project = name) %>%
add_clonotype(name = name)## Error in eval(parse(text = text, keep.source = FALSE), envir): promise already under evaluation: recursive default argument reference or earlier problems?name <- "m"
path <- glue::glue("{prefix}/{name}/outs/per_sample_outs/{name}/count/sample_filtered_feature_bc_matrix")
a_m <- Seurat::Read10X(path) %>%
CreateSeuratObject(project = name) %>%
add_clonotype(name = name)## Error in eval(parse(text = text, keep.source = FALSE), envir): promise already under evaluation: recursive default argument reference or earlier problems?name <- "n"
path <- glue::glue("{prefix}/{name}/outs/per_sample_outs/{name}/count/sample_filtered_feature_bc_matrix")
a_n <- Seurat::Read10X(path) %>%
CreateSeuratObject(project = name) %>%
add_clonotype(name = name)## Error in eval(parse(text = text, keep.source = FALSE), envir): promise already under evaluation: recursive default argument reference or earlier problems?For the moment I want to be able to play with the individual samples as well as the aggregate so that I can better understand the data. So I guess it works out that I didn’t figure out how to run all the samples at the same time via ‘cellranger multi’.
I am pretty sure Seurat’s merge() overload allows one to just do ‘merge(a,b,c,d,e…)’ but I am not using that.
all <- merge(a_control, a_mock) %>%
merge(a_m) %>%
merge(a_n)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'merge': error in evaluating the argument 'x' in selecting a method for function 'merge': error in evaluating the argument 'x' in selecting a method for function 'merge': object 'a_control' not foundcontrol_clono <- !is.na(a_control[["raw_clonotype_id"]])## Error in eval(expr, envir, enclos): object 'a_control' not foundsummary(control_clono)## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'control_clono' not foundmock_clono <- !is.na(a_mock[["raw_clonotype_id"]])## Error in eval(expr, envir, enclos): object 'a_mock' not foundsummary(mock_clono)## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'mock_clono' not foundm_clono <- !is.na(a_m[["raw_clonotype_id"]])## Error in eval(expr, envir, enclos): object 'a_m' not foundsummary(m_clono)## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'm_clono' not foundn_clono <- !is.na(a_n[["raw_clonotype_id"]])## Error in eval(expr, envir, enclos): object 'a_n' not foundsummary(n_clono)## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'n_clono' not foundI want to take a couple minutes to add some annotations to the seurat object, notably I want to state the identity relationships with some sort of name.
Thus I will make a vector of the the sample IDs and for each one make a category of self/not-self. Note that Seurat comes with a function ‘FindConservedMarkers()’ or something like that which compares each self to all other samples, so this may be redundant; but it is kind of nice to be able to see the categories as a set of binary indexes.
cluster_letters <- as.factor(LETTERS[Idents(object=all)])## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.factor': no applicable method for 'Idents' applied to an object of class "function"names(cluster_letters) <- colnames(x=all)## Error in names(cluster_letters) <- colnames(x = all): object 'cluster_letters' not foundcontrol_ids <- as.character(cluster_letters)## Error in eval(expr, envir, enclos): object 'cluster_letters' not foundmock_ids <- control## Error in eval(expr, envir, enclos): object 'control' not foundm_vaccine_ids <- control## Error in eval(expr, envir, enclos): object 'control' not foundn_vaccine_ids <- control## Error in eval(expr, envir, enclos): object 'control' not foundNow that I have 4 identical vectors, fill them with my chosen names for the samples and whether they do(nt) have that identity.
control_idx <- control == "A"## Error in eval(expr, envir, enclos): object 'control' not foundcontrol[control_idx] <- "Control"## Error in control[control_idx] <- "Control": object 'control' not foundcontrol[!control_idx] <- "Stimulated"## Error in control[!control_idx] <- "Stimulated": object 'control' not foundmock_idx <- mock == "B"## Error in eval(expr, envir, enclos): object 'mock' not foundmock[mock_idx] <- "Mock"## Error in mock[mock_idx] <- "Mock": object 'mock' not foundmock[!mock_idx] <- "Not mock"## Error in mock[!mock_idx] <- "Not mock": object 'mock' not foundm_vaccine_idx <- m_vaccine == "C"## Error in eval(expr, envir, enclos): object 'm_vaccine' not foundm_vaccine[m_vaccine_idx] <- "Muscular"## Error in m_vaccine[m_vaccine_idx] <- "Muscular": object 'm_vaccine' not foundm_vaccine[!m_vaccine_idx] <- "Not Muscular"## Error in m_vaccine[!m_vaccine_idx] <- "Not Muscular": object 'm_vaccine' not foundn_vaccine_idx <- n_vaccine == "D"## Error in eval(expr, envir, enclos): object 'n_vaccine' not foundn_vaccine[n_vaccine_idx] <- "Nasal"## Error in n_vaccine[n_vaccine_idx] <- "Nasal": object 'n_vaccine' not foundn_vaccine[!n_vaccine_idx] <- "Not Nasal"## Error in n_vaccine[!n_vaccine_idx] <- "Not Nasal": object 'n_vaccine' not foundNow add these categories to the sample metadata. I think this is a good place to consdier having a sample sheet from Dr. Park with whatever other random information might prove interesting about the samples.
all <- AddMetaData(
object=all,
metadata=cluster_letters,
col.name="cluster_letters")## Error in UseMethod(generic = "AddMetaData", object = object): no applicable method for 'AddMetaData' applied to an object of class "function"all <- AddMetaData(
object=all,
metadata=control,
col.name="control")## Error in UseMethod(generic = "AddMetaData", object = object): no applicable method for 'AddMetaData' applied to an object of class "function"all <- AddMetaData(
object=all,
metadata=mock,
col.name="mock")## Error in UseMethod(generic = "AddMetaData", object = object): no applicable method for 'AddMetaData' applied to an object of class "function"all <- AddMetaData(
object=all,
metadata=m_vaccine,
col.name="muscular")## Error in UseMethod(generic = "AddMetaData", object = object): no applicable method for 'AddMetaData' applied to an object of class "function"all <- AddMetaData(
object=all,
metadata=n_vaccine,
col.name="nasal")## Error in UseMethod(generic = "AddMetaData", object = object): no applicable method for 'AddMetaData' applied to an object of class "function"Let us start filtering the data, leading off with a definition of the minimum number of RNAs, minimum amount of rRNA, and maximum mitochondrial. In addition, let us print how much of each are observed before filtering. Before we can print/filter these attributes, we must use the PercentageFeatureSet() to get the numbers…
min_num_rna <- 200
min_pct_ribo <- 5
max_pct_mito <- 20
all[["percent_mt"]] <- PercentageFeatureSet(all, pattern="^mt-")## Error in UseMethod(generic = "DefaultAssay", object = object): no applicable method for 'DefaultAssay' applied to an object of class "function"all[["percent_ribo"]] <- PercentageFeatureSet(all, pattern="^Rp[sl]")## Error in UseMethod(generic = "DefaultAssay", object = object): no applicable method for 'DefaultAssay' applied to an object of class "function"Show the state before filtering on a per-cell basis across all samples. Start with the number of cells
sample_summaries <- as_tibble(data.frame(
"id" = c("control", "mock", "muscular", "nasal"),
"start_cells" = c(
sum(all@meta.data[["orig.ident"]] == "control"),
sum(all@meta.data[["orig.ident"]] == "mock"),
sum(all@meta.data[["orig.ident"]] == "m"),
sum(all@meta.data[["orig.ident"]] == "n"))))## Error in as_tibble(data.frame(id = c("control", "mock", "muscular", "nasal"), : could not find function "as_tibble"skim(all[["percent_mt"]])## Error in all[["percent_mt"]]: object of type 'builtin' is not subsettableskim(all[["percent_ribo"]])## Error in all[["percent_ribo"]]: object of type 'builtin' is not subsettableskim(all[["nFeature_RNA"]])## Error in all[["nFeature_RNA"]]: object of type 'builtin' is not subsettableskim(all[["nCount_RNA"]])## Error in all[["nCount_RNA"]]: object of type 'builtin' is not subsettable## Length and reads are for only those cells with clonotypes.
skim(all[["reads"]])## Error in all[["reads"]]: object of type 'builtin' is not subsettableskim(all[["length"]])## Error in all[["length"]]: object of type 'builtin' is not subsettable## How many cells have specific chains associated with them
sum(!is.na(all$chain))## Error in all$chain: object of type 'builtin' is not subsettableAnd on a per-sample basis with (new to me) skimr, which provides a pretty summary of the category of interest. The way I wrote the following stanzas should also append new columns to my sample_summaries table comprised of the mean values for these elements.
add_summaries <- function(sample_summaries, meta,
new_column = "unknown", group_column = "orig.ident",
meta_query = "percent_mt", summary_query = "numeric.mean") {
sample_summaries[[new_column]] <- meta %>%
group_by(!!rlang::sym(group_column)) %>%
skim_tee(meta_query) %>%
skim(meta_query) %>%
dplyr::select(summary_query)
return(sample_summaries)
}sample_summaries <- sample_summaries %>%
add_summaries(all@meta.data, new_column = "start_rna", meta_query = "nFeature_RNA") %>%
add_summaries(all@meta.data, new_column = "nCount_RNA", meta_query = "nCount_RNA") %>%
add_summaries(all@meta.data, new_column = "start_mt", meta_query = "percent_mt") %>%
add_summaries(all@meta.data, new_column = "start_ribo", meta_query = "percent_ribo") %>%
add_summaries(all@meta.data, new_column = "start_clono", meta_query = "reads")## Error in group_by(., !!rlang::sym(group_column)): trying to get slot "meta.data" from an object of a basic class ("function") with no slotssample_summaries## Error in eval(expr, envir, enclos): object 'sample_summaries' not foundOk, that was fun; lets look at this information as a series of plots:
VlnPlot(all, features="nFeature_RNA", pt.size=0)## Error in UseMethod(generic = "DefaultAssay", object = object): no applicable method for 'DefaultAssay' applied to an object of class "function"VlnPlot(all, features="percent_mt", pt.size=0)## Error in UseMethod(generic = "DefaultAssay", object = object): no applicable method for 'DefaultAssay' applied to an object of class "function"VlnPlot(all, features="percent_ribo", pt.size=0)## Error in UseMethod(generic = "DefaultAssay", object = object): no applicable method for 'DefaultAssay' applied to an object of class "function"VlnPlot(all, features="nCount_RNA", pt.size=0)## Error in UseMethod(generic = "DefaultAssay", object = object): no applicable method for 'DefaultAssay' applied to an object of class "function"VlnPlot(all, features="reads", pt.size=0)## Error in UseMethod(generic = "DefaultAssay", object = object): no applicable method for 'DefaultAssay' applied to an object of class "function"## I am curious about the length of the clonotype sequences.
VlnPlot(all, features="length", pt.size=0)## Error in UseMethod(generic = "DefaultAssay", object = object): no applicable method for 'DefaultAssay' applied to an object of class "function"FeatureScatter(all, "percent_ribo", "percent_mt")## Error in UseMethod(generic = "Idents", object = object): no applicable method for 'Idents' applied to an object of class "function"FeatureScatter(all, "nCount_RNA", "nFeature_RNA")## Error in UseMethod(generic = "Idents", object = object): no applicable method for 'Idents' applied to an object of class "function"FeatureScatter(all, "nCount_RNA", "percent_ribo")## Error in UseMethod(generic = "Idents", object = object): no applicable method for 'Idents' applied to an object of class "function"FeatureScatter(all, "nCount_RNA", "percent_mt")## Error in UseMethod(generic = "Idents", object = object): no applicable method for 'Idents' applied to an object of class "function"sufficient_rna_observed <- WhichCells(all, expression = nFeature_RNA >= min_num_rna)## Error in UseMethod(generic = "WhichCells", object = object): no applicable method for 'WhichCells' applied to an object of class "function"filt <- subset(all, cells = sufficient_rna_observed)## Error in subset.default(all, cells = sufficient_rna_observed): argument "subset" is missing, with no defaultsample_summaries[["ncells_filtrna"]] <- c(
sum(filt@meta.data[["orig.ident"]] == "control"),
sum(filt@meta.data[["orig.ident"]] == "mock"),
sum(filt@meta.data[["orig.ident"]] == "m"),
sum(filt@meta.data[["orig.ident"]] == "n"))## Error in eval(expr, envir, enclos): object 'filt' not foundsample_summaries %>%
add_summaries(filt@meta.data, new_column = "filt1_rna", meta_query = "nFeature_RNA") %>%
add_summaries(filt@meta.data, new_column = "filt1_RNA", meta_query = "nCount_RNA") %>%
add_summaries(filt@meta.data, new_column = "filt1_mt", meta_query = "percent_mt") %>%
add_summaries(filt@meta.data, new_column = "filt1_ribo", meta_query = "percent_ribo") %>%
add_summaries(filt@meta.data, new_column = "filt1_clono", meta_query = "reads") ->
sample_summaries## Error in group_by(., !!rlang::sym(group_column)): object 'filt' not foundIn the next I will check that the number of reads/rna across cells is sufficient, that filter does nothing currently, which I think is good.
## I think this filter does nothing in its current form.
sufficiently_observed_idx <- rowSums(filt) > 3## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'rowSums': object 'filt' not foundsummary(sufficiently_observed_idx)## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'sufficiently_observed_idx' not founddim(filt)## Error in eval(expr, envir, enclos): object 'filt' not foundfilt <- subset(filt, features = rownames(filt)[sufficiently_observed_idx])## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'subset': object 'filt' not founddim(filt)## Error in eval(expr, envir, enclos): object 'filt' not found## Keep cells with at least some ribosomal reads
## Note the Percent function above actually puts in a floating point
## number from 0-100, not (as I assumed from 0-1).
high_ribosomal <- WhichCells(filt, expression = percent_ribo >= min_pct_ribo)## Error in WhichCells(filt, expression = percent_ribo >= min_pct_ribo): object 'filt' not foundfilt <- subset(filt, cells = high_ribosomal)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'subset': object 'filt' not foundsample_summaries[["ncells_filtribo"]] <- c(
sum(filt@meta.data[["orig.ident"]] == "control"),
sum(filt@meta.data[["orig.ident"]] == "mock"),
sum(filt@meta.data[["orig.ident"]] == "m"),
sum(filt@meta.data[["orig.ident"]] == "n"))## Error in eval(expr, envir, enclos): object 'filt' not foundsample_summaries %>%
add_summaries(filt@meta.data, new_column = "filt2_rna", meta_query = "nFeature_RNA") %>%
add_summaries(filt@meta.data, new_column = "filt2_RNA", meta_query = "nCount_RNA") %>%
add_summaries(filt@meta.data, new_column = "filt2_mt", meta_query = "percent_mt") %>%
add_summaries(filt@meta.data, new_column = "filt2_ribo", meta_query = "percent_ribo") %>%
add_summaries(filt@meta.data, new_column = "filt2_clono", meta_query = "reads") ->
sample_summaries## Error in group_by(., !!rlang::sym(group_column)): object 'filt' not foundExclude cells with too much mitochondrial RNA
low_mitochondrial <- WhichCells(filt, expression = percent_mt <= max_pct_mito)## Error in WhichCells(filt, expression = percent_mt <= max_pct_mito): object 'filt' not foundfilt <- subset(filt, cells = low_mitochondrial)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'subset': object 'filt' not founddim(filt)## Error in eval(expr, envir, enclos): object 'filt' not foundsample_summaries[["ncells_filtmito"]] <- c(
sum(filt@meta.data[["orig.ident"]] == "control"),
sum(filt@meta.data[["orig.ident"]] == "mock"),
sum(filt@meta.data[["orig.ident"]] == "m"),
sum(filt@meta.data[["orig.ident"]] == "n"))## Error in eval(expr, envir, enclos): object 'filt' not foundsample_summaries %>%
add_summaries(filt@meta.data, new_column = "filt3_rna", meta_query = "nFeature_RNA") %>%
add_summaries(filt@meta.data, new_column = "filt3_RNA", meta_query = "nCount_RNA") %>%
add_summaries(filt@meta.data, new_column = "filt3_mt", meta_query = "percent_mt") %>%
add_summaries(filt@meta.data, new_column = "filt3_ribo", meta_query = "percent_ribo") %>%
add_summaries(filt@meta.data, new_column = "filt3_clono", meta_query = "reads") ->
sample_summaries## Error in group_by(., !!rlang::sym(group_column)): object 'filt' not foundas.matrix(sample_summaries)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.matrix': object 'sample_summaries' not foundall_norm <- NormalizeData(object=all) %>%
FindVariableFeatures() %>%
ScaleData() %>%
RunPCA() %>%
FindNeighbors() %>%
FindClusters() %>%
RunTSNE() %>%
RunUMAP(reduction = "pca", dims = 1:10)## Error in UseMethod(generic = "as.sparse", object = x): no applicable method for 'as.sparse' applied to an object of class "function"DimPlot(object=all_norm, reduction="tsne")## Error in is.data.frame(x): object 'all_norm' not foundplotted <- DimPlot(all_norm, reduction="umap", group.by="cluster_letters", label=TRUE)## Error in is.data.frame(x): object 'all_norm' not foundplotted## Error in eval(expr, envir, enclos): object 'plotted' not foundfilt_norm <- NormalizeData(object=filt) %>%
FindVariableFeatures() %>%
ScaleData() %>%
RunPCA() %>%
FindNeighbors() %>%
FindClusters() %>%
RunTSNE() %>%
RunUMAP(reduction = "pca", dims = 1:10)## Error in NormalizeData(object = filt): object 'filt' not foundDimPlot(object=filt_norm, reduction="tsne")## Error in is.data.frame(x): object 'filt_norm' not foundplotted <- DimPlot(filt_norm, reduction="umap", group.by="cluster_letters", label=TRUE)## Error in is.data.frame(x): object 'filt_norm' not foundplotted## Error in eval(expr, envir, enclos): object 'plotted' not foundfilt_norm <- JackStraw(filt_norm, num.replicate=10)## Error in DefaultAssay(object = object): object 'filt_norm' not foundfilt_norm <- ScoreJackStraw(filt_norm)## Error in ScoreJackStraw(filt_norm): object 'filt_norm' not foundJackStrawPlot(filt_norm)## Error in JS(object = object[[reduction]], slot = "empirical"): object 'filt_norm' not foundElbowPlot(filt_norm)## Error in Stdev(object = object, reduction = reduction): object 'filt_norm' not found## So I am thinking maybe 4-10?
wanted_dims <- 6
filt_norm <- FindNeighbors(filt_norm, dims=1:wanted_dims) %>%
FindClusters(resolution=0.5) %>%
StashIdent(save.name="res0p5_clusters")## Error in FindNeighbors(filt_norm, dims = 1:wanted_dims): object 'filt_norm' not foundRunUMAP(filt_norm, dims=1:9)## Error in RunUMAP(filt_norm, dims = 1:9): object 'filt_norm' not foundDimPlot(filt_norm, label=TRUE)## Error in is(x, "classRepresentation"): object 'filt_norm' not foundfilt_norm <- FindClusters(filt_norm, resolution=0.1) %>%
FindNeighbors(k.param=6) %>%
StashIdent(save.name="res0p1_clusters")## Error in FindClusters(filt_norm, resolution = 0.1): object 'filt_norm' not foundRunUMAP(filt_norm, dims=1:9)## Error in RunUMAP(filt_norm, dims = 1:9): object 'filt_norm' not foundDimPlot(filt_norm, label=TRUE)## Error in is(x, "classRepresentation"): object 'filt_norm' not foundAdd into the metadata a concatenation of the sample ID and the cluster ID
identity_vector <- filt_norm[["orig.ident"]][["orig.ident"]]## Error in eval(expr, envir, enclos): object 'filt_norm' not foundclass(identity_vector)## Error in eval(expr, envir, enclos): object 'identity_vector' not foundcluster_vector <- as.character(filt_norm[["res0p1_clusters"]][["res0p1_clusters"]])## Error in eval(expr, envir, enclos): object 'filt_norm' not foundconcatenated_vector <- paste0(identity_vector, "_", cluster_vector)## Error in paste0(identity_vector, "_", cluster_vector): object 'identity_vector' not foundfilt_norm[["cluster_sample"]] <- concatenated_vector## Error in eval(expr, envir, enclos): object 'concatenated_vector' not foundvar <- FindVariableFeatures(filt_norm)## Error in FindVariableFeatures(filt_norm): object 'filt_norm' not foundmost_var <- head(VariableFeatures(var), 30)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': no applicable method for 'VariableFeatures' applied to an object of class "function"variable_plot <- VariableFeaturePlot(var)## Error in UseMethod(generic = "HVFInfo", object = object): no applicable method for 'HVFInfo' applied to an object of class "function"variable_plot <- LabelPoints(plot=variable_plot, points=most_var, repel=TRUE)## Error in lapply(X = X, FUN = FUN, ...): object 'variable_plot' not foundvariable_plot## Error in eval(expr, envir, enclos): object 'variable_plot' not foundQuestion: Is it smart enough to use the raw data if I give FindAllMarkers the normalized data? For the moment I do not think I will risk it.
combined_markers <- FindAllMarkers(filt, only.pos=TRUE, logfc.threshold = 0.5)## Error in Idents(object = object): object 'filt' not foundhead(combined_markers)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'combined_markers' not foundbrief <- unique(annotations[, c("hgnc_symbol", "description")])
combined <- as.data.frame(combined_markers)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'combined_markers' not foundrownames(combined) <- toupper(rownames(combined))## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'toupper': error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'combined' not foundannotated_markers <- merge(combined, brief, by.x="row.names", by.y="hgnc_symbol",
all.x=TRUE)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'merge': object 'combined' not foundcombined_markers <- FindAllMarkers(filt, only.pos=TRUE, logfc.threshold=0.5)## Error in Idents(object = object): object 'filt' not foundcombined <- as.data.frame(combined_markers)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'combined_markers' not foundrownames(combined) <- toupper(rownames(combined))## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'toupper': error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'combined' not foundannotated_markers <- merge(combined, brief, by.x="row.names", by.y="hgnc_symbol",
all.x=TRUE)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'merge': object 'combined' not foundhead(annotated_markers)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'annotated_markers' not foundSince I am not using the filt_norm data structure, I will need to pull the cluster information from the normalized copy…
clusters <- filt## Error in eval(expr, envir, enclos): object 'filt' not foundIdents(clusters) <- filt_norm[["res0p1_clusters"]]## Error in eval(expr, envir, enclos): object 'filt_norm' not foundcluster_markers <- FindAllMarkers(clusters, only.pos=TRUE, logfc.threshold=0.5)## Error in Idents(object = object): object 'clusters' not foundclusters <- as.data.frame(cluster_markers)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'cluster_markers' not foundrownames(clusters) <- toupper(rownames(clusters))## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'toupper': error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'clusters' not foundannotated_clusters <- merge(clusters, brief, by.x="row.names", by.y="hgnc_symbol",
all.x=TRUE)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'merge': object 'clusters' not foundhead(annotated_clusters)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'annotated_clusters' not foundannotated_clusters %>%
group_by(cluster) %>%
dplyr::top_n(n=10, wt=avg_log2FC) %>%
as.data.frame()## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'annotated_clusters' not foundsum(filt_norm[["res0p1_clusters"]] == "0")## Error in eval(expr, envir, enclos): object 'filt_norm' not foundsum(filt_norm[["res0p1_clusters"]] == "0" &
!is.na(filt_norm[["raw_clonotype_id"]]))## Error in eval(expr, envir, enclos): object 'filt_norm' not foundsum(filt_norm[["res0p1_clusters"]] == "1")## Error in eval(expr, envir, enclos): object 'filt_norm' not foundsum(filt_norm[["res0p1_clusters"]] == "1" &
!is.na(filt_norm[["raw_clonotype_id"]]))## Error in eval(expr, envir, enclos): object 'filt_norm' not foundsum(filt_norm[["res0p1_clusters"]] == "2")## Error in eval(expr, envir, enclos): object 'filt_norm' not foundsum(filt_norm[["res0p1_clusters"]] == "2" &
!is.na(filt_norm[["raw_clonotype_id"]]))## Error in eval(expr, envir, enclos): object 'filt_norm' not foundsum(filt_norm[["res0p1_clusters"]] == "3")## Error in eval(expr, envir, enclos): object 'filt_norm' not foundsum(filt_norm[["res0p1_clusters"]] == "3" &
!is.na(filt_norm[["raw_clonotype_id"]]))## Error in eval(expr, envir, enclos): object 'filt_norm' not foundsum(filt_norm[["res0p1_clusters"]] == "4")## Error in eval(expr, envir, enclos): object 'filt_norm' not foundsum(filt_norm[["res0p1_clusters"]] == "4" &
!is.na(filt_norm[["raw_clonotype_id"]]))## Error in eval(expr, envir, enclos): object 'filt_norm' not foundsum(filt_norm[["res0p1_clusters"]] == "5")## Error in eval(expr, envir, enclos): object 'filt_norm' not foundsum(filt_norm[["res0p1_clusters"]] == "5" &
!is.na(filt_norm[["raw_clonotype_id"]]))## Error in eval(expr, envir, enclos): object 'filt_norm' not foundsum(filt_norm[["res0p1_clusters"]] == "6")## Error in eval(expr, envir, enclos): object 'filt_norm' not foundsum(filt_norm[["res0p1_clusters"]] == "6" &
!is.na(filt_norm[["raw_clonotype_id"]]))## Error in eval(expr, envir, enclos): object 'filt_norm' not foundClusters 0 and 5 have a great majority of the clonotypes. 0 has something like 90%, 5 has ~ 30%, the others ~ 10%
controln_0 <- FindMarkers(
filt, group.by="cluster_sample",
ident.1="control_0", ident.2="n_0") %>%
as.data.frame()## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'filt' not foundhead(controln_0)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'controln_0' not foundrownames(controln_0) <- toupper(rownames(controln_0))## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'toupper': error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'controln_0' not foundcontroln_0 <- merge(controln_0, brief, by="row.names", by.y="hgnc_symbol",
all.x=TRUE)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'merge': object 'controln_0' not foundannotated_clusters %>%
group_by(cluster) %>%
dplyr::top_n(n=10, wt=avg_log2FC) %>%
as.data.frame()## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'annotated_clusters' not foundmockvsn_0 <- FindMarkers(
filt_norm, group.by="cluster_sample",
ident.1="n_0", ident.2="mock_0") %>%
as.data.frame()## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'filt_norm' not foundhead(mockvsn_0)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'mockvsn_0' not foundrownames(mockvsn_0) <- toupper(rownames(mockvsn_0))## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'toupper': error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'mockvsn_0' not foundmockvsn_0 <- merge(mockvsn_0, brief, by="row.names", by.y="hgnc_symbol",
all.x=TRUE)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'merge': object 'mockvsn_0' not foundmockvsm_0 <- FindMarkers(
filt_norm, group.by="cluster_sample",
ident.1="m_0", ident.2="mock_0") %>%
as.data.frame()## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'filt_norm' not foundhead(mockvsm_0)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'mockvsm_0' not foundrownames(mockvsm_0) <- toupper(rownames(mockvsm_0))## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'toupper': error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'mockvsm_0' not foundmockvsm_0 <- merge(mockvsm_0, brief, by="row.names", by.y="hgnc_symbol",
all.x=TRUE)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'merge': object 'mockvsm_0' not foundhead(mockvsm_0, n=30)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'mockvsm_0' not foundThis function makes no sense.
DefaultAssay(filt_norm) <- "RNA"## Error in DefaultAssay(filt_norm) <- "RNA": object 'filt_norm' not foundconserved_markers <- FindConservedMarkers(
filt_norm, ident.1=c(0, 1), ident.2=c(2,3,4),
grouping.var="sample", only.pos=TRUE,
verbose=TRUE)## Error in FetchData(object = object, vars = grouping.var): object 'filt_norm' not foundmock_vs_control <- FindMarkers(filt_norm, group.by = "orig.ident",
ident.1 = "mock",
ident.2 = "control")## Error in FindMarkers(filt_norm, group.by = "orig.ident", ident.1 = "mock", : object 'filt_norm' not foundhead(mock_vs_control)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'mock_vs_control' not foundmuscular_vs_mock <- FindMarkers(filt_norm, group.by = "orig.ident",
ident.1 = "m",
ident.2 = "mock")## Error in FindMarkers(filt_norm, group.by = "orig.ident", ident.1 = "m", : object 'filt_norm' not foundsummary(muscular_vs_mock)## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'muscular_vs_mock' not foundnasal_vs_mock <- FindMarkers(filt, group.by = "orig.ident",
min.pct = 0.25, ident.1 = "n",
ident.2 = "mock")## Error in FindMarkers(filt, group.by = "orig.ident", min.pct = 0.25, ident.1 = "n", : object 'filt' not foundsummary(nasal_vs_mock)## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nasal_vs_mock' not foundFeaturePlot(filt, features=c("Rgcc"),
split.by="orig.ident", max.cutoff=3, cols=c("darkgreen", "darkred"))## Error in is(x, "classRepresentation"): object 'filt' not foundThis is a neat idea, I think we can repurpose it to immunology gene sets.
filt <- CellCycleScoring(
object = filt_norm,
g2m.features = cc.genes$g2m.genes,
s.features = cc.genes$s.genes)## Error in DefaultAssay(object = object): object 'filt_norm' not foundVlnPlot(filt, features = c("S.Score", "G2M.Score"),
group.by = "orig.ident",
ncol = 4, pt.size = 0)## Error in DefaultAssay(object = object): object 'filt' not foundHaving written the following I realized I used an older version of my mSigDB reference… FIXME: Redo it with the 7.5+ data.
broad_types <- load_gmt_signatures(signatures = "reference/m8.all.v2022.1.Mm.symbols.gmt")
broad_list <- list()
for (i in names(broad_types)) {
broad_list[[i]] <- geneIds(broad_types[[i]])
}
wtf <- AddModuleScore(object = filt_norm, features = broad_list,
name = "m8")## Error in DefaultAssay(object = object): object 'filt_norm' not foundchosen <- c(3, 9, 11, 36, 43, 42, 14)
names(broad_types)[chosen]## [1] "DESCARTES_ORGANOGENESIS_HEPATOCYTES"
## [2] "DESCARTES_ORGANOGENESIS_WHITE_BLOOD_CELLS"
## [3] "DESCARTES_ORGANOGENESIS_EPITHELIAL_CELLS"
## [4] "DESCARTES_ORGANOGENESIS_NUETROPHILS"
## [5] "TABULA_MURIS_SENIS_BROWN_ADIPOSE_TISSUE_T_CELL_AGEING"
## [6] "TABULA_MURIS_SENIS_BROWN_ADIPOSE_TISSUE_B_CELL_AGEING"
## [7] "DESCARTES_ORGANOGENESIS_JAW_AND_TOOTH_PROGENITORS"columns <- paste0("m8", chosen)
VlnPlot(wtf, features = columns,
group.by = "res0p1_clusters", same.y.lims = TRUE,
ncol = 4, pt.size = 0)## Error in DefaultAssay(object = object): object 'wtf' not foundchosen <- c(50, 51, 60, 61, 62, 65, 66)
names(broad_types)[chosen]## [1] "TABULA_MURIS_SENIS_BLADDER_LEUKOCYTE_AGEING"
## [2] "TABULA_MURIS_SENIS_BRAIN_MYELOID_MACROPHAGE_AGEING"
## [3] "TABULA_MURIS_SENIS_DIAPHRAGM_B_CELL_AGEING"
## [4] "TABULA_MURIS_SENIS_DIAPHRAGM_ENDOTHELIAL_CELL_AGEING"
## [5] "TABULA_MURIS_SENIS_DIAPHRAGM_MACROPHAGE_AGEING"
## [6] "TABULA_MURIS_SENIS_GONADAL_ADIPOSE_TISSUE_B_CELL_AGEING"
## [7] "TABULA_MURIS_SENIS_GONADAL_ADIPOSE_TISSUE_T_CELL_AGEING"columns <- paste0("m8", chosen)
VlnPlot(wtf, features = columns,
group.by = "res0p1_clusters", same.y.lims = TRUE,
ncol = 4, pt.size = 0)## Error in DefaultAssay(object = object): object 'wtf' not foundchosen <- c(115, 118, 119, 120, 121, 125, 128)
names(broad_types)[chosen]## [1] "TABULA_MURIS_SENIS_LIVER_MATURE_NK_T_CELL_AGEING"
## [2] "TABULA_MURIS_SENIS_LUNG_CD4_POSITIVE_ALPHA_BETA_T_CELL_AGEING"
## [3] "TABULA_MURIS_SENIS_LUNG_CD8_POSITIVE_ALPHA_BETA_T_CELL_AGEING"
## [4] "TABULA_MURIS_SENIS_LUNG_NK_CELL_AGEING"
## [5] "TABULA_MURIS_SENIS_LUNG_T_CELL_AGEING"
## [6] "TABULA_MURIS_SENIS_LUNG_CLASSICAL_MONOCYTE_AGEING"
## [7] "TABULA_MURIS_SENIS_LUNG_INTERMEDIATE_MONOCYTE_AGEING"columns <- paste0("m8", chosen)
VlnPlot(wtf, features = columns,
group.by = "res0p1_clusters", same.y.lims = TRUE,
ncol = 4, pt.size = 0)## Error in DefaultAssay(object = object): object 'wtf' not foundchosen <- c(212, 211, 210, 209)
names(broad_types)[chosen]## [1] "TABULA_MURIS_SENIS_TRACHEA_GRANULOCYTE_AGEING"
## [2] "TABULA_MURIS_SENIS_TRACHEA_FIBROBLAST_AGEING"
## [3] "TABULA_MURIS_SENIS_TRACHEA_ENDOTHELIAL_CELL_AGEING"
## [4] "TABULA_MURIS_SENIS_TRACHEA_BASAL_EPITHELIAL_CELL_OF_TRACHEOBRONCHIAL_TREE_AGEING"columns <- paste0("m8", chosen)
VlnPlot(wtf, features = columns,
group.by = "res0p1_clusters", same.y.lims = TRUE,
ncol = 4, pt.size = 0)## Error in DefaultAssay(object = object): object 'wtf' not foundchosen <- c(176:182)
names(broad_types)[chosen]## [1] "TABULA_MURIS_SENIS_PANCREAS_PANCREATIC_DELTA_CELL_AGEING"
## [2] "TABULA_MURIS_SENIS_PANCREAS_PANCREATIC_POLYPEPTIDE_CELL_AGEING"
## [3] "TABULA_MURIS_SENIS_PANCREAS_PANCREATIC_ACINAR_CELL_AGEING"
## [4] "TABULA_MURIS_SENIS_PANCREAS_PANCREATIC_DUCTAL_CELL_AGEING"
## [5] "TABULA_MURIS_SENIS_PANCREAS_PANCREATIC_STELLATE_CELL_AGEING"
## [6] "TABULA_MURIS_SENIS_SUBCUTANEOUS_ADIPOSE_TISSUE_B_CELL_AGEING"
## [7] "TABULA_MURIS_SENIS_SUBCUTANEOUS_ADIPOSE_TISSUE_ENDOTHELIAL_CELL_AGEING"columns <- paste0("m8", chosen)
VlnPlot(wtf, features = columns,
group.by = "res0p1_clusters", same.y.lims = TRUE,
ncol = 4, pt.size = 0)## Error in DefaultAssay(object = object): object 'wtf' not foundchosen <- c(42:47)
names(broad_types)[chosen]## [1] "TABULA_MURIS_SENIS_BROWN_ADIPOSE_TISSUE_B_CELL_AGEING"
## [2] "TABULA_MURIS_SENIS_BROWN_ADIPOSE_TISSUE_T_CELL_AGEING"
## [3] "TABULA_MURIS_SENIS_BROWN_ADIPOSE_TISSUE_ENDOTHELIAL_CELL_AGEING"
## [4] "TABULA_MURIS_SENIS_BROWN_ADIPOSE_TISSUE_MESENCHYMAL_STEM_CELL_OF_ADIPOSE_AGEING"
## [5] "TABULA_MURIS_SENIS_BROWN_ADIPOSE_TISSUE_MYELOID_CELL_AGEING"
## [6] "TABULA_MURIS_SENIS_BLADDER_BLADDER_CELL_AGEING"columns <- paste0("m8", chosen)
VlnPlot(wtf, features = columns,
group.by = "res0p1_clusters", same.y.lims = TRUE,
ncol = 4, pt.size = 0)## Error in DefaultAssay(object = object): object 'wtf' not foundt_groups_idx <- grepl(pattern="_T_CELL", x=names(broad_types))
t_groups <- names(broad_types)[t_groups_idx]
t_nums <- which(t_groups_idx, broad_types)
columns <- paste0("m8", t_nums)
VlnPlot(wtf, features = columns,
group.by = "res0p1_clusters", same.y.lims = TRUE,
ncol = 4, pt.size = 0)## Error in DefaultAssay(object = object): object 'wtf' not foundt_groups## [1] "TABULA_MURIS_SENIS_BROWN_ADIPOSE_TISSUE_T_CELL_AGEING"
## [2] "TABULA_MURIS_SENIS_GONADAL_ADIPOSE_TISSUE_T_CELL_AGEING"
## [3] "TABULA_MURIS_SENIS_HEART_T_CELL_AGEING"
## [4] "TABULA_MURIS_SENIS_KIDNEY_T_CELL_AGEING"
## [5] "TABULA_MURIS_SENIS_LIMB_MUSCLE_T_CELL_AGEING"
## [6] "TABULA_MURIS_SENIS_LIVER_MATURE_NK_T_CELL_AGEING"
## [7] "TABULA_MURIS_SENIS_LUNG_CD4_POSITIVE_ALPHA_BETA_T_CELL_AGEING"
## [8] "TABULA_MURIS_SENIS_LUNG_CD8_POSITIVE_ALPHA_BETA_T_CELL_AGEING"
## [9] "TABULA_MURIS_SENIS_LUNG_T_CELL_AGEING"
## [10] "TABULA_MURIS_SENIS_LUNG_MATURE_NK_T_CELL_AGEING"
## [11] "TABULA_MURIS_SENIS_MESENTERIC_ADIPOSE_TISSUE_CD4_POSITIVE_ALPHA_BETA_T_CELL_AGEING"
## [12] "TABULA_MURIS_SENIS_MESENTERIC_ADIPOSE_TISSUE_CD8_POSITIVE_ALPHA_BETA_T_CELL_AGEING"
## [13] "TABULA_MURIS_SENIS_MAMMARY_GLAND_T_CELL_AGEING"
## [14] "TABULA_MURIS_SENIS_MARROW_CD4_POSITIVE_ALPHA_BETA_T_CELL_AGEING"
## [15] "TABULA_MURIS_SENIS_MARROW_MATURE_ALPHA_BETA_T_CELL_AGEING"
## [16] "TABULA_MURIS_SENIS_MARROW_NAIVE_T_CELL_AGEING"
## [17] "TABULA_MURIS_SENIS_SPLEEN_CD4_POSITIVE_ALPHA_BETA_T_CELL_AGEING"
## [18] "TABULA_MURIS_SENIS_SPLEEN_CD8_POSITIVE_ALPHA_BETA_T_CELL_AGEING"
## [19] "TABULA_MURIS_SENIS_SPLEEN_T_CELL_AGEING"
## [20] "TABULA_MURIS_SENIS_SPLEEN_MATURE_NK_T_CELL_AGEING"
## [21] "TABULA_MURIS_SENIS_THYMUS_IMMATURE_T_CELL_AGEING"
## [22] "TABULA_MURIS_SENIS_TRACHEA_T_CELL_AGEING"t_groups_idx <- grepl(pattern="_EPITHELIAL_", x=names(broad_types))
t_groups <- names(broad_types)[t_groups_idx]
t_nums <- which(t_groups_idx, broad_types)
columns <- paste0("m8", t_nums)
VlnPlot(wtf, features = columns,
group.by = "res0p1_clusters", same.y.lims = TRUE,
ncol = 4, pt.size = 0)## Error in DefaultAssay(object = object): object 'wtf' not foundt_groups## [1] "DESCARTES_ORGANOGENESIS_EPITHELIAL_CELLS"
## [2] "TABULA_MURIS_SENIS_KIDNEY_EPITHELIAL_CELL_OF_PROXIMAL_TUBULE_AGEING"
## [3] "TABULA_MURIS_SENIS_KIDNEY_KIDNEY_COLLECTING_DUCT_EPITHELIAL_CELL_AGEING"
## [4] "TABULA_MURIS_SENIS_KIDNEY_KIDNEY_DISTAL_CONVOLUTED_TUBULE_EPITHELIAL_CELL_AGEING"
## [5] "TABULA_MURIS_SENIS_KIDNEY_KIDNEY_LOOP_OF_HENLE_ASCENDING_LIMB_EPITHELIAL_CELL_AGEING"
## [6] "TABULA_MURIS_SENIS_KIDNEY_KIDNEY_LOOP_OF_HENLE_THICK_ASCENDING_LIMB_EPITHELIAL_CELL_AGEING"
## [7] "TABULA_MURIS_SENIS_KIDNEY_KIDNEY_PROXIMAL_CONVOLUTED_TUBULE_EPITHELIAL_CELL_AGEING"
## [8] "TABULA_MURIS_SENIS_MAMMARY_GLAND_LUMINAL_EPITHELIAL_CELL_OF_MAMMARY_GLAND_AGEING"
## [9] "TABULA_MURIS_SENIS_SUBCUTANEOUS_ADIPOSE_TISSUE_EPITHELIAL_CELL_AGEING"
## [10] "TABULA_MURIS_SENIS_TRACHEA_BASAL_EPITHELIAL_CELL_OF_TRACHEOBRONCHIAL_TREE_AGEING"t_groups_idx <- grepl(pattern="_ENDOTHELIAL_", x=names(broad_types))
t_groups <- names(broad_types)[t_groups_idx]
t_nums <- which(t_groups_idx, broad_types)
columns <- paste0("m8", t_nums)
VlnPlot(wtf, features = columns,
group.by = "res0p1_clusters", same.y.lims = TRUE,
ncol = 4, pt.size = 0)## Error in DefaultAssay(object = object): object 'wtf' not foundt_groups## [1] "DESCARTES_ORGANOGENESIS_ENDOTHELIAL_CELLS"
## [2] "TABULA_MURIS_SENIS_AORTA_AORTIC_ENDOTHELIAL_CELL_AGEING"
## [3] "TABULA_MURIS_SENIS_BROWN_ADIPOSE_TISSUE_ENDOTHELIAL_CELL_AGEING"
## [4] "TABULA_MURIS_SENIS_BLADDER_ENDOTHELIAL_CELL_AGEING"
## [5] "TABULA_MURIS_SENIS_BRAIN_NON_MYELOID_ENDOTHELIAL_CELL_AGEING"
## [6] "TABULA_MURIS_SENIS_DIAPHRAGM_ENDOTHELIAL_CELL_AGEING"
## [7] "TABULA_MURIS_SENIS_GONADAL_ADIPOSE_TISSUE_ENDOTHELIAL_CELL_AGEING"
## [8] "TABULA_MURIS_SENIS_HEART_ENDOTHELIAL_CELL_OF_CORONARY_ARTERY_AGEING"
## [9] "TABULA_MURIS_SENIS_HEART_AND_AORTA_ENDOTHELIAL_CELL_OF_CORONARY_ARTERY_AGEING"
## [10] "TABULA_MURIS_SENIS_LIMB_MUSCLE_ENDOTHELIAL_CELL_AGEING"
## [11] "TABULA_MURIS_SENIS_LIVER_ENDOTHELIAL_CELL_OF_HEPATIC_SINUSOID_AGEING"
## [12] "TABULA_MURIS_SENIS_LUNG_ENDOTHELIAL_CELL_OF_LYMPHATIC_VESSEL_AGEING"
## [13] "TABULA_MURIS_SENIS_LUNG_VEIN_ENDOTHELIAL_CELL_AGEING"
## [14] "TABULA_MURIS_SENIS_MESENTERIC_ADIPOSE_TISSUE_ENDOTHELIAL_CELL_AGEING"
## [15] "TABULA_MURIS_SENIS_MAMMARY_GLAND_ENDOTHELIAL_CELL_AGEING"
## [16] "TABULA_MURIS_SENIS_PANCREAS_ENDOTHELIAL_CELL_AGEING"
## [17] "TABULA_MURIS_SENIS_SUBCUTANEOUS_ADIPOSE_TISSUE_ENDOTHELIAL_CELL_AGEING"
## [18] "TABULA_MURIS_SENIS_TRACHEA_ENDOTHELIAL_CELL_AGEING"pander::pander(sessionInfo())R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
locale: LC_CTYPE=en_US.UTF-8, LC_NUMERIC=C, LC_TIME=en_US.UTF-8, LC_COLLATE=en_US.UTF-8, LC_MONETARY=en_US.UTF-8, LC_MESSAGES=en_US.UTF-8, LC_PAPER=en_US.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.UTF-8 and LC_IDENTIFICATION=C
attached base packages: stats4, stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: GSEABase(v.1.60.0), graph(v.1.76.0), annotate(v.1.76.0), XML(v.3.99-0.13), AnnotationDbi(v.1.60.0), skimr(v.2.1.5), purrr(v.1.0.0), ggplot2(v.3.4.0), SeuratObject(v.4.1.3), Seurat(v.4.3.0), hpgltools(v.1.0), testthat(v.3.1.6), reticulate(v.1.26), SummarizedExperiment(v.1.28.0), GenomicRanges(v.1.50.2), GenomeInfoDb(v.1.34.6), IRanges(v.2.32.0), S4Vectors(v.0.36.1), MatrixGenerics(v.1.10.0), matrixStats(v.0.63.0), Biobase(v.2.58.0) and BiocGenerics(v.0.44.0)
loaded via a namespace (and not attached): ica(v.1.0-3), ps(v.1.7.2), Rsamtools(v.2.14.0), foreach(v.1.5.2), lmtest(v.0.9-40), rprojroot(v.2.0.3), crayon(v.1.5.2), rbibutils(v.2.2.11), MASS(v.7.3-58.1), nlme(v.3.1-161), backports(v.1.4.1), sva(v.3.46.0), GOSemSim(v.2.24.0), rlang(v.1.0.6), XVector(v.0.38.0), HDO.db(v.0.99.1), ROCR(v.1.0-11), irlba(v.2.3.5.1), nloptr(v.2.0.3), callr(v.3.7.3), limma(v.3.54.0), filelock(v.1.0.2), BiocParallel(v.1.32.5), rjson(v.0.2.21), bit64(v.4.0.5), glue(v.1.6.2), sctransform(v.0.3.5), pbkrtest(v.0.5.1), parallel(v.4.2.0), processx(v.3.8.0), spatstat.sparse(v.3.0-0), DOSE(v.3.24.2), spatstat.geom(v.3.0-3), tidyselect(v.1.2.0), usethis(v.2.1.6), fitdistrplus(v.1.1-8), variancePartition(v.1.28.0), tidyr(v.1.2.1), zoo(v.1.8-11), qqconf(v.1.3.1), GenomicAlignments(v.1.34.0), xtable(v.1.8-4), magrittr(v.2.0.3), evaluate(v.0.19), Rdpack(v.2.4), cli(v.3.5.0), zlibbioc(v.1.44.0), sn(v.2.1.0), rstudioapi(v.0.14), miniUI(v.0.1.1.1), sp(v.1.5-1), bslib(v.0.4.2), mathjaxr(v.1.6-0), fastmatch(v.1.1-3), aod(v.1.3.2), treeio(v.1.22.0), shiny(v.1.7.4), xfun(v.0.36), multtest(v.2.54.0), pkgbuild(v.1.4.0), gson(v.0.0.9), cluster(v.2.1.4), caTools(v.1.18.2), tidygraph(v.1.2.2), KEGGREST(v.1.38.0), tibble(v.3.1.8), ggrepel(v.0.9.2), ape(v.5.6-2), listenv(v.0.9.0), Biostrings(v.2.66.0), png(v.0.1-8), future(v.1.30.0), withr(v.2.5.0), bitops(v.1.0-7), ggforce(v.0.4.1), plyr(v.1.8.8), pillar(v.1.8.1), gplots(v.3.1.3), cachem(v.1.0.6), GenomicFeatures(v.1.50.3), multcomp(v.1.4-20), fs(v.1.5.2), clusterProfiler(v.4.6.0), vctrs(v.0.5.1), ellipsis(v.0.3.2), generics(v.0.1.3), devtools(v.2.4.5), metap(v.1.8), tools(v.4.2.0), munsell(v.0.5.0), tweenr(v.2.0.2), fgsea(v.1.24.0), DelayedArray(v.0.24.0), fastmap(v.1.1.0), compiler(v.4.2.0), pkgload(v.1.3.2), abind(v.1.4-5), httpuv(v.1.6.7), rtracklayer(v.1.58.0), sessioninfo(v.1.2.2), plotly(v.4.10.1), GenomeInfoDbData(v.1.2.9), gridExtra(v.2.3), edgeR(v.3.40.1), lattice(v.0.20-45), deldir(v.1.0-6), mutoss(v.0.1-12), utf8(v.1.2.2), later(v.1.3.0), dplyr(v.1.0.10), BiocFileCache(v.2.6.0), jsonlite(v.1.8.4), scales(v.1.2.1), tidytree(v.0.4.2), pbapply(v.1.6-0), genefilter(v.1.80.2), lazyeval(v.0.2.2), promises(v.1.2.0.1), doParallel(v.1.0.17), goftest(v.1.2-3), spatstat.utils(v.3.0-1), sandwich(v.3.0-2), rmarkdown(v.2.19), cowplot(v.1.1.1), Rtsne(v.0.16), pander(v.0.6.5), downloader(v.0.4), uwot(v.0.1.14), igraph(v.1.3.5), plotrix(v.3.8-2), survival(v.3.4-0), numDeriv(v.2016.8-1.1), yaml(v.2.3.6), htmltools(v.0.5.4), memoise(v.2.0.1), profvis(v.0.3.7), BiocIO(v.1.8.0), locfit(v.1.5-9.7), graphlayouts(v.0.8.4), viridisLite(v.0.4.1), digest(v.0.6.31), assertthat(v.0.2.1), RhpcBLASctl(v.0.21-247.1), mime(v.0.12), rappdirs(v.0.3.3), repr(v.1.1.4), RSQLite(v.2.2.20), yulab.utils(v.0.0.6), future.apply(v.1.10.0), remotes(v.2.4.2), data.table(v.1.14.6), urlchecker(v.1.0.1), blob(v.1.2.3), splines(v.4.2.0), RCurl(v.1.98-1.9), broom(v.1.0.2), hms(v.1.1.2), colorspace(v.2.0-3), base64enc(v.0.1-3), mnormt(v.2.1.1), aplot(v.0.1.9), sass(v.0.4.4), Rcpp(v.1.0.9), RANN(v.2.6.1), mvtnorm(v.1.1-3), enrichplot(v.1.18.3), fansi(v.1.0.3), brio(v.1.1.3), parallelly(v.1.33.0), R6(v.2.5.1), grid(v.4.2.0), ggridges(v.0.5.4), lifecycle(v.1.0.3), TFisher(v.0.2.0), curl(v.4.3.3), minqa(v.1.2.5), leiden(v.0.4.3), jquerylib(v.0.1.4), PROPER(v.1.30.0), Matrix(v.1.5-3), qvalue(v.2.30.0), TH.data(v.1.1-1), desc(v.1.4.2), RcppAnnoy(v.0.0.20), RColorBrewer(v.1.1-3), iterators(v.1.0.14), spatstat.explore(v.3.0-5), stringr(v.1.5.0), htmlwidgets(v.1.6.0), polyclip(v.1.10-4), biomaRt(v.2.54.0), shadowtext(v.0.1.2), gridGraphics(v.0.5-1), mgcv(v.1.8-41), globals(v.0.16.2), patchwork(v.1.1.2), spatstat.random(v.3.0-1), progressr(v.0.12.0), codetools(v.0.2-18), GO.db(v.3.16.0), gtools(v.3.9.4), prettyunits(v.1.1.1), dbplyr(v.2.2.1), gtable(v.0.3.1), DBI(v.1.1.3), ggfun(v.0.0.9), tensor(v.1.5), httr(v.1.4.4), KernSmooth(v.2.23-20), stringi(v.1.7.8), progress(v.1.2.2), reshape2(v.1.4.4), farver(v.2.1.1), viridis(v.0.6.2), ggtree(v.3.6.2), xml2(v.1.3.3), boot(v.1.3-28.1), lme4(v.1.1-31), restfulr(v.0.0.15), ggplotify(v.0.1.0), scattermore(v.0.8), bit(v.4.0.5), scatterpie(v.0.1.8), spatstat.data(v.3.0-0), ggraph(v.2.1.0), pkgconfig(v.2.0.3) and knitr(v.1.41)
message(paste0("This is hpgltools commit: ", get_git_commit()))## If you wish to reproduce this exact build of hpgltools, invoke the following:## > git clone http://github.com/abelew/hpgltools.git## > git reset 911e7d4beebdc73267ec4be631a305574289efd3## This is hpgltools commit: Tue Jan 17 10:36:44 2023 -0500: 911e7d4beebdc73267ec4be631a305574289efd3this_save <- paste0(gsub(pattern="\\.Rmd", replace="", x=rmd_file), "-v", ver, ".rda.xz")
##message(paste0("Saving to ", this_save))
##tmp <- sm(saveme(filename=this_save))