1 Notes:
1.1 Querying Dr. Mosser 20230123
- Question from Najib: Connection between 5’ libraries and VDJ, tags are in fact shared from VDJ/surface to the ‘parent’ GEM. April: Do GEM generation, then split the resulting cDNA at cleanup step to the protein barcode set (at bead cleanup), then go into regular 5’ expression library, from which a set of specific primers are used to enrich the VDJ portion. Thus all three have the same parental cell tag.
- Question from April: Do we need to change the inputs; from how many cells did we recover reads? He wants 10,000 cells / animal. April pooled according to 10x recommendations: 1:4 VDJ:expression library. Surface protein libraries have not yet been run, these have separate indexes and are much shorter; in contrast the VDJ/expression libraries are relatively similar (550 vs 630 nt).
- From Najib: why 5’ vs 3’ capture? Not entirely certain, but 5’ kit is used for immunology usually, apparently.
- Questions about the cell preparations: what was the status of the cells at GEM generation? Can we learn if that affected the number of cells observed, reads/cell? Can April tweak the sequencing library inputs to help even out the differences across samples for future runs (given that there are 3(I think) coming up)? Conversely, should I reprocess the samples so that all samples are perceived as having the least number of cells observed?
- Quick primer of recombination and V(D)J: each receptor is heterodimer, heavy chain has the constant, V, D, and J. Conversely the light chain has the constant and the VJ. In T-cells, allelic exclusion results in a single resulting expresison event. In B-cells there may be many. In-toto there is a near-infinite random set of possibilities; these are all randomly generated during development. B-cells are constantly making new combinations during the lifespan – T-cells are long-lived and constant (Dave explained an experiment where someone transferred T-cells from mouse to mouse over the course of many years). (orientation 5’->3’? constant->D->J?)
- Every cell that was sequenced is in theory a T-cell (“lung infected T-cells from mice), so should all have VDJ sequences. A small set of immune cells (NK) do not have these.
1.2 Checkin meeting with Dr. Park 20230124
- April describing observations, cell #s etc.
- Definitely not FACS, performed negative enrichment which in theory gets >= 90% pure samples.
- Najib query: limit to VDJ cells only? Unlikely.
- It sounds to me that the likely tasks I perform are not needed by Dr. Park.
2 TODO:
- Check expression of the samples for levels of CD3.
- Print out filtered amounts on a per-sample basis.
- Query mSigDB categories of interest (currently I grabbed the ~98 influenza categories arbitrarily): e.g. figure out which ones are actually relevant.
3 Changelog
4 Preprocessing with cellranger
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.
5 Rerun the pipeline with multi
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.
5.0.1 Shenanigans for combined VDJ samples
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 $cat_cmd
done
donemodule add cellranger
cellranger multi --id control --csv sample_sheets/multi_config_try05_control.csv
cellranger multi --id mock --csv sample_sheets/multi_config_try05_mock.csv
cellranger multi --id m --csv sample_sheets/multi_config_try05_m.csv
cellranger multi --id n --csv sample_sheets/multi_config_try05_n.csv
mv control mock m n 01multi_combined/6 Annotations
I wonder if I can put the gene annotations into the misc slot of the seurat data structure? And perhaps overload fData() to use it?
annotations <- load_biomart_annotations()$annotation## The biomart annotations file already exists, loading from it.brief <- unique(annotations[, c("hgnc_symbol", "description")])7 Set prefix of the data
#prefix <- "multi"
prefix <- "01multi_combined"8 Load the data into Seurat and poke at it
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.
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.
I wrote a little function to make loading the Seurat data from a sample sheet easier. My intention is to have some of this code write back to that sample sheet.
all <- create_seurat("sample_sheets/all_samples.csv", vdj_t_column = "vdjtcells")## Did not find the batch column in the sample sheet.## Filling it in as undefined.## Warning in CheckDuplicateCellNames(object.list = objects): Some cell names are
## duplicated across objects provided. Renaming to enforce unique cell names.
## Warning in CheckDuplicateCellNames(object.list = objects): Some cell names are
## duplicated across objects provided. Renaming to enforce unique cell names.control_cell_idx <- all[["orig.ident"]] == "control"
control_cells <- all[, control_cell_idx]
mock_cell_idx <- all[["orig.ident"]] == "mock"
mock_cells <- all[, mock_cell_idx]
muscular_cell_idx <- all[["orig.ident"]] == "m"
muscular_cells <- all[, muscular_cell_idx]
nasal_cell_idx <- all[["orig.ident"]] == "n"
nasal_cells <- all[, nasal_cell_idx]9 Query for clonotypes in the individual samples and the full dataset.
control_clono <- !is.na(control_cells[["raw_clonotype_id"]])
summary(control_clono)## raw_clonotype_id
## Mode :logical
## FALSE:13209
## TRUE :1971mock_clono <- !is.na(mock_cells[["raw_clonotype_id"]])
summary(mock_clono)## raw_clonotype_id
## Mode :logical
## FALSE:10835
## TRUE :3090m_clono <- !is.na(muscular_cells[["raw_clonotype_id"]])
summary(m_clono)## raw_clonotype_id
## Mode :logical
## FALSE:9664
## TRUE :4217n_clono <- !is.na(nasal_cells[["raw_clonotype_id"]])
summary(n_clono)## raw_clonotype_id
## Mode :logical
## FALSE:5545
## TRUE :306310 Initial Clusters
I 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)])
names(cluster_letters) <- colnames(x=all)
sample_ids <- as.character(cluster_letters)Now 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 <- sample_ids == "A"
all[["control_state"]] <- "Stimulated"
all@meta.data[control_idx, "control_state"] <- "Control"
mock_idx <- sample_ids == "B"
all[["mock_state"]] <- "Not Mock"
all@meta.data[mock_idx, "control_state"] <- "Mock"
mock_idx <- sample_ids == "C"
all[["muscular_state"]] <- "Not Muscular"
all@meta.data[mock_idx, "muscular_state"] <- "Muscular"
mock_idx <- sample_ids == "D"
all[["nasal_state"]] <- "Not Nasal"
all@meta.data[mock_idx, "nasal_state"] <- "Nasal"Now 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.
11 Filters and QC
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 <- record_seurat_samples(all, type="num_cells")
all[["percent_mt"]] <- PercentageFeatureSet(all, pattern="^mt-")
all[["percent_ribo"]] <- PercentageFeatureSet(all, pattern="^Rp[sl]")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"))))skim(all[["percent_mt"]])| Name | all[[“percent_mt”]] |
| Number of rows | 51594 |
| Number of columns | 1 |
| _______________________ | |
| Column type frequency: | |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| percent_mt | 0 | 1 | 2.26 | 3.32 | 0 | 1.28 | 1.88 | 2.69 | 98.86 | ▇▁▁▁▁ |
skim(all[["percent_ribo"]])| Name | all[[“percent_ribo”]] |
| Number of rows | 51594 |
| Number of columns | 1 |
| _______________________ | |
| Column type frequency: | |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| percent_ribo | 0 | 1 | 11.26 | 9.15 | 0 | 5.19 | 7.46 | 13.84 | 51.42 | ▇▂▁▁▁ |
skim(all[["nFeature_RNA"]])| Name | all[[“nFeature_RNA”]] |
| Number of rows | 51594 |
| Number of columns | 1 |
| _______________________ | |
| Column type frequency: | |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| nFeature_RNA | 0 | 1 | 2078 | 1242 | 18 | 1124 | 1702 | 2798 | 8128 | ▇▆▂▁▁ |
skim(all[["nCount_RNA"]])| Name | all[[“nCount_RNA”]] |
| Number of rows | 51594 |
| Number of columns | 1 |
| _______________________ | |
| Column type frequency: | |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| nCount_RNA | 0 | 1 | 5623 | 5052 | 500 | 2141 | 3659 | 7659 | 53836 | ▇▁▁▁▁ |
## Length and reads are for only those cells with clonotypes.
skim(all[["reads"]])| Name | all[[“reads”]] |
| Number of rows | 51594 |
| Number of columns | 1 |
| _______________________ | |
| Column type frequency: | |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| reads | 39253 | 0.24 | 4425 | 4534 | 24 | 1639 | 3089 | 5505 | 53188 | ▇▁▁▁▁ |
skim(all[["length"]])| Name | all[[“length”]] |
| Number of rows | 51594 |
| Number of columns | 1 |
| _______________________ | |
| Column type frequency: | |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| length | 39253 | 0.24 | 557.5 | 58.07 | 402 | 519 | 540 | 570 | 1004 | ▅▇▁▁▁ |
## How many cells have specific chains associated with them
sum(!is.na(all$chain))## [1] 12341And 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.
all <- record_seurat_samples(all, type="num_cells") %>%
record_seurat_samples(type="nFeature_RNA") %>%
record_seurat_samples(type="nCount_RNA") %>%
record_seurat_samples(type="reads", column_name="clonotype_reads") %>%
record_seurat_samples(type="pct_mito", pattern="^mt-") %>%
record_seurat_samples(type="pct_ribo", pattern="^Rp[sl]")## ── Data Summary ────────────────────────
## Values
## Name data
## Number of rows 51594
## Number of columns 45
## _______________________
## Column type frequency:
## numeric 1
## ________________________
## Group variables orig.ident
##
## ── Variable type: numeric ──────────────────────────────────────────────────────
## skim_variable orig.ident n_missing complete_rate mean sd p0 p25 p50
## 1 nFeature_RNA control 0 1 1999. 1113. 20 1158 1700.
## 2 nFeature_RNA m 0 1 2066. 1275. 22 1101 1651
## 3 nFeature_RNA mock 0 1 2086. 1218. 18 1149 1734
## 4 nFeature_RNA n 0 1 2224. 1416. 27 1063. 1734.
## p75 p100 hist
## 1 2637. 7532 ▇▇▃▁▁
## 2 2778 7590 ▇▆▂▁▁
## 3 2785 7442 ▇▇▃▁▁
## 4 3228 8128 ▇▅▃▁▁
## ── Data Summary ────────────────────────
## Values
## Name data
## Number of rows 51594
## Number of columns 45
## _______________________
## Column type frequency:
## numeric 1
## ________________________
## Group variables orig.ident
##
## ── Variable type: numeric ──────────────────────────────────────────────────────
## skim_variable orig.ident n_missing complete_rate mean sd p0 p25 p50
## 1 nCount_RNA control 0 1 5115. 4137. 500 2149 3618
## 2 nCount_RNA m 0 1 5729. 5383. 500 2155 3601
## 3 nCount_RNA mock 0 1 5539. 4862. 500 2144 3665
## 4 nCount_RNA n 0 1 6488. 6052. 500 2094. 3912
## p75 p100 hist
## 1 7057. 40853 ▇▂▁▁▁
## 2 7642 53707 ▇▁▁▁▁
## 3 7518 44494 ▇▂▁▁▁
## 4 9417. 53836 ▇▂▁▁▁
## ── Data Summary ────────────────────────
## Values
## Name data
## Number of rows 51594
## Number of columns 45
## _______________________
## Column type frequency:
## numeric 1
## ________________________
## Group variables orig.ident
##
## ── Variable type: numeric ──────────────────────────────────────────────────────
## skim_variable orig.ident n_missing complete_rate mean sd p0 p25 p50
## 1 reads control 13209 0.130 7066. 6037. 41 3114. 5365
## 2 reads m 9664 0.304 3685. 4114. 24 1403 2565
## 3 reads mock 10835 0.222 4709. 4085. 42 1998. 3618
## 4 reads n 5545 0.356 3460. 3604. 25 1377 2475
## p75 p100 hist
## 1 9344. 53188 ▇▂▁▁▁
## 2 4469 48039 ▇▁▁▁▁
## 3 6198. 40634 ▇▁▁▁▁
## 4 4232. 36105 ▇▁▁▁▁
## ── Data Summary ────────────────────────
## Values
## Name data
## Number of rows 51594
## Number of columns 46
## _______________________
## Column type frequency:
## numeric 1
## ________________________
## Group variables orig.ident
##
## ── Variable type: numeric ──────────────────────────────────────────────────────
## skim_variable orig.ident n_missing complete_rate mean sd p0 p25 p50 p75
## 1 pct_mito control 0 1 2.47 3.79 0 1.35 1.99 2.92
## 2 pct_mito m 0 1 2.32 3.42 0 1.35 1.97 2.75
## 3 pct_mito mock 0 1 2.10 2.53 0 1.19 1.77 2.55
## 4 pct_mito n 0 1 2.06 3.36 0 1.21 1.76 2.45
## p100 hist
## 1 98.7 ▇▁▁▁▁
## 2 98.4 ▇▁▁▁▁
## 3 98.9 ▇▁▁▁▁
## 4 97.3 ▇▁▁▁▁
## ── Data Summary ────────────────────────
## Values
## Name data
## Number of rows 51594
## Number of columns 47
## _______________________
## Column type frequency:
## numeric 1
## ________________________
## Group variables orig.ident
##
## ── Variable type: numeric ──────────────────────────────────────────────────────
## skim_variable orig.ident n_missing complete_rate mean sd p0 p25 p50 p75
## 1 pct_ribo control 0 1 10.0 7.39 0 5.48 7.69 10.9
## 2 pct_ribo m 0 1 13.3 10.7 0 5.54 8.34 18.9
## 3 pct_ribo mock 0 1 9.08 7.41 0 4.44 6.10 10.6
## 4 pct_ribo n 0 1 13.7 10.5 0 5.82 8.31 20.8
## p100 hist
## 1 51.4 ▇▂▁▁▁
## 2 50.1 ▇▂▂▂▁
## 3 45.0 ▇▂▁▁▁
## 4 48.8 ▇▂▂▂▁## The recorded information is here, but not printing it for now:
all@misc[["sample_metadata"]]## sampleid condition gexfile vdjtcells gexcells
## control control control 01multi_combined/control 100 100
## mock mock mock 01multi_combined/mock 100 100
## m m muscular 01multi_combined/m 100 100
## n n nasal 01multi_combined/n 100 100
## batch num_cells mean_nFeature_RNA mean_nCount_RNA
## control undefined 15180 1999 5115
## mock undefined 13925 2066 5729
## m undefined 13881 2086 5539
## n undefined 8608 2224 6488
## mean_clonotype_reads mean_pct_mito mean_pct_ribo
## control 7066 2.473 10.001
## mock 3685 2.322 13.305
## m 4709 2.096 9.083
## n 3460 2.065 13.675Ok, that was fun; lets look at this information as a series of plots:
VlnPlot(all, features="nFeature_RNA", pt.size=0)
VlnPlot(all, features="pct_mito", pt.size=0)
VlnPlot(all, features="pct_ribo", pt.size=0)
VlnPlot(all, features="nCount_RNA", pt.size=0)
VlnPlot(all, features="reads", pt.size=0)## Warning: Removed 39253 rows containing non-finite values (`stat_ydensity()`).
## I am curious about the length of the clonotype sequences.
VlnPlot(all, features="length", pt.size=0)## Warning: Removed 39253 rows containing non-finite values (`stat_ydensity()`).
FeatureScatter(all, "pct_ribo", "pct_mito")
FeatureScatter(all, "nCount_RNA", "nFeature_RNA")
FeatureScatter(all, "nCount_RNA", "pct_ribo")
FeatureScatter(all, "nCount_RNA", "pct_mito")
12 Filter samples using our guesstimates
Start with a minimum number of RNAs filter.
sufficient_rna_observed <- WhichCells(all, expression=nFeature_RNA >= min_num_rna)
filt <- subset(all, cells=sufficient_rna_observed)Second 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
summary(sufficiently_observed_idx)## Mode FALSE TRUE
## logical 11112 21173dim(filt)## [1] 32285 51536filt <- subset(filt, features=rownames(filt)[sufficiently_observed_idx])
dim(filt)## [1] 21173 51536## 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)
filt <- subset(filt, cells=high_ribosomal)Exclude cells with too much mitochondrial RNA
12.1 Now drop mitochondrial genes
low_mitochondrial <- WhichCells(filt, expression=percent_mt <= max_pct_mito)
filt <- subset(filt, cells=low_mitochondrial)13 Record post-filtered stats
filt <- record_seurat_samples(filt, type="num_cells") %>%
record_seurat_samples(type="nFeature_RNA") %>%
record_seurat_samples(type="nCount_RNA") %>%
record_seurat_samples(type="reads", column_name="clonotype_reads") %>%
record_seurat_samples(type="pct_mito", pattern="^mt-") %>%
record_seurat_samples(type="pct_ribo", pattern="^Rp[sl]")## ── Data Summary ────────────────────────
## Values
## Name data
## Number of rows 40008
## Number of columns 47
## _______________________
## Column type frequency:
## numeric 1
## ________________________
## Group variables orig.ident
##
## ── Variable type: numeric ──────────────────────────────────────────────────────
## skim_variable orig.ident n_missing complete_rate mean sd p0 p25 p50
## 1 nFeature_RNA control 0 1 2065. 1147. 240 1177 1774
## 2 nFeature_RNA m 0 1 2107. 1320. 221 1096 1648
## 3 nFeature_RNA mock 0 1 2044. 1237. 248 1089 1665
## 4 nFeature_RNA n 0 1 2249. 1440. 303 1057 1744
## p75 p100 hist
## 1 2757 7530 ▇▆▂▁▁
## 2 2891 7588 ▇▅▂▁▁
## 3 2775. 7442 ▇▅▂▁▁
## 4 3292 8128 ▇▃▃▁▁
## ── Data Summary ────────────────────────
## Values
## Name data
## Number of rows 40008
## Number of columns 47
## _______________________
## Column type frequency:
## numeric 1
## ________________________
## Group variables orig.ident
##
## ── Variable type: numeric ──────────────────────────────────────────────────────
## skim_variable orig.ident n_missing complete_rate mean sd p0 p25 p50
## 1 nCount_RNA control 0 1 5431. 4322. 500 2227 3898.
## 2 nCount_RNA m 0 1 6048. 5681. 500 2214 3689
## 3 nCount_RNA mock 0 1 5608. 5043. 502 2053 3587
## 4 nCount_RNA n 0 1 6715. 6246. 500 2131 4047
## p75 p100 hist
## 1 7649 40851 ▇▂▁▁▁
## 2 8338 53707 ▇▁▁▁▁
## 3 7821 44492 ▇▂▁▁▁
## 4 9926 53836 ▇▂▁▁▁
## ── Data Summary ────────────────────────
## Values
## Name data
## Number of rows 40008
## Number of columns 47
## _______________________
## Column type frequency:
## numeric 1
## ________________________
## Group variables orig.ident
##
## ── Variable type: numeric ──────────────────────────────────────────────────────
## skim_variable orig.ident n_missing complete_rate mean sd p0 p25 p50
## 1 reads control 10368 0.159 7089. 6045. 41 3116. 5402
## 2 reads m 7045 0.373 3694. 4120. 24 1408 2576.
## 3 reads mock 6107 0.333 4732. 4098. 42 2018 3647
## 4 reads n 4236 0.418 3473. 3612. 43 1388 2486
## p75 p100 hist
## 1 9356. 53188 ▇▂▁▁▁
## 2 4474. 48039 ▇▁▁▁▁
## 3 6223 40634 ▇▁▁▁▁
## 4 4252 36105 ▇▁▁▁▁
## ── Data Summary ────────────────────────
## Values
## Name data
## Number of rows 40008
## Number of columns 47
## _______________________
## Column type frequency:
## numeric 1
## ________________________
## Group variables orig.ident
##
## ── Variable type: numeric ──────────────────────────────────────────────────────
## skim_variable orig.ident n_missing complete_rate mean sd p0 p25 p50 p75
## 1 pct_mito control 0 1 2.49 1.45 0 1.56 2.20 3.10
## 2 pct_mito m 0 1 2.37 1.29 0 1.59 2.18 2.88
## 3 pct_mito mock 0 1 2.35 1.35 0 1.54 2.12 2.88
## 4 pct_mito n 0 1 2.03 1.05 0 1.36 1.90 2.54
## p100 hist
## 1 19.4 ▇▁▁▁▁
## 2 19.4 ▇▁▁▁▁
## 3 19.5 ▇▁▁▁▁
## 4 16.8 ▇▁▁▁▁
## ── Data Summary ────────────────────────
## Values
## Name data
## Number of rows 40008
## Number of columns 47
## _______________________
## Column type frequency:
## numeric 1
## ________________________
## Group variables orig.ident
##
## ── Variable type: numeric ──────────────────────────────────────────────────────
## skim_variable orig.ident n_missing complete_rate mean sd p0 p25 p50 p75
## 1 pct_ribo control 0 1 11.4 7.54 5 6.69 8.59 12.5
## 2 pct_ribo m 0 1 15.5 10.7 5 6.96 10.4 22.6
## 3 pct_ribo mock 0 1 11.8 7.84 5 6.15 8.24 15.0
## 4 pct_ribo n 0 1 15.5 10.4 5 6.75 10.5 23.3
## p100 hist
## 1 51.4 ▇▁▁▁▁
## 2 50.1 ▇▂▂▂▁
## 3 45.0 ▇▂▁▁▁
## 4 48.8 ▇▂▂▂▁Add the new filtered mean values onto the original set.
all@misc$sample_metadata <- cbind(all@misc$sample_metadata, filt@misc$sample_metadata)14 Distribution
14.1 Before filtering
all_norm <- NormalizeData(object=all) %>%
FindVariableFeatures() %>%
ScaleData() %>%
FindNeighbors() %>%
FindClusters() %>%
RunPCA() %>%
RunTSNE() %>%
RunUMAP(reduction = "pca", dims = 1:10)## Centering and scaling data matrix## Error: Cannot find 'pca' in this Seurat objectDimPlot(object=all_norm, reduction="tsne")## Error in is.data.frame(x): object 'all_norm' not foundplotted <- DimPlot(all_norm, reduction="umap", group.by="orig.ident", label=TRUE)## Error in is.data.frame(x): object 'all_norm' not foundplotted## Error in eval(expr, envir, enclos): object 'plotted' not found14.2 After filtering
filt_norm <- NormalizeData(object=filt) %>%
FindVariableFeatures() %>%
ScaleData() %>%
FindNeighbors() %>%
FindClusters() %>%
RunPCA() %>%
RunTSNE() %>%
RunUMAP(reduction="pca", dims=1:10)## Centering and scaling data matrix## Error: Cannot find 'pca' in this Seurat objectDimPlot(object=filt_norm, reduction="tsne")## Error in is.data.frame(x): object 'filt_norm' not foundplotted <- DimPlot(filt_norm, reduction="umap", group.by="orig.ident", 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 found15 Add these new clusters to our non-normalized data
I am not yet certain of how Seurat handles (non)normalized data for the various FindMarkers functions. Thus, I am adding the clusters from the dimension reductions to the non-normalized data here.
filt[["res0p1_clusters"]] <- filt_norm[["res0p1_clusters"]]## Error in eval(expr, envir, enclos): object 'filt_norm' not foundfilt[["cluster_sample"]] <- filt_norm[["cluster_sample"]]## Error in eval(expr, envir, enclos): object 'filt_norm' not found16 Variable features
var <- 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 found17 Various marker searches
17.1 All Markers
17.1.1 By sample
Question: 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)## Calculating cluster control## Calculating cluster m## Calculating cluster mock## Calculating cluster nhead(combined_markers)## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## Tmem252 0.000e+00 1.0024 0.491 0.311 0.000e+00 control Tmem252
## Plvap 0.000e+00 0.8014 0.599 0.410 0.000e+00 control Plvap
## Eng 0.000e+00 0.5932 0.625 0.446 0.000e+00 control Eng
## Ctla2a 2.069e-303 0.6391 0.672 0.518 4.382e-299 control Ctla2a
## Lyve1 3.612e-291 0.5805 0.411 0.240 7.647e-287 control Lyve1
## Atf3 1.289e-266 0.5544 0.721 0.543 2.730e-262 control Atf3combined <- as.data.frame(combined_markers)
rownames(combined) <- toupper(rownames(combined))
annotated_markers <- merge(combined, brief, by.x="row.names", by.y="hgnc_symbol",
all.x=TRUE)17.1.2 By sample
combined_markers <- FindAllMarkers(filt, only.pos=TRUE, logfc.threshold=0.5)## Calculating cluster control## Calculating cluster m## Calculating cluster mock## Calculating cluster ncombined <- as.data.frame(combined_markers)
rownames(combined) <- toupper(rownames(combined))
annotated_markers <- merge(combined, brief, by.x="row.names", by.y="hgnc_symbol",
all.x=TRUE)
head(annotated_markers)## Row.names p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## 1 AGER 6.586e-14 0.5312 0.442 0.422 1.394e-09 n Ager
## 2 APOD 0.000e+00 1.5162 0.236 0.062 0.000e+00 n Apod
## 3 ATF3 1.289e-266 0.5544 0.721 0.543 2.730e-262 control Atf3
## 4 B2M 2.881e-294 0.7894 0.990 0.961 6.099e-290 n B2m
## 5 BST2 0.000e+00 0.8906 0.844 0.659 0.000e+00 mock Bst2
## 6 BST21 2.627e-245 1.0587 0.786 0.683 5.562e-241 n Bst2
## description
## 1 advanced glycosylation end-product specific receptor [Source:HGNC Symbol;Acc:HGNC:320]
## 2 apolipoprotein D [Source:HGNC Symbol;Acc:HGNC:612]
## 3 activating transcription factor 3 [Source:HGNC Symbol;Acc:HGNC:785]
## 4 beta-2-microglobulin [Source:HGNC Symbol;Acc:HGNC:914]
## 5 bone marrow stromal cell antigen 2 [Source:HGNC Symbol;Acc:HGNC:1119]
## 6 <NA>17.1.3 By Cluster
Since I am not using the filt_norm data structure, I will need to pull the cluster information from the normalized copy…
clusters <- filt
Idents(clusters) <- clusters[["res0p1_clusters"]]## Error: Cannot find 'res0p1_clusters' in this Seurat objectcluster_markers <- FindAllMarkers(clusters, only.pos=TRUE, logfc.threshold=0.5)## Calculating cluster control## Calculating cluster m## Calculating cluster mock## Calculating cluster ncluster_genes <- as.data.frame(cluster_markers)
rownames(cluster_genes) <- toupper(rownames(cluster_genes))
annotated_clusters <- merge(cluster_genes, brief, by.x="row.names", by.y="hgnc_symbol",
all.x=TRUE)
head(annotated_clusters)## Row.names p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## 1 AGER 6.586e-14 0.5312 0.442 0.422 1.394e-09 n Ager
## 2 APOD 0.000e+00 1.5162 0.236 0.062 0.000e+00 n Apod
## 3 ATF3 1.289e-266 0.5544 0.721 0.543 2.730e-262 control Atf3
## 4 B2M 2.881e-294 0.7894 0.990 0.961 6.099e-290 n B2m
## 5 BST2 0.000e+00 0.8906 0.844 0.659 0.000e+00 mock Bst2
## 6 BST21 2.627e-245 1.0587 0.786 0.683 5.562e-241 n Bst2
## description
## 1 advanced glycosylation end-product specific receptor [Source:HGNC Symbol;Acc:HGNC:320]
## 2 apolipoprotein D [Source:HGNC Symbol;Acc:HGNC:612]
## 3 activating transcription factor 3 [Source:HGNC Symbol;Acc:HGNC:785]
## 4 beta-2-microglobulin [Source:HGNC Symbol;Acc:HGNC:914]
## 5 bone marrow stromal cell antigen 2 [Source:HGNC Symbol;Acc:HGNC:1119]
## 6 <NA>annotated_clusters %>%
group_by(cluster) %>%
dplyr::top_n(n=10, wt=avg_log2FC) %>%
as.data.frame()## Row.names p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## 1 APOD 0.000e+00 1.5162 0.236 0.062 0.000e+00 n Apod
## 2 ATF3 1.289e-266 0.5544 0.721 0.543 2.730e-262 control Atf3
## 3 BST2 0.000e+00 0.8906 0.844 0.659 0.000e+00 mock Bst2
## 4 BST21 2.627e-245 1.0587 0.786 0.683 5.562e-241 n Bst2
## 5 C3 1.558e-150 1.1778 0.347 0.220 3.299e-146 n C3
## 6 CCL5 5.247e-69 0.6441 0.157 0.093 1.111e-64 mock Ccl5
## 7 CFB 1.204e-241 0.8818 0.194 0.071 2.549e-237 n Cfb
## 8 CTLA2A 2.069e-303 0.6391 0.672 0.518 4.382e-299 control Ctla2a
## 9 CXCL10 3.844e-179 0.9916 0.238 0.121 8.139e-175 mock Cxcl10
## 10 ENG 0.000e+00 0.5932 0.625 0.446 0.000e+00 control Eng
## 11 GBP4 3.434e-184 0.5354 0.541 0.414 7.270e-180 m Gbp4
## 12 GPIHBP1 9.080e-262 0.5257 0.601 0.424 1.922e-257 control Gpihbp1
## 13 IFI27L2A 0.000e+00 1.3184 0.778 0.442 0.000e+00 n Ifi27l2a
## 14 IFIT1 0.000e+00 0.8667 0.542 0.234 0.000e+00 mock Ifit1
## 15 IFIT3 0.000e+00 0.8589 0.505 0.218 0.000e+00 mock Ifit3
## 16 INMT 1.396e-75 0.5051 0.438 0.348 2.956e-71 control Inmt
## 17 IRF7 0.000e+00 0.8388 0.730 0.407 0.000e+00 mock Irf7
## 18 IRF71 0.000e+00 1.2384 0.713 0.430 0.000e+00 n Irf7
## 19 ISG15 0.000e+00 1.0845 0.809 0.479 0.000e+00 mock Isg15
## 20 ISG151 0.000e+00 1.0299 0.732 0.516 0.000e+00 n Isg15
## 21 LCN2 7.538e-40 1.0603 0.278 0.218 1.596e-35 mock Lcn2
## 22 LCN21 2.069e-218 1.6999 0.359 0.204 4.381e-214 n Lcn2
## 23 LGALS1 2.710e-23 0.5359 0.486 0.450 5.737e-19 m Lgals1
## 24 LY6E 2.574e-270 0.8441 0.804 0.696 5.450e-266 mock Ly6e
## 25 LYVE1 3.612e-291 0.5805 0.411 0.240 7.647e-287 control Lyve1
## 26 MGP 1.052e-10 0.9769 0.318 0.294 2.228e-06 n Mgp
## 27 PLVAP 0.000e+00 0.8014 0.599 0.410 0.000e+00 control Plvap
## 28 RETNLA 6.190e-222 2.0144 0.100 0.025 1.311e-217 m Retnla
## 29 SAA3 1.090e-191 1.2510 0.164 0.065 2.308e-187 mock Saa3
## 30 SAA31 0.000e+00 1.8139 0.222 0.058 0.000e+00 n Saa3
## 31 SOCS3 2.601e-136 0.5480 0.689 0.636 5.506e-132 control Socs3
## 32 TMEM252 0.000e+00 1.0024 0.491 0.311 0.000e+00 control Tmem252
## 33 TSPAN7 2.750e-252 0.5211 0.638 0.463 5.822e-248 control Tspan7
## description
## 1 apolipoprotein D [Source:HGNC Symbol;Acc:HGNC:612]
## 2 activating transcription factor 3 [Source:HGNC Symbol;Acc:HGNC:785]
## 3 bone marrow stromal cell antigen 2 [Source:HGNC Symbol;Acc:HGNC:1119]
## 4 <NA>
## 5 complement C3 [Source:HGNC Symbol;Acc:HGNC:1318]
## 6 C-C motif chemokine ligand 5 [Source:HGNC Symbol;Acc:HGNC:10632]
## 7 complement factor B [Source:HGNC Symbol;Acc:HGNC:1037]
## 8 <NA>
## 9 C-X-C motif chemokine ligand 10 [Source:HGNC Symbol;Acc:HGNC:10637]
## 10 endoglin [Source:HGNC Symbol;Acc:HGNC:3349]
## 11 guanylate binding protein 4 [Source:HGNC Symbol;Acc:HGNC:20480]
## 12 glycosylphosphatidylinositol anchored high density lipoprotein binding protein 1 [Source:HGNC Symbol;Acc:HGNC:24945]
## 13 <NA>
## 14 interferon induced protein with tetratricopeptide repeats 1 [Source:HGNC Symbol;Acc:HGNC:5407]
## 15 interferon induced protein with tetratricopeptide repeats 3 [Source:HGNC Symbol;Acc:HGNC:5411]
## 16 indolethylamine N-methyltransferase [Source:HGNC Symbol;Acc:HGNC:6069]
## 17 interferon regulatory factor 7 [Source:HGNC Symbol;Acc:HGNC:6122]
## 18 <NA>
## 19 ISG15 ubiquitin like modifier [Source:HGNC Symbol;Acc:HGNC:4053]
## 20 <NA>
## 21 lipocalin 2 [Source:HGNC Symbol;Acc:HGNC:6526]
## 22 <NA>
## 23 galectin 1 [Source:HGNC Symbol;Acc:HGNC:6561]
## 24 lymphocyte antigen 6 family member E [Source:HGNC Symbol;Acc:HGNC:6727]
## 25 lymphatic vessel endothelial hyaluronan receptor 1 [Source:HGNC Symbol;Acc:HGNC:14687]
## 26 matrix Gla protein [Source:HGNC Symbol;Acc:HGNC:7060]
## 27 plasmalemma vesicle associated protein [Source:HGNC Symbol;Acc:HGNC:13635]
## 28 <NA>
## 29 <NA>
## 30 <NA>
## 31 suppressor of cytokine signaling 3 [Source:HGNC Symbol;Acc:HGNC:19391]
## 32 transmembrane protein 252 [Source:HGNC Symbol;Acc:HGNC:28537]
## 33 tetraspanin 7 [Source:HGNC Symbol;Acc:HGNC:11854]sum(clusters[["res0p1_clusters"]] == "0")## Error: Cannot find 'res0p1_clusters' in this Seurat objectsum(clusters[["res0p1_clusters"]] == "0" &
!is.na(clusters[["raw_clonotype_id"]]))## Error: Cannot find 'res0p1_clusters' in this Seurat objectsum(clusters[["res0p1_clusters"]] == "1")## Error: Cannot find 'res0p1_clusters' in this Seurat objectsum(clusters[["res0p1_clusters"]] == "1" &
!is.na(clusters[["raw_clonotype_id"]]))## Error: Cannot find 'res0p1_clusters' in this Seurat objectsum(clusters[["res0p1_clusters"]] == "2")## Error: Cannot find 'res0p1_clusters' in this Seurat objectsum(clusters[["res0p1_clusters"]] == "2" &
!is.na(clusters[["raw_clonotype_id"]]))## Error: Cannot find 'res0p1_clusters' in this Seurat objectsum(clusters[["res0p1_clusters"]] == "3")## Error: Cannot find 'res0p1_clusters' in this Seurat objectsum(clusters[["res0p1_clusters"]] == "3" &
!is.na(clusters[["raw_clonotype_id"]]))## Error: Cannot find 'res0p1_clusters' in this Seurat objectsum(clusters[["res0p1_clusters"]] == "4")## Error: Cannot find 'res0p1_clusters' in this Seurat objectsum(clusters[["res0p1_clusters"]] == "4" &
!is.na(clusters[["raw_clonotype_id"]]))## Error: Cannot find 'res0p1_clusters' in this Seurat objectsum(clusters[["res0p1_clusters"]] == "5")## Error: Cannot find 'res0p1_clusters' in this Seurat objectsum(clusters[["res0p1_clusters"]] == "5" &
!is.na(clusters[["raw_clonotype_id"]]))## Error: Cannot find 'res0p1_clusters' in this Seurat objectsum(clusters[["res0p1_clusters"]] == "6")## Error: Cannot find 'res0p1_clusters' in this Seurat objectsum(clusters[["res0p1_clusters"]] == "6" &
!is.na(clusters[["raw_clonotype_id"]]))## Error: Cannot find 'res0p1_clusters' in this Seurat objectClusters 0 and 5 have a great majority of the clonotypes. 0 has something like 90%, 5 has ~ 30%, the others ~ 10%
17.2 Compare specific clusters
17.2.1 Look at cluster 0, Nasal vs. Control
test_group <- clusters[["cluster_sample"]] == "control_0"## Error: Cannot find 'cluster_sample' in this Seurat objectsum(test_group)## Error in eval(expr, envir, enclos): object 'test_group' not foundtest_group <- clusters[["cluster_sample"]] == "n_0"## Error: Cannot find 'cluster_sample' in this Seurat objectsum(test_group)## Error in eval(expr, envir, enclos): object 'test_group' not foundcontroln_0 <- FindMarkers(
clusters, group.by="cluster_sample",
ident.1="control_0", ident.2="n_0")## Error in WhichCells.Seurat(object = object, idents = ident.1): Cannot find the following identities in the object: control_0controln_0 <- as.data.frame(controln_0)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': 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()## Row.names p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## 1 APOD 0.000e+00 1.5162 0.236 0.062 0.000e+00 n Apod
## 2 ATF3 1.289e-266 0.5544 0.721 0.543 2.730e-262 control Atf3
## 3 BST2 0.000e+00 0.8906 0.844 0.659 0.000e+00 mock Bst2
## 4 BST21 2.627e-245 1.0587 0.786 0.683 5.562e-241 n Bst2
## 5 C3 1.558e-150 1.1778 0.347 0.220 3.299e-146 n C3
## 6 CCL5 5.247e-69 0.6441 0.157 0.093 1.111e-64 mock Ccl5
## 7 CFB 1.204e-241 0.8818 0.194 0.071 2.549e-237 n Cfb
## 8 CTLA2A 2.069e-303 0.6391 0.672 0.518 4.382e-299 control Ctla2a
## 9 CXCL10 3.844e-179 0.9916 0.238 0.121 8.139e-175 mock Cxcl10
## 10 ENG 0.000e+00 0.5932 0.625 0.446 0.000e+00 control Eng
## 11 GBP4 3.434e-184 0.5354 0.541 0.414 7.270e-180 m Gbp4
## 12 GPIHBP1 9.080e-262 0.5257 0.601 0.424 1.922e-257 control Gpihbp1
## 13 IFI27L2A 0.000e+00 1.3184 0.778 0.442 0.000e+00 n Ifi27l2a
## 14 IFIT1 0.000e+00 0.8667 0.542 0.234 0.000e+00 mock Ifit1
## 15 IFIT3 0.000e+00 0.8589 0.505 0.218 0.000e+00 mock Ifit3
## 16 INMT 1.396e-75 0.5051 0.438 0.348 2.956e-71 control Inmt
## 17 IRF7 0.000e+00 0.8388 0.730 0.407 0.000e+00 mock Irf7
## 18 IRF71 0.000e+00 1.2384 0.713 0.430 0.000e+00 n Irf7
## 19 ISG15 0.000e+00 1.0845 0.809 0.479 0.000e+00 mock Isg15
## 20 ISG151 0.000e+00 1.0299 0.732 0.516 0.000e+00 n Isg15
## 21 LCN2 7.538e-40 1.0603 0.278 0.218 1.596e-35 mock Lcn2
## 22 LCN21 2.069e-218 1.6999 0.359 0.204 4.381e-214 n Lcn2
## 23 LGALS1 2.710e-23 0.5359 0.486 0.450 5.737e-19 m Lgals1
## 24 LY6E 2.574e-270 0.8441 0.804 0.696 5.450e-266 mock Ly6e
## 25 LYVE1 3.612e-291 0.5805 0.411 0.240 7.647e-287 control Lyve1
## 26 MGP 1.052e-10 0.9769 0.318 0.294 2.228e-06 n Mgp
## 27 PLVAP 0.000e+00 0.8014 0.599 0.410 0.000e+00 control Plvap
## 28 RETNLA 6.190e-222 2.0144 0.100 0.025 1.311e-217 m Retnla
## 29 SAA3 1.090e-191 1.2510 0.164 0.065 2.308e-187 mock Saa3
## 30 SAA31 0.000e+00 1.8139 0.222 0.058 0.000e+00 n Saa3
## 31 SOCS3 2.601e-136 0.5480 0.689 0.636 5.506e-132 control Socs3
## 32 TMEM252 0.000e+00 1.0024 0.491 0.311 0.000e+00 control Tmem252
## 33 TSPAN7 2.750e-252 0.5211 0.638 0.463 5.822e-248 control Tspan7
## description
## 1 apolipoprotein D [Source:HGNC Symbol;Acc:HGNC:612]
## 2 activating transcription factor 3 [Source:HGNC Symbol;Acc:HGNC:785]
## 3 bone marrow stromal cell antigen 2 [Source:HGNC Symbol;Acc:HGNC:1119]
## 4 <NA>
## 5 complement C3 [Source:HGNC Symbol;Acc:HGNC:1318]
## 6 C-C motif chemokine ligand 5 [Source:HGNC Symbol;Acc:HGNC:10632]
## 7 complement factor B [Source:HGNC Symbol;Acc:HGNC:1037]
## 8 <NA>
## 9 C-X-C motif chemokine ligand 10 [Source:HGNC Symbol;Acc:HGNC:10637]
## 10 endoglin [Source:HGNC Symbol;Acc:HGNC:3349]
## 11 guanylate binding protein 4 [Source:HGNC Symbol;Acc:HGNC:20480]
## 12 glycosylphosphatidylinositol anchored high density lipoprotein binding protein 1 [Source:HGNC Symbol;Acc:HGNC:24945]
## 13 <NA>
## 14 interferon induced protein with tetratricopeptide repeats 1 [Source:HGNC Symbol;Acc:HGNC:5407]
## 15 interferon induced protein with tetratricopeptide repeats 3 [Source:HGNC Symbol;Acc:HGNC:5411]
## 16 indolethylamine N-methyltransferase [Source:HGNC Symbol;Acc:HGNC:6069]
## 17 interferon regulatory factor 7 [Source:HGNC Symbol;Acc:HGNC:6122]
## 18 <NA>
## 19 ISG15 ubiquitin like modifier [Source:HGNC Symbol;Acc:HGNC:4053]
## 20 <NA>
## 21 lipocalin 2 [Source:HGNC Symbol;Acc:HGNC:6526]
## 22 <NA>
## 23 galectin 1 [Source:HGNC Symbol;Acc:HGNC:6561]
## 24 lymphocyte antigen 6 family member E [Source:HGNC Symbol;Acc:HGNC:6727]
## 25 lymphatic vessel endothelial hyaluronan receptor 1 [Source:HGNC Symbol;Acc:HGNC:14687]
## 26 matrix Gla protein [Source:HGNC Symbol;Acc:HGNC:7060]
## 27 plasmalemma vesicle associated protein [Source:HGNC Symbol;Acc:HGNC:13635]
## 28 <NA>
## 29 <NA>
## 30 <NA>
## 31 suppressor of cytokine signaling 3 [Source:HGNC Symbol;Acc:HGNC:19391]
## 32 transmembrane protein 252 [Source:HGNC Symbol;Acc:HGNC:28537]
## 33 tetraspanin 7 [Source:HGNC Symbol;Acc:HGNC:11854]17.2.2 Cluster 0 Nasal vs. Mock
mockvsn_0 <- FindMarkers(
clusters, 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': Cannot find the following identities in the object: n_0head(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(
clusters, 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': Cannot find the following identities in the object: m_0head(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 found17.3 Find Conserved Markers
This function makes no sense.
DefaultAssay(clusters) <- "RNA"
conserved_markers <- FindConservedMarkers(
clusters, ident.1=c(0, 1), ident.2=c(2,3,4),
grouping.var="sample", only.pos=TRUE,
verbose=TRUE)mock_vs_control <- FindMarkers(clusters, group.by="orig.ident",
ident.1="mock",
ident.2="control")
head(mock_vs_control)
muscular_vs_mock <- FindMarkers(clusters, group.by="orig.ident",
ident.1="m",
ident.2="mock")
summary(muscular_vs_mock)
nasal_vs_mock <- FindMarkers(clusters, group.by="orig.ident",
min.pct=0.25, ident.1="n",
ident.2="mock")
summary(nasal_vs_mock)
FeaturePlot(clusters, features=c("Rgcc"),
split.by="orig.ident", max.cutoff=3,
cols=c("darkgreen", "darkred"))18 Scan for likely cell cycle genes
This is a neat idea, I think we can repurpose it to immunology gene sets.
filt <- CellCycleScoring(
object=clusters,
g2m.features=cc.genes$g2m.genes,
s.features=cc.genes$s.genes)## Warning: The following features are not present in the object: MCM5, PCNA, TYMS,
## FEN1, MCM2, MCM4, RRM1, UNG, GINS2, MCM6, CDCA7, DTL, PRIM1, UHRF1, MLF1IP,
## HELLS, RFC2, RPA2, NASP, RAD51AP1, GMNN, WDR76, SLBP, CCNE2, UBR7, POLD3, MSH2,
## ATAD2, RAD51, RRM2, CDC45, CDC6, EXO1, TIPIN, DSCC1, BLM, CASP8AP2, USP1, CLSPN,
## POLA1, CHAF1B, BRIP1, E2F8, not searching for symbol synonyms## Warning: The following features are not present in the object: HMGB2, CDK1,
## NUSAP1, UBE2C, BIRC5, TPX2, TOP2A, NDC80, CKS2, NUF2, CKS1B, MKI67, TMPO, CENPF,
## TACC3, FAM64A, SMC4, CCNB2, CKAP2L, CKAP2, AURKB, BUB1, KIF11, ANP32E, TUBB4B,
## GTSE1, KIF20B, HJURP, CDCA3, HN1, CDC20, TTK, CDC25C, KIF2C, RANGAP1, NCAPD2,
## DLGAP5, CDCA2, CDCA8, ECT2, KIF23, HMMR, AURKA, PSRC1, ANLN, LBR, CKAP5, CENPE,
## CTCF, NEK2, G2E3, GAS2L3, CBX5, CENPA, not searching for symbol synonyms## Warning in AddModuleScore(object = object, features = features, name = name, :
## Could not find enough features in the object from the following feature lists:
## S.Score Attempting to match case...Could not find enough features in the object
## from the following feature lists: G2M.Score Attempting to match case...VlnPlot(clusters, features=c("S.Score", "G2M.Score"),
group.by="orig.ident",
ncol=4, pt.size=0)## Error in FetchData.Seurat(object = object, vars = features, slot = slot): None of the requested variables were found: S.Score, G2M.ScoreHaving 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=clusters, features=broad_list,
name="m8")## Warning: The following features are not present in the object: Gm29346, Pdyn,
## Iqcj, Iqschfp, Gm15578, Gm12724, A930005G22Rik, Gm38839, Adgra1, 5530401A14Rik,
## Gm16246, Adamts16, Crhbp, Lrtm1, Gm1604b, Galr1, Slit1, not searching for symbol
## synonyms## Warning: The following features are not present in the object: Ifi203-ps,
## 9630010A21Rik, Gm26236, Niban2, Pcsk2os2, 4930573H18Rik, Gm12426, 8030487O14Rik,
## E030026E10Rik, Gm36503, Gm36816, Garre1, 9530078K11Rik, Gm22060, Scn4b,
## 4632418H02Rik, Gm25410, Snord104, not searching for symbol synonyms## Warning: The following features are not present in the object: Spp2, Golt1a,
## F13b, Mtarc1, Mtarc2, Hnf4aos, Hnf4a, Hnf4g, Sertm1, Tm4sf4, Gm40055,
## 1700007F19Rik, Aadac, A330069K06Rik, Fgb, Gm16958, Acnat1, Ambp, Kif12, Gm12602,
## C8b, C8a, Gm19666, Gm42614, Klb, Ugt2b34, Ugt2b35, Ugt2b36, Ugt2b5, Afm,
## Hnf1a, Hnf1aos1, 1810017P11Rik, Mmd2, Gm20635, Gm3289, Akr1d1, 9930120I10Rik,
## Gm20426, Chst13, Uroc1, A2m, Gys2, Sult2a8, Prodh2, Slc7a9, Anks4b, Rps23rg1,
## Aadat, Hsd17b2, Gm27216, Smlr1, Gm29571, Mfsd4b3-ps, Creb3l3, Pah, Inhbc,
## Slc39a5, Igfbp1, Timd2, Shbg, 4930405D11Rik, Gm24233, Serpina6, Serpina1c,
## Mir337, DQ267102, Gm25357, Slc17a3, Cdhr2, Slc25a48, Lect2, Bhmt, Bhmt2, Dmgdh,
## Cpb2, 4930517O19Rik, Agxt2, Cyp2d26, Kng1, Spink1, Slc22a8, Keg1, A1cf, Pde6c,
## Cyp2c67, Cyp2c68, Cyp2c70, Abcc2, Pnliprp2, Rtl4, not searching for symbol
## synonyms## Warning: The following features are not present in the object: Gm29260, Gm13584,
## Gm37004, Tfap2c, AI849053, Gm15577, Gm35040, Gm25630, A330033J07Rik, Drd3,
## Gm23887, Emx2os, Emx2, Gm14664, not searching for symbol synonyms## Warning: The following features are not present in the object: Oprk1, Dusp27,
## 2900092N22Rik, A530058N18Rik, C130080G10Rik, Ctcflos, BC002189, C630028M04Rik,
## Gm12514, Gm12866, Trim63, Trim54, Myl2, Bmp10, Gm18066, 4930512H18Rik, Gm27211,
## Unc13c, Myl3, Gm10118, Hand1, 4932435O22Rik, Irx4, Thbs4, Gm3002, Gm8281,
## Mov10l1, Gm4335, Nkx2-5, Hdac1-ps, Mir133a-1hg, Gata6os, Tlx1, 6030498E09Rik,
## Gm14769, not searching for symbol synonyms## Warning: The following features are not present in the object: Vwc2l,
## Gm29514, Olah, Dbh, Pla2g4e, Gm9831, Gm12371, D130004A15Rik, A230006K03Rik,
## 4930567K12Rik, Hs3st4, 4930551E15Rik, 3110080E11Rik, Gm39244, Hcrtr2, Gm28905,
## Car10, Rab9b, not searching for symbol synonyms## Warning: The following features are not present in the object: Vxn, Gm13630,
## Prdm13, Tmprss11a, Srrm4os, Gm20501, Neurod4, Gm12224, Gm38534, Neurog1, Gm6999,
## Dcc, Drr1, not searching for symbol synonyms## Warning: The following features are not present in the object: Prdx2-ps1,
## 9630028B13Rik, Gm2061, Slc30a10, Epb42, Gm14049, Hsd3b6, 9830132P13Rik,
## 4933401H06Rik, 1700015C17Rik, Asb17os, Asb17, Gm22247, Lexm, Rnf212, Kel,
## Gm3793, Hbb-bh1, Hbb-bh0, Aqp8, Rusf1, Gypa, H2ax, Gm28530, Gm37249, Inhca,
## Ankrd36, Hba-x, Slc4a1, H2ac11, H2bc11, H1f4, Gm23127, Gm16867, Gm10110,
## 1810053B23Rik, Pdxk-ps, Marchf2, Gm20541, Rhag, Gm20517, Marchf3, Marchf5,
## Dennd10, Fmr1nb, not searching for symbol synonyms## Warning: The following features are not present in the object: A130048G24Rik,
## A630081D01Rik, A130071D04Rik, Gm5834, Cd5l, Gm15644, Clec7a, 4933406J09Rik,
## Gvin-ps6, Gvin-ps7, Gvin3, Gm15542, 6430710M23Rik, 2310008N11Rik, Gm23677,
## Hmgb1-ps8, C920009B18Rik, Nlrp1c-ps, 1700030C10Rik, Gm17160, Cyrib, Mx1, Pgap6,
## Ncr3-ps, Olfr111, Ms4a14, Tasl, not searching for symbol synonyms## Warning: The following features are not present in the object: Gm20172,
## Cracdl, Lmx1a, Ush2a, Gm13266, Dync2i2, Dcdc5, Gm4540, Gm15689, Dnai3, Dnai1,
## Ube2u, Dynlt5, Dnai4, Gm12930, Tex47, Smkr-ps, Gm44196, E330012B07Rik, Odad1,
## 3100003L05Rik, Katnip, Spef1l, Ins2, Gm36879, Poteg, Gm30504, Olfr370, Vat1l,
## 4933408N05Rik, Odad3, Dpy19l2, Gm1110, Hoatz, Gm20276, Odad4, Marchf10, Dnai2,
## 1700086L19Rik, Gm10735, Trhr, D930007P13Rik, Cfap91, Odad2, Gm16090, Ttr, Wnt8b,
## Frmpd4, not searching for symbol synonyms## Warning: The following features are not present in the object: Abca12,
## 4933417C20Rik, Mir205hg, Gm13219, Macrod2os1, Gpr87, Fhip1a, Gm12446, Ugt2a2,
## Gm33050, Vwde, Pyurf, Rfx6, Nepn, Fam174c, Tac2, B4galnt2, BC006965, Gm10406,
## Oc90, Gm15538, not searching for symbol synonyms## Warning: The following features are not present in the object: Lincmd1, Col19a1,
## Mstn, Chrnd, Chrng, Myog, Chrna1, 7530428D23Rik, Gm30735, Casq2, 4632404M16Rik,
## Frmpd1os, Pax7, Gm8091, Gm42875, Vgll2, Mybpc1, Myh8, Septin4, 4930544I03Rik,
## Cspg4b, 1520401A03Rik, Mymx, Pitx3, Tex16, Tceal7, not searching for symbol
## synonyms## Warning: The following features are not present in the object: Gm6209, Gm12829,
## Gm20485, Gm42397, Gm32531, Zic1, Gm12098, Zic2, Atp13a5, Htr1f, Mro, Slc22a6,
## Mageb18, not searching for symbol synonyms## Warning: The following features are not present in the object: Col9a1,
## 1700019A02Rik, Olfr1219, 9130410C08Rik, Gm12830, Gm10578, Epyc, not searching
## for symbol synonyms## Warning: The following features are not present in the object: Gm29455,
## 3110062G12Rik, Rapgef4os1, 2600014E21Rik, Neurod6, Gm26604, Kash5,
## C230057M02Rik, Camkv, 2900079G21Rik, Neurod2, Lrfn5, Gm20687, Mpped1, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Tfap2d, Gm16582,
## A930036I15Rik, Barhl2, not searching for symbol synonyms## Warning: The following features are not present in the object: Gm13629, Neurod1,
## Gm13791, Xkr7, 4930419G24Rik, Gm19445, 2610028E06Rik, Hmx1, Grxcr1, Exoc1l, Hrk,
## Gm16036, Ppp1r17, Tlx2, Hs3st2, Htr3a, Htr3b, Trhde, 4930473D10Rik, Gm15723,
## Tlx3, D130052B06Rik, Nrsn1, Prrxl1, Rxfp3, Gm2824, Olfr15, Ppef1, not searching
## for symbol synonyms## Warning: The following features are not present in the object: Dmbx1, Gm15637,
## Trh, not searching for symbol synonyms## Warning: The following features are not present in the object: Gm13377, Pax8,
## Gm13415, Lamp5, Gm27199, Lhx5, Slc6a5, Gm16010, Sox14, Lhx1, Lhx1os, Otp, Skor2,
## Pax2, not searching for symbol synonyms## Warning: The following features are not present in the object: Gm22786,
## 9430037O13Rik, Rrh, Kif19b, B130021K23Rik, C430039J01Rik, 4930413G21Rik,
## Septin1, Gm7972, Gm30238, Gpr137b-ps, H2bc22, Gm22208, Hoxc11, Dynlt2b, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Ibsp, Mir6240,
## Susd5, not searching for symbol synonyms## Warning: The following features are not present in the object: 6720464F23Rik,
## E430021H15Rik, 2310040G07Rik, Gm38825, Olfr959, 9430081H08Rik, Septin8, Btnl10,
## H2ac22, Slc66a2, not searching for symbol synonyms## Warning: The following features are not present in the object: Gm28175,
## 5930409G06Rik, Gsx1, Qrfprl, Gm18716, Cckbr, Sox1, Gm6607, Mcf2, not searching
## for symbol synonyms## Warning: The following features are not present in the object: Gm29865, Cyp2j8,
## Gm12688, Apc-ps1, Pramel47, Gjc3, Gm10046, 4930505M18Rik, Gm29507, Gm19514,
## Atp10b, AA914427, Gm16168, Slitrk2, not searching for symbol synonyms## Warning: The following features are not present in the object: Serpinb10,
## Gm33100, Olfr643, Irag1, 4933432K03Rik, Gm35657, H2bc4, H1f2, Pla2g10, Gm1720,
## not searching for symbol synonyms## Warning: The following features are not present in the object: Marchf4, Ecel1,
## Lhx4, Gm10530, Crp, Lhx3, Mnx1, Phox2b, G630064G18Rik, Gm31592, Grip1os2,
## Chat, Slc18a3, Gm2990, Cdh12, Marchf11, Hoxc8, Uts2b, Kcnh8, Slc5a7, Gm14696,
## A730046J19Rik, not searching for symbol synonyms## Warning: The following features are not present in the object: Sall4, Gm23445,
## Foxb1, A730062M13Rik, Gm30698, Sp8, not searching for symbol synonyms## Warning: The following features are not present in the object: D030025E07Rik,
## Gm10400, Hdnr, Olfr1372-ps1, Barx1, not searching for symbol synonyms## Warning: The following features are not present in the object: Cryge, Cryga,
## Cryba2, Olfr1062, Gm33206, Gm37359, Gja8, Tmprss11d, Gm31816, Gm20757, Mip,
## Gja3, not searching for symbol synonyms## Warning: The following features are not present in the object: Pth2r, Pax3,
## Gm30731, Slc6a11, Msx3, not searching for symbol synonyms## Warning: The following features are not present in the object: Gm19335, Myo3a,
## Nxph2, Dlx1as, Dlx2, Gm38505, Gm20515, Trpc4, Vmn2r1, Lhx8, Calb1, Dlx6os1,
## Dlx6os2, Dlx5, Slc6a1, C230062I16Rik, B020031H02Rik, Gm12068, 4933430M04Rik,
## Gm11346, Cntnap3, Smim45, Nol4, Arx, not searching for symbol synonyms## Warning: The following features are not present in the object: Tacr3, Tyrp1,
## Olfr1338, 2900064K03Rik, Oca2, Gabra5, Tyr, Gm15483, Clec18a, Slc38a8,
## 7630403G23Rik, Opn4, Slc45a2, AW822252, not searching for symbol synonyms## Warning: The following features are not present in the object: Gm13652, Gm31243,
## Gm5860, Gm15997, Alx1, Smc1b, Srd5a2, Dsc3, Mir6984, not searching for symbol
## synonyms## Warning: The following features are not present in the object: Otor, Vmn2r3,
## Gm14335, Kera, Gm22205, not searching for symbol synonyms## Warning: The following features are not present in the object: Gm17893,
## 4930509J09Rik, Gm23054, Rph3a, Iqsec3, A230077H06Rik, Cacng3, Esrrb, Pou6f2,
## Slc35f4, Cdh9, 1700123O21Rik, Gm15808, Akain1, Htr4, Gm15155, not searching for
## symbol synonyms## Warning: The following features are not present in the object: Fcnb, Gm16035,
## Prap1, Ngp, Slc13a5, Stfa1, Stfa3, not searching for symbol synonyms## Warning: The following features are not present in the object: Lmx1b,
## 9430024E24Rik, BB557941, Hoxd13, Hoxd12, Hoxd11, Gm14055, Gm10258, Hoxa10,
## Hoxa11, Hoxa11os, Hoxa13, Hottip, Sox5os4, A530021J07Rik, Gm31727, Hand2,
## Gm9143, not searching for symbol synonyms## Warning: The following features are not present in the object: 3110099E03Rik,
## Gm24492, not searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2, Ubb-ps,
## Slc2a5, not searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2, Ubb-ps,
## Gpr137b-ps, Fam3d, Cela2a, Smim30, Iqsec3, Irag1, Phxr4, Frmd7, not searching
## for symbol synonyms## Warning: The following features are not present in the object: Ins2, not
## searching for symbol synonyms## Warning: The following features are not present in the object: H1f2, Get1,
## Mir24-2, Exosc6, H2ax, not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, Tamalin,
## Ddx39a, Mir24-2, H2ax, not searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2, Ube2d-ps,
## Ubb-ps, H1f2, Zfp935, Cep20, Cldn25, Sting1, Slc66a2, Emx2, Atp5pb, Adprs,
## Smim30, Chaserr, Mir24-2, H2ax, not searching for symbol synonyms## Warning: The following features are not present in the object: Ins2, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Plac9, Slc66a2,
## H2az1, not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Niban1, Dnaaf10,
## H2bc4, Plac9, Sting1, Slc66a2, not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Clec7a, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, H2aj,
## Mir9-3hg, Psme3ip1, Erdr1, not searching for symbol synonyms## Warning: The following features are not present in the object: H2az2, Ubb-ps,
## H2bc4, Zic2, Atp5pb, Rpl34-ps1, Mir24-2, not searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2, H2az2,
## Ubb-ps, Zic2, Rpl34-ps1, H2az1, Fam110d, Smim30, Mir24-2, Mobp, not searching
## for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, Zic2,
## Neurod1, Cbln1, Calb2, not searching for symbol synonyms## Warning: The following features are not present in the object: Amer3, Zbed6,
## Clvs2, Diras1, Ano4, H2az2, Fem1al, Neurod2, Efcab15, Mideas, Tunar, Dync2i1,
## H2ac13, H2bc4, AW495222, Fam3d, Mir124a-1hg, Cacng2, Gm15760, Kcnj6, Lhfpl5,
## St6gal2, 2410021H03Rik, Niban2, Zfp804a, Pla2g4e, Rbm12, Gm20754, Fam110d,
## Srrm3, Particl, Ttc9b, Gm5113, 1500012K07Rik, Mir9-3hg, Hs3st4, Ins2, Ncan,
## Brme1, Get3, Exosc6, Gm6981, Erdr1, not searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2, H2az2,
## H2bu2, Ubb-ps, H2bc4, Tamalin, Cep20, Cerox1, Antkmt, Iftap, Nkx2-2, Dusp15,
## Hapln2, Atp5pb, H2az1, Smim30, Chaserr, H2ax, not searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2, H2bu2,
## Ubb-ps, Antkmt, Nkx2-2, Smim30, Pnma8b, H2ax, Sox3, not searching for symbol
## synonyms## Warning: The following features are not present in the object: Ubb-ps, Ngp, not
## searching for symbol synonyms## Warning: The following features are not present in the object: H2az2, Ubb-ps,
## Rpl34-ps1, Mir24-2, not searching for symbol synonyms## Warning: The following features are not present in the object: H2az2, Ubb-ps,
## Rpl34-ps1, Slc2a5, Mir24-2, Gm6981, not searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2, Mars1,
## Ubb-ps, not searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2, H2az2,
## Ubb-ps, Pyy, H2bc4, Antkmt, Polr1h, Slc66a2, Bambi-ps1, 5830417I10Rik, Atp5pb,
## Cibar1, Spring1, Smim30, Particl, Chaserr, 4930413G21Rik, not searching for
## symbol synonyms## Warning: The following features are not present in the object: Mtarc2, Ubb-ps,
## Tnfsfm13, Macroh2a1, Particl, not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, H1f2, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Abca12, Nkx2-5,
## Ambp, not searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2, Ubb-ps,
## Fam110d, not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, Slc2a5,
## Cd209f, Vsig4, not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps,
## Zfp862-ps, not searching for symbol synonyms## Warning: The following features are not present in the object: Trim63, Trim54,
## not searching for symbol synonyms## Warning: The following features are not present in the object: Gm5069,
## Rps15a-ps6, Rpl31-ps12, Morf4l1-ps1, Myoz2, Mir703, not searching for symbol
## synonyms## Warning: The following features are not present in the object: Rpl31-ps12, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Gm7609, H2bc4,
## Rpl31-ps12, Fam110d, Gm15421, Klra9, not searching for symbol synonyms## Warning: The following features are not present in the object: Rps15a-ps6,
## H2bc4, Plac9, Tmem254, Rpl31-ps12, Eef1a2, Gm15421, Mir703, H2aj, G530011O06Rik,
## not searching for symbol synonyms## Warning: The following features are not present in the object: Lilrb4b,
## Rps15a-ps6, Plac9, Cd209f, Cd209g, Vsig4, not searching for symbol synonyms## Warning: The following features are not present in the object: Plac9,
## Rpl31-ps12, not searching for symbol synonyms## Warning: The following features are not present in the object: Rdh16f2, H1f2,
## Tmem254, Cyp2d9, Miox, Hao2, not searching for symbol synonyms## Warning: The following features are not present in the object: Rdh16f2,
## Rps15a-ps6, Akr1c21, H2bc4, H1f2, Bhmt, Tmem254, Cyp2d12, Rpl31-ps12, Spink1,
## Slc22a8, Defb29, Cyp24a1, Hsd3b4, Hao2, H2az1, Cyp4a14, Guca2b, Mir703, Hpd,
## Slc13a1, Slc51b, Gsta5, not searching for symbol synonyms## Warning: The following features are not present in the object: Spink1, Fam110d,
## Kap, not searching for symbol synonyms## Warning: The following features are not present in the object: Rps15a-ps6,
## Rpl31-ps12, not searching for symbol synonyms## Warning: The following features are not present in the object: Spink1, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Foxi1, Tmem254,
## Entpd4, Miox, Rpl31-ps12, Spink1, Thoc2l, 6820431F20Rik, Erdr1, not searching
## for symbol synonyms## Warning: The following features are not present in the object: Plac9, Kap, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Tmem254, Kap, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Spink1, Kap, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Kcnj16, Tmem254,
## Entpd4, Miox, Rpl31-ps12, Cldn16, Spink1, Bbln, Clcnkb, Tmem52b, Kap, H2aj,
## Umod, Erdr1, not searching for symbol synonyms## Warning: The following features are not present in the object: Plac9, Kap, not
## searching for symbol synonyms## Warning: The following features are not present in the object: G6pc, H2bc4,
## H1f2, Tmem254, Cyp2d12, Defb29, Cyp24a1, Rps15a-ps4, Slc13a1, Clec2h, Kap,
## Slco1a6, not searching for symbol synonyms## Warning: The following features are not present in the object: Miox, Rpl31-ps12,
## Spink1, H2aj, not searching for symbol synonyms## Warning: The following features are not present in the object: 1700016C15Rik,
## Pah, Akr1c21, Tmem174, Tmem254, Miox, Mep1a, Spink1, Glyat, Keg1, Defb29,
## Hsd3b4, Hao2, Cyp2j5, Guca2b, Kap, H2aj, Acsm2, not searching for symbol
## synonyms## Warning: The following features are not present in the object: Ubb-ps, Gfus, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Golt1a, Mptx1,
## Mtarc2, Slc39a5, Rps15a-ps6, H2az2, Ubb-ps, Macroh2a1, Fam3d, Gsdmc2, Gfus,
## Cyp2d26, Pla2g10, Rpl31-ps12, Antkmt, Dpcd, Bbln, Hao2, Atp5pb, Fabp2, H2az1,
## Hyi, Guca2b, Rps15a-ps4, Sult1b1, Cdx2, Pals2, H2aj, Prap1, Defb37, Hsd17b2,
## Slc51b, Pigbos1, Gsta1, not searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2, H2az2,
## H2ac23, H1f2, Fam3d, Tmem254, Gfus, Rpl31-ps12, Antkmt, Gm6402, Gm9320, H2az1,
## Cdx2, Smim30, Aqp8, H2ax, not searching for symbol synonyms## Warning: The following features are not present in the object: Mptx1, H2az2,
## Ubb-ps, Hoxb13, H1f2, Fam3d, Gfus, Pla2g10, Cldn14, Tpsg1, Antkmt, Gm6402,
## Ttr, Cyp2c55, Bbln, Atp5pb, Fabp2, Guca2b, Pla2g2a, Smim30, H2aj, Scgb2b7, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, Tlcd3a,
## Cldn25, Cldn14, Aldh3b2, Cdx2, Hoxa11os, Ush1c, Ddx39a, Get3, H2ax, not
## searching for symbol synonyms## Warning: The following features are not present in the object: H2az1, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Plac9, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Plac9, Tmem254,
## Cldn25, H2az1, not searching for symbol synonyms## Warning: The following features are not present in the object: Plac9, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Rps15a-ps6,
## Cyrib, Rpl31-ps12, H2az1, Rps15a-ps4, Gm15421, not searching for symbol synonyms## Warning: The following features are not present in the object: Plac9,
## G530011O06Rik, not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, Plac9,
## Rpl31-ps12, not searching for symbol synonyms## Warning: The following features are not present in the object: Dnaaf10, Plac9,
## Erdr1, not searching for symbol synonyms## Warning: The following features are not present in the object: Rpl32l,
## Rps15a-ps6, Ubb-ps, H2ac23, Bhmt, Tmem254, Gm12191, Rpl31-ps12, Morf4l1-ps1,
## Ndufs5-ps, Gm6402, Gm9320, Rpl34-ps1, Ptma-ps2, Rps15a-ps4, Gm15421, Mir703,
## Npm3-ps1, Gm6654, H2ax, Gm6222, Erdr1, not searching for symbol synonyms## Warning: The following features are not present in the object: Gm7609, Rpl32l,
## Rps15a-ps6, H2az2, Ubb-ps, Rpl31-ps12, Morf4l1-ps1, Ndufs5-ps, Gm6402, Gm9320,
## Norad, Rpl34-ps1, Rps15a-ps4, Gm15421, Mir703, Gm6654, Gm6222, not searching for
## symbol synonyms## Warning: The following features are not present in the object: Agxt, Rdh16f2,
## Bhmt, Tmem254, Ugt3a1, A1bg, Rpl31-ps12, Mup2, Mup20, Ambp, Rps15a-ps4, Hpd,
## Cyp3a44, Sult2a2, Sult2a1, Sult2a7, Prodh2, Hamp2, Aqp8, Apoa5, not searching
## for symbol synonyms## Warning: The following features are not present in the object: H2bc4, Ins2, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Tmem254, Cldn25,
## H2az1, not searching for symbol synonyms## Warning: The following features are not present in the object: H2az1, not
## searching for symbol synonyms## Warning: The following features are not present in the object: H1f4, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Gm4956, Atp5pb,
## not searching for symbol synonyms## Warning: The following features are not present in the object: H2bc4, H1f2,
## H2aj, not searching for symbol synonyms## Warning: The following features are not present in the object: Fam110d, not
## searching for symbol synonyms## Warning: The following features are not present in the object: H2az2, H2ac23,
## H1f4, Macroh2a1, Antkmt, Bbln, H2az1, H2aj, H2ax, not searching for symbol
## synonyms## Warning: The following features are not present in the object: Ins1, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Gm4956, Mtarc2,
## H2bc4, H1f2, not searching for symbol synonyms## Warning: The following features are not present in the object: H2az2, H1f4,
## Bbln, H2az1, H2aj, not searching for symbol synonyms## Warning: The following features are not present in the object: Niban2, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2, Ubb-ps,
## Tamalin, H2ax, not searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2, H2az2,
## H1f2, Macroh2a1, Bbln, H2ac18, not searching for symbol synonyms## Warning: The following features are not present in the object: H1f2, Macroh2a1,
## Antkmt, H2az1, H2aj, not searching for symbol synonyms## Warning: The following features are not present in the object: H2az2, Tenm2,
## Plac9, Tmem254, not searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2, H2az2,
## H2bc4, H2ac18, not searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2, Btn1a1,
## Tmem254, Slc66a2, Erdr1, not searching for symbol synonyms## Warning: The following features are not present in the object: Wfdc18, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2, H2az2,
## Plac9, Tmem254, not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, Atp10d,
## Chaserr, not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, H1f2,
## H3c1, Antkmt, Polr1h, Bbln, Smim30, H2aj, Exosc6, not searching for symbol
## synonyms## Warning: The following features are not present in the object: H1f5, H1f3,
## H2ac8, H1f4, Cldn13, Ngp, Erdr1, not searching for symbol synonyms## Warning: The following features are not present in the object: Mrgpra2b, Ngp,
## not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, Exosc6,
## H2ax, Gm6981, not searching for symbol synonyms## Warning: The following features are not present in the object: H2ac23, Fcnb,
## Gypa, H2ax, Ngp, not searching for symbol synonyms## Warning: The following features are not present in the object: Tmem254, Ngp, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2, Ubb-ps,
## Atp5pb, Smim30, Ddx39a, H2ax, not searching for symbol synonyms## Warning: The following features are not present in the object: H2ac6, H2az1,
## Cldn13, Mrgpra2b, Gypa, Ddx39a, Ngp, not searching for symbol synonyms## Warning: The following features are not present in the object: H2bc4, Bbln,
## H2ac18, H2aj, Ngp, not searching for symbol synonyms## Warning: The following features are not present in the object: Ddx39a, Ngp, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Skp1, Ubb-ps,
## Polr1h, H2aj, 2610005L07Rik, not searching for symbol synonyms## Warning: The following features are not present in the object: Mrgpra2b, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Ngp, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2, Tmem254,
## Antkmt, not searching for symbol synonyms## Warning: The following features are not present in the object: Mrgpra2b, Ngp,
## not searching for symbol synonyms## Warning: The following features are not present in the object: Macroh2a1,
## Tmem254, Ngp, not searching for symbol synonyms## Warning: The following features are not present in the object: Fcnb, H2az1,
## Mrgpra2b, Ngp, not searching for symbol synonyms## Warning: The following features are not present in the object: Ngp, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, Tamalin,
## Tff2, Cela2a, 2610005L07Rik, not searching for symbol synonyms## Warning: The following features are not present in the object: Cela2a, Try4,
## Reg1, not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, Pyy,
## Dbpht2, H1f2, Scgn, Pnlip, Cela2a, Try4, Reg1, not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, Syndig1l,
## Ucn3, H1f2, Isl1, Ins1, Gm13498, Neurod1, Nkx2-2, Sertm1, Kif12, Cela2a, Try4,
## Ins2, H2ap, Erdr1, not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, Dbpht2,
## Cela2a, Try4, Ush1c, not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, Cela2a,
## Try4, not searching for symbol synonyms## Warning: The following features are not present in the object: 1810007D17Rik,
## not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, Pnliprp2,
## Ambp, Get3, Mmp7, Ngp, not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, Antkmt,
## Mir24-2, not searching for symbol synonyms## Warning: The following features are not present in the object: H2az2, Ubb-ps,
## Norad, not searching for symbol synonyms## Warning: The following features are not present in the object: Zbed6,
## Rps15a-ps6, B4galnt2, Slc4a1, Gm12191, Cyrib, Morf4l1-ps1, Ndufs5-ps,
## Tmem181c-ps, Mep1a, Cyp2c55, Niban2, Pck1, Fabp2, Sult1b1, Gm6654, Aqp8, Prap1,
## Gypa, Ces2a, Hsd17b2, Gcnt3, Erdr1, not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, Tamalin,
## Norad, Mrgprg, Mir24-2, not searching for symbol synonyms## Warning: The following features are not present in the object: Mtarc2,
## Rps15a-ps6, H2az2, Ubb-ps, Calm5, Rpl31-ps12, Antkmt, Gm9320, Gm94, Bbln,
## 2310050C09Rik, Hyi, Cela2a, Oas1f, Smim30, BC064078, H2aj, Krtdap, Mt4, Exosc6,
## Apoc3, not searching for symbol synonyms## Warning: The following features are not present in the object: Tfap2b, Mtarc2,
## H2az2, Tenm2, Ubb-ps, Krt24, Krt4, Hoxc8, Rpl34-ps1, Smim30, BC064078, H2aj,
## Irag2, Mir24-2, Erdr1, not searching for symbol synonyms## Warning: The following features are not present in the object: Rps15a-ps6,
## Ubb-ps, Calm5, Gfus, Krt4, Rpl31-ps12, Acer1, Gm9320, Hyi, Dlx5, BC064078,
## Exosc6, H2ax, not searching for symbol synonyms## Warning: The following features are not present in the object: Rps15a-ps4, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, H2az1,
## not searching for symbol synonyms
## Warning: The following features are not present in the object: Ubb-ps, H2az1,
## not searching for symbol synonyms## Warning: The following features are not present in the object: Gm4956,
## Rps15a-ps6, Gm9320, Rps15a-ps4, not searching for symbol synonyms## Warning: The following features are not present in the object: Tmem254, Cldn25,
## H2az1, not searching for symbol synonyms## Warning: The following features are not present in the object: Macir, Cyrib,
## Rpl31-ps12, H2az1, Mrgpra2b, Ngp, not searching for symbol synonyms## Warning: The following features are not present in the object: H2bc4, H2aj, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Rps15a-ps6,
## Rpl31-ps12, H2az1, Rps15a-ps4, H2aj, not searching for symbol synonyms## Warning: The following features are not present in the object: Slc4a1,
## Rpl31-ps12, Epb42, Gm6654, Gypa, not searching for symbol synonyms## Warning: The following features are not present in the object: H2az2, Polr1f,
## H2ac23, H2bc4, H1f2, Macroh2a1, Tmem254, Rpl31-ps12, Antkmt, Polr1h, Gm9320,
## Atp5pb, H2az1, Gm15421, Mir703, Smim30, H2ax, Pigbos1, Erdr1, not searching for
## symbol synonyms## Warning: The following features are not present in the object: Antkmt, Gm6402,
## H2aj, not searching for symbol synonyms## Warning: The following features are not present in the object: Erdr1, not
## searching for symbol synonyms## Warning: The following features are not present in the object: H2az2, Ubb-ps,
## H2ac23, H2ac11, H2ac8, Macroh2a1, Antkmt, H2-T10, Atp5pb, H2az1, H2aj, Ddx39a,
## Exosc6, H2ax, Erdr1, not searching for symbol synonyms## Warning: The following features are not present in the object: Serpinb3a, Krt36,
## H2ac23, Plac9, Gm94, Sprr1b, H2az1, Krtdap, Ddx39a, H2ax, not searching for
## symbol synonyms## Warning: The following features are not present in the object: Krtap3-3, Krt36,
## Plac9, Tmem254, Krt84, Sprr1b, Krtdap, Defb4, not searching for symbol synonyms## Warning: The following features are not present in the object: Ubb-ps, Mideas,
## H2ac13, H3c1, Obi1, Ins1, Adprs, Cela2a, Ins2, 6820431F20Rik, not searching for
## symbol synonyms## Warning: The following features are not present in the object: Tmem181c-ps, not
## searching for symbol synonyms## Warning: The following features are not present in the object: H2aw, H2bc4,
## Gm9320, Atp5pb, H2aj, not searching for symbol synonyms## Warning: The following features are not present in the object: Rps15a-ps6,
## Ubb-ps, Rpl31-ps12, Gm6402, Gm9320, not searching for symbol synonyms## Warning: The following features are not present in the object: Garre1, not
## searching for symbol synonyms## Warning: The following features are not present in the object: Dcpp3, Gm6402,
## Npm3-ps1, not searching for symbol synonymschosen <- 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: Cannot find 'res0p1_clusters' in this Seurat objectchosen <- 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: Cannot find 'res0p1_clusters' in this Seurat objectchosen <- 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: Cannot find 'res0p1_clusters' in this Seurat objectchosen <- 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: Cannot find 'res0p1_clusters' in this Seurat objectchosen <- 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: Cannot find 'res0p1_clusters' in this Seurat objectchosen <- 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: Cannot find 'res0p1_clusters' in this Seurat objectt_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: Cannot find 'res0p1_clusters' in this Seurat objectt_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: Cannot find 'res0p1_clusters' in this Seurat objectt_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: Cannot find 'res0p1_clusters' in this Seurat objectt_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: tibble(v.3.1.8), 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), vipor(v.0.4.5), 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), 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), rstudioapi(v.0.14), miniUI(v.0.1.1.1), sp(v.1.5-1), bslib(v.0.4.2), fastmatch(v.1.1-3), aod(v.1.3.2), treeio(v.1.22.0), shiny(v.1.7.4), xfun(v.0.36), 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), 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), 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), tools(v.4.2.0), beeswarm(v.0.4.0), 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), 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), R.utils(v.2.12.2), goftest(v.1.2-3), spatstat.utils(v.3.0-1), 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), survival(v.3.4-0), 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), R.oo(v.1.25.0), labeling(v.0.4.2), 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), ggbeeswarm(v.0.7.1), aplot(v.0.1.9), ggrastr(v.1.0.1), sass(v.0.4.4), Rcpp(v.1.0.9), RANN(v.2.6.1), enrichplot(v.1.18.3), fansi(v.1.0.3), tzdb(v.0.3.0), 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), 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), 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), R.methodsS3(v.1.8.2), gtable(v.0.3.1), DBI(v.1.1.3), ggfun(v.0.0.9), tensor(v.1.5), httr(v.1.4.4), highr(v.0.10), KernSmooth(v.2.23-20), stringi(v.1.7.8), vroom(v.1.6.0), 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), readr(v.2.1.3), 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))---
title: "Playing with some new scRNASeq data: A+B 202301."
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
  html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    keep_md: false
    mode: selfcontained
    number_sections: true
    self_contained: true
    theme: readable
    toc: true
    toc_float:
      collapsed: false
      smooth_scroll: false
---

<style type="text/css">
body, td {
  font-size: 16px;
}
code.r{
  font-size: 16px;
}
pre {
 font-size: 16px
}
</style>

```{r options, include=FALSE}
library("hpgltools")
library("reticulate")
tt <- devtools::load_all("~/hpgltools")
knitr::opts_knit$set(width=120,
                     progress=TRUE,
                     verbose=TRUE,
                     echo=TRUE)
knitr::opts_chunk$set(error=TRUE,
                      dpi=96)
lua_filters <- rmarkdown::pandoc_lua_filter_args("pandoc-zotxt.lua")
old_options <- options(digits=4,
                       stringsAsFactors=FALSE,
                       knitr.duplicate.label="allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size=10))
rundate <- format(Sys.Date(), format="%Y%m%d")
previous_file <- ""
ver <- "202301"

##tmp <- sm(loadme(filename=paste0(gsub(pattern="\\.Rmd", replace="", x=previous_file), "-v", ver, ".rda.xz")))
rmd_file <- "index_AB.Rmd"
library(Seurat)
library(ggplot2)
library(purrr)
library(skimr)
library(GSEABase)
library(tibble)
```

# Notes:

## Querying Dr. Mosser 20230123

* Question from Najib: Connection between 5' libraries and VDJ, tags
  are in fact shared from VDJ/surface to the 'parent' GEM.  April: Do
  GEM generation, then split the resulting cDNA at cleanup step to the
  protein barcode set (at bead cleanup), then go into regular 5'
  expression library, from which a set of specific primers are used to
  enrich the VDJ portion.  Thus all three have the same parental cell
  tag.
* Question from April: Do we need to change the inputs; from how many cells
  did we recover reads?  He wants 10,000 cells / animal.  April pooled
  according to 10x recommendations: 1:4 VDJ:expression library.
  Surface protein libraries have not yet been run, these have separate
  indexes and are much shorter; in contrast the VDJ/expression
  libraries are relatively similar (550 vs 630 nt).
* From Najib: why 5' vs 3' capture?  Not entirely certain, but 5' kit
  is used for immunology usually, apparently.
* Questions about the cell preparations: what was the status of the
  cells at GEM generation?  Can we learn if that affected the number
  of cells observed, reads/cell?  Can April tweak the sequencing
  library inputs to help even out the differences across samples for
  future runs (given that there are 3(I think) coming up)?
  Conversely, should I reprocess the samples so that all samples are
  perceived as having the least number of cells observed?
* Quick primer of recombination and V(D)J: each receptor is
  heterodimer, heavy chain has the constant, V, D, and J.  Conversely the
  light chain has the constant and the VJ.  In T-cells, allelic
  exclusion results in a single resulting expresison event.  In
  B-cells there may be many.  In-toto there is a near-infinite random
  set of possibilities; these are all randomly generated during
  development.  B-cells are constantly making new combinations during
  the lifespan -- T-cells are long-lived and constant (Dave explained
  an experiment where someone transferred T-cells from mouse to mouse
  over the course of many years).  (orientation 5'->3'?  constant->D->J?)
* Every cell that was sequenced is in theory a T-cell ("lung infected
  T-cells from mice), so should all have VDJ sequences.  A small set
  of immune cells (NK) do not have these.

## Checkin meeting with Dr. Park 20230124

* April describing observations, cell #s etc.
* Definitely not FACS, performed negative enrichment which in theory gets >= 90% pure samples.
* Najib query: limit to VDJ cells only?  Unlikely.
* It sounds to me that the likely tasks I perform are not needed by Dr. Park.

# TODO:

* Check expression of the samples for levels of CD3.
* Print out filtered amounts on a per-sample basis.
* Query mSigDB categories of interest (currently I grabbed the ~98
  influenza categories arbitrarily): e.g. figure out which ones are
  actually relevant.

# Changelog

# Preprocessing with cellranger

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.

# Rerun the pipeline with multi

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.

### Shenanigans for combined VDJ samples

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.

```{bash shenanigans, eval=FALSE}
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
done
```

```{bash cellrangerv2, eval=FALSE}
module 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

I wonder if I can put the gene annotations into the misc slot of the
seurat data structure?  And perhaps overload fData() to use it?

```{r hg38_fun}
annotations <- load_biomart_annotations()$annotation
brief <- unique(annotations[, c("hgnc_symbol", "description")])
```

# Set prefix of the data

```{r prefix}
#prefix <- "multi"
prefix <- "01multi_combined"
```

# Load the data into Seurat and poke at it

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.

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.

I wrote a little function to make loading the Seurat data from a
sample sheet easier.  My intention is to have some of this code write
back to that sample sheet.

```{r seurat_loading}
all <- create_seurat("sample_sheets/all_samples.csv", vdj_t_column = "vdjtcells")

control_cell_idx <- all[["orig.ident"]] == "control"
control_cells <- all[, control_cell_idx]
mock_cell_idx <- all[["orig.ident"]] == "mock"
mock_cells <- all[, mock_cell_idx]
muscular_cell_idx <- all[["orig.ident"]] == "m"
muscular_cells <- all[, muscular_cell_idx]
nasal_cell_idx <- all[["orig.ident"]] == "n"
nasal_cells <- all[, nasal_cell_idx]
```

# Query for clonotypes in the individual samples and the full dataset.

```{r query_clonotypes}
control_clono <- !is.na(control_cells[["raw_clonotype_id"]])
summary(control_clono)

mock_clono <- !is.na(mock_cells[["raw_clonotype_id"]])
summary(mock_clono)

m_clono <- !is.na(muscular_cells[["raw_clonotype_id"]])
summary(m_clono)

n_clono <- !is.na(nasal_cells[["raw_clonotype_id"]])
summary(n_clono)
```

# Initial Clusters

I 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.

```{r initial_clusters}
cluster_letters <- as.factor(LETTERS[Idents(object=all)])
names(cluster_letters) <- colnames(x=all)
sample_ids <- as.character(cluster_letters)
```

Now that I have 4 identical vectors, fill them with my chosen names
for the samples and whether they do(nt) have that identity.

```{r self_notself}
control_idx <- sample_ids == "A"
all[["control_state"]] <- "Stimulated"
all@meta.data[control_idx, "control_state"] <- "Control"

mock_idx <- sample_ids  == "B"
all[["mock_state"]] <- "Not Mock"
all@meta.data[mock_idx, "control_state"] <- "Mock"

mock_idx <- sample_ids  == "C"
all[["muscular_state"]] <- "Not Muscular"
all@meta.data[mock_idx, "muscular_state"] <- "Muscular"

mock_idx <- sample_ids  == "D"
all[["nasal_state"]] <- "Not Nasal"
all@meta.data[mock_idx, "nasal_state"] <- "Nasal"
```

Now 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.

# Filters and QC

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...

```{r filter_thresholds}
min_num_rna <- 200
min_pct_ribo <- 5
max_pct_mito <- 20

all <- record_seurat_samples(all, type="num_cells")

all[["percent_mt"]] <- PercentageFeatureSet(all, pattern="^mt-")
all[["percent_ribo"]] <- PercentageFeatureSet(all, pattern="^Rp[sl]")
```

Show the state before filtering on a per-cell basis across all
samples. Start with the number of cells


```{r show_num_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"))))
```

```{r show_all_summaries}
skim(all[["percent_mt"]])
skim(all[["percent_ribo"]])
skim(all[["nFeature_RNA"]])
skim(all[["nCount_RNA"]])
## Length and reads are for only those cells with clonotypes.
skim(all[["reads"]])
skim(all[["length"]])

## How many cells have specific chains associated with them
sum(!is.na(all$chain))
```

And 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.

```{r show_state_samples}
all <- record_seurat_samples(all, type="num_cells") %>%
  record_seurat_samples(type="nFeature_RNA") %>%
  record_seurat_samples(type="nCount_RNA") %>%
  record_seurat_samples(type="reads", column_name="clonotype_reads") %>%
  record_seurat_samples(type="pct_mito", pattern="^mt-") %>%
  record_seurat_samples(type="pct_ribo", pattern="^Rp[sl]")
```

```{r print_meta}
## The recorded information is here, but not printing it for now:
all@misc[["sample_metadata"]]
```

Ok, that was fun; lets look at this information as a series of plots:

```{r filters_qc}
VlnPlot(all, features="nFeature_RNA", pt.size=0)
VlnPlot(all, features="pct_mito", pt.size=0)
VlnPlot(all, features="pct_ribo", pt.size=0)
VlnPlot(all, features="nCount_RNA", pt.size=0)
VlnPlot(all, features="reads", pt.size=0)
## I am curious about the length of the clonotype sequences.
VlnPlot(all, features="length", pt.size=0)

FeatureScatter(all, "pct_ribo", "pct_mito")
FeatureScatter(all, "nCount_RNA", "nFeature_RNA")
FeatureScatter(all, "nCount_RNA", "pct_ribo")
FeatureScatter(all, "nCount_RNA", "pct_mito")
```

# Filter samples using our guesstimates

Start with a minimum number of RNAs filter.

```{r filter_min_rnas}
sufficient_rna_observed <- WhichCells(all, expression=nFeature_RNA >= min_num_rna)
filt <- subset(all, cells=sufficient_rna_observed)
```

Second I will check that the number of reads/rna across cells is
sufficient, that filter does nothing currently, which I think is good.

```{r filter_ribosomal}
## I think this filter does nothing in its current form.
sufficiently_observed_idx <- rowSums(filt) > 3
summary(sufficiently_observed_idx)
dim(filt)
filt <- subset(filt, features=rownames(filt)[sufficiently_observed_idx])
dim(filt)

## 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)
filt <- subset(filt, cells=high_ribosomal)
```

Exclude cells with too much mitochondrial RNA

## Now drop mitochondrial genes

```{r filter_mito}
low_mitochondrial <- WhichCells(filt, expression=percent_mt <= max_pct_mito)
filt <- subset(filt, cells=low_mitochondrial)
```

# Record post-filtered stats

```{r post_filt_stats}
filt <- record_seurat_samples(filt, type="num_cells") %>%
  record_seurat_samples(type="nFeature_RNA") %>%
  record_seurat_samples(type="nCount_RNA") %>%
  record_seurat_samples(type="reads", column_name="clonotype_reads") %>%
  record_seurat_samples(type="pct_mito", pattern="^mt-") %>%
  record_seurat_samples(type="pct_ribo", pattern="^Rp[sl]")
```

Add the new filtered mean values onto the original set.

```{r cbind_meta}
all@misc$sample_metadata <- cbind(all@misc$sample_metadata, filt@misc$sample_metadata)
```

# Distribution

## Before filtering

```{r distribution}
all_norm <- NormalizeData(object=all) %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  FindNeighbors() %>%
  FindClusters() %>%
  RunPCA() %>%
  RunTSNE() %>%
  RunUMAP(reduction = "pca", dims = 1:10)

DimPlot(object=all_norm, reduction="tsne")
plotted <- DimPlot(all_norm, reduction="umap", group.by="orig.ident", label=TRUE)
plotted
```

## After filtering

```{r distribution_post}
filt_norm <- NormalizeData(object=filt) %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  FindNeighbors() %>%
  FindClusters() %>%
  RunPCA() %>%
  RunTSNE() %>%
  RunUMAP(reduction="pca", dims=1:10)

DimPlot(object=filt_norm, reduction="tsne")
plotted <- DimPlot(filt_norm, reduction="umap", group.by="orig.ident", label=TRUE)
plotted
```

```{r choose_dims}
filt_norm <- JackStraw(filt_norm, num.replicate=10)
filt_norm <- ScoreJackStraw(filt_norm)
JackStrawPlot(filt_norm)
ElbowPlot(filt_norm)
## 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")
RunUMAP(filt_norm, dims=1:9)
DimPlot(filt_norm, label=TRUE)

filt_norm <- FindClusters(filt_norm, resolution=0.1) %>%
  FindNeighbors(k.param=6) %>%
  StashIdent(save.name="res0p1_clusters")
RunUMAP(filt_norm, dims=1:9)
DimPlot(filt_norm, label=TRUE)
```

Add into the metadata a concatenation of the sample ID and the cluster ID

```{r compare_clusters}
identity_vector <- filt_norm[["orig.ident"]][["orig.ident"]]
class(identity_vector)
cluster_vector <- as.character(filt_norm[["res0p1_clusters"]][["res0p1_clusters"]])
concatenated_vector <- paste0(identity_vector, "_", cluster_vector)
filt_norm[["cluster_sample"]] <- concatenated_vector
```

# Add these new clusters to our non-normalized data

I am not yet certain of how Seurat handles (non)normalized data for
the various FindMarkers functions.  Thus, I am adding the clusters
from the dimension reductions to the non-normalized data here.

```{r add_clusters_to_filt}
filt[["res0p1_clusters"]] <- filt_norm[["res0p1_clusters"]]
filt[["cluster_sample"]] <- filt_norm[["cluster_sample"]]
```

# Variable features

```{r variable_features}
var <- FindVariableFeatures(filt_norm)
most_var <- head(VariableFeatures(var), 30)
variable_plot <- VariableFeaturePlot(var)
variable_plot <- LabelPoints(plot=variable_plot, points=most_var, repel=TRUE)
variable_plot
```

# Various marker searches

## All Markers

### By sample

Question: 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.

```{r findallmarkers01}
combined_markers <- FindAllMarkers(filt, only.pos=TRUE, logfc.threshold=0.5)
head(combined_markers)

combined <- as.data.frame(combined_markers)
rownames(combined) <- toupper(rownames(combined))
annotated_markers <- merge(combined, brief, by.x="row.names", by.y="hgnc_symbol",
                           all.x=TRUE)
```

### By sample

```{r findallmarkers02}
combined_markers <- FindAllMarkers(filt, only.pos=TRUE, logfc.threshold=0.5)

combined <- as.data.frame(combined_markers)
rownames(combined) <- toupper(rownames(combined))
annotated_markers <- merge(combined, brief, by.x="row.names", by.y="hgnc_symbol",
                           all.x=TRUE)
head(annotated_markers)
```

### By Cluster

Since I am not using the filt_norm data structure, I will need to pull
the cluster information from the normalized copy...

```{r markers_by_cluster}
clusters <- filt
Idents(clusters) <- clusters[["res0p1_clusters"]]
cluster_markers <- FindAllMarkers(clusters, only.pos=TRUE, logfc.threshold=0.5)

cluster_genes <- as.data.frame(cluster_markers)
rownames(cluster_genes) <- toupper(rownames(cluster_genes))
annotated_clusters <- merge(cluster_genes, brief, by.x="row.names", by.y="hgnc_symbol",
                           all.x=TRUE)
head(annotated_clusters)

annotated_clusters %>%
  group_by(cluster) %>%
  dplyr::top_n(n=10, wt=avg_log2FC) %>%
  as.data.frame()
```

```{r count_cluster_clono}
sum(clusters[["res0p1_clusters"]] == "0")
sum(clusters[["res0p1_clusters"]] == "0" &
    !is.na(clusters[["raw_clonotype_id"]]))

sum(clusters[["res0p1_clusters"]] == "1")
sum(clusters[["res0p1_clusters"]] == "1" &
    !is.na(clusters[["raw_clonotype_id"]]))

sum(clusters[["res0p1_clusters"]] == "2")
sum(clusters[["res0p1_clusters"]] == "2" &
    !is.na(clusters[["raw_clonotype_id"]]))

sum(clusters[["res0p1_clusters"]] == "3")
sum(clusters[["res0p1_clusters"]] == "3" &
    !is.na(clusters[["raw_clonotype_id"]]))

sum(clusters[["res0p1_clusters"]] == "4")
sum(clusters[["res0p1_clusters"]] == "4" &
    !is.na(clusters[["raw_clonotype_id"]]))

sum(clusters[["res0p1_clusters"]] == "5")
sum(clusters[["res0p1_clusters"]] == "5" &
    !is.na(clusters[["raw_clonotype_id"]]))

sum(clusters[["res0p1_clusters"]] == "6")
sum(clusters[["res0p1_clusters"]] == "6" &
    !is.na(clusters[["raw_clonotype_id"]]))
```

Clusters 0 and 5 have a great majority of the clonotypes.
0 has something like 90%, 5 has ~ 30%, the others ~ 10%

## Compare specific clusters

### Look at cluster 0, Nasal vs. Control

```{r specific_cluster_markers}
test_group <- clusters[["cluster_sample"]] == "control_0"
sum(test_group)
test_group <- clusters[["cluster_sample"]] == "n_0"
sum(test_group)

controln_0 <- FindMarkers(
    clusters, group.by="cluster_sample",
    ident.1="control_0", ident.2="n_0")
controln_0 <- as.data.frame(controln_0)
rownames(controln_0) <- toupper(rownames(controln_0))
controln_0 <- merge(controln_0, brief, by="row.names", by.y="hgnc_symbol",
                    all.x=TRUE)

annotated_clusters %>%
  group_by(cluster) %>%
  dplyr::top_n(n=10, wt=avg_log2FC) %>%
  as.data.frame()
```

### Cluster 0 Nasal vs. Mock

```{r specific_0_nmock}
mockvsn_0 <- FindMarkers(
    clusters, group.by="cluster_sample",
    ident.1="n_0", ident.2="mock_0") %>%
  as.data.frame()
head(mockvsn_0)
rownames(mockvsn_0) <- toupper(rownames(mockvsn_0))
mockvsn_0 <- merge(mockvsn_0, brief, by="row.names", by.y="hgnc_symbol",
                   all.x=TRUE)

mockvsm_0 <- FindMarkers(
    clusters, group.by="cluster_sample",
    ident.1="m_0", ident.2="mock_0") %>%
  as.data.frame()
head(mockvsm_0)
rownames(mockvsm_0) <- toupper(rownames(mockvsm_0))
mockvsm_0 <- merge(mockvsm_0, brief, by="row.names", by.y="hgnc_symbol",
                   all.x=TRUE)
head(mockvsm_0, n=30)
```

## Find Conserved Markers

This function makes no sense.

```{r find_conserved_markers, eval=FALSE}
DefaultAssay(clusters) <- "RNA"
conserved_markers <- FindConservedMarkers(
    clusters, ident.1=c(0, 1), ident.2=c(2,3,4),
    grouping.var="sample", only.pos=TRUE,
    verbose=TRUE)
```

```{r compare_other_groups, eval=FALSE}
mock_vs_control <- FindMarkers(clusters, group.by="orig.ident",
                               ident.1="mock",
                               ident.2="control")
head(mock_vs_control)
muscular_vs_mock <- FindMarkers(clusters, group.by="orig.ident",
                                ident.1="m",
                                ident.2="mock")
summary(muscular_vs_mock)
nasal_vs_mock <- FindMarkers(clusters, group.by="orig.ident",
                             min.pct=0.25, ident.1="n",
                             ident.2="mock")
summary(nasal_vs_mock)

FeaturePlot(clusters, features=c("Rgcc"),
            split.by="orig.ident", max.cutoff=3,
            cols=c("darkgreen", "darkred"))
```

# Scan for likely cell cycle genes

This is a neat idea, I think we can repurpose it to immunology gene sets.

```{r cell_cycle}
filt <- CellCycleScoring(
    object=clusters,
    g2m.features=cc.genes$g2m.genes,
    s.features=cc.genes$s.genes)
VlnPlot(clusters, features=c("S.Score", "G2M.Score"),
        group.by="orig.ident",
        ncol=4, pt.size=0)
```

Having written the following I realized I used an older version of my
mSigDB reference...  FIXME: Redo it with the 7.5+ data.

```{r msigdb}
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=clusters, features=broad_list,
                      name="m8")

chosen <- c(3, 9, 11, 36, 43, 42, 14)
names(broad_types)[chosen]
columns <- paste0("m8", chosen)
VlnPlot(wtf, features=columns,
        group.by="res0p1_clusters", same.y.lims=TRUE,
        ncol=4, pt.size=0)


chosen <- c(50, 51, 60, 61, 62, 65, 66)
names(broad_types)[chosen]
columns <- paste0("m8", chosen)
VlnPlot(wtf, features=columns,
        group.by="res0p1_clusters", same.y.lims=TRUE,
        ncol=4, pt.size=0)

chosen <- c(115, 118, 119, 120, 121, 125, 128)
names(broad_types)[chosen]
columns <- paste0("m8", chosen)
VlnPlot(wtf, features=columns,
        group.by="res0p1_clusters", same.y.lims=TRUE,
        ncol=4, pt.size=0)

chosen <- c(212, 211, 210, 209)
names(broad_types)[chosen]
columns <- paste0("m8", chosen)
VlnPlot(wtf, features=columns,
        group.by="res0p1_clusters", same.y.lims=TRUE,
        ncol=4, pt.size=0)

chosen <- c(176:182)
names(broad_types)[chosen]
columns <- paste0("m8", chosen)
VlnPlot(wtf, features=columns,
        group.by="res0p1_clusters", same.y.lims=TRUE,
        ncol=4, pt.size=0)

chosen <- c(42:47)
names(broad_types)[chosen]
columns <- paste0("m8", chosen)
VlnPlot(wtf, features=columns,
        group.by="res0p1_clusters", same.y.lims=TRUE,
        ncol=4, pt.size=0)

t_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)
t_groups

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)
t_groups

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)
t_groups
```

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