Prepare dataset

To compare scRNAseq datasets with celaref, two inputs are needed for each dataset:

1. A counts matrix of number of reads per gene, per cell.

2. Cluster assignment for each cell.

See Input for details.

Cell clusters might be defined by any cell-clustering technique, such as those implemented in tools such as Seurat (Satija et al. 2015), cellRanger (10X genomics), SC3(Kiselev et al. 2017), among many others.

Within dataset differential expression

Every dataset, whether a query or a reference, is prepared the same way. For each cluster, cells within that cluster are compared to the rest of the cells pooled together, calculating differential gene expression using MAST (Finak et al. 2015). Because of the low counts and potential drop-out issues in single cell RNAseq data, only genes enriched in each cluster are considered. For every cluster – cells are ranked from most to least enriched according to their lower 95% CI of fold-change. Each gene is assigned a ‘rescaled rank’ from 0 (most enriched) to 1 (most absent).

That this step is the most time consuming, but only needs to be done once per dataset.

Query-Reference comparison

A list of ‘Up’ genes are extracted for each query cluster – defined as those that have significantly higher expression in that cluster versus the rest of the sample (p<0.01 after BH multiple hypothesis correction). The ‘Up’ gene list is capped at the top 100 (ranked by lower 95% FC). Then, those genes are looked up in the ranking of genes in each reference cell cluster. The distribution of these ‘up gene’ ranks is plotted to evaluate similarity of the query cell-group to a reference cell-group.

Output plots are described here.

Interpreting output

Typically, every cell cluster in the query data (each box) is plotted against everything in the reference data (X-axis). Each of the ‘up’ genes is represented by a tick mark, and the median generank is shown as a thick bar. A biased distribution near the top (i.e.. rescaled rank of 0) indicates similarity of the groups – essentially the same genes are representative of the clusters within their respective samples.

• A median gene rank of 0.5 would indicate a completely random distribution. However, much lower values are common. The reciprocal nature of the within-dataset differential expression can cause this - what’s up in one cluster is down in another.

• A small or heterogeneous cell group will not have much statistical power to select many ‘top’ genes (few tick marks) and these distributions will not be particuarly informative. If there are no ‘top’ genes it won’t be plotted at all.

• Because ‘top’ genes are compared to total reference rankings - the comparison between two datasets is not symmetrical. In ambiguous cases, it might helpful to plot the reverse comparison from reference to query. Note that these receiprocal comparisons are considered in Assigning labels to clusters. For instance - if a query cluster happens to be a mix of two reference cell groups, a reciprocal plot may make this more obvious.

Assigning labels to clusters

Lastly, there is a function to suggest some semi-sensible query cluster labels.

The first 4 columns of output (below) are the most interesting, the rest are described at bottom of section. The suggested cluster label is in the shortlab column. e.g.

There can be none, one or multiple reference group similarities for the query group. This is expected when there are similar cell sub-populations in the reference data. This can be visualised throught the relative shapes of the top gene distribution for the reference group, and reference group similarity labels are calculated as follows:

These labels are based on the distributions of the ranks of the query cluster’s ‘top’ genes in each of the reference groups (as plotted in the violin plots), rescaled to be in the 0-1 range.

1. The median gene rank for the ‘top’ genes in each reference group is calculated.

2. Reference groups are ordered from most to least similar (ascending median rank).

3. Mann-Whitney U tests are calculated between the adjacent reference groups - ie. 1st-2nd most similar, 2nd-3rd, 3rd-4th e.t.c. These are the stepped_pvals reported above - the last value will always be undefined NA. Essentially this is testing if the ‘top’ genes representative of the query group are significantly lower ranked (more similar) in one reference group vs the next most similar reference group. A genuine similarity of cell types should result in an abrupt change in these gene rank distributions.

4. Initial calls are made on which reference groups to include in the group label.
   • 1. If there is a significant difference (p < 0.01) between the first and second most similar groups - report on the top ranked reference group. The pval in this case will be the first stepped pvalue (which is significant).
   • 2. If there are no significant differences between any steps, report a lack of similarity. No p-value (NA) is reported - because no call could be made on what group(s) are more similar. Check the stepped_pvals to look at borderline cases.
   • 3. Otherwise, start at the most similar reference group, and report everything up to the first signifcantly different ranking. Effectively, this reports multiple reference group similarities when a call can’t be made between them e.g. subtypes, or a mixed query group. Here the reported pval describes the ‘jump’ between the bottom ranked similar reference group, and the first of the other groups. e.g. the 2nd or 3rd stepped pvalue.
5. The group assignment from step 4 is checked to ensure that the (median of the) gene ranks is significantly above a random distribution. Ie. above the 0.5 halfway point in the violin plots.
   • The distribution of the ‘top’ genes in the matched group (or last-ranked matching group when there are multiple matches) is tested to see if its median rank is significantly above 0.5 (sign test, using one-tailed binomial test on ranks < 0.5). If this fails (<0.01), ‘No_similarity’ will be reported, even if the p-value is significant. See Group1 and Group2 results in the quickstart example (occurs due to being only a 200 gene test). *B: This means that a significant ‘pval’ column can sometime occur on a ‘No_similarity’ label - but the pval_vs_random column (described below) will be non-significant.
   • This test exists for cases of significant difference from an ‘anti-some-other-celltype’ signature with genes distributed at the low end. That can occur when there are uneven gene cluster sizes and no genuine match for the query.
6. Reciprocal-only matches are added to cluster labels in brackets.
   • e.g. c1:celltypeA(celltypeB) or c2:(celltypeC)
   • The cluster groups are reciprocally tested against the query groups. All reciprocal matches are listed in the reciprocal_matches column. If there is a recriprocal match that isn’t in the query->ref match list, its added on to the end of the cluster label.
   • Reciprocal matches reported in the cluster names might indicate a mixed query cell type. It woudl be a good idea to look at the reciprocalviolin plot of the reference dataset vs the query.

The full version of this table is:

The next few columns of the ouput describe some of the heuristics used in the cluster labelling.

• pval_to_random : P-value of test of median rank (of last matched reference group) < random, from binomial test on top gene ranks (<0.5). If this isn’t signiicant, ‘No similarity’ will be reported. A completely random distribution would have a median rank in the middle of the violin plots, at 0.5.

• matches : List of all reference groups that ‘match’, as described, except it also includes (rare) examples where pval_to_random is not significant. “|” separated, in descending order of match.

• reciprocal_matches : List of all reference groups that flagged test group as a match when directon of comparison is reversed. (significant pval and pval_to_random). “|” separated, in descending order of match.

The last 3 columns of the output are usually empty. When defined they may indicate borderline labelling or edge cases - checking the violin plots is advised! Tests are again Mann-Whitney U, but on non-adjacently ranked groups.

• similar_non_match : This column lists any reference groups outside of the reported label match that are not signifcantly different to a reported match group. E.g If a query matches group A (because A is significantly different to the second-ranked B); if A is then not significantly different to the third -ranked C, then C will be reported here. This is more likely with smaller sets of ‘top genes’, for instance in Group3 of the quickstart example. Examples:
  • NA [Nothing]
  • C
  • microglia|interneurons
• similar_non_match_detail: P-values for any details about similar_non_match results. These p-values will always be non-significant (but again may be borderline). Examples:
  • NA [Nothing]
  • A > C (p=0.0214,n.s)
  • dunno > Exciting (p=0.0104,n.s)
• differences_within: This feild lists any pairs of matched groups (in shortlab) that are significantly different. This could happen if there are many matched groups listed in shortlab, that are all slightly different. Examples
  • NA [Nothing]
  • B > E (p=0.004)|C > E (p=0.009)

Input data

dataset_se <- load_se_from_files(counts_matrix   = "counts_matrix_file.tab", 
                                  cell_info_file = "cell_info_file.tab", 
                                  group_col_name = "Cluster")

dataset_se <- load_se_from_files(counts_matrix   = "counts_matrix_file.tab", 
                                  cell_info_file = "cell_info_file.tab",
                                  group_col_name  = "Cluster",
                                  cell_col_name   = "CellId" )

dataset_se <- load_se_from_files(counts_matrix   = "counts_matrix_file.tab", 
                                  cell_info_file = "cell_info_file.tab", 
                                  gene_info_file = "gene_info_file.tab",
                                  group_col_name = "Cluster")

dataset_se <- load_se_from_tables(counts_matrix   = counts_matrix, 
                                  cell_info_table = cell_info_table, 
                                  group_col_name  = "Cluster")

dataset_se <- load_dataset_10Xdata('~/path/to/data/10X_mydata', 
                                   dataset_genome = "GRCh38", 
                                   clustering_set = "kmeans_7_clusters") 

library(Matrix) 
#a sparse big M Matrix.
dataset_se.1 <- load_se_from_tables(counts_matrix = my_sparse_Matrix,
                                  cell_info_table = cell_info_table, 
                                  group_col_name  = "Cluster")

# A hdf5-backed SummarisedExperiment from elsewhere
dataset_se.2 <- loadHDF5SummarizedExperiment("a_SE_dir/")

# For consistant subsampling, use set.seed
set.seed(12)
de_table.demo_query.subset <- 
   contrast_each_group_to_the_rest(demo_query_se, "subsetted_example",
                                   n.group = 100, n.other = 200)
#> Found one, unnamed, assay in summarizedExperiment object. Assuming this is counts data ('counts')
#> Randomly sub sampling cells for Group1 contrast.
#> Found one, unnamed, assay in summarizedExperiment object. Assuming this is counts data ('counts')
#> 
#> Done!
#> Combining coefficients and standard errors
#> Calculating log-fold changes
#> Calculating likelihood ratio tests
#> Refitting on reduced model...
#> 
#> Done!
#> Randomly sub sampling cells for Group2 contrast.
#> Found one, unnamed, assay in summarizedExperiment object. Assuming this is counts data ('counts')
#> 
#> Done!
#> Combining coefficients and standard errors
#> Calculating log-fold changes
#> Calculating likelihood ratio tests
#> Refitting on reduced model...
#> 
#> Done!
#> Randomly sub sampling cells for Group3 contrast.
#> Found one, unnamed, assay in summarizedExperiment object. Assuming this is counts data ('counts')
#> 
#> Done!
#> Combining coefficients and standard errors
#> Calculating log-fold changes
#> Calculating likelihood ratio tests
#> Refitting on reduced model...
#> 
#> Done!
#> Randomly sub sampling cells for Group4 contrast.
#> Found one, unnamed, assay in summarizedExperiment object. Assuming this is counts data ('counts')
#> 
#> Done!
#> Combining coefficients and standard errors
#> Calculating log-fold changes
#> Calculating likelihood ratio tests
#> Refitting on reduced model...
#> 
#> Done!

set.seed(12)
demo_query_se.subset <- subset_cells_by_group(demo_query_se, n.group = 100)
#> Randomly sub sampling100 cells per group.

de_table.microarray <- contrast_each_group_to_the_rest_for_norm_ma_with_limma(
    norm_expression_table=demo_microarray_expr, 
    sample_sheet_table=demo_microarray_sample_sheet,
    dataset_name="DemoSimMicroarrayRef", 
    sample_name="cell_sample", group_name="group") 

Input filtering and Pre-processing

# Default filtering
dataset_se <- trim_small_groups_and_low_expression_genes(dataset_se)

# Also defaults, but specified
dataset_se <- trim_small_groups_and_low_expression_genes(dataset_se, 
                                    min_lib_size = 1000, 
                                    min_group_membership = 5,  
                                    min_detected_by_min_samples = 5)

# Count and store total reads/gene.
rowData(dataset_se)$total_count <- Matrix::rowSums(assay(dataset_se))
# rowData(dataset_se) must already list column 'GeneSymbol'
dataset_se <- convert_se_gene_ids(dataset_se, new_id='GeneSymbol', eval_col = 'total_count')

Within-experiment differential expression

Once data is loaded into summarizedExperiment objects, the groups in each dataset need to be analysed within-dataset before any cross-dataset comparisons can be done. This is the most time consuming step, but only needs to be done once per dataset.

Essentially, we want to rank all genes from most to least ‘distinctive’ for each group in the dataset.

Differential expression is calculated for every group versus the rest of the dataset pooled together using MAST(Finak et al. 2015). This will provide relative expression for everything relative to the rest of the tissue or sample as background.An independent experiment will have its own biases, but with any luck the same genes should be ‘distinctive’ for the same cell type regardless. Since single cell RNAseq data can have many zeros and drop outs, celaref focuses on overrepresented genes. Genes are ranked from most to least overrepresented on the basis of their most conservative (‘inner’) 95% confidence interval of log2FC. This rank is a simple compromise between expected size-of-effect (log2FC - which can change over-dramatically for low-expression genes), and statistical power (from a p-value ranking).

This is done with the contrast_each_group_to_the_rest function after filtering:

demo_query_se.filtered <- trim_small_groups_and_low_expression_genes(demo_query_se)
#> Found one, unnamed, assay in summarizedExperiment object. Assuming this is counts data ('counts')

de_table.demo_query <- contrast_each_group_to_the_rest(demo_query_se.filtered, "a_demo_query")
#> Found one, unnamed, assay in summarizedExperiment object. Assuming this is counts data ('counts')
#> Found one, unnamed, assay in summarizedExperiment object. Assuming this is counts data ('counts')
#> 
#> Done!
#> Combining coefficients and standard errors
#> Calculating log-fold changes
#> Calculating likelihood ratio tests
#> Refitting on reduced model...
#> 
#> Done!
#> Found one, unnamed, assay in summarizedExperiment object. Assuming this is counts data ('counts')
#> 
#> Done!
#> Combining coefficients and standard errors
#> Calculating log-fold changes
#> Calculating likelihood ratio tests
#> Refitting on reduced model...
#> 
#> Done!
#> Found one, unnamed, assay in summarizedExperiment object. Assuming this is counts data ('counts')
#> 
#> Done!
#> Combining coefficients and standard errors
#> Calculating log-fold changes
#> Calculating likelihood ratio tests
#> Refitting on reduced model...
#> 
#> Done!
#> Found one, unnamed, assay in summarizedExperiment object. Assuming this is counts data ('counts')
#> 
#> Done!
#> Combining coefficients and standard errors
#> Calculating log-fold changes
#> Calculating likelihood ratio tests
#> Refitting on reduced model...
#> 
#> Done!

Reference datasets are prepared with the same command, there’s no difference in the result.

demo_ref_se.filtered <- trim_small_groups_and_low_expression_genes(demo_ref_se)
#> Found one, unnamed, assay in summarizedExperiment object. Assuming this is counts data ('counts')
de_table.demo_ref   <- contrast_each_group_to_the_rest(demo_ref_se.filtered,   "a_demo_reference")
#> Found one, unnamed, assay in summarizedExperiment object. Assuming this is counts data ('counts')
#> Found one, unnamed, assay in summarizedExperiment object. Assuming this is counts data ('counts')
#> 
#> Done!
#> Combining coefficients and standard errors
#> Calculating log-fold changes
#> Calculating likelihood ratio tests
#> Refitting on reduced model...
#> 
#> Done!
#> Found one, unnamed, assay in summarizedExperiment object. Assuming this is counts data ('counts')
#> 
#> Done!
#> Combining coefficients and standard errors
#> Calculating log-fold changes
#> Calculating likelihood ratio tests
#> Refitting on reduced model...
#> 
#> Done!
#> Found one, unnamed, assay in summarizedExperiment object. Assuming this is counts data ('counts')
#> 
#> Done!
#> Combining coefficients and standard errors
#> Calculating log-fold changes
#> Calculating likelihood ratio tests
#> Refitting on reduced model...
#> 
#> Done!
#> Found one, unnamed, assay in summarizedExperiment object. Assuming this is counts data ('counts')
#> 
#> Done!
#> Combining coefficients and standard errors
#> Calculating log-fold changes
#> Calculating likelihood ratio tests
#> Refitting on reduced model...
#> 
#> Done!

This object can be now passed to subsequent comparison functions - see section Compare groups to reference.

For clarity, the results objects have names starting with de_table, but they are simply tibble (data.frame-like) objects that look like this:

As for what it contains, the important fields are:

• ID : Gene ID
• fdr : The multiple hypothesis corrected p-value (BH method)
• log2FC : Log2 fold-change of for this gene’s expression in the (test group) - (rest of sample)
• ci_inner : The inner (most conservative/nearest 0) 95% confidence interval of log2FC. This is used to rank genes from most-to-least overrepresented in this group, with respect to the rest of the sample.
• group : Group being tested (all are tested by default)
• sig_up : Is this gene significantly (fdr <=0.01) enriched (log2FC > 0) in this group.
• rank : Numerical rank of ci_inner from most (1) to least (n).
• rescaled_rank : Rank rescaled from most (0) to least (1) - used in analyses and plotting.
• dataset : Name of this dataset

This function is parallelised. Due to the differential expression calculations, this is a time-consuming step (e.g. a few hours, depending on data size). But the result can and should be saved and reused for any comparisons to other datasets. If num_cores is specified, up to that many groups will be processed in parallel. This is highly recommended. For best results num_cores should be set to the number of groups in the query so long as system resources permit.

Microarray reference data is treated differently, with function contrast_each_group_to_the_rest_for_norm_ma_with_limma() that both loads data and does within sample differential expression in one step. Its output is much the same. See section on microarray input for details.

de_table.microarray <- contrast_each_group_to_the_rest_for_norm_ma_with_limma(
    norm_expression_table=demo_microarray_expr, 
    sample_sheet_table=demo_microarray_sample_sheet,
    dataset_name="DemoSimMicroarrayRef", 
    sample_name="cell_sample", group_name="group") 

Compare groups to reference

Once the dataset has been compared to itself (see Prepare data with within-experiment differential expression), the groups can be compared to the reference dataset.

The main output of celaref are the violin plots of the reference group rankings of query group ‘top’ genes. Each query group gets its own panel, with a violin plot of its ‘top’ gene rankings in each reference group. See section Interpreting output and the overview diagram full description of these plots.

To make that output, run function make_ranking_violin_plot. (Note that de_table.test and de_table.ref parameters must be specified by name, not position.)

make_ranking_violin_plot(de_table.test=de_table.demo_query, de_table.ref=de_table.demo_ref)

To pull together the data for this plot the get_the_up_genes_for_all_possible_groups function is called internally. It can also be called by hand, see Special case: Saving get_the_up_genes_for_all_possible_groups output.

That get_the_up_genes_for_all_possible_groups function will do two things

1. Identify ‘top’ genes for each group in the query dataset. i.e what is most distinctively high for this cell-type/group in the context of this tissue/sample. This is defined as up to the first 100 genes with an inner log2FC 95% confidence interval >=1.
2. Lookup the rescaled rankings (again from most to least log2FC inner 95% CI) of ‘top’ genes in each reference group.

NB: There is scope for the ranking criteria to be changed, but currently only the inner log2FC 95% confidence interval is implemented. Future work: Use of ‘topconfects’ is planned (Harrison et al. 2018).

Compare groups within a single dataset

Its often useful to compare a dataset to itself. Just specify the same dataset for de_table.test and de_table.ref. This will show how similar the groups are. Clusters that can’t be distinguished from each other might be a sign that too many clusters were defined.

make_ranking_violin_plot(de_table.test=de_table.demo_query, de_table.ref=de_table.demo_query)

Make labels for groups

Lastly, celaref can parse these comparisons and suggest group names for the query groups.

The method for labelling used is described in section Assigning labels to clusters. The name in ‘shortlab’ might make a good starting point for downstream characterisation. These labels should generally be interperented alongside the violin plots.

group_names_table <- make_ref_similarity_names(de_table.demo_query, de_table.demo_ref)

Its unusual to have anything other than NA in the ‘similar_non_match’ column. Explained in section Assigning labels to clusters.

A note on the ‘num_steps’ parameter.

Function make_ref_similarity_names accepts an optional parameter ‘num_steps’. It doesn’t affect the construction of the suggested labels in ‘shortlab’, only the extra ‘similar_non_match’ columns. Only pairs of reference groups num_steps
away from each other when ranked by median generank are tested for difference - the nearer they are the more likely they’re similar.

It if is set too small though (e.g. 1), similar non-matched groups might be missed. Set to ‘NA’ for an exhastive test, but with many reference groups, this could be slow.

Special case: Saving get_the_up_genes_for_all_possible_groups output

Making the violin plots and cluster labels both use the get_the_up_genes_for_all_possible_groups function internally.

It is possible to run this manually and pass the result through. For most analysis this is uncessary neccessary, unless you want to look at the top genes rankings directly e.t.c.

de_table.marked.query_vs_ref <- get_the_up_genes_for_all_possible_groups(
   de_table.test=de_table.demo_query ,
   de_table.ref=de_table.demo_ref)
# Have to do do the reciprocal table too for labelling.
de_table.marked.ref_vs_query<- get_the_up_genes_for_all_possible_groups(
   de_table.test=de_table.demo_ref ,
   de_table.ref=de_table.demo_query)

kable(head(de_table.marked.query_vs_ref))

Equivalent plots and labels:

make_ranking_violin_plot(de_table.marked.query_vs_ref)

#use make_ref_similarity_names_using_marked instead:
similarity_label_table <- make_ref_similarity_names_using_marked(de_table.marked.query_vs_ref, de_table.recip.marked=de_table.marked.ref_vs_query)

kable(similarity_label_table)

Prepare 10X query dataset

First, load the dataset into a SummarizedExperiment object and filter out genes with low expression, or groups that have too few members.

library(celaref)
datasets_dir <- "~/celaref_extra_vignette_data/datasets"

dataset_se.10X_pbmc4k_k7 <- load_dataset_10Xdata(
   dataset_path   = file.path(datasets_dir,'10X_pbmc4k'), 
   dataset_genome = "GRCh38", 
   clustering_set = "kmeans_7_clusters", 
   id_to_use      = "GeneSymbol")
dataset_se.10X_pbmc4k_k7 <- trim_small_groups_and_low_expression_genes(dataset_se.10X_pbmc4k_k7)

Then prepare the datasets with the within-experiment comparisons. Setting the num-cores to 7 to let each group run in parallel (specify less to reduce RAM usage).

de_table.10X_pbmc4k_k7   <- contrast_each_group_to_the_rest(dataset_se.10X_pbmc4k_k7, dataset_name="10X_pbmc4k_k7", num_cores=7) 

Prepare reference microarray dataset

Next, do the same with the Watkins2009 reference data. However, because this is microarray data, it is a different process - the data loading and within-experiment comparisons are rolled into the single function contrast_each_group_to_the_rest_for_norm_ma_with_limma. That function needs two things:

• Logged, normalised expression values. Any low expression or poor quality measurements should have already been removed.
• Sample information (see contrast_each_group_to_the_rest_for_norm_ma_with_limma for details)

Note that for this to work, the arrays should be from the same experiment/study. The variation would probably be too much between samples pulled from different studies.

this_dataset_dir     <- file.path(datasets_dir,     'haemosphere_datasets','watkins')
norm_expression_file <- file.path(this_dataset_dir, "watkins_expression.txt")
samples_file         <- file.path(this_dataset_dir, "watkins_samples.txt")

norm_expression_table.full <- read.table(norm_expression_file, sep="\t", header=TRUE, quote="", comment.char="", row.names=1, check.names=FALSE)

samples_table              <- read_tsv(samples_file, col_types = cols())
samples_table$description  <- make.names( samples_table$description) # Avoid group or extra_factor names starting with numbers, for microarrays

From the sample table, can see that this dataset includes other tissues, but as a PBMC reference, we only want to consider the peripheral blood samples. Like the other data loading functions, to remove a sample (or cell) from the analysis, it is enough to remove it from the sample table.

samples_table        <- samples_table[samples_table$tissue == "Peripheral Blood",] 

As usually seems to be the case, the hardest part is formatting the input. Microarray expression values should be provided as normalised, log-transformed data using the same IDs as the query datset. Any probe or sample level filtering should also be performed beforehand. In this case, the data was normalised when acquired from the haemosphere website - but still need to match the IDs.

This data is from Illumina HumanWG-6 v2 Expression BeadChips, and gives expression at the probe level. These probes need to be converted to gene symbols to match the PBMC data.

NB: Converting between IDs is easier for single cell datasets using the convert_se_gene_ids function. But that function expects a SummarizedExperiment object, which isn’t used for microarray data. So it has to be done manually here.

NB: Note that it doesn’t matter if IDs are only present in one or the other dataset - just that they are the same type of ID and most match!

library("tidyverse")
library("illuminaHumanv2.db")
probes_with_gene_symbol_and_with_data <- intersect(keys(illuminaHumanv2SYMBOL),rownames(norm_expression_table.full))

# Get mappings - non NA
probe_to_symbol <- select(illuminaHumanv2.db, keys=rownames(norm_expression_table.full), columns=c("SYMBOL"), keytype="PROBEID")
probe_to_symbol <- unique(probe_to_symbol[! is.na(probe_to_symbol$SYMBOL),])
# no multimapping probes
genes_per_probe <- table(probe_to_symbol$PROBEID) # How many genes a probe is annotated against?
multimap_probes <- names(genes_per_probe)[genes_per_probe  > 1]
probe_to_symbol <- probe_to_symbol[!probe_to_symbol$PROBEID %in% multimap_probes, ]


convert_expression_table_ids<- function(expression_table, the_probes_table, old_id_name, new_id_name){
    
    the_probes_table <- the_probes_table[,c(old_id_name, new_id_name)]
    colnames(the_probes_table) <- c("old_id", "new_id")
    
    # Before DE, just pick the top expresed probe to represent the gene
    # Not perfect, but this is a ranking-based analysis.
    # hybridisation issues aside, would expect higher epressed probes to be more relevant to Single cell data anyway.
    probe_expression_levels <- rowSums(expression_table)
    the_probes_table$avgexpr <- probe_expression_levels[as.character(the_probes_table$old_id)]
    
    the_genes_table <-  the_probes_table %>% 
        group_by(new_id) %>%
        top_n(1, avgexpr)
    
    expression_table <- expression_table[the_genes_table$old_id,]
    rownames(expression_table) <- the_genes_table$new_id
    
    return(expression_table)
}

# Just the most highly expressed probe foreach gene.
norm_expression_table.genes <- convert_expression_table_ids(norm_expression_table.full, 
                                                            probe_to_symbol, old_id_name="PROBEID", new_id_name="SYMBOL")

Now read the data and run the within-experiment contrast with contrast_each_group_to_the_rest_for_norm_ma_with_limma.

Because there is information on which individual each sample is from in the ‘description’ field, this is specified with extra_factor_name, and is included as a factor in the linear model for limma. This is optional, and only one extra factor can be added this way.

# Go...
de_table.Watkins2009PBMCs <- contrast_each_group_to_the_rest_for_norm_ma_with_limma(
                 norm_expression_table = norm_expression_table.genes, 
                 sample_sheet_table    = samples_table, 
                 dataset_name          = "Watkins2009PBMCs", 
                 extra_factor_name     = 'description', 
                 sample_name           = "sampleId",
                 group_name            = 'celltype')

Compare 10X query PBMCs to to reference

Finally! Compare the single cell data to the purified PBMCs:

make_ranking_violin_plot(de_table.test=de_table.10X_pbmc4k_k7, de_table.ref=de_table.Watkins2009PBMCs)

Hmm, there’s a few clusters where different the top genes are bunched near the top for a couple of different reference cell types.

Logging the plot will be more informative at the top end for this dataset.

make_ranking_violin_plot(de_table.test=de_table.10X_pbmc4k_k7, de_table.ref=de_table.Watkins2009PBMCs, log10trans = TRUE)

Now get some some group labels.

As described in section Assigning lables to clusters, multiple similarities will be reported (in descending order of median rank) unless a clear (significantly different) frontrunner can be flagged.

label_table.pbmc4k_k7_vs_Watkins2009PBMCs <- make_ref_similarity_names(de_table.10X_pbmc4k_k7, de_table.Watkins2009PBMCs)

With a couple of (reciprocal-only matches) in the cluster names, it might be worth checking the reciprocal violin plots:

make_ranking_violin_plot(de_table.test=de_table.Watkins2009PBMCs, de_table.ref=de_table.10X_pbmc4k_k7, log10trans = TRUE)

Load and compare mouse brain datasets

First, start by loading the brain cell data from (Zeisel et al. 2015):

datasets_dir <- "~/celaref_extra_vignette_data/datasets"
zeisel_cell_info_file <- file.path(datasets_dir, "zeisel2015", "zeisel2015_mouse_scs_detail.tab")
zeisel_counts_file    <- file.path(datasets_dir, "zeisel2015", "zeisel2015_mouse_scs_counts.tab")

Note the sample data in zeisel2015_mouse_scs_detail.tab has the following information. They specify cell type groups at two different levels, and for this example, just going to use level1class. Also need to specify that cell_id is, unsurprisingly, the cell identifier.

dataset_se.zeisel <- load_se_from_files(zeisel_counts_file, zeisel_cell_info_file,
                                 group_col_name = "level1class", 
                                 cell_col_name  = "cell_id" )

That dataset_se object contains all the data, so subset it into two objects by tissue (its a SummarizedExperiment object). Then separately filter both for low-expression genes and groups with too few cells to analyse.

# Subset the summarizedExperiment object into two tissue-specific objects
dataset_se.cortex <- dataset_se.zeisel[,dataset_se.zeisel$tissue == "sscortex"]
dataset_se.hippo  <- dataset_se.zeisel[,dataset_se.zeisel$tissue == "ca1hippocampus"]

# And filter them
dataset_se.cortex  <- trim_small_groups_and_low_expression_genes(dataset_se.cortex )
dataset_se.hippo   <- trim_small_groups_and_low_expression_genes(dataset_se.hippo )

Next, need to do the within-dataset comparisons. There are 6 groups in each sample - so use 6 cores to run them all at once. This may take some time to finish, so be sure to save the result for reuse.

de_table.zeisel.cortex <- contrast_each_group_to_the_rest(dataset_se.cortex, dataset_name="zeisel_sscortex",       num_cores=6) 
de_table.zeisel.hippo  <- contrast_each_group_to_the_rest(dataset_se.hippo,  dataset_name="zeisel_ca1hippocampus", num_cores=6) 

Now compare the two:

make_ranking_violin_plot(de_table.test=de_table.zeisel.cortex, de_table.ref=de_table.zeisel.hippo)

Perhaps unsurprisingly given they’re from the same experiment, the cell-type annotations do almost perfectly correlate one-to-one.

Load lacrimal gland dataset

Next, compare that to a dissimilar tissue - lacrimal gland from Farmer et al. (2017). Only the more mature P4 timepoint will be used here.

The format of this data that is a little more complicated. There was a MatrixMarket formatted file for the counts matrix, and cell assignment and cluster information are in separate files. So this data needs to be converted into the form that load_se_from_tables expects.

library(Matrix) 

Farmer2017lacrimal_dir  <- file.path(datasets_dir, "Farmer2017_lacrimal", "GSM2671416_P4")

# Counts matrix
Farmer2017lacrimal_matrix_file   <- file.path(Farmer2017lacrimal_dir, "GSM2671416_P4_matrix.mtx")
Farmer2017lacrimal_barcodes_file <- file.path(Farmer2017lacrimal_dir, "GSM2671416_P4_barcodes.tsv")
Farmer2017lacrimal_genes_file    <- file.path(Farmer2017lacrimal_dir, "GSM2671416_P4_genes.tsv")

counts_matrix <- readMM(Farmer2017lacrimal_matrix_file) 
counts_matrix <- as.matrix(counts_matrix)
storage.mode(counts_matrix) <- "integer"

genes <- read.table(Farmer2017lacrimal_genes_file,    sep="", stringsAsFactors = FALSE)[,1]
cells <- read.table(Farmer2017lacrimal_barcodes_file, sep="", stringsAsFactors = FALSE)[,1]
rownames(counts_matrix) <- genes
colnames(counts_matrix) <- cells


# Gene info table
gene_info_table.Farmer2017lacrimal <- as.data.frame(read.table(Farmer2017lacrimal_genes_file, sep="", stringsAsFactors = FALSE), stringsAsFactors = FALSE)
colnames(gene_info_table.Farmer2017lacrimal) <- c("ensemblID","GeneSymbol") # ensemblID is first, will become ID

## Cell/sample info
Farmer2017lacrimal_cells2groups_file  <- file.path(datasets_dir, "Farmer2017_lacrimal", "Farmer2017_supps", paste0("P4_cellinfo.tab"))
Farmer2017lacrimal_clusterinfo_file   <- file.path(datasets_dir, "Farmer2017_lacrimal", "Farmer2017_supps", paste0("Farmer2017_clusterinfo_P4.tab"))


# Cells to cluster number (just a number)
Farmer2017lacrimal_cells2groups_table <- read_tsv(Farmer2017lacrimal_cells2groups_file, col_types=cols())
# Cluster info - number to classification
Farmer2017lacrimal_clusterinfo_table <- read_tsv(Farmer2017lacrimal_clusterinfo_file, col_types=cols())  
# Add in cluster info
Farmer2017lacrimal_cells2groups_table <- merge(x=Farmer2017lacrimal_cells2groups_table, y=Farmer2017lacrimal_clusterinfo_table, by.x="cluster", by.y="ClusterNum")

# Cell sample2group
cell_sample_2_group.Farmer2017lacrimal <- Farmer2017lacrimal_cells2groups_table[,c("Cell identity","ClusterID", "nGene", "nUMI")]
colnames(cell_sample_2_group.Farmer2017lacrimal) <- c("cell_sample", "group", "nGene", "nUMI")
# Add -1 onto each of the names, that seems to be in the counts
cell_sample_2_group.Farmer2017lacrimal$cell_sample <- paste0(cell_sample_2_group.Farmer2017lacrimal$cell_sample, "-1")

# Create a summarised experiment object.
dataset_se.P4  <- load_se_from_tables(counts_matrix, 
                                   cell_info_table = cell_sample_2_group.Farmer2017lacrimal,
                                   gene_info_table = gene_info_table.Farmer2017lacrimal )

After all that, the dataset has the cell information (colData):

… and the gene information (rowData):

Note that ‘ID’ is the ensembl gene id, and it needs to switch to the gene symbol to match the Zeisel data. Could equally well use ensembl ids for both.

Gene symbol to ID is almost a one to one mapping, so a few genes are lost in this step. Calculating the total read count for each gene is a simple way of producing a tie-breaker. This is also the reason why the data was loaded with ensemblID as the ID in load_se_from_tables, because GeneSymbol is not unique.

rowData(dataset_se.P4)$total_count <- rowSums(assay(dataset_se.P4))
dataset_se.P4  <-  convert_se_gene_ids( dataset_se.P4,  new_id='GeneSymbol', eval_col='total_count')

Filter and do the within-experiment comparisons

dataset_se.P4 <- trim_small_groups_and_low_expression_genes(dataset_se.P4)
de_table.Farmer2017lacrimalP4  <- contrast_each_group_to_the_rest(dataset_se.P4,  dataset_name="Farmer2017lacrimalP4", num_cores = 4) 

Cross-tissue comparision

Now compare the mouse cortex samples to the lacrimal gland. Being completely different tissues there shouldn’t be many cell types in common.

make_ranking_violin_plot(de_table.test=de_table.zeisel.cortex, de_table.ref=de_table.Farmer2017lacrimalP4)

label_table.cortex_vs_lacrimal <- 
   make_ref_similarity_names(de_table.zeisel.cortex, de_table.Farmer2017lacrimalP4)

This cross-tissue comparison looks very different to the brain-brain contrast - as expected, most clusters have ‘no similarity’.

Not all though. The cortex ‘microglia’ have their similarity with the ‘Macrophage/Monocyte’ group highlighted. This makes sense - as they are biological similar cell types.

Interestingly, there’s also a reciprocal match from the Lacrimal gland endothelial cells to endothelial-mural cell in the brain sample.