Bioconductor package to visualize spatially-resolved transcriptomics data

layer_stat_cor

R Documentation

Layer modeling correlation of statistics

Description

Layer modeling correlation of statistics

Usage

layer_stat_cor(
  stats,
  modeling_results = fetch_data(type = "modeling_results"),
  model_type = names(modeling_results)[1],
  reverse = FALSE,
  top_n = NULL
)

Arguments

`stats`	A query `data.frame` where the row names are ENSEMBL gene IDs, the column names are labels for clusters of cells or cell types, and where each cell contains the given statistic for that gene and cell type. These statistics should be computed similarly to the modeling results from the data we provide. For example, like the `enrichment` t-statistics that are derived from comparing one layer against the rest. The `stats` will be matched and then correlated with the reference statistics. If using the output of `registration_wrapper()` then use `⁠$enrichment⁠` to access the results from `registration_stats_enrichment()`. This function will automatically extract the statistics and assign the ENSEMBL gene IDs to the row names of the query matrix.
`modeling_results`	Defaults to the output of `fetch_data(type = 'modeling_results')`. This is a list of tables with the columns `⁠f_stat_⁠` or `⁠t_stat_⁠` as well as `⁠p_value_⁠` and `⁠fdr_⁠` plus `ensembl`. The column name is used to extract the statistic results, the p-values, and the FDR adjusted p-values. Then the `ensembl` column is used for matching in some cases. See `fetch_data()` for more details. Typically this is the set of reference statistics used in `layer_stat_cor()`.
`model_type`	A named element of the `modeling_results` list. By default that is either `enrichment` for the model that tests one human brain layer against the rest (one group vs the rest), `pairwise` which compares two layers (groups) denoted by `layerA-layerB` such that `layerA` is greater than `layerB`, and `anova` which determines if any layer (group) is different from the rest adjusting for the mean expression level. The statistics for `enrichment` and `pairwise` are t-statistics while the `anova` model ones are F-statistics.
`reverse`	A `logical(1)` indicating whether to multiply by `-1` the input statistics and reverse the `layerA-layerB` column names (using the `-`) into `layerB-layerA`.
`top_n`	An `integer(1)` specifying whether to filter to the top n marker genes. The default is `NULL` in which case no filtering is done.

Details

Check https://github.com/LieberInstitute/HumanPilot/blob/master/Analysis/Layer_Guesses/dlpfc_snRNAseq_annotation.R for a full analysis from which this family of functions is derived from.

Value

A correlation matrix between the query stats and the reference statistics using only the ENSEMBL gene IDs present in both tables. The columns are sorted using hierarchical clustering.

Author(s)

Andrew E Jaffe, Leonardo Collado-Torres

Examples


## Obtain the necessary data
if (!exists("modeling_results")) {
    modeling_results <- fetch_data(type = "modeling_results")
}

## Compute the correlations
cor_stats_layer <- layer_stat_cor(
    tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer,
    modeling_results,
    model_type = "enrichment"
)

## Explore the correlation matrix
head(cor_stats_layer[, seq_len(3)])
summary(cor_stats_layer)

## Repeat with top_n set to 10
summary(layer_stat_cor(
    tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer,
    modeling_results,
    model_type = "enrichment",
    top_n = 10
))

LieberInstitute/spatialLIBD documentation built on Dec. 19, 2024, 7:12 p.m.