Package 'lemur' reference manual

Title:	Latent Embedding Multivariate Regression
Description:	Fit a latent embedding multivariate regression (LEMUR) model to multi-condition single-cell data. The model provides a parametric description of single-cell data measured with treatment vs. control or more complex experimental designs. The parametric model is used to (1) align conditions, (2) predict log fold changes between conditions for all cells, and (3) identify cell neighborhoods with consistent log fold changes. For those neighborhoods, a pseudobulked differential expression test is conducted to assess which genes are significantly changed.
Authors:	Constantin Ahlmann-Eltze [aut, cre]
Maintainer:	Constantin Ahlmann-Eltze <[email protected]>
License:	MIT + file LICENSE
Version:	1.3.3
Built:	2024-09-19 03:16:11 UTC
Source:	https://github.com/bioc/lemur

Access values from a `lemur_fit`

Description

Access values from a lemur_fit

Usage

## S3 method for class 'lemur_fit'
.DollarNames(x, pattern = "")

## S4 method for signature 'lemur_fit'
x$name

## S4 replacement method for signature 'lemur_fit'
x$name <- value
## S3 method for class 'lemur_fit'
.DollarNames(x, pattern = "")

## S4 method for signature 'lemur_fit'
x$name

## S4 replacement method for signature 'lemur_fit'
x$name <- value

Arguments

`x`	the `lemur_fit`
`pattern`	the pattern from looking up potential values interactively
`name`	the name of the value behind the dollar
`value`	the replacement value. This only works for `colData` and `rowData`.

Value

The respective value stored in the lemur_fit object.

Enforce additional alignment of cell clusters beyond the direct differential embedding

Description

Enforce additional alignment of cell clusters beyond the direct differential embedding

Usage

align_harmony(
  fit,
  design = fit$alignment_design,
  ridge_penalty = 0.01,
  max_iter = 10,
  ...,
  verbose = TRUE
)

align_by_grouping(
  fit,
  grouping,
  design = fit$alignment_design,
  ridge_penalty = 0.01,
  preserve_position_of_NAs = FALSE,
  verbose = TRUE
)
align_harmony(
  fit,
  design = fit$alignment_design,
  ridge_penalty = 0.01,
  max_iter = 10,
  ...,
  verbose = TRUE
)

align_by_grouping(
  fit,
  grouping,
  design = fit$alignment_design,
  ridge_penalty = 0.01,
  preserve_position_of_NAs = FALSE,
  verbose = TRUE
)

Arguments

`fit`	a `lemur_fit` object
`design`	a specification of the design (matrix or formula) that is used for the transformation. Default: `fit$design_matrix`
`ridge_penalty`	specification how much the flexibility of the transformation should be regularized. Default: `0.01`
`max_iter`	argument specific for `align_harmony`. The number of iterations. Default: `10`
`...`	additional parameters that are passed on to relevant functions
`verbose`	Should the method print information during the fitting. Default: `TRUE`.
`grouping`	argument specific for `align_by_grouping`. Either a vector which assigns each cell to one group or a matrix with `ncol(fit)` columns where the rows are a soft-assignment to a cluster (i.e., columns sum to `1`). `NA`'s are allowed.
`preserve_position_of_NAs`	argument specific for `align_by_grouping`. Boolean flag to decide if `NA`s in the `grouping` mean that these cells should stay where they are (if possible) or if they are free to move around. Default: `FALSE`

Value

The fit object with the updated fit$embedding and fit$alignment_coefficients.

Examples

data(glioblastoma_example_data)
fit <- lemur(glioblastoma_example_data, design = ~ patient_id + condition,
             n_emb = 5, verbose = FALSE)
# Creating some grouping for illustration
cell_types <- sample(c("tumor cell", "neuron", "leukocyte"), size = ncol(fit), replace = TRUE)
fit_al1 <- align_by_grouping(fit, grouping = cell_types)

# Alternatively, use harmony to automatically group cells
fit_al2 <- align_harmony(fit)
fit_al2

# The alignment coefficients are a 3D array
fit_al2$alignment_coefficients

data(glioblastoma_example_data)
fit <- lemur(glioblastoma_example_data, design = ~ patient_id + condition,
             n_emb = 5, verbose = FALSE)
# Creating some grouping for illustration
cell_types <- sample(c("tumor cell", "neuron", "leukocyte"), size = ncol(fit), replace = TRUE)
fit_al1 <- align_by_grouping(fit, grouping = cell_types)

# Alternatively, use harmony to automatically group cells
fit_al2 <- align_harmony(fit)
fit_al2

# The alignment coefficients are a 3D array
fit_al2$alignment_coefficients

Find differential expression neighborhoods

Description

Find differential expression neighborhoods

Usage

find_de_neighborhoods(
  fit,
  group_by,
  contrast = fit$contrast,
  selection_procedure = c("zscore", "contrast"),
  directions = c("random", "contrast", "axis_parallel"),
  min_neighborhood_size = 50,
  de_mat = SummarizedExperiment::assays(fit)[["DE"]],
  test_data = fit$test_data,
  test_data_col_data = NULL,
  test_method = c("glmGamPoi", "edgeR", "limma", "none"),
  continuous_assay_name = fit$use_assay,
  count_assay_name = "counts",
  size_factor_method = NULL,
  design = fit$design,
  alignment_design = fit$alignment_design,
  add_diff_in_diff = TRUE,
  make_neighborhoods_consistent = FALSE,
  skip_confounded_neighborhoods = FALSE,
  control_parameters = NULL,
  verbose = TRUE
)
find_de_neighborhoods(
  fit,
  group_by,
  contrast = fit$contrast,
  selection_procedure = c("zscore", "contrast"),
  directions = c("random", "contrast", "axis_parallel"),
  min_neighborhood_size = 50,
  de_mat = SummarizedExperiment::assays(fit)[["DE"]],
  test_data = fit$test_data,
  test_data_col_data = NULL,
  test_method = c("glmGamPoi", "edgeR", "limma", "none"),
  continuous_assay_name = fit$use_assay,
  count_assay_name = "counts",
  size_factor_method = NULL,
  design = fit$design,
  alignment_design = fit$alignment_design,
  add_diff_in_diff = TRUE,
  make_neighborhoods_consistent = FALSE,
  skip_confounded_neighborhoods = FALSE,
  control_parameters = NULL,
  verbose = TRUE
)

Arguments

`fit`	the `lemur_fit` generated by `lemur()`
`group_by`	If the `independent_matrix` is provided, `group_by` defines how the pseudobulks are formed. This is typically the variable in the column data that represents the independent unit of replication of the experiment (e.g., the mouse or patient ID). The argument has to be wrapped in `vars(...)`.
`contrast`	a specification which contrast to fit. This defaults to the `contrast` argument that was used for `test_de` and is stored in `fit$contrast`.
`selection_procedure`	specify the algorithm that is used to select the neighborhoods for each gene. Broadly, `selection_procedure = "zscore"` is faster but less precise than `selection_procedure = "contrast"`.
`directions`	a string to define the algorithm to select the direction onto which the cells are projected before searching for the neighborhood. `directions = "random"` produces denser neighborhoods, whereas `directions = "contrast"` has usually more power. Alternatively, this can also be a matrix with one direction for each gene (i.e., a matrix of size `nrow(fit) * fit$n_embedding`).
`min_neighborhood_size`	the minimum number of cells per neighborhood. Default: `50`.
`de_mat`	the matrix with the differential expression values and is only relevant if `selection_procedure = "zscore"` or `directions = "random"`. Defaults to an assay called `"DE"` that is produced by `lemur::test_de()`.
`test_data`	a `SummarizedExperiment` object or a named list of matrices. The data is used to test if the neighborhood inferred on the training data contain a reliable significant change. If `test_method` is `"glmGamPoi"` or `"edgeR"` a test using raw counts is conducted and two matching assays are needed: (1) the continuous assay (with `continuous_assay_name`) is projected onto the LEMUR fit to find the latent position of each cell and (2) the count assay (`count_assay_name`) is used for forming the pseudobulk. If `test_method == "limma"`, only the continuous assay is needed. The arguments defaults to the test data split of when calling `lemur()`.
`test_data_col_data`	additional column data for the `test_data` argument.
`test_method`	choice of test for the pseudobulked differential expression. glmGamPoi and edgeR work on an count assay. limma works on the continuous assay.
`continuous_assay_name`, `count_assay_name`	the assay or list names of `independent_data`.
`size_factor_method`	Set the procedure to calculate the size factor after pseudobulking. This argument is only relevant if `test_method` is `"glmGamPoi"` or `"edgeR"`. If `fit` is subsetted, using a vector with the sequencing depth per cell ensures reasonable results. Default: `NULL` which means that `colSums(assay(fit$test_data, count_assay_name))` is used.
`design`, `alignment_design`	the design to use for the fit. Default: `fit$design`
`add_diff_in_diff`	a boolean to specify if the log-fold change (plus significance) of the DE in the neighborhood against the DE in the complement of the neighborhood is calculated. If `TRUE`, the result includes three additional columns starting with `"did_"` short for difference-in-difference. Default: `TRUE`.
`make_neighborhoods_consistent`	Include cells from outside the neighborhood if they are at least 10 times in the k-nearest neighbors of the cells inside the neighborhood. Secondly, remove cells from the neighborhood which are less than 10 times in the k-nearest neighbors of the other cells in the neighborhood. Default `FALSE`
`skip_confounded_neighborhoods`	Sometimes the inferred neighborhoods are not limited to a single cell state; this becomes problematic if the cells of the conditions compared in the contrast are unequally distributed between the cell states. Default: `FALSE`
`control_parameters`	named list with additional parameters passed to underlying functions.
`verbose`	Should the method print information during the fitting. Default: `TRUE`.

Value

a data frame with one entry per gene

name: The gene name.
neighborhood: A list column where each element is a vector with the cell names included in that neighborhood.
n_cells: the number of cells in the neighborhood (lengths(neighborhood)).
sel_statistic: The statistic that is maximized by the selection_procedure.
pval, adj_pval, t_statistic, lfc: The p-value, Benjamini-Hochberg adjusted p-value (FDR), the t-statistic, and the log2 fold change of the differential expression test defined by contrast for the cells inside the neighborhood (calculated using test_method). Only present if test_data is not NULL.
did_pval, did_adj_pval, did_lfc: The measurement if the differential expression of the cells inside the neighborhood is significantly different from the differential expression of the cells outside the neighborhood. Only present if add_diff_in_diff = TRUE.

Examples

data(glioblastoma_example_data)
fit <- lemur(glioblastoma_example_data, design = ~ patient_id + condition,
             n_emb = 5, verbose = FALSE)
# Optional alignment
# fit <- align_harmony(fit)
fit <- test_de(fit, contrast = cond(condition = "panobinostat") - cond(condition = "ctrl"))
nei <- find_de_neighborhoods(fit, group_by = vars(patient_id))
head(nei)

data(glioblastoma_example_data)
fit <- lemur(glioblastoma_example_data, design = ~ patient_id + condition,
             n_emb = 5, verbose = FALSE)
# Optional alignment
# fit <- align_harmony(fit)
fit <- test_de(fit, contrast = cond(condition = "panobinostat") - cond(condition = "ctrl"))
nei <- find_de_neighborhoods(fit, group_by = vars(patient_id))
head(nei)

The `glioblastoma_example_data` dataset

Description

The dataset is a SingleCellExperiment object subset to 5,000 cells and 300 genes. The colData contain an entry for each cell from which patient it came and to which treatment condition it belonged ("ctrl" or "panobinostat").

Details

The original data was collected by Zhao et al. (2021).

Value

A SingleCellExperiment object.

References

Zhao, Wenting, Athanassios Dovas, Eleonora Francesca Spinazzi, Hanna Mendes Levitin, Matei Alexandru Banu, Pavan Upadhyayula, Tejaswi Sudhakar, et al. “Deconvolution of Cell Type-Specific Drug Responses in Human Tumor Tissue with Single-Cell RNA-Seq.” Genome Medicine 13, no. 1 (December 2021): 82. https://doi.org/10.1186/s13073-021-00894-y.

Main function to fit the latent embedding multivariate regression (LEMUR) model

Description

Main function to fit the latent embedding multivariate regression (LEMUR) model

Usage

lemur(
  data,
  design = ~1,
  col_data = NULL,
  n_embedding = 15,
  linear_coefficient_estimator = c("linear", "mean", "cluster_median", "zero"),
  use_assay = "logcounts",
  test_fraction = 0.2,
  ...,
  verbose = TRUE
)
lemur(
  data,
  design = ~1,
  col_data = NULL,
  n_embedding = 15,
  linear_coefficient_estimator = c("linear", "mean", "cluster_median", "zero"),
  use_assay = "logcounts",
  test_fraction = 0.2,
  ...,
  verbose = TRUE
)

Arguments

`data`	a matrix with observations in the columns and features in the rows. Or a `SummarizedExperiment` / `SingleCellExperiment` object
`design`	a formula referring to global objects or column in the `colData` of `data` and `col_data` argument
`col_data`	an optional data frame with `ncol(data)` rows.
`n_embedding`	the dimension of the $k$-plane that is rotated through space.
`linear_coefficient_estimator`	specify which estimator is used to center the conditions. `"linear"` runs simple regression it works well in many circumstances but can produce poor results if the composition of the cell types changes between conditions (e.g., one cell type disappears). `"mean"`, `"cluster_median"` and `"zero"` are alternative estimators, which are each supposed to be more robust against compositional changes but cannot account for genes that change for all cells between conditions. `"linear"` is the default as it works best with subsequent alignment steps.
`use_assay`	if `data` is a `SummarizedExperiment` / `SingleCellExperiment` object, which assay should be used.
`test_fraction`	the fraction of cells that are split of before the model fit to keep an independent set of test observations. Alternatively, a logical vector of length `ncol(data)`. Default: 20% (`0.2`).
`...`	additional parameters that are passed on to the internal function `lemur_impl`.
`verbose`	Should the method print information during the fitting. Default: `TRUE`.

Value

An object of class lemur_fit which extends SingleCellExperiment. Accordingly, all functions that work for sce's also work for lemur_fit's. In addition, we give easy access to the fitted values using the dollar notation (e.g., fit$embedding). For details see the lemur_fit help page.

References

Ahlmann-Eltze, C. & Huber, W. (2023). Analysis of multi-condition single-cell data with latent embedding multivariate regression. bioRxiv https://doi.org/10.1101/2023.03.06.531268

Examples

data(glioblastoma_example_data)
fit <- lemur(glioblastoma_example_data, design = ~ patient_id + condition, n_emb = 5)
fit


data(glioblastoma_example_data)
fit <- lemur(glioblastoma_example_data, design = ~ patient_id + condition, n_emb = 5)
fit

The `lemur_fit` class

Description

The lemur_fit class extends SingleCellExperiment and provides additional accessors to get the values of the values produced by lemur.

Usage

## S4 method for signature 'lemur_fit,ANY,ANY,ANY'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'lemur_fit'
design(object)
## S4 method for signature 'lemur_fit,ANY,ANY,ANY'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'lemur_fit'
design(object)

Arguments

`x`, `i`, `j`, `...`, `drop`	the `lemur_fit` object and indices for the `[` subsetting operator
`object`	the `lemur_fit` object for the `BiocGenerics::design` generic

Details

To access the values produced by lemur, use the dollar notation ($):

fit$n_embedding: the number of embedding dimensions.
fit$design: the specification of the design in lemur. Usually this is a stats::formula.
fit$base_point: a matrix (nrow(fit) * fit$n_embedding) with the base point for the Grassmann exponential map.
fit$coefficients: a three-dimensional tensor (nrow(fit) * fit$n_embedding * ncol(fit$design_matrix)) with the coefficients for the exponential map.
fit$embedding: a matrix (fit$n_embedding * ncol(fit)) with the low dimensional position for each cell.
fit$design_matrix: a matrix with covariates for each cell (ncol(fit) * ncol(fit$design_matrix)).
fit$linear_coefficients: a matrix (nrow(fit) * ncol(fit$design_matrix)) with the coefficients for the linear regression.
fit$alignment_coefficients: a 3D tensor with the coefficients for the alignment (fit$n_embedding * fit$n_embedding * ncol(fit$design_matrix))
fit$alignment_design: an alternative design specification for the alignment. This is typically a stats::formula.
fit$alignment_design_matrix: an alternative design matrix specification for the alignment.
fit$contrast: a parsed version of the contrast specification from the test_de function or NULL.
fit$colData: the column annotation DataFrame.
fit$rowData: the row annotation DataFrame.

Value

An object of class lemur_fit.

Examples

# The easiest way to make a lemur_fit object, is to call `lemur`
data(glioblastoma_example_data)
fit <- lemur(glioblastoma_example_data, design = ~ patient_id + condition,
             n_emb = 5, verbose = FALSE)

fit$n_embedding
fit$embedding[,1:10]
fit$n_embedding
fit$embedding[,1:10]
fit$design_matrix[1:10,]
fit$coefficients[1:3,,]

# The easiest way to make a lemur_fit object, is to call `lemur`
data(glioblastoma_example_data)
fit <- lemur(glioblastoma_example_data, design = ~ patient_id + condition,
             n_emb = 5, verbose = FALSE)

fit$n_embedding
fit$embedding[,1:10]
fit$n_embedding
fit$embedding[,1:10]
fit$design_matrix[1:10,]
fit$coefficients[1:3,,]

Predict values from `lemur_fit` object

Description

Predict values from lemur_fit object

Usage

## S3 method for class 'lemur_fit'
predict(
  object,
  newdata = NULL,
  newdesign = NULL,
  newcondition = NULL,
  embedding = object$embedding,
  with_linear_model = TRUE,
  with_embedding = TRUE,
  with_alignment = TRUE,
  ...
)
## S3 method for class 'lemur_fit'
predict(
  object,
  newdata = NULL,
  newdesign = NULL,
  newcondition = NULL,
  embedding = object$embedding,
  with_linear_model = TRUE,
  with_embedding = TRUE,
  with_alignment = TRUE,
  ...
)

Arguments

`object`	an `lemur_fit` object
`newdata`	a data.frame which passed to `model.matrix` with `design` to make the `newdesign` matrix
`newdesign`	a matrix with the covariates for which the output is predicted. If `NULL`, the `object$design_matrix` is used. If it is a vector it is repeated `ncol(embedding)` times to create a design matrix with the same entry for each cell.
`newcondition`	an unquoted expression with a call to `cond()` specifying the covariates of the prediction. See the `contrast` argument in test_de for more details. Note that combinations of multiple calls to `cond()` are not allowed (e.g., `cond(a = 1) - cond(a = 2)`). If specified, `newdata` and `newdesign` are ignored.
`embedding`	the low-dimensional cell position for which the output is predicted.
`with_linear_model`	a boolean to indicate if the linear regression offset is included in the prediction.
`with_embedding`	a boolean to indicate if the embedding contributes to the output.
`with_alignment`	a boolean to indicate if the alignment effect is removed from the output.
`...`	additional parameters passed to `predict_impl`.

Value

A matrix with the same dimension nrow(object) * nrow(newdesign).

Examples


data(glioblastoma_example_data)
fit <- lemur(glioblastoma_example_data, design = ~ patient_id + condition,
             n_emb = 5, verbose = FALSE)

pred <- predict(fit)

pred_ctrl <- predict(fit, newdesign = c(1, 0, 0, 0, 0, 0))
pred_trt <-  predict(fit, newdesign = c(1, 0, 0, 0, 0, 1))
# This is the same as the test_de result
fit <- test_de(fit, cond(condition = "panobinostat") - cond(condition = "ctrl"))
all.equal(SummarizedExperiment::assay(fit, "DE"), pred_trt - pred_ctrl,
          check.attributes = FALSE)

data(glioblastoma_example_data)
fit <- lemur(glioblastoma_example_data, design = ~ patient_id + condition,
             n_emb = 5, verbose = FALSE)

pred <- predict(fit)

pred_ctrl <- predict(fit, newdesign = c(1, 0, 0, 0, 0, 0))
pred_trt <-  predict(fit, newdesign = c(1, 0, 0, 0, 0, 1))
# This is the same as the test_de result
fit <- test_de(fit, cond(condition = "panobinostat") - cond(condition = "ctrl"))
all.equal(SummarizedExperiment::assay(fit, "DE"), pred_trt - pred_ctrl,
          check.attributes = FALSE)

Project new data onto the latent spaces of an existing lemur fit

Description

Project new data onto the latent spaces of an existing lemur fit

Usage

project_on_lemur_fit(
  fit,
  data,
  col_data = NULL,
  use_assay = "logcounts",
  design = fit$design,
  alignment_design = fit$alignment_design,
  return = c("matrix", "lemur_fit")
)
project_on_lemur_fit(
  fit,
  data,
  col_data = NULL,
  use_assay = "logcounts",
  design = fit$design,
  alignment_design = fit$alignment_design,
  return = c("matrix", "lemur_fit")
)

Arguments

`fit`	an `lemur_fit` object
`data`	a matrix with observations in the columns and features in the rows. Or a `SummarizedExperiment` / `SingleCellExperiment` object. The features must match the features in `fit`.
`col_data`	col_data an optional data frame with `ncol(data)` rows.
`use_assay`	if `data` is a `SummarizedExperiment` / `SingleCellExperiment` object, which assay should be used.
`design`, `alignment_design`	the design formulas or design matrices that are used to project the data on the correct latent subspace. Both default to the designs from the `fit` object.
`return`	which data structure is returned.

Value

Either a matrix with the low-dimensional embeddings of the data or an object of class lemur_fit wrapping that embedding.

Examples


data(glioblastoma_example_data)

subset1 <- glioblastoma_example_data[,1:2500]
subset2 <- glioblastoma_example_data[,2501:5000]

fit <- lemur(subset1, design = ~ condition, n_emb = 5,
             test_fraction = 0, verbose = FALSE)

# Returns a `lemur_fit` object with the projection of `subset2`
fit2 <- project_on_lemur_fit(fit, subset2, return = "lemur_fit")
fit2



data(glioblastoma_example_data)

subset1 <- glioblastoma_example_data[,1:2500]
subset2 <- glioblastoma_example_data[,2501:5000]

fit <- lemur(subset1, design = ~ condition, n_emb = 5,
             test_fraction = 0, verbose = FALSE)

# Returns a `lemur_fit` object with the projection of `subset2`
fit2 <- project_on_lemur_fit(fit, subset2, return = "lemur_fit")
fit2

Predict values from `lemur_fit` object

Description

Predict values from lemur_fit object

Usage

## S4 method for signature 'lemur_fit'
residuals(object, with_linear_model = TRUE, with_embedding = TRUE, ...)
## S4 method for signature 'lemur_fit'
residuals(object, with_linear_model = TRUE, with_embedding = TRUE, ...)

Arguments

`object`	an `lemur_fit` object
`with_linear_model`	a boolean to indicate if the linear regression offset is included in the prediction.
`with_embedding`	a boolean to indicate if the embedding contributes to the output.
`...`	ignored.

Value

A matrix with the same dimension dim(object).

Examples

data(glioblastoma_example_data)
fit <- lemur(glioblastoma_example_data, design = ~ patient_id + condition,
             n_emb = 5, verbose = FALSE)

resid <- residuals(fit)
dim(resid)


data(glioblastoma_example_data)
fit <- lemur(glioblastoma_example_data, design = ~ patient_id + condition,
             n_emb = 5, verbose = FALSE)

resid <- residuals(fit)
dim(resid)

Predict log fold changes between conditions for each cell

Description

Predict log fold changes between conditions for each cell

Usage

test_de(
  fit,
  contrast,
  embedding = NULL,
  consider = c("embedding+linear", "embedding", "linear"),
  new_assay_name = "DE"
)
test_de(
  fit,
  contrast,
  embedding = NULL,
  consider = c("embedding+linear", "embedding", "linear"),
  new_assay_name = "DE"
)

Arguments

`fit`	the result of calling `lemur()`
`contrast`	Specification of the contrast: a call to `cond()` specifying a full observation (e.g. `cond(treatment = "A", sex = "male") - cond(treatment = "C", sex = "male")` to compare treatment A vs C for male observations). Unspecified factors default to the reference level.
`embedding`	matrix of size `n_embedding` $\times$ `n` that specifies where in the latent space the differential expression is tested. It defaults to the position of all cells from the original fit.
`consider`	specify which part of the model are considered for the differential expression test.
`new_assay_name`	the name of the assay added to the `fit` object. Default: `"DE"`.

Value

If is.null(embedding) the fit object with a new assay called "DE". Otherwise return a matrix with the differential expression values.

Examples

library(SummarizedExperiment)
library(SingleCellExperiment)

data(glioblastoma_example_data)
fit <- lemur(glioblastoma_example_data, design = ~ patient_id + condition,
             n_emb = 5, verbose = FALSE)
# Optional alignment
# fit <- align_harmony(fit)
fit <- test_de(fit, contrast = cond(condition = "panobinostat") - cond(condition = "ctrl"))

# The fit object contains a new assay called "DE"
assayNames(fit)

# The DE assay captures differences between conditions
is_ctrl_cond <- fit$colData$condition == "ctrl"
mean(logcounts(fit)[1,!is_ctrl_cond]) - mean(logcounts(fit)[1,is_ctrl_cond])
mean(assay(fit, "DE")[1,])

library(SummarizedExperiment)
library(SingleCellExperiment)

data(glioblastoma_example_data)
fit <- lemur(glioblastoma_example_data, design = ~ patient_id + condition,
             n_emb = 5, verbose = FALSE)
# Optional alignment
# fit <- align_harmony(fit)
fit <- test_de(fit, contrast = cond(condition = "panobinostat") - cond(condition = "ctrl"))

# The fit object contains a new assay called "DE"
assayNames(fit)

# The DE assay captures differences between conditions
is_ctrl_cond <- fit$colData$condition == "ctrl"
mean(logcounts(fit)[1,!is_ctrl_cond]) - mean(logcounts(fit)[1,is_ctrl_cond])
mean(assay(fit, "DE")[1,])

Differential embedding for each condition

Description

Differential embedding for each condition

Usage

test_global(
  fit,
  contrast,
  reduced_design = NULL,
  consider = c("embedding+linear", "embedding", "linear"),
  variance_est = c("analytical", "resampling", "none"),
  verbose = TRUE,
  ...
)
test_global(
  fit,
  contrast,
  reduced_design = NULL,
  consider = c("embedding+linear", "embedding", "linear"),
  variance_est = c("analytical", "resampling", "none"),
  verbose = TRUE,
  ...
)

Arguments

`fit`	the result of calling `lemur()`
`contrast`	Specification of the contrast: a call to `cond()` specifying a full observation (e.g. `cond(treatment = "A", sex = "male") - cond(treatment = "C", sex = "male")` to compare treatment A vs C for male observations). Unspecified factors default to the reference level.
`reduced_design`	an alternative specification of the null hypothesis.
`consider`	specify which part of the model are considered for the differential expression test.
`variance_est`	How or if the variance should be estimated. `'analytical'` is only compatible with `consider = "linear"`. `'resampling'` is the most flexible (to adapt the number of resampling iterations, set `n_resampling_iter`. Default: `100`)
`verbose`	should the method print information during the fitting. Default: `TRUE`.
`...`	additional arguments.

Value

a data.frame

Package 'lemur'

Help Index

Access values from a lemur_fit

Description

Usage

Arguments

Value

See Also

Enforce additional alignment of cell clusters beyond the direct differential embedding

Description

Usage

Arguments

Value

Examples

Find differential expression neighborhoods

Description

Usage

Arguments

Value

Examples

The glioblastoma_example_data dataset

Description

Details

Value

References

Main function to fit the latent embedding multivariate regression (LEMUR) model

Description

Usage

Arguments

Value

References

See Also

Examples

The lemur_fit class

Description

Usage

Arguments

Details

Value

See Also

Examples

Predict values from lemur_fit object

Description

Usage

Arguments

Value

See Also

Examples

Project new data onto the latent spaces of an existing lemur fit

Description

Usage

Arguments

Value

Examples

Predict values from lemur_fit object

Description

Usage

Arguments

Value

See Also

Examples

Predict log fold changes between conditions for each cell

Description

Usage

Arguments

Value

See Also

Examples

Differential embedding for each condition

Description

Usage

Arguments

Value

Access values from a `lemur_fit`

The `glioblastoma_example_data` dataset

The `lemur_fit` class

Predict values from `lemur_fit` object

Predict values from `lemur_fit` object