Package 'iCellR'

An object of class iCellR.

A character vector of list object names. Lists should be made using "image.capture.10x" function. .

A character vector of condition names.

An object of class iCellR.

A data frame containing ADT counts for cells.

An object of class iCellR.

A data frame containing VDJ information for cells.

An object of class iCellR.

Choose from raw or main (normalized) ADT data, default = "raw".

A directory that includes the 10X image files (scalefactors_json.json, tissue_lowres_image.png and tissue_positions_list.csv).

A data frame containing gene counts for cells.

Genes that are used as a marker for S phase.

Genes that are used as a marker for G2 and M phase.

A data frame containing gene counts for cells.

Genes that are used as a marker for phases.

Return the data or object. If FALSE the object would be returned.

Choose from "coverage" or "tirosh" for scoring method.

An object of class iCellR.

Min rate for mitochondrial gene expression per cell, default = 0.

Max rate for mitochondrial gene expression per cell, default = 1.

Min number genes per cell, default = 0.

Max number genes per cell, default = Inf.

Min number UMIs per cell, default = 0.

Max number UMIs per cell, default = Inf.

A character vector of cell ids to be filtered out.

A character vector of cell ids to keep.

A character vector of gene names to be filtered by thier expression. If more then one gene is defined it would be OR not AND.

Minimum gene expression to be filtered by the genes set in filter.by.gene, default = 1.

An object of class iCellR.

The plot to use for gating. Must be a 2D plot.

Choose from knetl, umap and tsne, default = NULL.

Choose from ,"GSE109125","GSE122108","GSE122597","GSE124829","GSE15907","GSE37448", rna", "uli.rna" or "mca", default = "rna"

A set of gene names to used to predict cell type.

Top cell types sorted by cumulative expression, default = 25.

Choose from "heatmap" od "point.plot", default = "heatmap"

Colors for heatmap, default = c("blue" ,"white", "red").

An object of class iCellR.

The name of the cluster to be changed.

The new name for the cluster.

Reset to the original clustering.

An object of class iCellR.

Choose from "tsne" and "pca", default = "tsne".

The column which has the clonotype IDs, default = 2.

The column which has the barcode IDs, default = 1.

A clonotype name to be plotted, default = NULL.

Choose one condition you want to see in the plot, default = NULL (all conditions).

2 for 2D plots and 3 for 3D plots, default = 2.

A number for the size of the points in the plot, default = 1.

Colors for heat mapping the points in "scatterplot", default = c("gray","red").

Choose a color for box default = "black".

A color for the plot background, default = "black".

Color transparency for points, default = 0.5.

If set to TRUE an intractive HTML file will be created, default = TRUE.

If "interactive" is set to TRUE, the out put name for HTML, default = "plot".

An object of class iCellR.

Choose from "main", "atac", "atac.imputed" and "imputed", default = "main"

Choose the conditions you want to average, default = NULL (all conditions).

integer indicating the number of decimal places (round) or significant digits (signif) to be used.

filter out clusters with low number of cells, default = 5.

Rounding of Numbers, default = FALSE.

An object of class iCellR.

Choose from pie/pie.cond or bar/bar.cond, defult = pie.

Chose from "data" or "plot", default = "data".

If TRUE the values will be normalized to the number of cells by downsampling.

Chose from "sf" (size factor) or "percentage", default = "percentage".

An object of class iCellR.

A number. Taking the top genes ranked by base mean, default = 500.

Choose from "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski", default = "euclidean".

Choose from "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median" or "centroid", default = "complete".

Choose from "distance" and "random".

A data frame containing gene counts for cells.

The name of the cluster to be removed.

An object of class iCellR.

Choose from "bar.cc", "pie.cc" , box.umi", "box.mito", "box.gene", default = "box.mito".

Choose the conditions you want to see in the plot, default = NULL (all conditions).

Choose a color for points in the plot.

A number for the size of the points in the plot, default = 1.

Color transparency for points in "scatterplot" and "boxplot", default = 0.5.

A color for the boxes in the "boxplot", default = "red".

A color for the lines around the "boxplot", default = "green".

Background color, default = "white"

Notch the box plots, default = FALSE.

If set to TRUE an interactive HTML file will be created, default = TRUE.

If "interactive" is set to TRUE, the out put name for HTML, default = "plot".

An object of class iCellR.

A numeric value for the size of the cells, default = 1.

Choose between "tsne", "pca", "umap", "knetl", "diffusion", default = "tsne".

Choose cell color if col.by = "monochrome", default = "black".

Choose background color, default = "black".

Choose between "clusters", "conditions", "cc" (cell cycle) or "monochrome", default = "clusters".

Show the conditions in separate plots.

If TRUE the conditions will be shown in shapes.

Annotate cluster names on the plot, default = TRUE.

If anno.clust is TRUE set font size, default = 3.

A numeric value between 0 to 1, default = 0.5.

A numeric value for plot dimensions. Choose either 2 or 3, default = 2.

A number to rotate the non-interactive 3D plot.

Number of clonotype to plot, default = 10.

If TRUE the density plots for PCA/tSNE second dimension will be created, default = FALSE.

If TRUE an html interactive file will be made, default = TRUE.

If TRUE a non-interactive 3D plot will be made.

Output name for html file if interactive = TRUE, default = "plot".

A character vector of data.frame object names.

A character vector of data.frame condition names.

An object of class iCellR.

An object of class iCellR.

matrix; input data matrix

integer; number of nearest neighbours

An object of class iCellR.

PC dimentions to be used.

Number of top positive marker genes to be taken from each PC, default = 15.

Number of top negative marker genes to be taken from each PC, default = 5.

An object of class iCellR.

Choose from "main", "atac", "atac.imputed" and "imputed", default = "main"

Choose from "t.test", "wilcox.test", default = "t.test".

Correction method. Choose from "holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr", "none", default = "hochberg".

A number that designates the minimum fold change for out put, default = 2.

Minimum adjusted p value for out put, default = 0.1.

If set to FALSE the infinite fold changes would be filtered from out put, default = FALSE.

If set to TRUE only genes that are a marker for only one cluster would be in the out put, default = FALSE.

If set to FALSE both the up regulated (positive) and down regulated (negative) markers would be in the out put, default = TRUE.

An object of class iCellR.

A vector of cell ids.

A cluster id to be assigned to the provided cell ids.

An object of class iCellR.

Gene name/names to be plotted.

If TRUE the conditions will be shown in shapes.

Choose the conditions you want to see in the plot, default = NULL (all conditions).

Choose from "main", "atac, "atac.imputed" and "imputed", default = "main".

A cluster number so that all the boxes in the box plot would be compared to. If set to "0" the cluster with the highest avrage would be choosen, default = 0.

Choose from "sig.values" and "sig.signs". If set to "sig.signs" p values would be replaced with signs ("na", "*", "**", "***"), default = "sig.signs".

Choose between "tsne", "pca", "umap", "knetl", "diffusion", "pseudo.A" and "pseudo.B", default = "tsne".

Scale the colors, default = FALSE.

If scaleValue = TRUE, set a number for min, default = -2.5.

If scaleValue = TRUE, set a number for max, default = 2.5.

2 for 2D plots and 3 for 3D plots, default = 2.

Choose from "clusters" and "conditions", default = "clusters".

Choose from "scatterplot", "boxplot" and "barplot", default = "scatterplot".

A number for the size of the points in the plot, default = 1.

Colors for heat mapping the points in "scatterplot", default = c("gray","red").

A color for the points in the box plot, default = "black".

A color for the boxes in the "boxplot", default = "red".

A color for the lines around the "boxplot", default = "green".

A color for the plot background, default = "black".

Color transparency for points in "scatterplot" and "boxplot", default = 1.

Color transparency for box in "boxplot", default = 0.5.

If set to TRUE an interactive HTML file will be created, default = TRUE.

If "interactive" is set to TRUE, the out put name for HTML, default = "plot".

Write export the data used for the plot plot, default = TFALSE.

An object of class iCellR.

Choose from "raw.data" or "main.data", default = "raw.data".

If TRUE each condition will be calculated, default = FALSE.

An object of class iCellR.

Choose from imputed and main, default = "imputed".

First gene name.

Second gene name.

Filter only one condition (only one), default is all conditions.

Choose clusters to plot.

A numeric value for the size of the cells, default = 1.

A numeric value between 0 to 1, default = 0.5.

If TRUE an html interactive file will be made, default = TRUE.

Output name for html file if interactive = TRUE, default = "plot".

A data frame containing gene counts for cells.

A set of gene names to be heatmapped.

If FALSE the cells will not be sorted based on their distance, default = TRUE.

Choose from "main", "atac", atac.imputed and "imputed", default = "main".

Choose from "clusters" or "none", default = "clusters".

Choose the conditions you want to see in the plot, default = NULL (all conditions).

Set a minimum color scale, default = -2.5.

Set a maximum color scale, default = 2.5.

If TRUE an html interactive file will be made, default = TRUE.

Chhose a size, default = 10.

Choose a size, default = 10.

If you want a color legend key, default = FALSE.

Output name for html file if interactive = TRUE, default = "plot".

Colors for heatmap, default = c("blue" ,"white", "red").

HTO raw data

A number which average coverage is divided by to set a threshold for low coverage. For example 10 means it is 10 time less than the average. default = 10.

A percent above which you decide to set as a good sample assignment/HTO, default = 80.

A data frame containing gene counts for cells.

Choose from "raw.data" or "main.data", "imputed.data", default = "main.data".

Genes that are used as a marker for phases.

Return the data or object. If FALSE the object would be returned.

Choose from "tirosh (Tirosh, et. al. 2016), mean, sum, gsva, ssgsea, zscore and plage. , default = "tirosh".

An object of class iCellR.

PC dimentions to be used

number of neighboring cells for KNN, default = 10.

Batch alignment method. Choose from "CCCA" and "CPCA", default = "CPCA".

Choose from "base.mean.rank" or "gene.model", default is "base.mean.rank". If gene.model is chosen you need to provide gene.list.

A number. Taking the top genes ranked by base mean, default = 500.

A number to add to each value in the matrix before log transformasion to aviond Inf numbers, default = 0.1.

If TRUE the data will be scaled (log2 + plus.log.value), default = TRUE.

A charactor vector of genes to be used for PCA. If "clust.method" is set to "gene.model", default = "my_model_genes.txt".

An object of class iCellR.

the distance measure to be used to compute the dissimilarity matrix. This must be one of: "euclidean", "maximum", "mandatattan", "canberra", "binary", "minkowski" or "NULL". By default, distance="euclidean". If the distance is "NULL", the dissimilarity matrix (diss) should be given by the user. If distance is not "NULL", the dissimilarity matrix should be "NULL".

The higher the number the less sensitivity, default = 100.

Choose between "tsne", "pca", "umap", default = "pca".

PCA dimentions to be use for clustering, default = 1:10.

return igraph object, default = FALSE.

path to the input (h5) file

row names to be feature names or ID numbers.

make row names unique.

A directory that includes the 10X barcodes.tsv, genes.tsv and matrix.mtx files.

Gene names or ids column number, default = 2.

Peak marker file.

An object of class iCellR.

A number for taking the genes that have dispersion above this number, default = 1.5.

A number taking the top genes ranked by base mean, default = 500.

Maximum number of genes , default = 2000.

Color for the genes not used for the model, default = "darkgray".

Color for the genes above the dispersion limit, default = "chartreuse3".

Color for the genes above the rank limit, default = "cadetblue3".

Color of the line for dispersion limit, default = "black".

Color of the line for rank limit, default = "red".

Chose from "data" or "plot", default = "data".

A number for the size of the points in the plot, default = 1.75.

Color transparency for the points in the plot, default = 0.5.

If set to TRUE, mitochondrial genes would be excluded from the gene list made for clustering, default = TRUE.

If TRUE the cell cycle genes will be removed (s.phase and g2m.phase), default = TRUE.

Mark mitochondrial genes in the plot, default = TRUE.

If set to TRUE an interactive HTML file will be created, default = TRUE.

If "interactive" is set to TRUE, the out put name for HTML, default = "plot".

A data frame containing gene counts for cells.

An object of class iCellR.

An object of class iCellR.

An object of class iCellR.

Choose a normalization method, there are three option currently. Choose from "global.glsf", "ranked.glsf","spike.in" or no.norm, default = "ranked.glsf".

If the method is set to "ranked.glsf", you need to set top number of genes sorted based on global base mean, default = 500.

A numeric vector of spike-in values with the same cell id order as the main data.

If the norm.method is set to "rpm" the library sizes would be divided by this number, default = 1000 (higher numbers recomanded for bulk RNA-Seq).

integer indicating the number of decimal places (round) or significant digits (signif) to be used.

Rounding of Numbers, default = FALSE.

If TURE, it would normalize ATAC-Seq data and not RNA-Seq, default = FALSE.

If TURE, all the cells filtered in RNA-Seq will be filtered in ATAC-Seq. This needs to be done for both data to match, default = TRUE.

An object of class iCellR.

Number of PCs to show in plot, defult = 50.

A data frame containing vdj information.

Conditions.

An object of class iCellR.

A list of marker genes for clusters.

PC dimentions to be used, , default = 1:10.

An object of class iCellR.

the distance measure to be used to compute the dissimilarity matrix. This must be one of: "euclidean", "maximum", "mandatattan", "canberra", "binary", "minkowski" or "NULL". By default, distance="euclidean". If the distance is "NULL", the dissimilarity matrix (diss) should be given by the user. If distance is not "NULL", the dissimilarity matrix should be "NULL".

KNN the higher the number the less sensitivity, default = 5.

Draw all the cells or clusters, , default = TRUE.

Choose between "tsne", "pca", "umap", default = "pca". We highly recommend PCA.

PCA dimentions to be use for clustering, default = 1:20.

Choose the conditions you want to see in the plot, default = NULL (all conditions).

Choose a layout, default = "layout_with_fr".

Size of the nodes, , default = 10.

Calculate memberships based on distance.

If set to TRUE an interactive HTML file will be created, default = TRUE.

Color of the nodes, default = random colors.

Solor of the edges, default = "gray".

If "interactive" is set to TRUE, the out put name for HTML, default = "Abstract.KNetL".

seed number, default = 1.

An object of class iCellR.

A list of marker genes for clusters.

A list of names for clusters.

Choose from "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski", default = "euclidean".

Choose from "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median" or "centroid", default = "complete".

Space between names and tree, default = 0.5.

Choose from "classic", "jitter", "unrooted", "fan", "cladogram", "radial", default = "classic".

Hang, default = 1.

Text size, default = 1.

A data frame containing gene counts for cells.

Choose from "raw.data" or "main.data", "imputed.data", default = "raw.data".

A character vector of mitochondrial genes names , default is the genes starting with mt.

A character vector of gene names for S phase, default = s.phase.

A character vector of gene names for G2 and M phase, default = g2m.phase.

matrix; input data matrix

integer; number of nearest neighbours (default:30)

returns the communities object itself, invisibly.

returns an integer scalar.

returns a numeric vector.

returns a numeric vector, one number for each vertex in the graph that was the input of the community detection.

returns a numeric scalar.

returns a character scalar.

returns a logical vector.

returns a logical scalar.

returns a two-column numeric matrix.

returns a numeric vector, the membership vector of the vertices.

returns a dendrogram object.

returns a character vector.

returns a numeric scalar for communities found with the InfoMAP method and NULL for other methods.

for communities objects returns NULL, invisibly.

An object of class iCellR.

Choose from "base.mean.rank" or "gene.model", default is "base.mean.rank". If gene.model is chosen you need to provide gene.list.

A number taking the top genes ranked by base mean, default = 500.

A charactor vector of genes to be used for PCA. If "clust.method" is set to "gene.model", default = "my_model_genes.txt".

Choose from "main" and "imputed", default = "main"

Choose from "LogNormalize", "CLR" and "RC". LogNormalize: Feature counts for each cell are divided by the total counts for that cell and multiplied by the scale.factor. This is then natural-log transformed using log1p. CLR: Applies a centered log ratio transformation. RC: Relative counts. Feature counts for each cell are divided by the total counts for that cell and multiplied by the scale.factor. No log-transformation is applied. For counts per million (CPM) set ‘scale.factor = 1e6’

Sets the scale factor for cell-level normalization.

If performing CLR normalization, normalize across features (1) or cells (2)

How many cells should be run in each chunk, will try to split evenly across threads

Choose from "vst","mean.var.plot (mvp)","dispersion (disp)".

Number of features to select as top variable features; only used when ‘selection.method’ is set to ‘'dispersion'’ or ‘'vst'’

A numeric value. This will call ‘SelectIntegrationFeatures’ to select the provided number of features to be used in anchor finding

Whether or not to scale the features provided. Only set to FALSE if you have previously scaled the features you want to use for each object in the object.list

Numeric of length two specifying the min and max values the Pearson residual will be clipped to

cca: Canonical correlation analysis. rpca: Reciprocal PCA

Perform L2 normalization on the CCA cell embeddings after dimensional reduction

Which dimensions to use from the CCA to specify the neighbor search space

How many neighbors (k) to use when picking anchors

How many neighbors (k) to use when filtering anchors

How many neighbors (k) to use when scoring anchors

The maximum number of features to use when specifying the neighborhood search space in the anchor filtering

Method for nearest neighbor finding. Options include: rann, annoy

Error bound on the neighbor finding algorithm (from RANN)

Number of neighbors to consider when weighting

An object of class iCellR.

Chose top genes to use for CCA, default = 1000.

Choose a number, default = 30.

Choose the CCA dimentions to align, default = 1:20.

TRUE or FALSE, default = TRUE.

TRUE or FALSE, default = TRUE.

Choose a method, default = "LogNormalize".

Scaling factor, default = 10000.

Show progress, default = TRUE.

An object of class iCellR.

the cluster analysis method to be used. This should be one of: "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", "centroid", "kmeans".

the distance measure to be used to compute the dissimilarity matrix. This must be one of: "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski" or "NULL". By default, distance="euclidean". If the distance is "NULL", the dissimilarity matrix (diss) should be given by the user. If distance is not "NULL", the dissimilarity matrix should be "NULL".

the index to be calculated. This should be one of : "kl", "ch", "hartigan", "ccc", "scott", "marriot", "trcovw", "tracew", "friedman", "rubin", "cindex", "db", "silhouette", "duda", "pseudot2", "beale", "ratkowsky", "ball", "ptbiserial", "gap", "frey", "mcclain", "gamma", "gplus", "tau", "dunn", "hubert", "sdindex", "dindex", "sdbw", "all" (all indices except GAP, Gamma, Gplus and Tau), "alllong" (all indices with Gap, Gamma, Gplus and Tau included).

maximal number of clusters, between 2 and (number of objects - 1), greater or equal to min.nc.

minimum number of clusters, default = 2.

PCA dimentions to be use for clustering, default = 1:10.

An object of class iCellR.

Choose from "main" and "imputed", default = "main"

Choose from "t.test", "wilcox.test", default = "t.test".

Correction method. Choose from "holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr", "none", default = "hochberg".

Choose from "clusters", "conditions", "clustBase.condComp" or "condBase.clustComp".

First condition to do DE analysis on.

Second condition to do DE analysis on.

A base condition or cluster if de.by is either cond.clust or clust.cond

An object of class iCellR.

PC dimentions to be used for UMAP analysis.

diffusion map method, default = "phate".

int, optional, default: 2 number of dimensions in which the data will be embedded

int, optional, default: 5 number of nearest neighbors on which to build kernel

int, optional, default: 40 sets decay rate of kernel tails. If NULL, alpha decaying kernel is not used

int, optional, default: 2000 number of landmarks to use in fast PHATE

float, optional, default: 1 Informational distance constant between -1 and 1. gamma=1 gives the PHATE log potential, gamma=0 gives a square root potential.

int, optional, default: 'auto' power to which the diffusion operator is powered sets the level of diffusion

string, optional, default: 'euclidean'. recommended values: 'euclidean', 'cosine', 'precomputed' Any metric from scipy.spatial.distance can be used distance metric for building kNN graph. If 'precomputed', data should be an n_samples x n_samples distance or affinity matrix. Distance matrices are assumed to have zeros down the diagonal, while affinity matrices are assumed to have non-zero values down the diagonal. This is detected automatically using data[0,0]. You can override this detection with knn.dist.method='precomputed_distance' or knn.dist.method='precomputed_affinity'.

phate object, optional object to use for initialization. Avoids recomputing intermediate steps if parameters are the same.

string, optional, default: 'metric' choose from 'classic', 'metric', and 'nonmetric' which MDS algorithm is used for dimensionality reduction

string, optional, default: 'euclidean' recommended values: 'euclidean' and 'cosine'

int, optional, default: 100. Maximum value of t to test for automatic t selection.

int, optional, default: 100 Number of principal components to use for calculating neighborhoods. For extremely large datasets, using n_pca < 20 allows neighborhoods to be calculated in log(n_samples) time.

boolean, optional, if TRUE, produce a plot showing the Von Neumann Entropy curve for automatic t selection.

int or boolean, optional (default : 1) If TRUE or > 0, message verbose updates.

int, optional (default: 1) The number of jobs to use for the computation. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n.cpus + 1 + n.jobs) are used. Thus for n_jobs = -2, all CPUs but one are used

int or NULL, random state (default: NULL)

Deprecated. For log potential, use gamma=1. For sqrt potential, use gamma=0.

Deprecated To disable alpha decay, use alpha=NULL

Deprecated.

Deprecated.

Deprecated.

Deprecated.

Deprecated.

Deprecated.

Deprecated.

An object of class iCellR.

Choose between "iCellR.imp" and "magic", defualt = "iCellR.imp".

PC dimentions to be used for the analysis, default = 10.

Number of neighboring cells to find, default = 10.

If TURE, it would normalize ATAC-Seq data and not RNA-Seq, default = FALSE.

integer indicating the number of decimal places (round) or significant digits (signif) to be used.

Rounding of Numbers, default = FALSE.

Choose between "tsne", "pca", "umap", "diffusion", "knetl", default = "pca".

character or integer vector, default: NULL vector of column names or column indices for which to return smoothed data If 'all_genes' or NULL, the entire smoothed matrix is returned

if imp.method is magic; int, optional, default: 10 number of nearest neighbors on which to build kernel

if imp.method is magic; int, optional, default: 15 sets decay rate of kernel tails. If NULL, alpha decaying kernel is not used

if imp.method is magic; int, optional, default: 'auto' power to which the diffusion operator is powered sets the level of diffusion. If 'auto', t is selected according to the Procrustes disparity of the diffused data.'

number of PCA components that should be used; default: 100.

magic object, optional object to use for initialization. Avoids recomputing intermediate steps if parameters are the same.

if imp.method is magic; int, optional, default: 20 Maximum value of t to test for automatic t selection.

string, optional, default: 'euclidean'. recommended values: 'euclidean', 'cosine' Any metric from 'scipy.spatial.distance' can be used distance metric for building kNN graph.

'int' or 'boolean', optional (default : 1) If 'TRUE' or '> 0', message verbose updates.

'int', optional (default: 1) The number of jobs to use for the computation. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n.cpus + 1 + n.jobs) are used. Thus for n_jobs = -2, all CPUs but one are used

int or 'NULL', random state (default: 'NULL')

An object of class iCellR.

the distance measure to be used to compute the dissimilarity matrix. This must be one of: "euclidean", "maximum", "mandatattan", "canberra", "binary", "minkowski" or "NULL". By default, distance="euclidean". If the distance is "NULL", the dissimilarity matrix (diss) should be given by the user. If distance is not "NULL", the dissimilarity matrix should be "NULL".

Adjusting zoom the higher the number the less sensitivity, default = 400.

Choose between "tsne", "pca", "umap", default = "pca".

PCA dimentions to be use for clustering, default = 1:20.

Run in Combined or joint fashion as in CCCA and CPCA, default = FALSE.

If return.graph is TRUE the choose the cluster colors. Choose between "clusters", "conditions".

seed number, default = 1.

Choose your 2D layout, default = "layout_nicely".

Choose your 3D layout, default = "layout_with_fr".

Add 3D KNetL as well, default = FALSE.

Choose between "tsne", "pca", "umap" to unpack the nodes, default = "umap".

Perform dim reudx for unpaking the nodes, default = TRUE.

Perform clustering as well (nor recomanded), default = FALSE.

return igraph object, default = FALSE.

An object of class iCellR.

Choose from "base.mean.rank" or "gene.model", default is "base.mean.rank". If gene.model is chosen you need to provide gene.list.

A number taking the top genes ranked by base mean, default = 500.

A charactor vector of genes to be used for PCA. If "clust.method" is set to "gene.model", default = "my_model_genes.txt".

Choose from "main" and "imputed", default = "main"

An integer scalar specifying the number of nearest neighbors to consider when identifying MNNs.

A logical scalar indicating whether cosine normalization should be performed on the input data prior to calculating distances between cells.

A numeric scalar specifying the threshold beyond which neighbours are to be ignored when computing correction vectors. Each threshold is defined in terms of the number of median distances.

Number of dimentions to pass to ‘multiBatchPCA’.

Further arguments to pass to ‘multiBatchPCA’. Setting ‘approximate=TRUE’ is recommended for large data sets with many cells.

Further arguments to pass to ‘multiBatchPCA’. Setting ‘approximate=TRUE’ is recommended for large data sets with many cells.

See ‘?"scran-gene-selection"’.

Logical scalar indicating whether re-ordering of batches should be performed to maximize the number of MNN pairs at each step. Alternatively an integer vector containing a permutation of ‘1:N’ where ‘N’ is the number of batches.

Logical scalar indicating whether the values in ‘...’ are already low-dimensional, e.g., the output of ‘multiBatchPCA’.

Logical scalar indicating whether the percentage of variance lost due to non-orthogonality should be computed.

A string or integer scalar specifying the assay containing the expression values, if SingleCellExperiment objects are present in ‘...’.

See ‘?"scran-gene-selection"’. Only relevant if ‘...’ contains SingleCellExperiment objects.

A BiocNeighborParam object specifying the nearest neighbor algorithm. Defaults to an exact algorithm if ‘NULL’, see ‘?findKNN’ for more details.

A BiocParallelParam object specifying whether the PCA and nearest-neighbor searches should be parallelized.

An object of class iCellR.

PC dimentions to be used for tSNE analysis.

seed number, default = 0.

Add 3D tSNE as well, default = TRUE.

integer; the number of dimensions that should be retained in the initial PCA step (default: 50)

numeric; Perplexity parameter

numeric; Speed/accuracy trade-off (increase for less accuracy), set to 0.0 for exact TSNE (default: 0.5)

logical; Checks whether duplicates are present. It is best to make sure there are no duplicates present and set this option to FALSE, especially for large datasets (default: TRUE)

logical; Whether an initial PCA step should be performed (default: TRUE)

integer; Number of iterations (default: 1000)

logical; Whether progress updates should be messageed (default: FALSE)

logical; Indicate whether X is a distance matrix (experimental, default: FALSE)

matrix; Initial locations of the objects. If NULL, random initialization will be used (default: NULL). Note that when using this, the initial stage with exaggerated perplexity values and a larger momentum term will be skipped.

logical; Should data be centered before pca is applied? (default: TRUE)

logical; Should data be scaled before pca is applied? (default: FALSE)

integer; Iteration after which the perplexities are no longer exaggerated (default: 250, except when Y_init is used, then 0)

integer; Iteration after which the final momentum is used (default: 250, except when Y_init is used, then 0)

numeric; Momentum used in the first part of the optimization (default: 0.5)

numeric; Momentum used in the final part of the optimization (default: 0.8)

numeric; Learning rate (default: 200.0)

numeric; Exaggeration factor used to multiply the P matrix in the first part of the optimization (default: 12.0)

An object of class iCellR.

Choose from "main" and "imputed", default = "main"

Choose from "base.mean.rank" or "gene.model", default is "base.mean.rank". If gene.model is chosen you need to provide gene.list.

A number. Taking the top genes ranked by base mean, default = 500.

A number to add to each value in the matrix before log transformasion to aviond Inf numbers, default = 0.1.

If TRUE the data will be scaled (log2 + plus.log.value), default = TRUE.

A charactor vector of genes to be used for PCA. If "clust.method" is set to "gene.model", default = "my_model_genes.txt".

An object of class iCellR.

integer; number of nearest neighbours (default:45)

Choose between "tsne", "pca", "umap", default = "pca".

PCA dimentions to be use for clustering, default = 1:10.

An object of class iCellR.

Choose from "base.mean.rank" or "gene.model", defult is "base.mean.rank".

A number taking the top genes ranked by base mean, defult = 500.

A list of genes to be used for tSNE analysis. If "clust.method" is set to "gene.model", defult = "my_model_genes.txt".

Add 3D tSNE as well, default = TRUE.

integer; the number of dimensions that should be retained in the initial PCA step (default: 50)

numeric; Perplexity parameter

numeric; Speed/accuracy trade-off (increase for less accuracy), set to 0.0 for exact TSNE (default: 0.5)

logical; Checks whether duplicates are present. It is best to make sure there are no duplicates present and set this option to FALSE, especially for large datasets (default: TRUE)

logical; Whether an initial PCA step should be performed (default: TRUE)

integer; Number of iterations (default: 1000)

logical; Whether progress updates should be messageed (default: FALSE)

logical; Indicate whether X is a distance matrix (experimental, default: FALSE)

matrix; Initial locations of the objects. If NULL, random initialization will be used (default: NULL). Note that when using this, the initial stage with exaggerated perplexity values and a larger momentum term will be skipped.

logical; Should data be centered before pca is applied? (default: TRUE)

logical; Should data be scaled before pca is applied? (default: FALSE)

integer; Iteration after which the perplexities are no longer exaggerated (default: 250, except when Y_init is used, then 0)

integer; Iteration after which the final momentum is used (default: 250, except when Y_init is used, then 0)

numeric; Momentum used in the first part of the optimization (default: 0.5)

numeric; Momentum used in the final part of the optimization (default: 0.8)

numeric; Learning rate (default: 200.0)

numeric; Exaggeration factor used to multiply the P matrix in the first part of the optimization (default: 12.0)

An object of class iCellR.

seed number, default = 0.

PC dimentions to be used for UMAP analysis.

The size of local neighborhood (in terms of number of neighboring sample points) used for manifold approximation. Larger values result in more global views of the manifold, while smaller values result in more local data being preserved. In general values should be in the range 2 to 100.

The dimension of the space to embed into. This defaults to 2 to provide easy visualization, but can reasonably be set to any integer value in the range 2 to 100.

Type of distance metric to use to find nearest neighbors. One of:

• "euclidean" (the default)

• "cosine"

• "manhattan"

• "hamming"

• "categorical" (see below)

Only applies if nn_method = "annoy" (for nn_method = "fnn", the distance metric is always "euclidean").

If X is a data frame or matrix, then multiple metrics can be specified, by passing a list to this argument, where the name of each item in the list is one of the metric names above. The value of each list item should be a vector giving the names or integer ids of the columns to be included in a calculation, e.g. metric = list(euclidean = 1:4, manhattan = 5:10).

Each metric calculation results in a separate fuzzy simplicial set, which are intersected together to produce the final set. Metric names can be repeated. Because non-numeric columns are removed from the data frame, it is safer to use column names than integer ids.

Factor columns can also be used by specifying the metric name "categorical". Factor columns are treated different from numeric columns and although multiple factor columns can be specified in a vector, each factor column specified is processed individually. If you specify a non-factor column, it will be coerced to a factor.

For a given data block, you may override the pca and pca_center arguments for that block, by providing a list with one unnamed item containing the column names or ids, and then any of the pca or pca_center overrides as named items, e.g. metric = list(euclidean = 1:4, manhattan = list(5:10, pca_center = FALSE)). This exists to allow mixed binary and real-valued data to be included and to have PCA applied to both, but with centering applied only to the real-valued data (it is typical not to apply centering to binary data before PCA is applied).

Number of epochs to use during the optimization of the embedded coordinates. By default, this value is set to 500 for datasets containing 10,000 vertices or less, and 200 otherwise.

Initial learning rate used in optimization of the coordinates.

Scaling to apply to X if it is a data frame or matrix:

• "none" or FALSE or NULL No scaling.

• "Z" or "scale" or TRUE Scale each column to zero mean and variance 1.

• "maxabs" Center each column to mean 0, then divide each element by the maximum absolute value over the entire matrix.

• "range" Range scale the entire matrix, so the smallest element is 0 and the largest is 1.

• "colrange" Scale each column in the range (0,1).

For UMAP, the default is "none".

Type of initialization for the coordinates. Options are:

• "spectral" Spectral embedding using the normalized Laplacian of the fuzzy 1-skeleton, with Gaussian noise added.

• "normlaplacian". Spectral embedding using the normalized Laplacian of the fuzzy 1-skeleton, without noise.

• "random". Coordinates assigned using a uniform random distribution between -10 and 10.

• "lvrandom". Coordinates assigned using a Gaussian distribution with standard deviation 1e-4, as used in LargeVis (Tang et al., 2016) and t-SNE.

• "laplacian". Spectral embedding using the Laplacian Eigenmap (Belkin and Niyogi, 2002).

• "pca". The first two principal components from PCA of X if X is a data frame, and from a 2-dimensional classical MDS if X is of class "dist".

• "spca". Like "pca", but each dimension is then scaled so the standard deviation is 1e-4, to give a distribution similar to that used in t-SNE. This is an alias for init = "pca", init_sdev = 1e-4.

• "agspectral" An "approximate global" modification of "spectral" which all edges in the graph to a value of 1, and then sets a random number of edges (negative_sample_rate edges per vertex) to 0.1, to approximate the effect of non-local affinities.

• A matrix of initial coordinates.

For spectral initializations, ("spectral", "normlaplacian", "laplacian"), if more than one connected component is identified, each connected component is initialized separately and the results are merged. If verbose = TRUE the number of connected components are logged to the console. The existence of multiple connected components implies that a global view of the data cannot be attained with this initialization. Either a PCA-based initialization or increasing the value of n_neighbors may be more appropriate.

If non-NULL, scales each dimension of the initialized coordinates (including any user-supplied matrix) to this standard deviation. By default no scaling is carried out, except when init = "spca", in which case the value is 0.0001. Scaling the input may help if the unscaled versions result in initial coordinates with large inter-point distances or outliers. This usually results in small gradients during optimization and very little progress being made to the layout. Shrinking the initial embedding by rescaling can help under these circumstances. Scaling the result of init = "pca" is usually recommended and init = "spca" as an alias for init = "pca", init_sdev = 1e-4 but for the spectral initializations the scaled versions usually aren't necessary unless you are using a large value of n_neighbors (e.g. n_neighbors = 150 or higher).

The effective scale of embedded points. In combination with min_dist, this determines how clustered/clumped the embedded points are.

The effective minimum distance between embedded points. Smaller values will result in a more clustered/clumped embedding where nearby points on the manifold are drawn closer together, while larger values will result on a more even dispersal of points. The value should be set relative to the spread value, which determines the scale at which embedded points will be spread out.

Interpolate between (fuzzy) union and intersection as the set operation used to combine local fuzzy simplicial sets to obtain a global fuzzy simplicial sets. Both fuzzy set operations use the product t-norm. The value of this parameter should be between 0.0 and 1.0; a value of 1.0 will use a pure fuzzy union, while 0.0 will use a pure fuzzy intersection.

The local connectivity required – i.e. the number of nearest neighbors that should be assumed to be connected at a local level. The higher this value the more connected the manifold becomes locally. In practice this should be not more than the local intrinsic dimension of the manifold.

The effective bandwidth of the kernel if we view the algorithm as similar to Laplacian Eigenmaps. Larger values induce more connectivity and a more global view of the data, smaller values concentrate more locally.

Weighting applied to negative samples in low dimensional embedding optimization. Values higher than one will result in greater weight being given to negative samples.

The number of negative edge/1-simplex samples to use per positive edge/1-simplex sample in optimizing the low dimensional embedding.

More specific parameters controlling the embedding. If NULL these values are set automatically as determined by min_dist and spread.

More specific parameters controlling the embedding. If NULL these values are set automatically as determined by min_dist and spread.

Method for finding nearest neighbors. Options are:

• "fnn". Use exact nearest neighbors via the FNN package.

• "annoy" Use approximate nearest neighbors via the RcppAnnoy package.

By default, if X has less than 4,096 vertices, the exact nearest neighbors are found. Otherwise, approximate nearest neighbors are used. You may also pass precalculated nearest neighbor data to this argument. It must be a list consisting of two elements:

• "idx". A n_vertices x n_neighbors matrix containing the integer indexes of the nearest neighbors in X. Each vertex is considered to be its own nearest neighbor, i.e. idx[, 1] == 1:n_vertices.

• "dist". A n_vertices x n_neighbors matrix containing the distances of the nearest neighbors.

Multiple nearest neighbor data (e.g. from two different precomputed metrics) can be passed by passing a list containing the nearest neighbor data lists as items. The n_neighbors parameter is ignored when using precomputed nearest neighbor data.

Number of trees to build when constructing the nearest neighbor index. The more trees specified, the larger the index, but the better the results. With search_k, determines the accuracy of the Annoy nearest neighbor search. Only used if the nn_method is "annoy". Sensible values are between 10 to 100.

Number of nodes to search during the neighbor retrieval. The larger k, the more the accurate results, but the longer the search takes. With n_trees, determines the accuracy of the Annoy nearest neighbor search. Only used if the nn_method is "annoy".

If TRUE, use an approximation to the power function in the UMAP gradient, from https://martin.ankerl.com/2012/01/25/optimized-approximative-pow-in-c-and-cpp/.

Optional target data for supervised dimension reduction. Can be a vector, matrix or data frame. Use the target_metric parameter to specify the metrics to use, using the same syntax as metric. Usually either a single numeric or factor column is used, but more complex formats are possible. The following types are allowed:

• Factor columns with the same length as X. NA is allowed for any observation with an unknown level, in which case UMAP operates as a form of semi-supervised learning. Each column is treated separately.

• Numeric data. NA is not allowed in this case. Use the parameter target_n_neighbors to set the number of neighbors used with y. If unset, n_neighbors is used. Unlike factors, numeric columns are grouped into one block unless target_metric specifies otherwise. For example, if you wish columns a and b to be treated separately, specify target_metric = list(euclidean = "a", euclidean = "b"). Otherwise, the data will be effectively treated as a matrix with two columns.

• Nearest neighbor data, consisting of a list of two matrices, idx and dist. These represent the precalculated nearest neighbor indices and distances, respectively. This is the same format as that expected for precalculated data in nn_method. This format assumes that the underlying data was a numeric vector. Any user-supplied value of the target_n_neighbors parameter is ignored in this case, because the the number of columns in the matrices is used for the value. Multiple nearest neighbor data using different metrics can be supplied by passing a list of these lists.

Unlike X, all factor columns included in y are automatically used.

Number of nearest neighbors to use to construct the target simplicial set. Default value is n_neighbors. Applies only if y is non-NULL and numeric.

The metric used to measure distance for y if using supervised dimension reduction. Used only if y is numeric.

Weighting factor between data topology and target topology. A value of 0.0 weights entirely on data, a value of 1.0 weights entirely on target. The default of 0.5 balances the weighting equally between data and target. Only applies if y is non-NULL.

If set to a positive integer value, reduce data to this number of columns using PCA. Doesn't applied if the distance metric is "hamming", or the dimensions of the data is larger than the number specified (i.e. number of rows and columns must be larger than the value of this parameter). If you have > 100 columns in a data frame or matrix, reducing the number of columns in this way may substantially increase the performance of the nearest neighbor search at the cost of a potential decrease in accuracy. In many t-SNE applications, a value of 50 is recommended, although there's no guarantee that this is appropriate for all settings.

If TRUE, center the columns of X before carrying out PCA. For binary data, it's recommended to set this to FALSE.

If TRUE, use the PCG random number generator (O'Neill, 2014) during optimization. Otherwise, use the faster (but probably less statistically good) Tausworthe "taus88" generator. The default is TRUE.

If TRUE, then the following combination of parameters is set: pcg_rand = TRUE, n_sgd_threads = "auto" and approx_pow = TRUE. The default is FALSE. Setting this to TRUE will speed up the stochastic optimization phase, but give a potentially less accurate embedding, and which will not be exactly reproducible even with a fixed seed. For visualization, fast_sgd = TRUE will give perfectly good results. For more generic dimensionality reduction, it's safer to leave fast_sgd = FALSE. If fast_sgd = TRUE, then user-supplied values of pcg_rand, n_sgd_threads, and approx_pow are ignored.

If TRUE, then return extra data that can be used to add new data to an existing embedding via umap_transform. The embedded coordinates are returned as the list item embedding. If FALSE, just return the coordinates. This parameter can be used in conjunction with ret_nn. Note that some settings are incompatible with the production of a UMAP model: external neighbor data (passed via a list to nn_method), and factor columns that were included via the metric parameter. In the latter case, the model produced is based only on the numeric data. A transformation using new data is possible, but the factor columns in the new data are ignored.

If TRUE, then in addition to the embedding, also return nearest neighbor data that can be used as input to nn_method to avoid the overhead of repeatedly calculating the nearest neighbors when manipulating unrelated parameters (e.g. min_dist, n_epochs, init). See the "Value" section for the names of the list items. If FALSE, just return the coordinates. Note that the nearest neighbors could be sensitive to data scaling, so be wary of reusing nearest neighbor data if modifying the scale parameter. This parameter can be used in conjunction with ret_model.

Number of threads to use.

Number of threads to use during stochastic gradient descent. If set to > 1, then results will not be reproducible, even if 'set.seed' is called with a fixed seed before running. Set to "auto" go use the same value as n_threads.

Minimum batch size for multithreading. If the number of items to process in a thread falls below this number, then no threads will be used. Used in conjunction with n_threads and n_sgd_threads.

Temporary directory to store nearest neighbor indexes during nearest neighbor search. Default is tempdir. The index is only written to disk if n_threads > 1 and nn_method = "annoy"; otherwise, this parameter is ignored.

If TRUE, log details to the console.

An object of class iCellR.

A numeric value for the size of the cells, default = 1.

Colors for heat mapping the points in "scatterplot", default = c("gray","red").

A color for the plot background, default = "black".

Choose between "clusters", "mt","UMIs","nGenes", "cc" (cell cycle) or "gene", default = "clusters".

Choose the conditions you want to see in the plot, default = NULL (all conditions).

Gene name/names to be plotted, if col.by = "gene".

Choose from "main" or "imputed", default = "main".

Scale the colors, default = FALSE.

If scaleValue = TRUE, set a number for min, default = -2.5.

If scaleValue = TRUE, set a number for max, default = 2.5.

Annotate cluster names on the plot, default = TRUE.

If anno.clust is TRUE set font size, default = 3.

If anno.clust is TRUE set color, default = "white".

Color transparency for points in "scatterplot" and "boxplot", default = 1.

If set to TRUE an interactive HTML file will be created, default = TRUE.

If "interactive" is set to TRUE, the out put name for HTML, default = "plot".

An object of class iCellR.

Choose from "box.umi", "box.mito", "box.gene", "box.s.phase", "box.g2m.phase","all.in.one","three.in.one", "point.mito.umi", "point.gene.umi".

Choose a color for points in the plot.

A number for the size of the points in the plot, default = 1.

Color transparency for points in "scatterplot" and "boxplot", default = 0.5.

A color for the boxes in the "boxplot", default = "red".

A color for the lines around the "boxplot", default = "green".

Background color, default = "white"

If set to TRUE an interactive HTML file will be created, default = TRUE.

If "interactive" is set to TRUE, the out put name for HTML, default = "plot".

An object of class iCellR.

Number of top differentially expressed genes to be choosen from each cluster, default = 10.

Minimum base mean of the genes to be chosen, default = 0.5.

Filter markers that are seen for more than one cluster, default = TRUE.

Choose a cluster to find markers for. If 0, it would find markers for all clusters, , default = 0.

A data frame containing VDJ data for cells.

A data frame containing differential expression (DE) analysis results.

Choose from "pval" or "padj", default = "padj".

A number to draw the line for the significant genes based on sig.value type, default = 0.1.

Choose from "ma" or "volcano", default = "volcano".

A number to set a limit for the x axis.

A number to set a limit for the y axis.

If set to TRUE the x.limit and y.limit will be forced, default = FALSE.

If set to TRUE the y axis will be scaled to include all the points, default = TRUE.

A number for the size of the points in the plot, default = 1.75.

Color transparency for points in "scatterplot" and "boxplot", default = 0.5.

A set of three colors for the points in the volcano plot, default = c("#E64B35","#3182bd","#636363").

If set to TRUE an interactive HTML file will be created, default = TRUE.

If "interactive" is set to TRUE, the output name for HTML, default = "plot".