Package 'roots'

Animation plot

Description

Animation plot

Usage

animPlot(data, ccm, delay = 0.1, darken = 1, lwd = 1, c.cex = 1,
  main = "", ...)

Arguments

data	Dimensionality reduction plot
ccm	Dataframe of indices and momentums
delay	Delay between frames in seconds
darken	Passed to colGrad() function
lwd	Line width
c.cex	Size of poiints.
main	Plot title
...	Passed to plot() function

Details

Animation plot Generates plot in base R that gradually updates giving the impression of an animation

Value

Generates plot

Author(s)

Wajid Jawaid

Examples

## Not run: 
xx <- animPlot(x, ccm)

## End(Not run)

---

Generates a GIF animating

Description

Generates a GIF animation

Usage

animPlotGif(data, ccm, delay = 0.1, darken = 1, lwd = 1, c.cex = 1,
  main = "", gif = "animation", img.name = "tempPlot", plot.par = NULL,
  point.col = "#333333", arrowLength = 0.1, ...)

Arguments

data	Reduced dimensionality map to be used for visualisation
ccm	Dataframe of indices and momentums
delay	Delay between frames in seconds
darken	Passed to colGrad() function
lwd	Line width
c.cex	Size of poiints.
main	Title
gif	Name of movie
img.name	Name of temporary image files generated
plot.par	Passed to R base par() function
point.col	Colour of background points
arrowLength	Modify length of arrow
...	Passed to plot() function

Details

Generates a GIF animation

Value

Produces an animated GIF with given file name

Author(s)

Wajid Jawaid

Examples

## Not run: 
xx <- animPlotGif(x, ccm, gif = "animation")

## End(Not run)

---

Apply Gaussian Kernel using Laleh Haghverdi's variable sigma

Description

Apply Gaussian Kernel using Laleh Haghverdi's variable sigma

Usage

applyGaussianKernelwithVariableSigma(d2, rsigmas, csigmas = NULL)

Arguments

d2	Squared distance metric
rsigmas	Sigmas for cells in the rows
csigmas	Sigmas for cells in the columns

Details

Apply Gaussian Kernel using Laleh Haghverdi's variable sigma

Value

Returns matrix of same size as d2.

Author(s)

Wajid Jawaid

Examples

## Not run: 
d <- applyGaussianKernelwithVariableSigma(dist, sigmas)

## End(Not run)

---

Normalise by background gene set

Description

Normalise by background gene set

Usage

bgGeneNorm(x, threshold = 0.05)

Arguments

x	Matrix to be normalised with cells in rows and genes in columns
threshold	Default 0.05. The threshold below which a gene is deemed background

Details

Normalise by background gene set. Find background genes that are expressed at a lower percentage of the total library size per cell than 'threshold' parameter. These genes are used to calculate a normalisation factor.

Value

Returns a normalised matrix of same dimenions as 'x'

Author(s)

Wajid Jawaid

Examples

## Not run: 
normGenes <- bgGeneNorm(x)

## End(Not run)

---

Calculates sigmas for a distance matrix

Description

Calculates sigmas for a distance matrix

Usage

calculateVariableSigmas(d, knn)

Arguments

d	Square distance matrix with 0 diagonal
knn	Number of nearest neighbours to use for calculation

Details

Calculates sigmas for a distance matrix Using Laleh Hagherverdi's method

Value

Returns a vector of sigmas

Author(s)

wj241

Examples

## Not run: 
sigmas <- calculateVariableSigmas(dist, 5)

## End(Not run)

---

Generates a smooth colour gradient

Description

Generates a smooth colour gradient

Usage

colGrad(x, darken = 1)

Arguments

x	Number of colours required
darken	Multiplication factor. Must be less than 1. Smaller the darker.

Details

Generates a smooth colour gradient Goes from red to red/green to green to green/blue to blue to blu/red

Value

Returns vector of RGB colours

Author(s)

Wajid Jawaid

Examples

gradientColors <- colGrad(10)

---

Generic diffusion function

Description

Generic diffusion function using automated individualised sigma calculation

Usage

diffuseMat(data, ndims = 20, nsig = 5, removeFirst = TRUE,
  useARPACK = TRUE, distfun = NULL, sigmas = NULL, sqdistmat = NULL)

Arguments

data	Matrix of data with genes in rows and cells in columns.
ndims	Number of dimensions to return
nsig	For automatic sigma calculation
removeFirst	Default TRUE. Removes the first eigenvector
useARPACK	Default TRUE. Uses Arnoldi algorithm for eignvector calculations
distfun	A different distance function that returns the squared distance
sigmas	Manually provide sigma
sqdistmat	Squared distance matrix. Give your own squared distance matrix.

Details

Generic diffusion function using automated individualised sigma calculation.

A Gaussian kernel is applied to the chosen distance metric producing an $n \times n$ square unnormalised symmetric transition matrix, $A$. Let $D$ be an $n \times n$ diagonal matrix with row(column) sums of $A$ as entries. The density corrected transition matrix will now be:

$D^{-1} A D^{-1}$

and can be normalised:

$B^{-1} D^{-1} A D^{-1}$

where $B$ is an $n \times n$ diagonal matrix with row sums of the density corrected transition matrix as entries. The eigen decomposition of this matrix can be simplified by solving the symmetric system:

$B^{-\frac{1}{2}} D^{-1} A D^{-1} B^{-\frac{1}{2}} R^\prime = R^\prime \lambda^\prime$

where $R^\prime$ is a matrix of the right eigenvectors that solve the system and $\lambda^\prime$ is the corresponding eigenvalue diagonal matrix. Now the solution of:

$B^{-1} D^{-1} A D^{-1} R = R \lambda$

in terms of $R^\prime$ and $B^{-\frac{1}{2}}$ is:

$B^{-1} D^{-1} A D^{-1} B^{-\frac{1}{2}} R^\prime = B^{-\frac{1}{2}} R^\prime \lambda^\prime$

and

$R = B^{-\frac{1}{2}} R^\prime$

This $R$ without the first eigen vector is returned as the diffusion map.

Value

List output containing:

values	Eigenvalues, excluding the first eigenvalue, which should always be 1.
vectors	Matrix of eigen vectors in columns, first eigen vector removed.
nconv	Number of eigen vectors/values that converged.
niter	Iterations taken for Arnoldi algorithm to converge.
nops	Number of operations.
val0	1st eigen value - should be 1. If not be suspicious!
vec0	1st eigen vector - should be $n^{-\frac{1}{2}}$, where n is the number of cells/samples.
usedARPACK	Predicates use of ARPACK for spectral decomposition.
distfun	Function used to calculate the squared distance.
nn	Number of nearest neighbours used for calculating sigmas.
d2	Matrix of squared distances, returned from distfun.
sigmas	Vector of sigmas. Same length as number of cells if individual
	sigmas were calculated, otherwise a scalar if was supplied.
gaussian	Unnormalised transition matrix after applying Gaussian.
markov	Normalised gaussian matrix.
densityCorrected	Matrix after applying density correction to markov.

Author(s)

Wajid Jawaid

References

Haghverdi, L., Buettner, F., Theis, F.J., 2015. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, 2989–2998.

Haghverdi, L., Büttner, M., Wolf, F.A., Buettner, F., Theis, F.J., 2016. Diffusion pseudotime robustly reconstructs lineage branching. Nat Meth 13, 845–848.

Angerer, P., Haghverdi, L., Büttner, M., Theis, F.J., Marr, C., Buettner, F., 2016. destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics 32, 1241–1243.

Examples

## Not run: 
xx <- diffuseMat(x)

## End(Not run)

---

Predicts diffusion map projection from new data points

Description

Predicts diffusion map projection from new data points

Usage

diffuseProj(dm, x, data, distfun)

Arguments

dm	Output from diffuseMat2 function
x	Matrix of new data points. Features in rows and cells in columns.
data	Original data used to generate diffusion map
distfun	A distance function that takes new data as first paramter and previous data as second variable returning a squared distance measure, with each sample in the rows and distance to previous data points in columns, e.g. function(x, y) (1 - cor(x, y))^2.

Details

Predicts diffusion map projection from new data points

Value

Returns a matrix with projected diffusion components.

Author(s)

Wajid Jawaid

Examples

## Not run: 
y <- diffuseProj(xx, newData, oldData, function(z) (1-cor(z))^2)

## End(Not run)

---

Fast vectorised Euclidean distance calculator

Description

Fast vectorised Euclidean distance calculator

Usage

fastDist(x, squared = FALSE)

Arguments

x	Matrix with vectors in columns.
squared	Will not perform the square root, i.e. will return the squared ‘L2-norm’.

Details

Calculates Euclidean distances between vectors arranged as columns in a matrix.

Value

Returns a matrix of pairwise distances

Author(s)

Wajid Jawaid

Examples

## Not run: 
dist <- fastDist(x)

## End(Not run)

---

Filter genes

Description

Filter genes

Usage

filterGenes(x, mu = 0.01, cv = 2, fano = FALSE)

Arguments

x	Matrix to be normalised with cells in rows and genes in columns
mu	Meam threshold
cv	Coefficient of variation or Fano factor threshold.
fano	Default TRUE. Predicate treat CV as Fano factor or CV

Details

Filter genes Filter genes by mean and either coefficient of variation, cv or Fano factor.

Value

Returns a filtered matrix with same number of cells but fewer genes than 'x'

Author(s)

Wajid Jawaid

Examples

## Not run: 
expressionGenesFiltered <- filterGenes(x)

## End(Not run)

---

Louvain clustering on transition matrix

Description

Louvain clustering on transition matrix

Usage

findLouvain(mkv)

Arguments

mkv	Transition matrix

Details

Louvain clustering on transition matrix

Value

Returns a list with graph, dataframe and community object

Author(s)

Wajid Jawaid

Examples

## Not run: 
xx <- findLouvain(mkv)
xx$cll

## End(Not run)

---

Find next cell function

Description

Find next cell function

Usage

fnc(rdmap, tm, curInd, mom = NULL, momAdj = 0.5, w1 = exp(1), w2 = 1,
  varEst = 10)

Arguments

rdmap	reduced dimensionality matrix with cells in rows and dims in columns
tm	Transition matrix
curInd	Current state on tm
mom	Current momentum vector
momAdj	Weighting to adjust momentum. From 0-1. Lower numbers make smaller adjustment to momentum vector.
w1	Parameter - Base used for modifying of tm probs.
w2	Parameter - Multiplifaction factor used for modifying tm probs.
varEst	Number of alternatives to sample for estimating variance.

Details

Find next cell function. Transitioin probabilities are modifed by calulating the cosine of the angle between the current momentum vector and the vector on the rdmap required for each transtion. The tranisiton probability is adjusted by multiplying by w1^(w2 * (cosine_angle)) and then normalising.

Value

Returns index of new cell and new momentum vector

Author(s)

Wajid Jawaid

Examples

## Not run: 
nextCell <- fnc(rdmap, tm, curInd)

## End(Not run)

---

Find a plausible developmental journey

Description

Return a plausible developmental journey

Usage

getTraj(rdmap, tm, sourceCellInds, terminalCellsInd = NULL, momAdj = 0.5,
  w1 = exp(1), w2 = 1, simLen = 50, sim.seed = NULL, varEst = 10)

Arguments

rdmap	reduced dimensionality matrix with cells in rows and dims in columns
tm	Transition matrix
sourceCellInds	Starting sell indices
terminalCellsInd	Terminal cell indices
momAdj	Weighting to adjust momentum. From 0-1. Lower numbers make smaller adjustment to momentum vector.
w1	Parameter - Base used for modifying of tm probs.
w2	Parameter - Multiplifaction factor used for modifying tm probs.
simLen	Maximum number of allowable tranisitons
sim.seed	Random seed for reproducibility
varEst	Number of alternatives to sample for estimating variance.

Details

Return a plausible developmental journey

Value

Returns a data.frame of ordered indices and momentums

Author(s)

Wajid Jawaid

Examples

## Not run: 
traj <- getTraj(rdmap, tm, startCells, terminalCells)

## End(Not run)

---

View single cell dataset

Description

View single cell dataset

Usage

goggles(x, pcaDims = 90, nsig = 5, dmat = NULL, mkv = NULL,
  plotDims = 2, kernSq = 2, ...)

Arguments

x	Matrix with cells in rows and gene in columns
pcaDims	Number of PCA dimensions to keep for distance measure
nsig	Number of significant neighbours to keep for Gaussian kernel
dmat	Optional. Give your own distance matrix
mkv	Optional. Give your own markov matrix.
plotDims	Default 2. Number of dimensions to plot
kernSq	Factor to tighten kernel - operates on sigmas.
...	Additonal parameters not currently in use

Details

View single cell dataset

Value

A list of l, dimensionality reduced data.frame; clust, returned from louvainClust(); adj, Sparse, pruned adjacency matrix; dmat, distance matrix; pca, PCA reduced matrix. sparse, diagnostics on adj prior to applying sparseMarkov().

Author(s)

Wajid Jawaid

Examples

## Not run: 
xx <- goggles(x)
plot(xx$l)

## End(Not run)

---

Make markov matrix sparse

Description

Make markov matrix sparse

Usage

sparseMarkov(mkv, knn)

Arguments

mkv	Markov matric
knn	Number of nearest neighbours. See above.

Details

Make markov matrix sparse Choose knn as the maximum number of similar cells are likely to exist in your dataset.

Value

Markovian sparse matrix.

Author(s)

Wajid Jawaid

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