Animation plot

Description

Animation plot

Usage

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

Arguments

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

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

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

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

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

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

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:

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

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

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

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

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

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

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

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

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