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I just installed Ubuntu 16.04 and can't make the sound work - have been trying for good ten hours. I am a linux newbie so I will appreciate any help.This is how my sound settings look like, there is no output and the slider is gray.The speakers are internal and headphones are connected via the aux cable - sound is coming out from none of them. There is however no hardware issue with the headphones, they work perfectly when connected to a phone, and I don't expect any hardware problem with the internal speakers.I followed the advices such as unmute the AlsaMixer master, but I can't see it at all: sudo aplay -l**** List of PLAYBACK Hardware Devices ****card 0: PCH [HDA Intel PCH], device 3: HDMI 0 [HDMI 0] Subdevices: 1/1 Subdevice #0: subdevice #0card 0: PCH [HDA Intel PCH], device 7: HDMI 1 [HDMI 1] Subdevices: 1/1 Subdevice #0: subdevice #0card 0: PCH [HDA Intel PCH], device 8: HDMI 2 [HDMI 2] Subdevices: 1/1 Subdevice #0: subdevice #0card 0: PCH [HDA Intel PCH], device 9: HDMI 3 [HDMI 3] Subdevices: 1/1 Subdevice #0: subdevice #0card 0: PCH [HDA Intel PCH], device 10: HDMI 4 [HDMI 4] Subdevices: 1/1 Subdevice #0: subdevice #0lspci -v | grep -A7 -i "audio"00:1f.3 Audio device: Intel Corporation Device 9d71 (rev 21) Subsystem: Dell Device 0786 Flags: bus master, fast devsel, latency 32, IRQ 16 Memory at d5228000 (64-bit, non-prefetchable) [size=16K] Memory at d5200000 (64-bit, non-prefetchable) [size=64K] Capabilities: Kernel driver in use: snd_hda_intel Kernel

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For each dataset by running the optimizer function. More information can be found in the reference supplemental materials and in the RTAM script.norm.data Plotting transcript counts per gene per cellTo compare the normalized transcriptomes, the normalized expression of each gene in each cell is plotted. As transcript counts are integers, the expression values for many lowly expressed genes (with n=1,2,3 .. transcripts) are identical and align on tiers. Each dot may represent the expression level of multiple genes in any single cell.0]))), log2(as.matrix(norm.data[,1][norm.data[,1]>0]) +1 ), pch=16, cex=0.3, col="darkgray" , xlim=c(-ncol(norm.data)*.1, ncol(norm.data)*1.1), ylim=c(0, 4.2), xlab = "Cells (ranked by UMI)", ylab = expression("Normalized gene expression ("*Log[2]*"(.+1 ))"), cex.lab = 2, cex.axis = 2, cex.main=2)for(i in 2:ncol(norm.data)){ par(new=TRUE) points( matrix(i,ncol=1,nrow=nrow(as.matrix(norm.data[,i][norm.data[,i]>0]))), log2(as.matrix(norm.data[,i][norm.data[,i]>0]) +1 ), pch=16, cex=0.3, col="darkgray")}title( paste("Sample1, RTAM2-normalization, cutoff ", 250," nGene ",250,sep=""), col.main = "brown", cex.main = 2)">graphics.off()plot.new()par(mar=c(5,5,4,2)+1,mgp=c(3,1,0))par(xaxs="i", yaxs="i") par(bty="l")plot(matrix(1,ncol=1,nrow=nrow(as.matrix(norm.data[,1][norm.data[,1]>0]))), log2(as.matrix(norm.data[,1][norm.data[,1]>0]) +1 ), pch=16, cex=0.3, col="darkgray" , xlim=c(-ncol(norm.data)*.1, ncol(norm.data)*1.1), ylim=c(0, 4.2), xlab = "Cells (ranked by UMI)", ylab = expression("Normalized gene expression ("*Log[2]*"(.+1 ))"), cex.lab = 2, cex.axis = 2, cex.main=2)for(i in 2:ncol(norm.data)){ par(new=TRUE) points( matrix(i,ncol=1,nrow=nrow(as.matrix(norm.data[,i][norm.data[,i]>0]))), log2(as.matrix(norm.data[,i][norm.data[,i]>0]) +1 ), pch=16, cex=0.3, col="darkgray")}title( paste("Sample1, RTAM2-normalization, cutoff ", 250," nGene ",250,sep=""), col.main = "brown", cex.main = 2)QC of normalized dataTo assess the alignment of the normalized cellular transcriptomes, the mean or median expression in each cell of large sets of uniquitous genes is examined, including:(1) a curated set of housekeeping genes (HKG), or(2) the set of all commonly-expressed genes (detected in > 95% of the cells studied)Whilst the expression of any individual gene is expected to vary between cells for both biological and technical reasons, the average expression per cell of a large set of ubiquitous genes should be similar, particularly amongst cells of the same lineage.The average expression of commonly-expressed genes (with detectable expression in >95% cells in the dataset)95% cells in the dataset)" href="#the-average-expression-of-commonly-expressed-genes-with-detectable-expression-in-95-cells-in-the-dataset"> 0.95*ncol(norm.data) ), ] )library(robustbase)for(j in 1:ncol(norm.data)){ par( new=TRUE) points(matrix(j,ncol=1,nrow=1), log2(mean(as.matrix(Common.mat[,j][Common.mat[,j]>0])) + 1 ) , axis = FALSE , col="red" , pch=15, cex =0.5 )} legend(0,0.75,bty="n",pch=16,col=c("red",NA), cex=1.5, legend=paste("Mean of commonly-expressed genes")) ">Sqnce 0.95*ncol(norm.data) ), ] )library(robustbase)for(j in 1:ncol(norm.data)){ par( new=TRUE) points(matrix(j,ncol=1,nrow=1), log2(mean(as.matrix(Common.mat[,j][Common.mat[,j]>0])) + 1 ) , axis = FALSE , col="red" , pch=15, cex =0.5 )} legend(0,0.75,bty="n",pch=16,col=c("red",NA), cex=1.5, legend=paste("Mean of commonly-expressed genes")) The average expression of housekeeping genes0])) }par( new=TRUE)points(log2(Mean_HK_mat[1,] +1 ), col="blue" , pch=15, cex =0.5 )legend(0,0.5,bty="n",pch=16,col=c("blue",NA), cex=1.5, legend=paste("Mean of houskeeping genes"))">Houskeeping_gene_list 0])) }par( new=TRUE)points(log2(Mean_HK_mat[1,] +1 ), col="blue" , pch=15, cex =0.5 )legend(0,0.5,bty="n",pch=16,col=c("blue",NA), cex=1.5, legend=paste("Mean of houskeeping genes"))#Clustering to cell phenotypesCells are clustered into {lineages/phenotypes/subclones} by dimensionality reduction of normalized transcriptomes, using principle component analysis or tSNE/UMAP methods. As this clustering is driven by differential gene expression, non informative genes which are not expressed are excluded:= 1 ), ] )# Updating list of cells in nUMI and nGene for lateron analysesnUMI train1 = 1 ), ] )# Updating list of cells in nUMI and nGene for lateron analysesnUMI tSNE/UMAP clustering of normalized data is performed on a matrix of single cells (MSC), generated as a Seurat object.MSC Single cell inferred CNV (sciCNV) analysis from scRNA-seq dataThe sciCNV. PCH Search Win; PCH Lotto; PCH Frontpage; Winners; FAQ's

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Plot box again# box()Color of the axesYou can also customize the color of the axis and the ticks with the col and col.ticks arguments.plot(x, y, pch = 19, axes = FALSE)# Add X-axisaxis(1, col = "blue", # Axis line color col.ticks = "green", # Ticks color col.axis = "red") # Labels color# Add Y-axisaxis(2, col = "blue", col.ticks = "green", col.axis = "red")Axis tick marksIn addition to axes labels, the tick marks of each axis can be customized in different ways.xaxp and yaxp argumentsThe xaxp (X-axis) and yaxp (Y-axis) arguments allow customizing where the ticks of each axis start and end and the number of regions to divide the axis specifying vectors of the form c(start, end, number_regions).plot(x, y, pch = 19, xaxp = c(-3, 3, 3), yaxp = c(-70, 70, 5))Minor ticksIt is possible to add minor ticks to the axes with the minor.tick function of the Hmisc library. The function will allow you to specify the tick density, the size and addition arguments to each axis.# install.packages("Hmisc")library(Hmisc)plot(x, y, pch = 19)minor.tick(nx = 2, ny = 2, # Ticks density tick.ratio = 0.5) # Ticks sizeInterior ticksYou can set interior ticks passing positive values to tck argument. The greater the value, the longer the ticks. Default value is tck = -0.5.# Interior ticksplot(x, y, pch = 19, tck = 0.02)Rotate tick mark labelsIt is possible to rotate the tick mark labels in several ways making use of the las argument.Option 1. Parallel to axis (default).plot(x, y, pch = 19, las = 0, main = "Parallel")Option 2. Horizontal.plot(x, y, pch = 19, las = 1, main = "Horizontal")Option 3. Perpendicular to axis.plot(x, y, pch = 19, las = 2, main = "Perpendicular")Option 4. Vertical.plot(x, y, pch = 19, las = 3, main = "Vertical")Custom tick mark labelsThe labels argument of the axis function allows customizing the tick mark labels.# Change X-axis tick labelsplot(x, y, pch = 19, xaxt = "n")axis(1, at = seq(round(min(x)), round(max(x)), by = 1), labels = 1:11)Note that you can also set texts in addition to numbers, or even expressions.# X-axis tick labels with textplot(x, y, pch = 19, xaxt = "n")axis(1, at = c(-2, 2), labels = c("Some text", "Other text"))Remove tick marksYou can remove the tick marks of one of the two axis or of both at the same time setting arguments xaxt and yaxt to "".Option 1. Remove only X-axis ticks.# Remove X axis

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Tick labelsplot(x, y, pch = 19, xaxt = "n", main = "xaxt = 'n'")Option 2. Remove only Y-axis ticks.# Remove Y axis tick labelsplot(x, y, pch = 19, yaxt = "n", main = "yaxt = 'n'")Option 3. Remove both X-axis and Y-axis ticks.# Remove both axis tick labelsplot(x, y, pch = 19, yaxt = "n", xaxt = "n", main = "xaxt = 'n', yaxt = 'n'")Option 4. Remove both X-axis and Y-axis ticks and the box.# Remove axis tick labels and the boxplot(x, y, pch = 19, axes = FALSE, main = "axes = FALSE")Axis limitsThe axis limits can be customized with the xlim and ylim arguments for the X and Y axis, respectively.# Axis limitsplot(x, y, pch = 19, xlim = c(0, 4), # X-axis limits ylim = c(0, 100)) # Y-axis limitsAxis scaleOriginal data.# Positive dataw 0]z 0]# Defaultplot(w, z, main = "Untransformed")X-axis transformed.# Log scale. X-axisplot(w, z, log = "x", main = "X-axis transformed")Y-axis transformed.# Log scale. Y-axisplot(w, z, log = "y", main = "Y-axis transformed")Both transformed.# Log scale. X and Y axisplot(w, z, log = "xy", main = "Both transformed")Dual axisYou can join together two plots with different Y-axis scale increasing the margins of the plot, using par(new = TRUE), creating a new plot without axis or labels and setting a new axis with the axis function.# Increase the plot marginspar(mar = c(5, 4, 4, 4) + 0.25)# Dataplot(x, y, pch = 19, ylab = "Var 1")# Needed to merge the plotspar(new = TRUE)# More dataplot(x ^ 2, y ^ 2, col = 4, pch = 19, axes = FALSE, # No axes bty = "n", # No box xlab = "", ylab = "") # No axis labels# New axisaxis(4)# Axis labelmtext("Var 2", side = 4, line = 3, col = 4)

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