vignettes/Analysis_kidney.md
In Siliegia/SIGMA: A clusterability measure for scRNA-seq data
library(SIGMA)
library(ggplot2)
library(Seurat)
The authors who have anlyzed this data already normalized the data set
with the R package “scran” and determined clusters by hierachical
clustering. In total they have found 22 clusters.
data("force_gr_kidney")
data("sce_kidney")
paga.coord$Group <- sce_kidney$cell.type
ggplot(paga.coord, aes(x = V1, y = V2, colour = Group)) +
geom_point(shape = 16)
With SIGMA, we are now able to assess the variability for each cluster
and see if possible sub-clusters can be found. First, we load the
preprocessed SingleCellObject of the kidney data.
#Load kidney data from package
#Extract scran normalized counts and log-transform
expr.norm.log <- as.matrix(log(assay(sce_kidney, "scran")+1))
#Change the name of the rows to readable gene names
rownames(expr.norm.log) <- as.character(rowData(sce_kidney)$HUGO)
rownames(sce_kidney) <- as.character(rowData(sce_kidney)$HUGO)
In the next step we would like to exclude certain variances from
appearing in the measure. For example, in this fetal kidney data set,
several factors would not be of interest to cluster on: cell cycle
related variances, ribosomal and mitochondrial gene expression. As, well
as stress related genes, which arise during dissociation. Cycling genes,
we determine here with the Seurat package, so for that we first need to
create a Seurat object and normalize it. Another important factor is
technical variability, for example the varying number of transcripts.
It’s important to also include that in the data frame.
#Creating Seurat object
cnts <- counts(sce_kidney)
colnames(cnts) <- 1:ncol(cnts)
rownames(cnts) <- as.character(rowData(sce_kidney)$HUGO)
fetalkidney <- CreateSeuratObject(cnts)
#> Warning: Non-unique features (rownames) present in the input matrix, making unique
#> Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
fetalkidney <- NormalizeData(fetalkidney)
#Cell cycle analysis
s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes
fetalkidney <- CellCycleScoring(fetalkidney, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE)
#> Warning: The following features are not present in the object: MLF1IP, not searching for symbol synonyms
#Determining the expression of MT-genes, Rb-genes and stress genes:
data("ribosomal_genes")
data("stress_genes")
rb <- rownames(fetalkidney) %in% rb.genes
stress.genes <- intersect(stress.genes, rownames(expr.norm.log))
#Creating the final data frame with all the factors to be excluded from considering while calculating the clusterability measure:
exclude <- data.frame(clsm = log(colSums(cnts) + 1), cellcycle = fetalkidney$G2M.Score,
mt = colMeans(expr.norm.log[grep("^MT-", rownames(expr.norm.log)),]),
ribosomal = colMeans(expr.norm.log[rb,]), stress = colMeans(expr.norm.log[stress.genes,]))
Now we are ready to apply the main function to determine clusterability:
#Main funcion SIGMA
out_kidney <- sigma_funct(expr.norm.log, clusters = sce_kidney$cell.type, exclude = exclude)
#> Calculating values for cluster NPCc
#> Dim: 9300 596
#> Calculating svd ...
#> Scaled by: 0.9936921
#> Market Mode: 596
#> Calculating values for cluster DTLH
#> Dim: 2561 67
#> Calculating svd ...
#> Scaled by: 0.9549873
#> Market Mode: 67
#> Calculating values for cluster ICa
#> Dim: 9703 653
#> Calculating svd ...
#> Scaled by: 0.9941114
#> Market Mode: 653
#> Calculating values for cluster CnT
#> Dim: 2116 80
#> Calculating svd ...
#> Scaled by: 0.971555
#> Market Mode: 80
#> Calculating values for cluster UBCD
#> Dim: 4435 317
#> Calculating svd ...
#> Scaled by: 0.9752237
#> Market Mode: 317
#> Calculating values for cluster ErPrT
#> Dim: 5580 338
#> Calculating svd ...
#> Scaled by: 0.9872483
#> Market Mode: 338
#> Calculating values for cluster RVCSBa
#> Dim: 6180 367
#> Calculating svd ...
#> Scaled by: 0.9881706
#> Market Mode: 367
#> Calculating values for cluster ICb
#> Dim: 10031 677
#> Calculating svd ...
#> Scaled by: 0.9956775
#> Market Mode: 677
#> Calculating values for cluster SSBpr
#> Dim: 3862 213
#> Calculating svd ...
#> Scaled by: 0.9705424
#> Market Mode: 213
#> Calculating values for cluster NPCd
#> Dim: 5201 280
#> Calculating svd ...
#> Scaled by: 0.9815733
#> Market Mode: 280
#> Calculating values for cluster SSBpod
#> Dim: 7331 257
#> Calculating svd ...
#> Scaled by: 0.9920918
#> Market Mode: 257
#> Calculating values for cluster IPC
#> Dim: 6395 177
#> Calculating svd ...
#> Scaled by: 0.98267
#> Market Mode: 177
#> Calculating values for cluster Pod
#> Dim: 9328 430
#> Calculating svd ...
#> Scaled by: 0.9889339
#> Market Mode: 430
#> Calculating values for cluster PTA
#> Dim: 6682 459
#> Calculating svd ...
#> Scaled by: 0.9861411
#> Market Mode: 459
#> Calculating values for cluster NPCa
#> Dim: 9150 522
#> Calculating svd ...
#> Scaled by: 0.9945157
#> Market Mode: 522
#> Calculating values for cluster RVCSBb
#> Dim: 8449 312
#> Calculating svd ...
#> Scaled by: 0.9893211
#> Market Mode: 312
#> Calculating values for cluster End
#> Dim: 7027 223
#> Calculating svd ...
#> Scaled by: 0.9868303
#> Market Mode: 223
#> Calculating values for cluster SSBm/d
#> Dim: 2647 151
#> Calculating svd ...
#> Scaled by: 0.977434
#> Market Mode: 151
#> Calculating values for cluster NPCb
#> Dim: 6334 296
#> Calculating svd ...
#> Scaled by: 0.9918222
#> Market Mode: 296
#> Calculating values for cluster Leu
#> Dim: 1352 65
#> Calculating svd ...
#> Scaled by: 0.9256314
#> Market Mode: 65
#> Calculating values for cluster Mes
#> Dim: 1133 37
#> Calculating svd ...
#> Scaled by: 0.9470118
#> Market Mode: 37
#> Calculating values for cluster Prolif
#> Dim: 2132 85
#> Calculating svd ...
#> Scaled by: 0.9699548
#> Market Mode: 85
We can have a look at the main output of this function. For each
cluster, the corresponding clusterability measure is shown.
#Evaluate the output of the measure
#plot all values for sigma
plot_sigma(out_kidney)
If you would like to go into more detail, then you can have a look at
all sigmas and g-sigmas that are available per cluster.
#Plot all values for sigma and g_sigma
plot_all_sigmas(out_kidney)
plot_all_g_sigmas(out_kidney)
If you are interested in the values of all sigmas, g-sigmas and singular
values of the signal matrix, then this information can be obtained with
the help of this function.
#obtain the values for sigma and additional information
get_info(out_kidney, "UBCD")
#> sigma g_sigma theta r2vals singular_value celltype
#> 16 0.9718702 0.7595932 1.8030720 0.4468447 1 UBCD
#> 17 0.9613534 0.7073134 1.5854881 0.1810414 2 UBCD
#> 18 0.8545601 0.4459294 0.9704636 0.4134855 3 UBCD
#> 19 0.8649745 0.4617228 0.9958318 0.1402408 4 UBCD
#> 20 0.8749372 0.4779268 1.0228843 0.1069279 5 UBCD
#> 21 0.0000000 0.0000000 0.5170606 0.4340084 6 UBCD
#> 22 0.0000000 0.0000000 0.5170606 0.2978763 7 UBCD
#> 23 0.0000000 0.0000000 0.5170606 0.2157584 8 UBCD
Now, to determine if the clustrs with a high clusterability measure have
variances that are meaningful for you to sub-cluster, have a look at the
variance driving genes, which will tell you which genes cause the signal
to appear. For example, if genes are only related to differentiation,
then sub-clustering might not be necessary but could be of interest.
#See which genes cause variances in the data
get_var_genes(out_kidney, "UBCD")[,1:3]
#> Singular.vector.1 Singular.vector.2 Singular.vector.3
#> Highest-1 RPS6 HES1 CLU
#> Highest-2 DHRS2 FOS CTSH
#> Highest-3 SPINK1 ID2 MGST3
#> Highest-4 S100A6 ID1 EPCAM
#> Highest-5 HPGD JUN CYB5A
#> Highest-6 VSIG2 DDIT4 GSTM3
#> Highest-7 KRT7 JUNB CD9
#> Highest-8 FXYD3 DUSP1 GSTP1
#> Highest-9 FBLN1 GADD45B CD24
#> Highest-10 S100P HSP90AB1 TUBA4A
#> Highest-11 S100A11 ADIRF DDX5
#> Highest-12 ADIRF RGS16 SKP1
#> Highest-13 SNCG IER2 AGR2
#> Highest-14 PVALB FABP5 S100A11
#> Highest-15 UPK1A ID3 MYL6
#> Highest-16 UQCRQ TXNIP HSP90AB1
#> Highest-17 RPS18 H3F3A ENO1
#> Highest-18 HMGCS2 UBB TSPAN1
#> Highest-19 FTH1 HSPA1A ITM2B
#> Highest-20 PSCA RBP1 MYL12B
#> Highest-21 ADH1C GPC3 ARG2
#> Highest-22 RPL34 IGF2 CALM2
#> Highest-23 LGALS3 SPARC KRT8
#> Highest-24 RPL31 HSP90AA1 MYL12A
#> Highest-25 SHROOM1 TPM2 H3F3B
#> Highest-26 LEAP2 SNCG SYPL1
#> Highest-27 UPK2 TSC22D1 LGALS3BP
#> Highest-28 CISD3 HSPA1B KRT19
#> Highest-29 RPLP1 LGALS1 GAPDH
#> Highest-30 MT-ND3 SMC2 CLIC1
#> Highest-31 RPL12 HNRNPA2B1 RGS2
#> Highest-32 IGF2 MYLIP MALAT1
#> Highest-33 S100A4 CALD1 CAPG
#> Highest-34 PERP HMGB2 LINC00675
#> Highest-35 MT-ND4 NR2F1 AOC1
#> Highest-36 FABP5 YME1L1 GATA2
#> Highest-37 FBP1 COL1A2 SCPEP1
#> Highest-38 GDF15 NR2F2 TMEM176B
#> Highest-39 RPL26 SEPT7 ACTG1
#> Highest-40 MT-ATP6 IDH1 HSPA5
#> Highest-41 C9orf16 TMSB15A DEGS2
#> Highest-42 RPS14 ZNF503 UBC
#> Highest-43 RPL41 DNAJA1 CLDN7
#> Highest-44 FAM3B DDIT3 CAPS
#> Lowest-1 TMSB4X PHLDA2 RPS2
#> Lowest-2 NGFRAP1 AQP2 COL1A2
#> Lowest-3 ACTB TNFRSF12A COL3A1
#> Lowest-4 IGFBP7 KRT18 COL1A1
#> Lowest-5 WFDC2 ERRFI1 RPL13A
#> Lowest-6 CLDN3 RPL41 RPL13
#> Lowest-7 NDUFA4 HES4 PTN
#> Lowest-8 MEST SAT1 RPL28
#> Lowest-9 HINT1 CLU RPS3
#> Lowest-10 STMN1 SDF2L1 RPS12
#> Lowest-11 PTMA TPM4 LGALS1
#> Lowest-12 ACTG1 CLDN4 GPC3
#> Lowest-13 BCAM RPL3 ZEB2
#> Lowest-14 VIM TMSB4X RPLP1
#> Lowest-15 STC1 PHLDA1 RPL18A
#> Lowest-16 NREP ZFAND5 CTSC
#> Lowest-17 CYR61 MAP2K3 RPS20
#> Lowest-18 PTGER1 GUK1 RPS16
#> Lowest-19 HNRNPA1 FTL RPS19
#> Lowest-20 HMGN1 CREM RPL10A
#> Lowest-21 MAL COTL1 RPL15
#> Lowest-22 MDK LRRFIP1 RPS17
#> Lowest-23 SBSPON MAL2 SPARC
#> Lowest-24 TPM4 EPHA2 RPS18
#> Lowest-25 HMGB1 TMEM176A VIM
#> Lowest-26 SUMO2 FOSL1 CALD1
#> Lowest-27 EPCAM CYSTM1 CNKSR3
#> Lowest-28 MYH10 LINC00152 RPS15A
#> Lowest-29 S100A13 GADD45A SOCS3
#> Lowest-30 CNKSR3 ZNF430 RPL18
#> Lowest-31 HES4 ODC1 RPL32
#> Lowest-32 TNFRSF12A MIR4435-2HG NR2F1
#> Lowest-33 TUBB2B CD24 RPL37A
#> Lowest-34 MEG3 S100A10 TCF21
#> Lowest-35 C6orf48 CDK2AP2 RPS14
#> Lowest-36 SNRPN WFDC2 RPS8
#> Lowest-37 TUBB RPLP0 RPL10
#> Lowest-38 TUBA1B PTP4A1 EEF1B2
#> Lowest-39 NAP1L1 HN1 TNC
#> Lowest-40 BCAT1 KRT8 MFAP4
#> Lowest-41 COL18A1 TES RPL19
#> Lowest-42 EFNB2 KTN1 RPL9
#> Lowest-43 HMGN2 MAFF RPL30
#> Lowest-44 JUND SERP1 TPT1
You can also check out the fit of the MP distribution for each cluster.
#Check if the MP distribution fits to the data
plot_MP(out_kidney, "UBCD")
And for fruther validation, see if the singular vectors of the
significant singular values look meaningful. By plotting either clusters
or genes with the singular vectors.
#Plot clusters
plot_singular_vectors(out_kidney, "UBCD", colour = sce_kidney@metadata$ubcd.cluster)
#Plot variance driving genes
plot_singular_vectors(out_kidney, "UBCD", colour = "UPK1A", scaled = FALSE)
Siliegia/SIGMA documentation built on Dec. 18, 2021, 2 p.m.
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library(SIGMA)
library(ggplot2)
library(Seurat)
The authors who have anlyzed this data already normalized the data set with the R package “scran” and determined clusters by hierachical clustering. In total they have found 22 clusters.
data("force_gr_kidney")
data("sce_kidney")
paga.coord$Group <- sce_kidney$cell.type
ggplot(paga.coord, aes(x = V1, y = V2, colour = Group)) +
geom_point(shape = 16)
With SIGMA, we are now able to assess the variability for each cluster
and see if possible sub-clusters can be found. First, we load the
preprocessed SingleCellObject of the kidney data.
#Load kidney data from package
#Extract scran normalized counts and log-transform
expr.norm.log <- as.matrix(log(assay(sce_kidney, "scran")+1))
#Change the name of the rows to readable gene names
rownames(expr.norm.log) <- as.character(rowData(sce_kidney)$HUGO)
rownames(sce_kidney) <- as.character(rowData(sce_kidney)$HUGO)
In the next step we would like to exclude certain variances from appearing in the measure. For example, in this fetal kidney data set, several factors would not be of interest to cluster on: cell cycle related variances, ribosomal and mitochondrial gene expression. As, well as stress related genes, which arise during dissociation. Cycling genes, we determine here with the Seurat package, so for that we first need to create a Seurat object and normalize it. Another important factor is technical variability, for example the varying number of transcripts. It’s important to also include that in the data frame.
#Creating Seurat object
cnts <- counts(sce_kidney)
colnames(cnts) <- 1:ncol(cnts)
rownames(cnts) <- as.character(rowData(sce_kidney)$HUGO)
fetalkidney <- CreateSeuratObject(cnts)
#> Warning: Non-unique features (rownames) present in the input matrix, making unique
#> Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
fetalkidney <- NormalizeData(fetalkidney)
#Cell cycle analysis
s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes
fetalkidney <- CellCycleScoring(fetalkidney, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE)
#> Warning: The following features are not present in the object: MLF1IP, not searching for symbol synonyms
#Determining the expression of MT-genes, Rb-genes and stress genes:
data("ribosomal_genes")
data("stress_genes")
rb <- rownames(fetalkidney) %in% rb.genes
stress.genes <- intersect(stress.genes, rownames(expr.norm.log))
#Creating the final data frame with all the factors to be excluded from considering while calculating the clusterability measure:
exclude <- data.frame(clsm = log(colSums(cnts) + 1), cellcycle = fetalkidney$G2M.Score,
mt = colMeans(expr.norm.log[grep("^MT-", rownames(expr.norm.log)),]),
ribosomal = colMeans(expr.norm.log[rb,]), stress = colMeans(expr.norm.log[stress.genes,]))
Now we are ready to apply the main function to determine clusterability:
#Main funcion SIGMA
out_kidney <- sigma_funct(expr.norm.log, clusters = sce_kidney$cell.type, exclude = exclude)
#> Calculating values for cluster NPCc
#> Dim: 9300 596
#> Calculating svd ...
#> Scaled by: 0.9936921
#> Market Mode: 596
#> Calculating values for cluster DTLH
#> Dim: 2561 67
#> Calculating svd ...
#> Scaled by: 0.9549873
#> Market Mode: 67
#> Calculating values for cluster ICa
#> Dim: 9703 653
#> Calculating svd ...
#> Scaled by: 0.9941114
#> Market Mode: 653
#> Calculating values for cluster CnT
#> Dim: 2116 80
#> Calculating svd ...
#> Scaled by: 0.971555
#> Market Mode: 80
#> Calculating values for cluster UBCD
#> Dim: 4435 317
#> Calculating svd ...
#> Scaled by: 0.9752237
#> Market Mode: 317
#> Calculating values for cluster ErPrT
#> Dim: 5580 338
#> Calculating svd ...
#> Scaled by: 0.9872483
#> Market Mode: 338
#> Calculating values for cluster RVCSBa
#> Dim: 6180 367
#> Calculating svd ...
#> Scaled by: 0.9881706
#> Market Mode: 367
#> Calculating values for cluster ICb
#> Dim: 10031 677
#> Calculating svd ...
#> Scaled by: 0.9956775
#> Market Mode: 677
#> Calculating values for cluster SSBpr
#> Dim: 3862 213
#> Calculating svd ...
#> Scaled by: 0.9705424
#> Market Mode: 213
#> Calculating values for cluster NPCd
#> Dim: 5201 280
#> Calculating svd ...
#> Scaled by: 0.9815733
#> Market Mode: 280
#> Calculating values for cluster SSBpod
#> Dim: 7331 257
#> Calculating svd ...
#> Scaled by: 0.9920918
#> Market Mode: 257
#> Calculating values for cluster IPC
#> Dim: 6395 177
#> Calculating svd ...
#> Scaled by: 0.98267
#> Market Mode: 177
#> Calculating values for cluster Pod
#> Dim: 9328 430
#> Calculating svd ...
#> Scaled by: 0.9889339
#> Market Mode: 430
#> Calculating values for cluster PTA
#> Dim: 6682 459
#> Calculating svd ...
#> Scaled by: 0.9861411
#> Market Mode: 459
#> Calculating values for cluster NPCa
#> Dim: 9150 522
#> Calculating svd ...
#> Scaled by: 0.9945157
#> Market Mode: 522
#> Calculating values for cluster RVCSBb
#> Dim: 8449 312
#> Calculating svd ...
#> Scaled by: 0.9893211
#> Market Mode: 312
#> Calculating values for cluster End
#> Dim: 7027 223
#> Calculating svd ...
#> Scaled by: 0.9868303
#> Market Mode: 223
#> Calculating values for cluster SSBm/d
#> Dim: 2647 151
#> Calculating svd ...
#> Scaled by: 0.977434
#> Market Mode: 151
#> Calculating values for cluster NPCb
#> Dim: 6334 296
#> Calculating svd ...
#> Scaled by: 0.9918222
#> Market Mode: 296
#> Calculating values for cluster Leu
#> Dim: 1352 65
#> Calculating svd ...
#> Scaled by: 0.9256314
#> Market Mode: 65
#> Calculating values for cluster Mes
#> Dim: 1133 37
#> Calculating svd ...
#> Scaled by: 0.9470118
#> Market Mode: 37
#> Calculating values for cluster Prolif
#> Dim: 2132 85
#> Calculating svd ...
#> Scaled by: 0.9699548
#> Market Mode: 85
We can have a look at the main output of this function. For each cluster, the corresponding clusterability measure is shown.
#Evaluate the output of the measure
#plot all values for sigma
plot_sigma(out_kidney)
If you would like to go into more detail, then you can have a look at all sigmas and g-sigmas that are available per cluster.
#Plot all values for sigma and g_sigma
plot_all_sigmas(out_kidney)
plot_all_g_sigmas(out_kidney)
If you are interested in the values of all sigmas, g-sigmas and singular values of the signal matrix, then this information can be obtained with the help of this function.
#obtain the values for sigma and additional information
get_info(out_kidney, "UBCD")
#> sigma g_sigma theta r2vals singular_value celltype
#> 16 0.9718702 0.7595932 1.8030720 0.4468447 1 UBCD
#> 17 0.9613534 0.7073134 1.5854881 0.1810414 2 UBCD
#> 18 0.8545601 0.4459294 0.9704636 0.4134855 3 UBCD
#> 19 0.8649745 0.4617228 0.9958318 0.1402408 4 UBCD
#> 20 0.8749372 0.4779268 1.0228843 0.1069279 5 UBCD
#> 21 0.0000000 0.0000000 0.5170606 0.4340084 6 UBCD
#> 22 0.0000000 0.0000000 0.5170606 0.2978763 7 UBCD
#> 23 0.0000000 0.0000000 0.5170606 0.2157584 8 UBCD
Now, to determine if the clustrs with a high clusterability measure have variances that are meaningful for you to sub-cluster, have a look at the variance driving genes, which will tell you which genes cause the signal to appear. For example, if genes are only related to differentiation, then sub-clustering might not be necessary but could be of interest.
#See which genes cause variances in the data
get_var_genes(out_kidney, "UBCD")[,1:3]
#> Singular.vector.1 Singular.vector.2 Singular.vector.3
#> Highest-1 RPS6 HES1 CLU
#> Highest-2 DHRS2 FOS CTSH
#> Highest-3 SPINK1 ID2 MGST3
#> Highest-4 S100A6 ID1 EPCAM
#> Highest-5 HPGD JUN CYB5A
#> Highest-6 VSIG2 DDIT4 GSTM3
#> Highest-7 KRT7 JUNB CD9
#> Highest-8 FXYD3 DUSP1 GSTP1
#> Highest-9 FBLN1 GADD45B CD24
#> Highest-10 S100P HSP90AB1 TUBA4A
#> Highest-11 S100A11 ADIRF DDX5
#> Highest-12 ADIRF RGS16 SKP1
#> Highest-13 SNCG IER2 AGR2
#> Highest-14 PVALB FABP5 S100A11
#> Highest-15 UPK1A ID3 MYL6
#> Highest-16 UQCRQ TXNIP HSP90AB1
#> Highest-17 RPS18 H3F3A ENO1
#> Highest-18 HMGCS2 UBB TSPAN1
#> Highest-19 FTH1 HSPA1A ITM2B
#> Highest-20 PSCA RBP1 MYL12B
#> Highest-21 ADH1C GPC3 ARG2
#> Highest-22 RPL34 IGF2 CALM2
#> Highest-23 LGALS3 SPARC KRT8
#> Highest-24 RPL31 HSP90AA1 MYL12A
#> Highest-25 SHROOM1 TPM2 H3F3B
#> Highest-26 LEAP2 SNCG SYPL1
#> Highest-27 UPK2 TSC22D1 LGALS3BP
#> Highest-28 CISD3 HSPA1B KRT19
#> Highest-29 RPLP1 LGALS1 GAPDH
#> Highest-30 MT-ND3 SMC2 CLIC1
#> Highest-31 RPL12 HNRNPA2B1 RGS2
#> Highest-32 IGF2 MYLIP MALAT1
#> Highest-33 S100A4 CALD1 CAPG
#> Highest-34 PERP HMGB2 LINC00675
#> Highest-35 MT-ND4 NR2F1 AOC1
#> Highest-36 FABP5 YME1L1 GATA2
#> Highest-37 FBP1 COL1A2 SCPEP1
#> Highest-38 GDF15 NR2F2 TMEM176B
#> Highest-39 RPL26 SEPT7 ACTG1
#> Highest-40 MT-ATP6 IDH1 HSPA5
#> Highest-41 C9orf16 TMSB15A DEGS2
#> Highest-42 RPS14 ZNF503 UBC
#> Highest-43 RPL41 DNAJA1 CLDN7
#> Highest-44 FAM3B DDIT3 CAPS
#> Lowest-1 TMSB4X PHLDA2 RPS2
#> Lowest-2 NGFRAP1 AQP2 COL1A2
#> Lowest-3 ACTB TNFRSF12A COL3A1
#> Lowest-4 IGFBP7 KRT18 COL1A1
#> Lowest-5 WFDC2 ERRFI1 RPL13A
#> Lowest-6 CLDN3 RPL41 RPL13
#> Lowest-7 NDUFA4 HES4 PTN
#> Lowest-8 MEST SAT1 RPL28
#> Lowest-9 HINT1 CLU RPS3
#> Lowest-10 STMN1 SDF2L1 RPS12
#> Lowest-11 PTMA TPM4 LGALS1
#> Lowest-12 ACTG1 CLDN4 GPC3
#> Lowest-13 BCAM RPL3 ZEB2
#> Lowest-14 VIM TMSB4X RPLP1
#> Lowest-15 STC1 PHLDA1 RPL18A
#> Lowest-16 NREP ZFAND5 CTSC
#> Lowest-17 CYR61 MAP2K3 RPS20
#> Lowest-18 PTGER1 GUK1 RPS16
#> Lowest-19 HNRNPA1 FTL RPS19
#> Lowest-20 HMGN1 CREM RPL10A
#> Lowest-21 MAL COTL1 RPL15
#> Lowest-22 MDK LRRFIP1 RPS17
#> Lowest-23 SBSPON MAL2 SPARC
#> Lowest-24 TPM4 EPHA2 RPS18
#> Lowest-25 HMGB1 TMEM176A VIM
#> Lowest-26 SUMO2 FOSL1 CALD1
#> Lowest-27 EPCAM CYSTM1 CNKSR3
#> Lowest-28 MYH10 LINC00152 RPS15A
#> Lowest-29 S100A13 GADD45A SOCS3
#> Lowest-30 CNKSR3 ZNF430 RPL18
#> Lowest-31 HES4 ODC1 RPL32
#> Lowest-32 TNFRSF12A MIR4435-2HG NR2F1
#> Lowest-33 TUBB2B CD24 RPL37A
#> Lowest-34 MEG3 S100A10 TCF21
#> Lowest-35 C6orf48 CDK2AP2 RPS14
#> Lowest-36 SNRPN WFDC2 RPS8
#> Lowest-37 TUBB RPLP0 RPL10
#> Lowest-38 TUBA1B PTP4A1 EEF1B2
#> Lowest-39 NAP1L1 HN1 TNC
#> Lowest-40 BCAT1 KRT8 MFAP4
#> Lowest-41 COL18A1 TES RPL19
#> Lowest-42 EFNB2 KTN1 RPL9
#> Lowest-43 HMGN2 MAFF RPL30
#> Lowest-44 JUND SERP1 TPT1
You can also check out the fit of the MP distribution for each cluster.
#Check if the MP distribution fits to the data
plot_MP(out_kidney, "UBCD")
And for fruther validation, see if the singular vectors of the significant singular values look meaningful. By plotting either clusters or genes with the singular vectors.
#Plot clusters
plot_singular_vectors(out_kidney, "UBCD", colour = sce_kidney@metadata$ubcd.cluster)
#Plot variance driving genes
plot_singular_vectors(out_kidney, "UBCD", colour = "UPK1A", scaled = FALSE)
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