QCConditions
2024-03-31
library(Seurat)## Warning: Paket 'Seurat' wurde unter R Version 4.3.3 erstellt## Lade nötiges Paket: SeuratObject## Lade nötiges Paket: sp## Warning: Paket 'sp' wurde unter R Version 4.3.3 erstellt##
## Attache Paket: 'SeuratObject'## Das folgende Objekt ist maskiert 'package:base':
##
## intersectlibrary(SeuratData)
library(patchwork)
library(dplyr)##
## Attache Paket: 'dplyr'## Die folgenden Objekte sind maskiert von 'package:stats':
##
## filter, lag## Die folgenden Objekte sind maskiert von 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)## Warning: Paket 'ggplot2' wurde unter R Version 4.3.3 erstelltlibrary(sctransform)
setwd("C:/Users/thoma/OneDrive/Desktop/Analysis_treated_november")
A1.data <- Read10X(data.dir = "C:/Users/thoma/OneDrive/Desktop/T.c_2024/analysis_march_2024.tar/analysis_march_2024/analysis_march_2024/count_A1/outs/filtered_feature_bc_matrix")
A2.data <- Read10X(data.dir = "C:/Users/thoma/OneDrive/Desktop/T.c_2024/analysis_march_2024.tar/analysis_march_2024/analysis_march_2024/count_A2/outs/filtered_feature_bc_matrix")
A3.data <- Read10X(data.dir = "C:/Users/thoma/OneDrive/Desktop/T.c_2024/analysis_march_2024.tar/analysis_march_2024/analysis_march_2024/count_A3/outs/filtered_feature_bc_matrix")
A4.data <- Read10X(data.dir = "C:/Users/thoma/OneDrive/Desktop/T.c_2024/analysis_march_2024.tar/analysis_march_2024/analysis_march_2024/count_A4/outs/filtered_feature_bc_matrix")
A5.data <- Read10X(data.dir = "C:/Users/thoma/OneDrive/Desktop/T.c_2024/analysis_march_2024.tar/analysis_march_2024/analysis_march_2024/count_A5/outs/filtered_feature_bc_matrix")
A6.data <- Read10X(data.dir = "C:/Users/thoma/OneDrive/Desktop/T.c_2024/analysis_march_2024.tar/analysis_march_2024/analysis_march_2024/count_A6/outs/filtered_feature_bc_matrix")
btt2022.data <- Read10X(data.dir = "C:/Users/thoma/OneDrive/Desktop/priming2022/analysis_priming01_repeat.tar/analysis_priming01_repeat/Btt/outs/filtered_feature_bc_matrix")
pbs2022.data <- Read10X(data.dir = "C:/Users/thoma/OneDrive/Desktop/priming2022/analysis_priming01_repeat.tar/analysis_priming01_repeat/PBS/outs/filtered_feature_bc_matrix")
naive2022.data <- Read10X(data.dir = "C:/Users/thoma/OneDrive/Desktop/priming2022/analysis_priming01_repeat.tar/analysis_priming01_repeat/Naive/outs/filtered_feature_bc_matrix")
A1 <- CreateSeuratObject(counts = A1.data, project = "A1")## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')A2 <- CreateSeuratObject(counts = A2.data, project = "A2")## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')A3 <- CreateSeuratObject(counts = A3.data, project = "A3")## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')A4 <- CreateSeuratObject(counts = A4.data, project = "A4")## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')A5 <- CreateSeuratObject(counts = A5.data, project = "A5")## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')A6 <- CreateSeuratObject(counts = A6.data, project = "A6")## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')btt2022 <- CreateSeuratObject(counts = btt2022.data, project = "btt2022")## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')pbs2022 <- CreateSeuratObject(counts = pbs2022.data, project = "pbs2022")## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')naive2022 <- CreateSeuratObject(counts = naive2022.data, project = "naive2022")## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')A1@meta.data$Condition <- "naive"
A4@meta.data$Condition <- "naive"
A5@meta.data$Condition <- "PBS"
A2@meta.data$Condition <- "PBS"
A3@meta.data$Condition <- "Btt"
A6@meta.data$Condition <- "Btt"
btt2022@meta.data$Condition <- "Btt"
naive2022@meta.data$Condition <- "naive"
pbs2022@meta.data$Condition <- "PBS"
A1## An object of class Seurat
## 14492 features across 115 samples within 1 assay
## Active assay: RNA (14492 features, 0 variable features)
## 1 layer present: countsCheck if mitochondrial genes are present “ND1” = “KEF75-p01”, “ND2” = “KEF75-p13”, “ND3” = “KEF75-p07”, “ND4” = “KEF75-p05”, “ND4L” = “KEF75-p04”, “ND5” = “KEF75-p06”, “ND6” = “KEF75-p03”, “COX1” = “KEF75-p12”, “COX2” = “KEF75-p11”, “COX3” = “KEF75-p08”, “ATP6” = “KEF75-p09”, “ATP8” = “KEF75-p10”, “CYTB” = “KEF75-p02”
grep("KEF75-p10", rownames(A1), value = TRUE)## [1] "KEF75-p10"feature.listA1=rownames(A1)
mt.featuresA1=c("KEF75-p01", "KEF75-p13", "KEF75-p07", "KEF75-p05", "KEF75-p04", "KEF75-p06", "KEF75-p03", "KEF75-p12", "KEF75-p11", "KEF75-p08", "KEF75-p09", "KEF75-p10", "KEF75-p02")
mt.featuresA1=intersect(mt.featuresA1, feature.listA1)
A1[["percent.mtA1"]] <- PercentageFeatureSet(A1, features = mt.featuresA1)
feature.listA2=rownames(A2)
mt.featuresA2=c("KEF75-p01", "KEF75-p13", "KEF75-p07", "KEF75-p05", "KEF75-p04", "KEF75-p06", "KEF75-p03", "KEF75-p12", "KEF75-p11", "KEF75-p08", "KEF75-p09", "KEF75-p10", "KEF75-p02")
mt.featuresA2=intersect(mt.featuresA2, feature.listA2)
A2[["percent.mtA2"]] <- PercentageFeatureSet(A2, features = mt.featuresA2)
feature.listA3=rownames(A3)
mt.featuresA3=c("KEF75-p01", "KEF75-p13", "KEF75-p07", "KEF75-p05", "KEF75-p04", "KEF75-p06", "KEF75-p03", "KEF75-p12", "KEF75-p11", "KEF75-p08", "KEF75-p09", "KEF75-p10", "KEF75-p02")
mt.featuresA3=intersect(mt.featuresA3, feature.listA3)
A3[["percent.mtA3"]] <- PercentageFeatureSet(A3, features = mt.featuresA3)
feature.listA4=rownames(A4)
mt.featuresA4=c("KEF75-p01", "KEF75-p13", "KEF75-p07", "KEF75-p05", "KEF75-p04", "KEF75-p06", "KEF75-p03", "KEF75-p12", "KEF75-p11", "KEF75-p08", "KEF75-p09", "KEF75-p10", "KEF75-p02")
mt.featuresA4=intersect(mt.featuresA4, feature.listA4)
A4[["percent.mtA4"]] <- PercentageFeatureSet(A4, features = mt.featuresA4)
feature.listA5=rownames(A5)
mt.featuresA5=c("KEF75-p01", "KEF75-p13", "KEF75-p07", "KEF75-p05", "KEF75-p04", "KEF75-p06", "KEF75-p03", "KEF75-p12", "KEF75-p11", "KEF75-p08", "KEF75-p09", "KEF75-p10", "KEF75-p02")
mt.featuresA5=intersect(mt.featuresA5, feature.listA5)
A5[["percent.mtA5"]] <- PercentageFeatureSet(A5, features = mt.featuresA5)
feature.listA6=rownames(A6)
mt.featuresA6=c("KEF75-p01", "KEF75-p13", "KEF75-p07", "KEF75-p05", "KEF75-p04", "KEF75-p06", "KEF75-p03", "KEF75-p12", "KEF75-p11", "KEF75-p08", "KEF75-p09", "KEF75-p10", "KEF75-p02")
mt.featuresA6=intersect(mt.featuresA6, feature.listA6)
A6[["percent.mtA6"]] <- PercentageFeatureSet(A6, features = mt.featuresA6)
feature.listbtt2022=rownames(btt2022)
mt.featuresbtt2022=c("KEF75-p01", "KEF75-p13", "KEF75-p07", "KEF75-p05", "KEF75-p04", "KEF75-p06", "KEF75-p03", "KEF75-p12", "KEF75-p11", "KEF75-p08", "KEF75-p09", "KEF75-p10", "KEF75-p02")
mt.featuresbtt2022=intersect(mt.featuresbtt2022, feature.listbtt2022)
btt2022[["percent.mtbtt2022"]] <- PercentageFeatureSet(btt2022, features = mt.featuresbtt2022)
feature.listpbs2022=rownames(pbs2022)
mt.featurespbs2022=c("KEF75-p01", "KEF75-p13", "KEF75-p07", "KEF75-p05", "KEF75-p04", "KEF75-p06", "KEF75-p03", "KEF75-p12", "KEF75-p11", "KEF75-p08", "KEF75-p09", "KEF75-p10", "KEF75-p02")
mt.featurespbs2022=intersect(mt.featurespbs2022, feature.listpbs2022)
pbs2022[["percent.mtpbs2022"]] <- PercentageFeatureSet(pbs2022, features = mt.featurespbs2022)
feature.listnaive2022=rownames(naive2022)
mt.featuresnaive2022=c("KEF75-p01", "KEF75-p13", "KEF75-p07", "KEF75-p05", "KEF75-p04", "KEF75-p06", "KEF75-p03", "KEF75-p12", "KEF75-p11", "KEF75-p08", "KEF75-p09", "KEF75-p10", "KEF75-p02")
mt.featuresnaive2022=intersect(mt.featuresnaive2022, feature.listnaive2022)
naive2022[["percent.mtnaive2022"]] <- PercentageFeatureSet(naive2022, features = mt.featuresnaive2022)QC Visualization
VlnPlot(A1, features = c("nFeature_RNA", "nCount_RNA", "percent.mtA1"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(A2, features = c("nFeature_RNA", "nCount_RNA", "percent.mtA2"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(A3, features = c("nFeature_RNA", "nCount_RNA", "percent.mtA3"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(A4, features = c("nFeature_RNA", "nCount_RNA", "percent.mtA4"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(A5, features = c("nFeature_RNA", "nCount_RNA", "percent.mtA5"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(A6, features = c("nFeature_RNA", "nCount_RNA", "percent.mtA6"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(btt2022, features = c("nFeature_RNA", "nCount_RNA", "percent.mtbtt2022"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(pbs2022, features = c("nFeature_RNA", "nCount_RNA", "percent.mtpbs2022"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(naive2022, features = c("nFeature_RNA", "nCount_RNA", "percent.mtnaive2022"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
plot2A1 <- FeatureScatter(A1, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot2A1
plot2A2 <- FeatureScatter(A2, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot2A2
plot2A3 <- FeatureScatter(A3, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot2A3
plot2A4 <- FeatureScatter(A4, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot2A4
plot2A5 <- FeatureScatter(A5, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot2A5
plot2A6 <- FeatureScatter(A6, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot2A6
plot2btt2022 <- FeatureScatter(btt2022, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot2btt2022
plot2pbs2022 <- FeatureScatter(pbs2022, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot2pbs2022
plot2naive2022 <- FeatureScatter(naive2022, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot2naive2022
sctransform each single
scA1 <- SCTransform(A1, vars.to.regress = "percent.mtA1", verbose = FALSE)
scA2 <- SCTransform(A2, vars.to.regress = "percent.mtA2", verbose = FALSE)
scA3 <- SCTransform(A3, vars.to.regress = "percent.mtA3", verbose = FALSE)
scA4 <- SCTransform(A4, vars.to.regress = "percent.mtA4", verbose = FALSE)
scA5 <- SCTransform(A5, vars.to.regress = "percent.mtA5", verbose = FALSE)
scA6 <- SCTransform(A6, vars.to.regress = "percent.mtA6", verbose = FALSE)
scbtt2022 <- SCTransform(btt2022, vars.to.regress = "percent.mtbtt2022", verbose = FALSE)
scpbs2022 <- SCTransform(pbs2022, vars.to.regress = "percent.mtpbs2022", verbose = FALSE)
scnaive2022 <- SCTransform(naive2022, vars.to.regress = "percent.mtnaive2022", verbose = FALSE)Repeat QC
VlnPlot(A6, features = c("nFeature_RNA", "nCount_RNA", "percent.mtA6"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(A5, features = c("nFeature_RNA", "nCount_RNA", "percent.mtA5"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(A4, features = c("nFeature_RNA", "nCount_RNA", "percent.mtA4"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(A3, features = c("nFeature_RNA", "nCount_RNA", "percent.mtA3"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(A2, features = c("nFeature_RNA", "nCount_RNA", "percent.mtA2"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(A1, features = c("nFeature_RNA", "nCount_RNA", "percent.mtA1"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(btt2022, features = c("nFeature_RNA", "nCount_RNA", "percent.mtbtt2022"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(pbs2022, features = c("nFeature_RNA", "nCount_RNA", "percent.mtpbs2022"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(naive2022, features = c("nFeature_RNA", "nCount_RNA", "percent.mtnaive2022"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
scA1QC <- RunPCA(scA1, features = VariableFeatures(object = scA1))## PC_ 1
## Positive: LOC658140, LOC655756, LOC661860, LOC656464, LOC654949, LOC664368, LOC103314322, LOC661223, LOC100141601, LOC664436
## LOC659377, LOC664555, LOC103312846, LOC655475, LOC663871, LOC655614, LOC658590, LOC658478, LOC664315, LOC655190
## LOC100142212, LOC107397762, LOC657747, Parp, LOC659866, LOC103312310, LOC655639, LOC660418, LOC660822, LOC659879
## Negative: LOC103315122, LOC103313909, LOC655533, LOC103313579, Tyr2, LOC103315121, LOC660769, LOC103312143, LOC663147, LOC661355
## LOC661796, LOC654950, LOC659613, LOC657969, LOC103314806, LOC103313573, LOC100142559, LOC658936, LOC658955, LOC655232
## LOC660520, LOC658754, LOC103313568, LOC654924, Esyt2a, LOC664266, LOC661858, LOC661588, LOC103312946, LOC662417
## PC_ 2
## Positive: LOC103313573, LOC103313568, LOC107398426, LOC659010, LOC661812, LOC661343, LOC657224, LOC107399012, LOC661354, LOC661678
## LOC661278, LOC655019, LOC107398245, LOC658088, LOC103314806, LOC664339, LOC658929, LOC660883, LOC103312469, LOC661798
## LOC663820, LOC103313325, LOC659958, Nag2, LOC662101, LOC103312841, LOC663832, LOC663003, LOC656637, LOC103313913
## Negative: LOC656560, LOC663390, LOC663391, LOC663153, LOC664315, LOC656356, LOC664393, LOC662445, LOC662754, LOC655649
## LOC656270, LOC657695, LOC103313909, Tyr1, LOC664143, LOC662319, LOC661296, LOC656682, LOC662141, LOC663962
## LOC658540, LOC103313660, LOC658058, LOC660379, LOC663023, LOC662436, LOC663894, LOC663982, LOC656416, LOC661185
## PC_ 3
## Positive: LOC661588, LOC662396, LOC656198, LOC663147, LOC103315108, LOC103315017, LOC103312806, LOC103312486, LOC655174, LOC100141837
## LOC658333, LOC661488, LOC664339, LOC656464, LOC658864, LOC100141670, LOC658914, LOC657364, LOC656087, LOC103312946
## LOC659558, LOC664282, LOC658754, LOC654924, LOC658646, LOC661876, LOC661835, LOC663567, LOC103315250, LOC663025
## Negative: LOC660379, LOC663962, Syx1A, LOC655640, LOC658375, LOC659820, LOC657715, LOC657258, LOC658141, LOC658559
## LOC660441, LOC660675, LOC660371, LOC663822, LOC656499, LOC656164, LOC103313111, LOC659951, LOC664359, LOC658603
## LOC662427, LOC656833, LOC656797, LOC660098, LOC663628, LOC662907, LOC659982, LOC657081, LOC656458, LOC664026
## PC_ 4
## Positive: LOC661968, LOC656270, Tyr1, LOC662595, LOC662980, LOC103313909, LOC656935, LOC660431, LOC654950, LOC656649
## LOC658603, LOC103313660, LOC661353, LOC103312419, LOC661396, LOC663391, LOC661354, LOC100142538, LOC658276, LOC657209
## LOC663695, LOC658899, LOC659637, LOC100141654, LOC659010, Spz3, LOC656309, LOC100141546, LOC103313573, LOC655651
## Negative: LOC100142175, LOC655492, LOC103314806, LOC658984, LOC663912, LOC661343, LOC660215, LOC655420, LOC656070, LOC100142307
## LOC103315156, LOC656846, LOC655174, LOC664531, LOC658006, LOC103315250, LOC657686, LOC657036, LOC103313467, LOC660233
## LOC657364, LOC103313607, LOC103313785, LOC661259, LOC659949, LOC656170, LOC658171, Y-e3, LOC655127, LOC659765
## PC_ 5
## Positive: LOC662396, LOC659147, LOC103312486, LOC660355, LOC657686, LOC103312345, LOC103314176, LOC660193, LOC656521, LOC660020
## LOC100142538, LOC660302, LOC658140, LOC103313825, LOC663663, LOC659968, LOC659879, LOC662291, LOC661552, LOC664406
## LOC654888, LOC656993, LOC661588, LOC107397983, LOC663309, LOC663391, LOC664393, LOC658333, LOC656170, LOC660605
## Negative: LOC655533, LOC656232, LOC103313909, Tyr2, LOC100142559, LOC103312143, LOC103315122, cec2, LOC658991, LOC658253
## LOC664209, LOC654930, LOC103313660, LOC103315220, LOC659982, LOC103313913, LOC663482, LOC655019, LOC663106, LOC659085
## LOC103313582, LOC103314285, LOC660465, LOC103313715, LOC100142122, LOC657732, LOC103312928, LOC655889, LOC663232, LOC103313115scA2QC <- RunPCA(scA2, features = VariableFeatures(object = scA2))## PC_ 1
## Positive: KEF75-r02, LOC103314122, HEX2, LOC662773, KEF75-r01, LOC661814, HEX1B, LOC662902, HEX1A, LOC662738
## LOC661756, LOC655732, LOC661778, Cyp9f2, LOC656825, LOC660033, LOC656243, LOC103313126, KEF75-p12, LOC663001
## LOC107398513, KEF75-p06, LOC664598, LOC100142066, LOC660127, Idgf4, LOC661561, LOC655734, LOC103313295, LOC103314462
## Negative: Arp1, Rpl41, LOC656276, LOC663147, LOC654924, LOC103312806, LOC658333, Tyr1, LOC103315107, LOC661185
## LOC663390, LOC103315108, LOC659357, LOC663209, LOC662400, LOC662141, LOC658148, LOC656653, LOC100141837, LOC658195
## LOC661665, LOC656198, LOC662445, LOC656528, LOC662265, RpS6, LOC659478, LOC657360, LOC661488, LOC663175
## PC_ 2
## Positive: LOC103315108, LOC658333, LOC103315107, Tyr1, LOC103312806, LOC656198, LOC100141837, LOC654924, LOC661488, LOC103315121
## LOC661588, LOC661796, LOC663982, LOC658366, LOC659357, LOC103313361, LOC654950, LOC103312946, LOC662396, LOC100142054
## LOC100142559, LOC663567, LOC660060, Tyr2, LOC103313909, LOC658936, LOC659624, LOC100141670, LOC659396, LOC100142172
## Negative: LOC663822, LOC103315154, LOC103313111, LOC658512, LOC657051, LOC656320, LOC661583, LOC664364, LOC660033, LOC660371
## LOC103314685, LOC657844, nAChRa10, LOC659949, LOC103313498, LOC657715, LOC663134, LOC662708, LOC103313785, LOC103314353
## Y-e3, LOC100142175, LOC103313978, LOC664362, LOC100142307, LOC663310, LOC661624, LOC661797, LOC657828, LOC659879
## PC_ 3
## Positive: LOC655373, Tcjheh-r4, LOC663845, Y-b, LOC663220, LOC660769, LOC663904, LOC656921, LOC661589, LOC656486
## LOC659147, LOC655232, LOC662470, LOC655972, LOC103313579, LOC661654, LOC103314176, LOC103312345, LOC659820, LOC103313909
## LOC658984, LOC663408, LOC662417, LOC659226, LOC660189, LOC103313299, LOC661354, LOC656761, LOC664028, LOC664126
## Negative: Rpl41, LOC656930, LOC663013, LOC660705, LOC664058, LOC656653, LOC656276, LOC662265, LOC662743, LOC661665
## LOC658148, LOC663390, RpS6, LOC659536, LOC662436, LOC659112, LOC659458, LOC662141, LOC662445, LOC656416
## LOC663209, LOC659478, LOC103313825, LOC654949, LOC657536, LOC663175, LOC660748, LOC660558, LOC662400, LOC664459
## PC_ 4
## Positive: LOC660302, LOC656270, LOC659147, LOC103315068, LOC103313909, LOC657382, LOC663153, LOC658685, LOC658262, LOC663982
## LOC660273, LOC103314884, LOC663732, LOC657072, LOC660294, LOC657119, LOC663769, LOC656093, LOC103313271, LOC663118
## LOC659982, LOC656367, 5MP, LOC100141642, LOC660576, LOC660855, LOC663663, LOC658693, LOC656683, LOC656279
## Negative: LOC660701, LOC664486, LOC103314322, LOC103314929, LOC103313660, LOC100142084, LOC103315220, LOC103313407, LOC658186, LOC657276
## LOC103314638, LOC658527, LOC655239, LOC664260, LOC655765, LOC100142047, LOC658234, Vasa, LOC664294, LOC656643
## LOC659951, LOC659892, LOC655985, LOC658605, LOC659671, Mlpt, LOC657969, LOC661780, LOC103313624, LOC661365
## PC_ 5
## Positive: LOC659652, LOC661858, LOC103315122, LOC656091, LOC103313909, LOC100141658, LOC655258, LOC660038, LOC664088, LOC664209
## LOC100142105, LOC656367, LOC660707, D12des, LOC662029, LOC656797, LOC662785, LOC654998, Star, LOC659982
## Fim, LOC660675, LOC662291, LOC656803, LOC659325, LOC103314979, LOC103313467, LOC103312143, LOC103314937, LOC655073
## Negative: LOC658165, LOC657300, LOC661098, LOC663138, LOC655651, LOC103312486, LOC660590, LOC656266, LOC654879, LOC660215
## LOC659081, LOC658773, LOC103313170, LOC656567, LOC103312319, LOC661774, LOC659129, LOC100142084, LOC660871, LOC659969
## LOC659127, LOC103312208, LOC655160, LOC659933, LOC100142316, LOC663411, LOC655354, LOC656250, LOC658015, LOC657841scA3QC <- RunPCA(scA3, features = VariableFeatures(object = scA3))## PC_ 1
## Positive: LOC660033, LOC103313111, LOC663822, LOC664364, LOC657051, LOC103313785, LOC656320, LOC661583, Y-e3, LOC100142175
## LOC103313498, LOC103314353, LOC658512, LOC664362, LOC657844, LOC663310, LOC103315154, LOC663134, LOC662646, LOC100142307
## LOC660215, LOC658706, LOC656376, LOC662708, LOC660371, LOC661797, LOC103314685, LOC661913, LOC662176, LOC100142582
## Negative: LOC103315107, Tyr1, LOC103315108, LOC658333, LOC103315121, LOC103312806, LOC661796, LOC663982, LOC656198, LOC659357
## LOC661588, LOC661488, LOC100141837, LOC663147, LOC654924, LOC654950, LOC103312946, LOC100142054, LOC659613, LOC103313361
## LOC103315017, LOC664282, LOC663391, LOC100142559, Tyr2, Y-b, LOC103315122, LOC662396, LOC103315256, LOC655533
## PC_ 2
## Positive: LOC103313909, LOC660769, LOC664209, LOC655533, LOC103313573, LOC100142559, LOC103312946, LOC655048, LOC103313568, LOC103315122
## LOC655232, LOC657715, LOC660675, LOC655694, LOC655492, Tyr2, LOC658949, LOC660520, LOC103315121, Tyr1
## LOC661226, LOC664531, LOC100141741, LOC661858, LOC654958, LOC662417, LOC103313361, LOC659941, LOC663153, LOC660098
## Negative: LOC658140, LOC656464, LOC664368, LOC656807, LOC655756, LOC661860, LOC103314322, LOC656189, LOC100141601, LOC657732
## LOC655614, LOC662857, LOC658590, LOC661223, LOC656003, LOC103313715, LOC103314617, LOC658478, LOC662223, LOC664436
## LOC656251, LOC664555, LOC107397762, LOC103312846, LOC664086, LOC100141706, LOC662517, LOC103312878, LOC103312684, LOC660822
## PC_ 3
## Positive: LOC662585, LOC103313568, LOC103313573, LOC660007, LOC656637, LOC661354, LOC656538, LOC655429, LOC659010, LOC660520
## LOC661278, LOC103313361, LOC100142155, LOC654950, LOC103313170, LOC103313579, LOC658955, LOC661737, LOC663345, LOC657922
## LOC662567, LOC662102, LOC659765, LOC662516, LOC664047, LOC103312838, LOC100141837, LOC658493, LOC659671, LOC655096
## Negative: LOC100142578, LOC664531, LOC662265, LOC103313429, LOC655282, LOC663153, Rpl41, LOC658651, LOC664530, LOC662907
## LOC663175, LOC662141, LOC662400, LOC664058, LOC659478, LOC659226, LOC659184, LOC660153, LOC659112, LOC663695
## LOC659338, LOC662954, LOC662773, LOC659357, LOC663663, LOC657582, LOC657360, LOC100141906, LOC662275, LOC658540
## PC_ 4
## Positive: LOC657969, LOC103313573, LOC103313568, LOC660379, LOC659085, LOC661655, LOC660314, LOC100141615, LOC657076, LOC658936
## LOC656626, LOC660098, LOC662980, LOC655273, LOC660417, LOC657867, LOC659901, LOC658799, KEF75-r01, LOC107397609
## LOC100142152, LOC658540, LOC656862, LOC664073, LOC664546, LOC661968, LOC103313053, LOC657107, LOC657042, LOC655011
## Negative: LOC660302, LOC659690, LOC659147, LOC663650, LOC661588, LOC660355, LOC103314884, LOC660754, LOC659539, LOC663982
## LOC103314176, LOC662544, LOC662933, LOC659558, LOC658685, LOC661293, LOC656560, LOC103312990, LOC664530, LOC660767
## LOC655373, LOC656798, LOC655614, LOC662029, LOC658824, LOC662497, LOC663220, LOC103313825, LOC662495, LOC655017
## PC_ 5
## Positive: LOC663832, LOC103313909, LOC100142559, LOC664406, LOC103313825, LOC656906, LOC659499, LOC103313361, LOC661309, LOC659652
## LOC661461, LOC663902, LOC103312757, LOC103315164, LOC661444, LOC660215, LOC662214, Tyr2, LOC655576, LOC661756
## LOC661937, LOC655639, LOC103313730, LOC100142105, LOC103312415, LOC655533, LOC661699, LOC659849, LOC663412, LOC103312112
## Negative: LOC662396, LOC103313854, LOC656170, LOC663823, LOC103312486, LOC661257, LOC103314812, LOC662746, LOC656279, LOC659154
## LOC662645, LOC661480, LOC658754, LOC656645, LOC656298, LOC658362, LOC658234, LOC656816, LOC663391, LOC103313108
## LOC663873, LOC100142441, LOC656373, LOC664464, LOC659840, LOC654958, LOC664494, LOC659836, LOC664544, LOC103314679scA4QC <- RunPCA(scA4, features = VariableFeatures(object = scA4))## PC_ 1
## Positive: LOC103315122, LOC103313909, LOC659613, LOC103315107, LOC661796, LOC655694, LOC655232, LOC663153, Y-b, LOC664266
## LOC107398543, LOC664209, LOC103315121, Fim, LOC103312685, LOC103312143, LOC660769, Tyr2, LOC103313361, LOC103313170
## Mlpt, LOC660431, LOC660520, LOC655533, LOC658685, LOC655115, cec2, LOC103315256, LOC103313299, LOC103313579
## Negative: LOC658140, LOC655756, LOC664368, LOC659879, LOC664086, LOC656807, LOC657724, LOC656189, LOC100141601, LOC662517
## LOC662206, LOC662305, LOC662335, LOC658478, LOC100142212, LOC662223, LOC654968, LOC656464, LOC656003, LOC658380
## LOC655181, LOC103312878, LOC664436, LOC661860, LOC662396, LOC657179, LOC655158, LOC103314322, LOC659991, LOC103312846
## PC_ 2
## Positive: LOC664531, LOC661651, LOC662907, LOC663695, LOC654958, LOC664530, LOC661473, LOC664447, LOC100141906, LOC103313854
## LOC656682, LOC107397983, LOC659302, HEX2, LOC107398271, LOC662738, HEX1A, LOC662858, LOC660843, LOC662333
## LOC658651, LOC662339, LOC103313429, LOC663823, LOC663141, LOC659649, LOC661756, LOC655732, LOC107398513, LOC656243
## Negative: LOC662585, LOC655414, LOC655426, LOC107397973, LOC659010, LOC662516, LOC655429, LOC656229, LOC654933, LOC661812
## LOC103313398, LOC658165, LOC659768, LOC103313325, LOC662527, LOC103312803, LOC661209, LOC656637, LOC656538, LOC664216
## LOC103314708, LOC661588, LOC660494, LOC655855, Syx1A, LOC103313170, LOC663146, LOC657959, LOC661737, LOC660883
## PC_ 3
## Positive: LOC103315121, LOC103315107, LOC103315108, LOC103312803, LOC663695, LOC662785, Tyr1, LOC658540, LOC661588, LOC661796
## LOC660465, LOC664282, LOC661876, LOC663982, LOC103312806, LOC662396, LOC658754, LOC663004, LOC658936, LOC660297
## LOC103312143, LOC656170, LOC655329, LOC654886, LOC659902, LOC100142172, LOC658315, LOC660273, LOC656198, LOC659357
## Negative: LOC657844, LOC660328, LOC658375, LOC103313615, LOC103313878, LOC100141642, LOC661343, LOC103312214, LOC103314546, LOC100141647
## LOC103313299, LOC657371, LOC103312853, LOC664364, LOC664457, LOC107398726, LOC658225, LOC100142578, LOC662654, LOC663354
## LOC658718, LOC656250, LOC659623, LOC661909, LOC662949, LOC103314029, LOC103313926, LOC660371, LOC661438, LOC656594
## PC_ 4
## Positive: LOC662701, LOC656314, LOC659302, LOC658821, Fpps, LOC103314884, LOC661756, LOC656318, LOC657852, LOC658335
## LOC663181, LOC657528, LOC659777, LOC658019, LOC100141896, LOC660583, LOC660215, LOC663885, LOC656087, LOC664362
## LOC662738, LOC662954, LOC656328, LOC655732, LOC661814, LOC658705, HEX1A, LOC663832, LOC656239, LOC663066
## Negative: LOC656464, LOC658140, LOC659325, LOC659549, LOC659764, LOC103315108, LOC663390, LOC662551, LOC658987, LOC657295
## LOC658100, LOC655756, LOC660333, LOC103312853, LOC656270, LOC664266, LOC656560, LOC663871, LOC662708, LOC657699
## Mlpt, LOC657212, LOC664353, LOC656565, LOC664112, LOC103313926, LOC658095, LOC107397762, LOC656153, LOC656057
## PC_ 5
## Positive: LOC659879, LOC656170, LOC658005, LOC100142175, LOC103312486, LOC661480, LOC100141919, LOC657796, LOC100141621, LOC654949
## LOC655475, LOC103315250, LOC660934, LOC657686, LOC658093, LOC657003, LOC658173, LOC103312214, LOC657980, LOC664073
## LOC661820, LOC656397, LOC661172, LOC657662, LOC103312461, LOC103313607, LOC103312356, LOC664149, LOC662095, LOC657060
## Negative: LOC103313909, LOC659675, LOC662335, LOC103315122, LOC663832, LOC656876, Rpa2, LOC662486, LOC103312143, LOC662517
## LOC103314773, LOC664209, LOC658625, LOC657179, LOC658099, LOC659465, LOC660567, Tyr1, LOC662206, LOC655533
## LOC659225, LOC657724, LOC657098, LOC659164, LOC657061, LOC664086, LOC656820, LOC660174, LOC659838, LOC655220scA5QC <- RunPCA(scA5, features = VariableFeatures(object = scA5))## Warning in irlba(A = t(x = object), nv = npcs, ...): You're computing too large
## a percentage of total singular values, use a standard svd instead.## PC_ 1
## Positive: LOC103313498, LOC659649, LOC658171, LOC103313785, LOC663516, LOC103313111, LOC659879, LOC664364, LOC657051, LOC656320
## LOC103313926, LOC663822, LOC655420, LOC103314353, LOC657844, LOC659512, Y-e3, LOC656846, LOC100142553, LOC658512
## LOC661797, LOC103312461, LOC103313962, LOC658387, LOC664026, LOC663823, LOC657085, nAChRa10, LOC660033, LOC659949
## Negative: LOC658333, LOC103315108, LOC103315107, LOC103315121, LOC661588, Tyr1, LOC103312806, LOC660769, LOC656198, LOC661796
## LOC654950, LOC100141837, LOC103315017, LOC662396, LOC103313361, LOC663025, Arp1, Y-b, LOC659357, LOC663147
## LOC656761, LOC661488, LOC664282, LOC103312803, LOC103315122, LOC661876, LOC660394, LOC660431, LOC654924, LOC659613
## PC_ 2
## Positive: LOC660583, LOC103313909, LOC103313530, LOC100141741, LOC656318, LOC103315122, LOC103314134, LOC659613, LOC655796, LOC655533
## LOC100142559, LOC658821, LOC656279, LOC103315098, LOC656443, Tcjheh-r4, LOC663894, LOC656087, Tyr2, LOC655654
## LOC662687, LOC658651, LOC655174, LOC100141952, LOC662390, LOC659485, LOC658824, LOC656271, LOC657969, LOC655157
## Negative: LOC658140, LOC664368, LOC655756, LOC656464, LOC658478, LOC107397762, LOC654949, LOC658987, LOC655378, LOC663871
## LOC659377, LOC661223, LOC661860, LOC662223, LOC103312684, LOC657231, LOC656342, LOC100142484, LOC659879, LOC656003
## LOC100141601, LOC656232, LOC656298, LOC655475, LOC661209, LOC100142152, LOC657872, LOC662857, LOC657695, LOC661551
## PC_ 3
## Positive: LOC103313573, LOC100142175, LOC103313568, LOC661444, LOC103313738, LOC656486, LOC656067, LOC662642, LOC657715, LOC663650
## LOC656906, LOC103313825, Syx1A, LOC656538, LOC659820, Fim, LOC661278, LOC100141776, LOC659250, LOC664222
## LOC658469, LOC661354, LOC103313299, LOC660769, LOC662417, LOC664266, LOC663832, LOC103315156, LOC103313579, LOC657452
## Negative: LOC661860, LOC661203, LOC660771, LOC663141, LOC660325, LOC663391, LOC657695, LOC661223, LOC663178, LOC658140
## LOC656485, LOC657130, LOC659377, LOC656464, LOC656189, LOC656983, LOC660464, LOC662858, LOC658478, LOC656807
## LOC661229, LOC655756, LOC662223, LOC659465, LOC656279, LOC657657, LOC661139, LOC103313707, LOC656612, LOC103313530
## PC_ 4
## Positive: LOC658776, LOC107398173, LOC656807, LOC659405, LOC662266, LOC664286, LOC657584, LOC661396, LOC103313715, LOC664224
## LOC663732, LOC659090, LOC656538, LOC659809, LOC661737, LOC664505, LOC657732, LOC656189, LOC660923, LOC656174
## LOC103314322, LOC661788, LOC663635, LOC660900, LOC656817, LOC103314884, LOC664187, LOC659285, LOC103312310, LOC656325
## Negative: LOC663147, LOC103312727, LOC662708, LOC100188941, LOC656070, LOC654924, LOC663390, LOC100142122, LOC663220, LOC657715
## LOC656298, LOC657025, LOC664222, LOC657065, LOC661355, LOC103313909, LOC656881, LOC655694, LOC663310, LOC664086
## LOC657295, LOC103312990, LOC659860, LOC656930, LOC656425, LOC662716, LOC103313467, LOC103312287, LOC664162, LOC103315218
## PC_ 5
## Positive: LOC658590, LOC659090, LOC660822, LOC103313198, LOC656350, LOC103312928, LOC103314322, LOC100141706, LOC654930, LOC100187736
## LOC664555, LOC103313715, LOC658740, LOC656649, LOC103314638, LOC664436, LOC657096, LOC103313003, LOC100142212, LOC103313548
## LOC664187, LOC662520, LOC103312846, LOC657732, LOC103314889, LOC658310, LOC656807, LOC657649, LOC662857, LOC658192
## Negative: LOC663378, LOC664073, LOC103312803, LOC662214, LOC660294, LOC103314708, LOC659690, LOC657687, LOC658984, LOC657582
## LOC103313670, LOC661480, LOC661834, LOC661444, LOC103312115, LOC659526, LOC658019, LOC660708, LOC661780, LOC658284
## LOC100142007, LOC662402, LOC657040, LOC659177, LOC660746, LOC656089, LOC659770, LOC659445, LOC657826, LOC664225scA6QC <- RunPCA(scA6, features = VariableFeatures(object = scA6))## PC_ 1
## Positive: LOC659879, LOC664368, LOC658140, LOC655756, LOC662517, LOC103314322, LOC664086, LOC103312461, LOC103313715, LOC656807
## LOC662206, LOC103313926, LOC662335, LOC660233, Y-e3, LOC664073, LOC664436, LOC656342, LOC656189, LOC656003
## LOC656464, LOC657724, LOC663265, LOC103312846, LOC664555, LOC100142212, LOC103312684, LOC655158, LOC655054, LOC657179
## Negative: LOC103315121, LOC103313909, LOC103315107, LOC103315122, LOC661796, LOC655533, Tyr1, LOC664209, Tyr2, LOC663220
## LOC103313361, LOC103312946, LOC103315108, LOC100142559, LOC100141896, LOC662785, LOC661968, LOC655232, LOC663147, LOC659613
## LOC661588, LOC664266, LOC103315256, Y-b, LOC660431, LOC663153, LOC107398543, LOC660465, LOC658754, LOC660769
## PC_ 2
## Positive: LOC658333, LOC103315108, LOC103312806, LOC103315107, LOC658140, LOC103315121, LOC656464, LOC664368, LOC656198, LOC661588
## Tyr1, LOC662396, LOC662095, LOC656003, LOC655614, LOC661488, LOC103314322, LOC656232, LOC103312486, LOC655756
## LOC662223, Y-b, LOC658366, LOC107397762, LOC103315017, LOC655741, LOC655232, LOC655019, LOC654924, LOC661876
## Negative: LOC660215, LOC659302, LOC663066, LOC660583, LOC662907, LOC658171, LOC659229, LOC103314842, LOC656005, LOC662670
## LOC658356, LOC656271, LOC660825, LOC661756, LOC655732, LOC103313498, LOC662831, LOC661791, LOC661555, LOC107398513
## Idgf4, LOC662738, LOC656243, LOC103313962, LOC655420, LOC664364, LOC661814, LOC661778, LOC663483, LOC660127
## PC_ 3
## Positive: LOC100142175, LOC660371, LOC657095, LOC664364, Nag2, LOC661583, LOC657844, LOC663822, LOC656250, nAChRa10
## LOC100142553, LOC657051, LOC656320, LOC661913, LOC100142307, LOC664026, LOC656376, LOC662708, LOC659949, Y-e3
## LOC657715, LOC659544, LOC100141776, LOC103312461, LOC659879, LOC663832, LOC656067, LOC657862, LOC664483, LOC658512
## Negative: LOC658333, LOC659690, LOC100141706, LOC656003, LOC661651, LOC103315108, LOC658140, LOC660418, LOC661860, LOC654958
## LOC664086, LOC661223, LOC664393, LOC663695, LOC658590, LOC655614, LOC656485, LOC103314322, LOC659164, LOC664068
## LOC100142212, Arp1, LOC663023, LOC664555, LOC656464, LOC656983, LOC664368, LOC656807, LOC103313429, LOC656964
## PC_ 4
## Positive: LOC655796, lgl, LOC103313785, LOC663642, LOC663706, LOC103314201, LOC103314086, LOC662942, LOC103312884, LOC655834
## LOC661777, LOC103314134, LOC663453, LOC660229, LOC662789, LOC103313718, LOC657157, LOC103313731, LOC103314372, LOC100142105
## LOC103314373, LOC103313186, LOC661766, LOC656039, LOC661323, LOC103313498, LOC103313746, Pmp2-c, Pmp2-b, LOC107397412
## Negative: LOC660692, LOC100141718, LOC100142559, LOC661756, LOC656318, LOC661516, LOC656066, LOC658841, LOC107398075, LOC663832
## LOC662773, LOC659226, LOC662954, LOC661091, LOC655732, LOC662738, LOC656243, LOC100142578, LOC661814, LOC661778
## LOC100142328, HEX2, LOC662005, LOC663181, LOC659318, LOC660127, LOC662570, Cyp9f2, LOC663354, LOC656237
## PC_ 5
## Positive: LOC103312143, LOC103315122, LOC656270, LOC657715, LOC663220, LOC664266, LOC664209, LOC661796, LOC656303, LOC656276
## LOC655694, LOC663147, LOC664531, LOC660379, LOC664143, LOC662792, LOC103314663, LOC663390, Tyr1, LOC658984
## LOC103315256, LOC103313909, LOC662785, LOC656653, LOC659949, LOC664162, LOC103315121, LOC664315, LOC661868, LOC662265
## Negative: LOC103313573, LOC662585, LOC656637, LOC103313568, LOC100142155, LOC661737, LOC103313325, LOC662516, LOC100141790, LOC663146
## LOC659146, LOC654933, LOC657224, LOC659010, LOC103313773, LOC100141546, LOC103313170, LOC661278, LOC655414, LOC656538
## LOC655832, LOC656419, LOC103313333, LOC658899, LOC103313398, LOC663281, LOC100141919, LOC658776, LOC661070, LOC662818scbtt2022QC <- RunPCA(scbtt2022, features = VariableFeatures(object = scbtt2022))## PC_ 1
## Positive: LOC656243, LOC655732, LOC661778, LOC662738, LOC661814, LOC107398513, LOC100142328, LOC655734, LOC662773, LOC103313295
## LOC658401, LOC100142066, LOC107398155, LOC660127, Idgf2, LOC662005, LOC661756, LOC663181, LOC100141856, LOC103313009
## LOC660583, HEX1A, LOC663483, LOC103314536, LOC656849, LOC100141718, LOC656868, LOC103314776, LOC656629, LOC656917
## Negative: LOC658333, LOC103315122, Arp1, LOC100141837, LOC103315108, LOC103315107, LOC103312806, LOC103315121, LOC103313909, Tyr1
## LOC656198, LOC661588, LOC659357, LOC661796, LOC100142054, LOC661488, LOC654950, LOC660431, LOC658366, LOC656464
## LOC103315017, LOC654924, LOC660769, LOC663147, Tyr2, LOC103313361, Y-b, LOC663025, LOC662396, LOC655533
## PC_ 2
## Positive: LOC103315108, LOC100142559, LOC103315107, LOC103315121, LOC658333, Tyr1, LOC661796, LOC103312806, LOC100141837, LOC656198
## LOC103315122, LOC659357, LOC103313909, LOC655533, LOC661588, Tyr2, LOC662785, LOC658936, LOC660465, LOC100142054
## LOC658438, LOC661968, LOC661488, LOC654950, LOC658262, LOC658366, LOC664282, LOC103312803, LOC660297, LOC662396
## Negative: LOC657844, LOC663822, LOC656320, LOC103313111, LOC657051, LOC661583, LOC658512, LOC664364, LOC103313785, LOC659879
## nAChRa10, LOC103314353, LOC103313615, LOC662708, LOC664026, LOC661797, LOC660371, Y-e3, LOC663193, LOC103315154
## LOC664373, LOC103314685, LOC103315128, LOC100142307, LOC661297, LOC100142175, LOC100142553, Tsl, LOC103313498, LOC659949
## PC_ 3
## Positive: LOC103315122, LOC103313909, cec2, LOC103315121, LOC103313579, LOC659613, LOC100141741, LOC103315107, LOC660431, LOC103315218
## Arp1, LOC107398726, LOC656797, LOC103313790, LOC103315256, LOC661796, LOC664266, LOC663153, LOC103313053, LOC100142559
## Tyr1, LOC655533, LOC657969, LOC103313092, LOC661357, LOC664373, Esyt2a, Tyr2, LOC659085, LOC103313615
## Negative: LOC664086, LOC659879, LOC662206, LOC662517, LOC664368, LOC658140, LOC655756, LOC103314322, LOC103312846, LOC664555
## LOC103313715, LOC656464, LOC100141601, LOC103313723, LOC659164, LOC656807, LOC656342, LOC656003, LOC664436, LOC661860
## LOC656189, LOC662335, LOC657724, LOC658478, LOC656876, LOC103312878, LOC100142212, LOC660233, LOC662223, LOC663668
## PC_ 4
## Positive: LOC658936, LOC107398075, LOC100142066, LOC660127, LOC107398513, LOC655732, LOC663181, LOC661778, LOC662738, LOC656243
## LOC658401, LOC662773, LOC655734, LOC654950, Idgf4, LOC661814, LOC103313009, LOC657017, LOC100142559, LOC100141856
## LOC662701, LOC103314776, HEX1A, LOC103313295, LOC100141718, LOC107398155, LOC100141837, LOC660829, Idgf2, LOC663832
## Negative: LOC103314086, LOC659441, LOC659502, LOC661206, LOC658268, LOC660229, LOC663642, LOC664063, LOC660577, LOC655909
## LOC660232, Pmp2-b, LOC103314623, KEF75-t12, LOC658137, LOC657908, LOC655599, LOC662942, LOC103313498, LOC663145
## LOC662274, LOC103312884, LOC660368, LOC656855, LOC664014, LOC661766, LOC657602, LOC662108, LOC657157, LOC103314127
## PC_ 5
## Positive: Tyr1, LOC103315121, LOC661796, LOC103315108, LOC103315107, LOC655533, Tyr2, LOC662785, LOC658438, Nag2
## LOC656170, LOC660465, LOC657715, LOC661968, LOC659357, LOC661588, LOC103313789, LOC661355, LOC656470, LOC100141837
## LOC663625, LOC103314685, LOC103312820, LOC103314353, LOC103313498, LOC663912, LOC655329, LOC655232, LOC661797, LOC103315068
## Negative: LOC103312853, LOC663436, LOC107398726, LOC103314081, LOC107399088, LOC660328, LOC660275, LOC103312214, LOC661343, LOC103313878
## LOC103314386, LOC661516, LOC656797, LOC663266, LOC656594, LOC656565, LOC658100, LOC661369, LOC661909, LOC663941
## LOC657295, LOC661204, LOC655844, LOC103313615, LOC661208, cec2, LOC656782, LOC661651, LOC107398056, LOC660518scpbs2022QC <- RunPCA(scpbs2022, features = VariableFeatures(object = scpbs2022))## PC_ 1
## Positive: LOC662738, HEX2, LOC661756, HEX1A, LOC103315107, LOC660583, LOC656243, LOC103315108, LOC661355, LOC662773
## LOC103315121, LOC103312806, LOC658333, LOC655732, LOC100142559, LOC103315122, LOC659357, Tyr1, LOC661796, LOC661778
## LOC103313909, LOC660692, LOC663147, LOC658936, LOC660127, LOC661814, LOC107398513, LOC663391, KEF75-t22, LOC654958
## Negative: LOC103313568, LOC107398960, LOC659010, LOC661572, LOC100141760, LOC658188, LOC100141790, LOC659879, LOC663146, LOC656419
## LOC658776, LOC100142155, LOC103313573, LOC664565, LOC103313325, LOC103313398, LOC103313773, LOC100141546, LOC656007, LOC656637
## LOC658088, LOC659146, LOC655429, LOC663822, LOC658901, LOC659405, LOC656017, LOC660371, LOC656325, LOC657051
## PC_ 2
## Positive: LOC662738, HEX1A, LOC655732, LOC656243, HEX2, LOC661778, LOC660127, LOC661756, LOC100142328, LOC661814
## HEX1B, LOC662176, LOC658401, LOC107398513, LOC103313111, LOC656005, LOC663181, LOC659271, KEF75-t14, LOC660033
## Cyp9f2, KEF75-t22, LOC662773, LOC662005, LOC663822, LOC661583, LOC656369, KEF75-r02, LOC657447, LOC660583
## Negative: LOC658333, LOC103315108, LOC103315107, LOC103312806, LOC103315121, Tyr1, LOC103315122, Arp1, LOC663147, LOC659357
## LOC662396, LOC661796, Rpl41, LOC656198, LOC103315017, LOC656276, LOC662400, LOC657698, LOC100141670, LOC663390
## LOC103313909, LOC656560, LOC659624, LOC103312946, LOC656464, LOC662785, LOC655614, LOC657360, LOC664282, LOC658914
## PC_ 3
## Positive: Tyr2, LOC663146, LOC103315122, Tyr1, LOC655533, LOC103313568, LOC103313909, LOC103315124, LOC661572, LOC659613
## LOC100141546, LOC100141760, LOC660888, LOC103313573, LOC107397553, LOC656419, LOC100142559, LOC103315256, LOC661796, LOC103313333
## LOC654933, LOC659450, LOC658188, LOC103312685, LOC664325, LOC654969, LOC663002, LOC103313053, LOC655096, LOC664565
## Negative: LOC664368, LOC658140, LOC655756, LOC664555, LOC659879, LOC103313785, LOC660470, LOC664390, LOC662206, LOC656807
## LOC663823, LOC103312878, LOC658171, LOC660754, LOC662520, LOC100142553, LOC100142307, LOC657051, LOC657732, LOC657513
## LOC657085, LOC664014, LOC656342, LOC656251, LOC656170, LOC658478, nAChRa10, LOC103313926, LOC660233, LOC657290
## PC_ 4
## Positive: LOC664531, LOC655492, LOC664373, LOC103314663, LOC100142175, LOC664266, LOC664364, LOC663832, LOC103313615, LOC658141
## LOC100141741, LOC657715, LOC103315218, LOC660215, LOC655420, LOC660675, LOC660371, LOC659849, LOC657844, LOC659941
## LOC662708, LOC656369, LOC103315122, LOC660576, LOC659226, LOC663438, LOC660379, LOC663924, LOC661583, LOC661297
## Negative: LOC103314322, LOC664436, LOC100142212, LOC656807, LOC657732, LOC658108, LOC656003, LOC660418, LOC662206, LOC658590
## LOC659090, LOC656251, LOC100142549, LOC100141706, LOC103312859, LOC103313715, LOC662223, LOC658720, LOC103312878, LOC661369
## LOC103312684, LOC100142122, LOC658310, LOC662922, LOC656464, LOC664368, LOC103312310, LOC656476, LOC664555, LOC656102
## PC_ 5
## Positive: LOC662396, LOC664393, LOC661968, LOC100141621, LOC656549, LOC103314134, LOC662095, LOC655796, LOC655736, LOC103315068
## LOC655614, LOC655809, LOC660190, LOC103313108, LOC659747, LOC103312806, LOC663982, LOC658986, LOC658140, LOC103312486
## LOC658333, LOC659922, LOC655520, LOC663118, LOC656193, LOC661089, LOC663147, LOC663437, LOC662754, LOC656170
## Negative: LOC655533, LOC103313032, Tyr2, LOC655157, LOC660215, LOC103314693, LOC100142175, LOC658708, LOC660417, LOC659849
## LOC100142152, LOC655639, LOC663557, LOC657076, LOC103314012, LOC657672, LOC661229, LOC655337, LOC659085, LOC103313909
## LOC658296, LOC661223, LOC662765, LOC662214, LOC662315, LOC659224, LOC658002, LOC655542, LOC103312495, LOC100142456scnaive2022QC <- RunPCA(scnaive2022, features = VariableFeatures(object = scnaive2022))## PC_ 1
## Positive: LOC103313909, Tyr1, LOC103315107, LOC103315122, LOC103315108, LOC100142559, LOC103315121, Tyr2, LOC655533, LOC661588
## LOC103312806, LOC661796, LOC662785, LOC661355, LOC656198, LOC103313789, LOC658936, LOC658262, LOC661488, LOC654950
## LOC100142054, LOC660465, LOC658333, LOC100141837, LOC659357, LOC660769, LOC103312803, LOC103312946, LOC661876, LOC100141657
## Negative: LOC659879, LOC103313785, LOC664364, LOC660033, LOC663822, LOC656320, LOC103313111, LOC661583, LOC658512, nAChRa10
## LOC657051, Y-e3, LOC657844, LOC100142553, LOC660371, LOC100142307, LOC100142175, LOC103313498, LOC103314353, LOC659949
## LOC664026, LOC103313615, LOC662708, LOC661797, LOC658171, LOC660233, LOC659544, LOC103314685, LOC658987, LOC103314029
## PC_ 2
## Positive: LOC658140, LOC656464, LOC655756, LOC664368, LOC662396, LOC664555, LOC103314322, LOC103313715, LOC655614, LOC656807
## LOC658590, LOC103312878, LOC662206, LOC656232, LOC656189, LOC664436, LOC658333, LOC100141706, LOC103312846, LOC664086
## LOC658108, LOC663265, LOC662517, LOC103314617, LOC107397762, LOC661860, LOC656381, LOC103313548, LOC659879, LOC656003
## Negative: LOC103315122, cec2, LOC103315218, LOC103313579, LOC103313615, LOC100141741, LOC664373, LOC103314806, LOC103314663, LOC660583
## LOC658225, LOC655420, LOC663083, LOC656797, LOC103312853, LOC660328, LOC664266, LOC107398726, pain, LOC655260
## LOC661357, LOC656565, LOC660675, LOC664457, LOC664364, LOC658100, LOC658141, LOC103313299, LOC655492, LOC103313878
## PC_ 3
## Positive: LOC103312806, LOC662396, LOC658333, LOC661588, LOC662585, LOC658140, LOC663025, LOC103315107, LOC654950, LOC661488
## LOC656198, LOC658237, LOC103313579, LOC664528, LOC655614, LOC661179, LOC658262, LOC663146, LOC103315108, LOC103313361
## LOC103312486, Fim, LOC660769, LOC656637, LOC658646, LOC662470, LOC656464, LOC100141670, LOC655225, LOC660431
## Negative: LOC660215, LOC655157, LOC661756, LOC100142559, Tyr2, LOC655533, LOC658708, LOC662738, LOC662907, LOC655732
## LOC661778, LOC656243, HEX1A, LOC657672, HEX2, LOC661814, LOC662773, LOC103313032, HEX1B, LOC660127
## LOC103313295, LOC658002, LOC659302, LOC658401, LOC660417, LOC658171, LOC100142328, LOC662005, LOC107398513, LOC659318
## PC_ 4
## Positive: LOC103312806, LOC662396, LOC662738, LOC656243, LOC655732, LOC661778, LOC662773, HEX1A, LOC661814, HEX1B
## LOC660127, LOC658401, HEX2, LOC103313295, LOC658333, LOC662005, LOC107398075, LOC659318, LOC103315107, LOC662701
## LOC107398513, Idgf4, Est-6, LOC661588, LOC658820, LOC100141856, LOC103314884, Cyp9f2, LOC659136, LOC100142328
## Negative: Tyr2, LOC655533, LOC655157, LOC100142559, LOC660215, LOC103313032, LOC658708, LOC660417, LOC657672, LOC103314386
## LOC656270, LOC103313909, LOC107399088, LOC657844, LOC663557, LOC657592, LOC103313053, LOC660098, LOC660576, LOC100142175
## LOC103314693, LOC659085, Tyr1, LOC659272, LOC658375, LOC103312482, LOC107398726, LOC659540, LOC662842, LOC659849
## PC_ 5
## Positive: LOC107398726, LOC107399088, LOC103312853, LOC661516, LOC661651, cec2, LOC103314386, LOC660583, LOC656565, LOC103314081
## LOC660328, LOC655844, LOC663372, LOC661343, LOC663131, LOC656782, LOC663436, LOC656797, LOC659485, LOC660307
## LOC659226, LOC658237, LOC661909, LOC103313579, LOC661369, pain, LOC664457, LOC103313878, LOC661208, LOC656594
## Negative: LOC103315107, LOC656170, LOC662708, LOC103315121, LOC103313909, Tyr1, LOC103315108, LOC660379, LOC657715, LOC100142175
## LOC103312806, LOC103313467, LOC103313825, LOC661588, Nag2, LOC103312143, LOC663832, LOC100142307, LOC657051, LOC103315154
## LOC103314353, LOC663822, nAChRa10, LOC663962, LOC661355, Mae, Y-e3, LOC661797, LOC103314685, LOC662933scA1QC <- RunUMAP(scA1QC, dims = 1:10)## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## 09:40:35 UMAP embedding parameters a = 0.9922 b = 1.112## 09:40:35 Read 115 rows and found 10 numeric columns## 09:40:35 Using Annoy for neighbor search, n_neighbors = 30## 09:40:35 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 09:40:35 Writing NN index file to temp file C:\Users\thoma\AppData\Local\Temp\RtmpQFINd1\file454c27f44e1f
## 09:40:35 Searching Annoy index using 1 thread, search_k = 3000
## 09:40:35 Annoy recall = 100%
## 09:40:35 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 09:40:37 Initializing from normalized Laplacian + noise (using RSpectra)
## 09:40:37 Commencing optimization for 500 epochs, with 3710 positive edges
## 09:40:37 Optimization finishedDimPlot(scA1QC, reduction = "umap")
scA2QC <- RunUMAP(scA2QC, dims = 1:10)## 09:40:38 UMAP embedding parameters a = 0.9922 b = 1.112## 09:40:38 Read 553 rows and found 10 numeric columns## 09:40:38 Using Annoy for neighbor search, n_neighbors = 30## 09:40:38 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 09:40:38 Writing NN index file to temp file C:\Users\thoma\AppData\Local\Temp\RtmpQFINd1\file454c6b292f01
## 09:40:38 Searching Annoy index using 1 thread, search_k = 3000
## 09:40:38 Annoy recall = 100%
## 09:40:39 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 09:40:40 Initializing from normalized Laplacian + noise (using RSpectra)
## 09:40:40 Commencing optimization for 500 epochs, with 26706 positive edges
## 09:40:42 Optimization finishedDimPlot(scA2QC, reduction = "umap")
scA3QC <- RunUMAP(scA3QC, dims = 1:10)## 09:40:42 UMAP embedding parameters a = 0.9922 b = 1.112## 09:40:42 Read 111 rows and found 10 numeric columns## 09:40:42 Using Annoy for neighbor search, n_neighbors = 30## 09:40:42 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 09:40:42 Writing NN index file to temp file C:\Users\thoma\AppData\Local\Temp\RtmpQFINd1\file454c7233298f
## 09:40:42 Searching Annoy index using 1 thread, search_k = 3000
## 09:40:42 Annoy recall = 100%
## 09:40:43 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 09:40:44 Initializing from normalized Laplacian + noise (using RSpectra)
## 09:40:44 Commencing optimization for 500 epochs, with 3592 positive edges
## 09:40:44 Optimization finishedDimPlot(scA3QC, reduction = "umap")
scA4QC <- RunUMAP(scA4QC, dims = 1:10)## 09:40:45 UMAP embedding parameters a = 0.9922 b = 1.112## 09:40:45 Read 180 rows and found 10 numeric columns## 09:40:45 Using Annoy for neighbor search, n_neighbors = 30## 09:40:45 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 09:40:45 Writing NN index file to temp file C:\Users\thoma\AppData\Local\Temp\RtmpQFINd1\file454c38c878bd
## 09:40:45 Searching Annoy index using 1 thread, search_k = 3000
## 09:40:45 Annoy recall = 100%
## 09:40:45 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 09:40:46 Initializing from normalized Laplacian + noise (using RSpectra)
## 09:40:46 Commencing optimization for 500 epochs, with 6232 positive edges
## 09:40:47 Optimization finishedDimPlot(scA4QC, reduction = "umap")
scA5QC <- RunUMAP(scA5QC, dims = 1:10)## 09:40:48 UMAP embedding parameters a = 0.9922 b = 1.112## 09:40:48 Read 83 rows and found 10 numeric columns## 09:40:48 Using Annoy for neighbor search, n_neighbors = 30## 09:40:48 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 09:40:48 Writing NN index file to temp file C:\Users\thoma\AppData\Local\Temp\RtmpQFINd1\file454c742ffbf
## 09:40:48 Searching Annoy index using 1 thread, search_k = 3000
## 09:40:48 Annoy recall = 100%
## 09:40:48 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 09:40:49 Initializing from normalized Laplacian + noise (using RSpectra)
## 09:40:49 Commencing optimization for 500 epochs, with 2632 positive edges
## 09:40:50 Optimization finishedDimPlot(scA5QC, reduction = "umap")
scA6QC <- RunUMAP(scA6QC, dims = 1:10)## 09:40:50 UMAP embedding parameters a = 0.9922 b = 1.112## 09:40:50 Read 346 rows and found 10 numeric columns## 09:40:50 Using Annoy for neighbor search, n_neighbors = 30## 09:40:50 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 09:40:51 Writing NN index file to temp file C:\Users\thoma\AppData\Local\Temp\RtmpQFINd1\file454c75ec21c
## 09:40:51 Searching Annoy index using 1 thread, search_k = 3000
## 09:40:51 Annoy recall = 100%
## 09:40:51 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 09:40:52 Initializing from normalized Laplacian + noise (using RSpectra)
## 09:40:52 Commencing optimization for 500 epochs, with 12594 positive edges
## 09:40:54 Optimization finishedDimPlot(scA6QC, reduction = "umap")
scbtt2022QC <- RunUMAP(scbtt2022QC, dims = 1:10)## 09:40:54 UMAP embedding parameters a = 0.9922 b = 1.112## 09:40:54 Read 394 rows and found 10 numeric columns## 09:40:54 Using Annoy for neighbor search, n_neighbors = 30## 09:40:54 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 09:40:54 Writing NN index file to temp file C:\Users\thoma\AppData\Local\Temp\RtmpQFINd1\file454c219f3204
## 09:40:54 Searching Annoy index using 1 thread, search_k = 3000
## 09:40:54 Annoy recall = 100%
## 09:40:55 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 09:40:56 Initializing from normalized Laplacian + noise (using RSpectra)
## 09:40:56 Commencing optimization for 500 epochs, with 12760 positive edges
## 09:40:57 Optimization finishedDimPlot(scbtt2022QC, reduction = "umap")
scpbs2022QC <- RunUMAP(scpbs2022QC, dims = 1:10)## 09:40:57 UMAP embedding parameters a = 0.9922 b = 1.112## 09:40:57 Read 292 rows and found 10 numeric columns## 09:40:57 Using Annoy for neighbor search, n_neighbors = 30## 09:40:57 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 09:40:57 Writing NN index file to temp file C:\Users\thoma\AppData\Local\Temp\RtmpQFINd1\file454c7a2a38e3
## 09:40:57 Searching Annoy index using 1 thread, search_k = 3000
## 09:40:57 Annoy recall = 100%
## 09:40:58 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 09:40:59 Initializing from normalized Laplacian + noise (using RSpectra)
## 09:40:59 Commencing optimization for 500 epochs, with 10502 positive edges
## 09:41:00 Optimization finishedDimPlot(scpbs2022QC, reduction = "umap")
scnaive2022QC <- RunUMAP(scnaive2022QC, dims = 1:10)## 09:41:01 UMAP embedding parameters a = 0.9922 b = 1.112## 09:41:01 Read 723 rows and found 10 numeric columns## 09:41:01 Using Annoy for neighbor search, n_neighbors = 30## 09:41:01 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 09:41:01 Writing NN index file to temp file C:\Users\thoma\AppData\Local\Temp\RtmpQFINd1\file454ca123e36
## 09:41:01 Searching Annoy index using 1 thread, search_k = 3000
## 09:41:01 Annoy recall = 100%
## 09:41:01 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 09:41:03 Initializing from normalized Laplacian + noise (using RSpectra)
## 09:41:03 Commencing optimization for 500 epochs, with 25508 positive edges
## 09:41:05 Optimization finishedDimPlot(scnaive2022QC, reduction = "umap")
FeaturePlot(scA1QC, features = c("nCount_RNA"))
FeaturePlot(scA1QC, features = c("percent.mtA1"))
FeaturePlot(scA2QC, features = c("percent.mtA2"))
FeaturePlot(scA3QC, features = c("percent.mtA3"))
FeaturePlot(scA4QC, features = c("percent.mtA4"))
FeaturePlot(scA5QC, features = c("percent.mtA5"))
FeaturePlot(scA6QC, features = c("percent.mtA6"))
FeaturePlot(scbtt2022QC, features = c("percent.mtbtt2022"))
FeaturePlot(scpbs2022QC, features = c("percent.mtpbs2022"))
FeaturePlot(scnaive2022QC, features = c("percent.mtnaive2022"))
FeaturePlot(scA1QC, features = c("nCount_RNA"))
FeaturePlot(scA2QC, features = c("nCount_RNA"))
FeaturePlot(scA3QC, features = c("nCount_RNA"))
FeaturePlot(scA4QC, features = c("nCount_RNA"))
FeaturePlot(scA5QC, features = c("nCount_RNA"))
FeaturePlot(scA6QC, features = c("nCount_RNA"))
FeaturePlot(scbtt2022QC, features = c("nCount_RNA"))
FeaturePlot(scpbs2022QC, features = c("nCount_RNA"))
FeaturePlot(scnaive2022QC, features = c("nCount_RNA"))
Filter out Cells with >15% mitochondrial gene expression
filtered.A1 <- subset(A1, subset = percent.mtA1 < 15)
filtered.A2 <- subset(A2, subset = percent.mtA2 < 15)
filtered.A3 <- subset(A3, subset = percent.mtA3 < 15)
filtered.A4 <- subset(A4, subset = percent.mtA4 < 15)
filtered.A5 <- subset(A5, subset = percent.mtA5 < 15)
filtered.A6 <- subset(A6, subset = percent.mtA6 < 15)
filtered.btt2022 <- subset(btt2022, subset = percent.mtbtt2022 < 15)
filtered.pbs2022 <- subset(pbs2022, subset = percent.mtpbs2022 < 15)
filtered.naive2022 <- subset(naive2022, subset = percent.mtnaive2022 < 15)Normalize with sctransform; no variable to regress out
sc.filtered.A1 <- SCTransform(filtered.A1, verbose = FALSE)
sc.filtered.A2 <- SCTransform(filtered.A2, verbose = FALSE)
sc.filtered.A3 <- SCTransform(filtered.A3, verbose = FALSE)
sc.filtered.A4 <- SCTransform(filtered.A4, verbose = FALSE)
sc.filtered.A5 <- SCTransform(filtered.A5, verbose = FALSE)
sc.filtered.A6 <- SCTransform(filtered.A6, verbose = FALSE)
sc.filtered.btt2022 <- SCTransform(filtered.btt2022, verbose = FALSE)
sc.filtered.naive2022 <- SCTransform(filtered.naive2022, verbose = FALSE)
sc.filtered.pbs2022 <- SCTransform(filtered.pbs2022, verbose = FALSE)Before Integration, visualize Sample clustering
test.sc.filtered.A1 <- RunPCA(sc.filtered.A1, verbose = FALSE)
test.sc.filtered.A1 <- RunUMAP(test.sc.filtered.A1, dims = 1:30, verbose = FALSE)
test.sc.filtered.A1 <- FindNeighbors(test.sc.filtered.A1, dims = 1:30, verbose = FALSE)
test.sc.filtered.A1 <- FindClusters(test.sc.filtered.A1, verbose = FALSE)
DimPlot(test.sc.filtered.A1, label = TRUE)
test.sc.filtered.A2 <- RunPCA(sc.filtered.A2, verbose = FALSE, npcs = 27)## Warning in irlba(A = t(x = object), nv = npcs, ...): You're computing too large
## a percentage of total singular values, use a standard svd instead.test.sc.filtered.A2 <- RunUMAP(test.sc.filtered.A2, dims = 1:27, verbose = FALSE, n.neighbors = 27)
test.sc.filtered.A2 <- FindNeighbors(test.sc.filtered.A2, dims = 1:27, verbose = FALSE)
test.sc.filtered.A2 <- FindClusters(test.sc.filtered.A2, verbose = FALSE)
DimPlot(test.sc.filtered.A2, label = TRUE)
test.sc.filtered.A3 <- RunPCA(sc.filtered.A3, verbose = FALSE)## Warning in irlba(A = t(x = object), nv = npcs, ...): You're computing too large
## a percentage of total singular values, use a standard svd instead.test.sc.filtered.A3 <- RunUMAP(test.sc.filtered.A3, dims = 1:30, verbose = FALSE)
test.sc.filtered.A3 <- FindNeighbors(test.sc.filtered.A3, dims = 1:30, verbose = FALSE)
test.sc.filtered.A3 <- FindClusters(test.sc.filtered.A3, verbose = FALSE)
DimPlot(test.sc.filtered.A3, label = TRUE)
test.sc.filtered.A3 <- RunPCA(sc.filtered.A3, verbose = FALSE)## Warning in irlba(A = t(x = object), nv = npcs, ...): You're computing too large
## a percentage of total singular values, use a standard svd instead.test.sc.filtered.A3 <- RunUMAP(test.sc.filtered.A3, dims = 1:30, verbose = FALSE)
test.sc.filtered.A3 <- FindNeighbors(test.sc.filtered.A3, dims = 1:30, verbose = FALSE)
test.sc.filtered.A3 <- FindClusters(test.sc.filtered.A3, verbose = FALSE)
DimPlot(test.sc.filtered.A3, label = TRUE)
test.sc.filtered.A4 <- RunPCA(sc.filtered.A4, verbose = FALSE)
test.sc.filtered.A4 <- RunUMAP(test.sc.filtered.A4, dims = 1:30, verbose = FALSE)
test.sc.filtered.A4 <- FindNeighbors(test.sc.filtered.A4, dims = 1:30, verbose = FALSE)
test.sc.filtered.A4 <- FindClusters(test.sc.filtered.A4, verbose = FALSE)
DimPlot(test.sc.filtered.A4, label = TRUE)
test.sc.filtered.A5 <- RunPCA(sc.filtered.A5, verbose = FALSE)## Warning in irlba(A = t(x = object), nv = npcs, ...): You're computing too large
## a percentage of total singular values, use a standard svd instead.test.sc.filtered.A5 <- RunUMAP(test.sc.filtered.A5, dims = 1:30, verbose = FALSE)
test.sc.filtered.A5 <- FindNeighbors(test.sc.filtered.A5, dims = 1:30, verbose = FALSE)
test.sc.filtered.A5 <- FindClusters(test.sc.filtered.A5, verbose = FALSE)
DimPlot(test.sc.filtered.A5, label = TRUE)
test.sc.filtered.A6 <- RunPCA(sc.filtered.A6, verbose = FALSE)
test.sc.filtered.A6 <- RunUMAP(test.sc.filtered.A6, dims = 1:30, verbose = FALSE)
test.sc.filtered.A6 <- FindNeighbors(test.sc.filtered.A6, dims = 1:30, verbose = FALSE)
test.sc.filtered.A6 <- FindClusters(test.sc.filtered.A6, verbose = FALSE)
DimPlot(test.sc.filtered.A6, label = TRUE)
test.sc.filtered.btt2022 <- RunPCA(sc.filtered.btt2022, verbose = FALSE)
test.sc.filtered.btt2022 <- RunUMAP(test.sc.filtered.btt2022, dims = 1:30, verbose = FALSE)
test.sc.filtered.btt2022 <- FindNeighbors(test.sc.filtered.btt2022, dims = 1:30, verbose = FALSE)
test.sc.filtered.btt2022 <- FindClusters(test.sc.filtered.btt2022, verbose = FALSE)
DimPlot(test.sc.filtered.btt2022, label = TRUE)
test.sc.filtered.pbs2022 <- RunPCA(sc.filtered.pbs2022, verbose = FALSE)## Warning in irlba(A = t(x = object), nv = npcs, ...): You're computing too large
## a percentage of total singular values, use a standard svd instead.test.sc.filtered.pbs2022 <- RunUMAP(test.sc.filtered.pbs2022, dims = 1:30, verbose = FALSE)
test.sc.filtered.pbs2022 <- FindNeighbors(test.sc.filtered.pbs2022, dims = 1:30, verbose = FALSE)
test.sc.filtered.pbs2022 <- FindClusters(test.sc.filtered.pbs2022, verbose = FALSE)
DimPlot(test.sc.filtered.pbs2022, label = TRUE)
test.sc.filtered.naive2022 <- RunPCA(sc.filtered.naive2022, verbose = FALSE)
test.sc.filtered.naive2022 <- RunUMAP(test.sc.filtered.naive2022, dims = 1:30, verbose = FALSE)
test.sc.filtered.naive2022 <- FindNeighbors(test.sc.filtered.naive2022, dims = 1:30, verbose = FALSE)
test.sc.filtered.naive2022 <- FindClusters(test.sc.filtered.naive2022, verbose = FALSE)
DimPlot(test.sc.filtered.naive2022, label = TRUE)
QC
FeaturePlot(test.sc.filtered.A1, features = c("percent.mtA1"))
FeaturePlot(test.sc.filtered.A2, features = c("percent.mtA2"))
FeaturePlot(test.sc.filtered.A3, features = c("percent.mtA3"))
FeaturePlot(test.sc.filtered.A4, features = c("percent.mtA4"))
FeaturePlot(test.sc.filtered.A5, features = c("percent.mtA5"))
FeaturePlot(test.sc.filtered.A6, features = c("percent.mtA6"))
FeaturePlot(test.sc.filtered.btt2022, features = c("percent.mtbtt2022"))
FeaturePlot(test.sc.filtered.pbs2022, features = c("percent.mtpbs2022"))
FeaturePlot(test.sc.filtered.naive2022, features = c("percent.mtnaive2022"))
FeaturePlot(test.sc.filtered.A1, features = c("nCount_RNA"))
FeaturePlot(test.sc.filtered.A2, features = c("nCount_RNA"))
FeaturePlot(test.sc.filtered.A3, features = c("nCount_RNA"))
FeaturePlot(test.sc.filtered.A4, features = c("nCount_RNA"))
FeaturePlot(test.sc.filtered.A5, features = c("nCount_RNA"))
FeaturePlot(test.sc.filtered.A6, features = c("nCount_RNA"))
FeaturePlot(test.sc.filtered.btt2022, features = c("nCount_RNA"))
FeaturePlot(test.sc.filtered.pbs2022, features = c("nCount_RNA"))
FeaturePlot(test.sc.filtered.naive2022, features = c("nCount_RNA"))
Select Integration Features with 3000 features (without A2)
runs.list <- list(sc.filtered.A1 = sc.filtered.A1, sc.filtered.A3 = sc.filtered.A3, sc.filtered.A4 = sc.filtered.A4, sc.filtered.A5 = sc.filtered.A5, sc.filtered.A6 = sc.filtered.A6, sc.filtered.btt2022 = sc.filtered.btt2022, sc.filtered.pbs2022 = sc.filtered.pbs2022, sc.filtered.naive2022 = sc.filtered.naive2022)
features3000 <- SelectIntegrationFeatures(object.list = runs.list, nfeatures = 3000)
runs.list <- PrepSCTIntegration(object.list = runs.list, anchor.features = features3000)
runs.list <- lapply(X = runs.list, FUN = RunPCA, features = features3000)## PC_ 1
## Positive: LOC658140, LOC655756, LOC656464, LOC661860, LOC654949, LOC664368, LOC103314322, LOC661223, LOC100141601, LOC664315
## LOC664436, LOC659377, LOC663871, LOC655614, LOC664555, LOC107397762, LOC103312846, LOC655475, LOC658478, LOC658590
## LOC655190, LOC658195, LOC100142212, LOC657747, LOC659866, LOC658058, Parp, LOC103312310, LOC655639, LOC660418
## Negative: LOC103315122, LOC103313909, LOC103315107, LOC655533, LOC103313579, Tyr2, LOC103315121, LOC661355, LOC660769, LOC663147
## LOC658955, LOC103312143, LOC654950, LOC661796, Tyr1, LOC658936, LOC103313573, LOC657969, LOC658754, LOC660520
## LOC659613, LOC655232, LOC103314806, LOC654924, LOC103313568, LOC100142559, LOC664266, LOC658685, Esyt2a, LOC660394
## PC_ 2
## Positive: LOC103313573, LOC103313568, LOC661678, LOC661343, LOC661812, LOC661354, LOC659010, LOC657224, LOC655019, LOC664339
## LOC107398426, LOC107399012, LOC661798, LOC656637, LOC662101, LOC661278, LOC658929, LOC107398245, LOC103313325, LOC663820
## LOC658088, LOC103314806, LOC663003, LOC103312841, LOC656538, LOC103313913, Nag2, LOC103313679, LOC659958, LOC659396
## Negative: LOC656560, LOC663390, LOC663391, Tyr1, LOC664315, LOC662445, LOC663153, Arp1, LOC655649, LOC662141
## LOC664393, LOC103313909, LOC656270, LOC662754, LOC658058, LOC663962, LOC657695, LOC660379, LOC662436, LOC664143
## LOC661296, LOC661185, LOC103313660, LOC662319, LOC659536, LOC659478, LOC658540, LOC663023, LOC657360, LOC656438
## PC_ 3
## Positive: Syx1A, LOC660379, LOC655640, LOC657715, LOC658375, LOC663962, LOC660675, LOC659820, LOC658141, LOC658559
## LOC660441, LOC660371, LOC658603, LOC103313111, LOC656164, LOC660098, LOC662954, LOC659982, LOC664222, LOC663822
## LOC656833, LOC656797, LOC662427, LOC656499, LOC664359, LOC663628, LOC659207, LOC662907, LOC659951, LOC656458
## Negative: LOC658333, LOC662396, LOC656198, LOC103315108, LOC661588, LOC663147, LOC103312806, LOC100141837, LOC103312486, LOC103315017
## LOC661488, LOC656464, LOC658914, LOC655174, LOC100141670, LOC658864, LOC664339, LOC659558, LOC664282, LOC663567
## LOC656087, LOC103312946, LOC655225, LOC657364, LOC663025, LOC655649, LOC661876, LOC661835, LOC658754, LOC100141621
## PC_ 4
## Positive: LOC655492, LOC100142175, LOC103314806, LOC658984, LOC661343, LOC663912, LOC655420, LOC103315156, LOC100142307, LOC656070
## LOC655174, LOC660215, LOC657364, LOC103313467, LOC656846, LOC658006, LOC664531, LOC103315250, LOC657036, LOC657686
## LOC661259, LOC656170, LOC661678, LOC660233, LOC103313607, LOC662421, LOC655127, LOC659949, LOC659765, LOC103313785
## Negative: LOC661968, Tyr1, LOC656270, LOC656649, KEF75-p09, LOC661354, LOC662595, LOC660431, LOC662980, LOC658603
## LOC661396, LOC663391, LOC654950, LOC103312419, LOC659010, LOC661353, LOC100142538, LOC656309, LOC103313573, LOC663695
## LOC659637, LOC658276, LOC100141654, LOC662567, LOC103313909, LOC663025, LOC103313568, KEF75-p13, Spz3, LOC103313660
## PC_ 5
## Positive: LOC662396, LOC103312486, LOC659147, LOC660355, LOC103314176, LOC657686, LOC658333, LOC103312345, LOC656170, LOC663663
## LOC660193, LOC658140, LOC659879, LOC656521, LOC661552, LOC656298, LOC660020, LOC660302, LOC103313825, LOC661251
## LOC100142538, LOC654888, LOC656993, LOC662291, LOC107397983, LOC663391, LOC657828, LOC664530, LOC664393, LOC664406
## Negative: LOC655533, LOC656232, Tyr2, LOC103313909, LOC103312143, LOC103315122, LOC100142559, cec2, LOC658991, LOC659982
## LOC659085, LOC103315220, LOC654930, LOC664209, LOC103314285, LOC103313913, LOC655019, LOC658253, LOC103313660, LOC103313582
## LOC663482, LOC661355, LOC100142122, LOC103313053, LOC660465, LOC660309, LOC663106, LOC100142054, LOC657239, LOC103313715## Warning in irlba(A = t(x = object), nv = npcs, ...): You're computing too large
## a percentage of total singular values, use a standard svd instead.## PC_ 1
## Positive: LOC660033, LOC103313785, LOC664364, LOC103313111, LOC100142175, LOC663822, Y-e3, LOC103313498, LOC656320, LOC100142307
## LOC661583, LOC663310, LOC657844, LOC662646, LOC664362, LOC658512, LOC656376, LOC660215, LOC659949, LOC661913
## LOC660233, nAChRa10, LOC660371, LOC662708, LOC662176, LOC664026, LOC657085, LOC659649, LOC654860, LOC100142553
## Negative: LOC103315107, Tyr1, LOC103315121, LOC103315108, LOC663147, LOC661796, LOC658333, LOC663982, LOC103312806, LOC661588
## LOC654924, LOC659357, LOC654950, LOC661488, LOC100141837, LOC656198, LOC103313361, LOC103312946, LOC100142054, LOC659613
## LOC664282, Tyr2, LOC103315017, LOC663391, LOC100142559, Y-b, Arp1, LOC655533, LOC663025, LOC655232
## PC_ 2
## Positive: LOC103313573, LOC103313568, LOC655492, LOC655694, LOC655048, LOC657715, LOC660675, LOC660769, LOC654958, LOC103313909
## LOC664209, LOC661226, LOC658949, LOC664531, LOC662417, LOC100141741, LOC655533, LOC661858, LOC663845, LOC659941
## LOC660520, LOC655232, LOC655420, LOC660591, LOC663390, LOC662907, Fim, LOC664287, LOC103312685, LOC660098
## Negative: LOC656464, LOC658140, LOC661860, LOC664368, LOC656807, LOC655756, LOC656189, LOC100141601, LOC103314322, LOC655614
## LOC107397762, LOC661223, LOC657732, LOC662223, LOC656003, LOC658478, LOC662857, LOC103314617, LOC658590, LOC656232
## LOC103313715, LOC103312846, LOC664555, LOC664436, LOC658240, LOC656251, LOC661326, LOC660822, LOC100141706, LOC664143
## PC_ 3
## Positive: LOC664531, LOC100142578, LOC664315, LOC659338, LOC656276, LOC662265, LOC659478, LOC655282, LOC656438, LOC659112
## LOC663153, LOC662400, Rpl41, LOC103313429, LOC664058, LOC659226, LOC660379, LOC662141, LOC658540, LOC658559
## LOC662907, LOC657360, LOC659184, LOC657969, LOC103313053, LOC662954, LOC658195, RpS6, LOC662040, LOC659536
## Negative: LOC661354, LOC662567, LOC659765, LOC656637, LOC660007, LOC655429, LOC662585, LOC661488, LOC662396, LOC103313568
## LOC663025, LOC660520, LOC657922, LOC103312838, LOC656538, LOC661737, LOC103313170, LOC661678, LOC658333, LOC659671
## LOC655414, LOC661249, LOC103313108, LOC103312684, LOC658955, LOC662102, LOC663833, LOC655930, LOC661588, LOC658864
## PC_ 4
## Positive: LOC660302, LOC659690, LOC659147, LOC660355, LOC103314176, LOC664530, LOC663650, LOC656560, LOC660754, LOC662544
## LOC662092, LOC661588, LOC659558, LOC103314884, LOC663663, LOC663982, LOC658824, LOC659539, LOC663220, LOC660344
## LOC662933, LOC660767, LOC657243, LOC655614, LOC658071, LOC658375, LOC662497, chic, LOC103312990, LOC658685
## Negative: LOC103313568, LOC657969, LOC103313573, KEF75-r02, LOC660314, LOC661655, LOC660379, LOC657076, LOC659085, LOC660098
## KEF75-r01, LOC656626, KEF75-p12, LOC658799, LOC662980, LOC656862, LOC659901, LOC654933, LOC658936, LOC100142152
## LOC100141615, LOC661235, LOC100141837, LOC657867, LOC655273, LOC107397609, LOC660934, LOC655011, LOC103312320, LOC657042
## PC_ 5
## Positive: LOC662396, LOC662746, LOC663823, LOC103312486, LOC661480, LOC656298, LOC103313854, LOC658362, LOC100141557, LOC656816
## LOC656170, LOC662645, LOC656645, LOC656941, LOC655649, LOC659154, LOC657107, LOC655563, LOC656373, LOC656279
## LOC662468, LOC662516, LOC656198, LOC663391, LOC103313181, LOC661098, LOC654958, LOC656302, LOC658140, LOC663537
## Negative: LOC663832, LOC103313909, LOC103313825, LOC100142559, LOC664406, LOC659499, LOC656906, LOC103313361, LOC659652, LOC661444
## LOC661309, LOC661461, LOC103315164, LOC100142105, LOC663902, LOC663275, LOC661235, LOC103312757, LOC659982, LOC103312990
## LOC655576, LOC661164, LOC103314169, LOC655533, LOC103312415, LOC661756, LOC660215, LOC661937, Tyr2, LOC659849## PC_ 1
## Positive: LOC658140, LOC655756, LOC656464, LOC664368, LOC659879, LOC662396, LOC107397762, LOC654949, LOC100141601, LOC656807
## LOC664086, LOC656189, LOC658478, LOC654968, LOC661860, LOC662305, LOC662223, LOC657724, LOC100142212, LOC662517
## LOC658380, LOC662206, LOC659377, LOC656003, LOC103312878, LOC661223, LOC655378, LOC103312846, LOC103314322, LOC661229
## Negative: LOC103315122, LOC103313909, LOC103315107, LOC658955, LOC660769, LOC103313579, LOC659613, LOC663147, LOC661796, LOC103315121
## LOC655232, Y-b, LOC655694, LOC663390, LOC663153, LOC661355, LOC660520, LOC664266, LOC103312685, Arp1
## LOC660431, Fim, LOC664209, LOC107398543, Tyr1, LOC103312143, LOC656761, LOC103313170, LOC103313361, Tyr2
## PC_ 2
## Positive: LOC103313568, LOC103313573, LOC661354, LOC662585, LOC656637, LOC662516, LOC661488, LOC659010, LOC656229, LOC661812
## LOC655426, LOC107397973, LOC655414, LOC100141837, LOC662642, LOC661678, LOC661588, LOC103312803, LOC662527, LOC658165
## LOC655019, LOC103314708, LOC661209, LOC659768, LOC654969, LOC661653, LOC656719, LOC654933, LOC658366, LOC655429
## Negative: LOC664531, LOC661651, LOC654958, LOC662907, LOC662544, LOC660038, LOC659147, LOC664447, LOC107397983, LOC663695
## LOC662708, LOC658394, LOC103314176, LOC664530, LOC663769, LOC659649, LOC103313854, LOC658202, LOC659226, LOC100141906
## LOC657036, LOC658651, LOC663823, LOC658071, LOC663002, LOC655420, LOC656560, LOC661139, LOC661191, LOC658171
## PC_ 3
## Positive: LOC657844, LOC658375, LOC660328, LOC100141647, LOC657371, LOC661343, LOC103313615, LOC100141642, LOC103313878, LOC103314546
## LOC659623, LOC100142578, LOC103312853, LOC662654, LOC103312214, LOC103313926, LOC107398726, LOC663354, LOC664364, LOC661909
## LOC103313299, LOC656594, LOC663099, LOC664457, LOC662949, LOC658718, LOC658225, LOC100141776, LOC100141741, LOC656250
## Negative: LOC103315121, LOC103315107, LOC103315108, LOC100141837, LOC661588, LOC663982, LOC663695, Tyr1, LOC103312803, LOC662785
## LOC658914, LOC662743, LOC662396, LOC661355, LOC103312806, LOC660465, LOC656198, LOC661796, LOC658333, LOC658540
## LOC664282, LOC657360, LOC655685, LOC658754, LOC662265, LOC662445, LOC655329, LOC654886, LOC656528, LOC658195
## PC_ 4
## Positive: LOC656464, LOC658140, LOC103315108, LOC656560, LOC654949, chic, LOC663871, LOC103315017, LOC655649, LOC659325
## Arp1, LOC657932, LOC663390, LOC655756, LOC107397762, Mlpt, LOC659549, Rpl41, LOC656507, LOC658914
## LOC659764, LOC658100, LOC656270, LOC103312853, LOC657295, LOC658987, LOC661860, LOC655130, LOC662551, LOC664315
## Negative: KEF75-p03, LOC663832, KEF75-r02, KEF75-p06, KEF75-p05, KEF75-p02, LOC100141896, KEF75-p08, KEF75-p11, KEF75-p12
## KEF75-p09, KEF75-p01, LOC659777, KEF75-p07, KEF75-r01, LOC656318, KEF75-p13, LOC658019, LOC656314, Fpps
## LOC656239, LOC662701, LOC658821, LOC660215, LOC658438, LOC659302, LOC661756, LOC657852, LOC100142307, LOC656164
## PC_ 5
## Positive: LOC103313909, Tyr1, LOC103315122, LOC103315107, LOC103314773, LOC662335, LOC659675, Rpa2, LOC656876, LOC103315121
## LOC660769, LOC660431, LOC662206, LOC103312928, LOC659465, LOC660567, LOC662517, LOC656650, LOC662486, LOC660174
## LOC657098, LOC656003, LOC103312143, LOC655533, LOC663832, LOC658625, LOC664209, LOC657179, LOC659838, LOC658099
## Negative: LOC656170, LOC658005, LOC100142175, LOC103312486, LOC659879, LOC100141919, LOC103312214, LOC661172, LOC103315250, LOC656298
## LOC661678, LOC100141621, LOC657686, LOC654949, LOC658173, LOC660934, LOC661480, Nag2, LOC657980, LOC661820
## LOC664149, LOC659248, LOC657796, LOC658014, LOC655889, LOC100142477, LOC657003, LOC655685, LOC103313705, Tcjheh-r3## Warning in irlba(A = t(x = object), nv = npcs, ...): You're computing too large
## a percentage of total singular values, use a standard svd instead.## PC_ 1
## Positive: LOC103313498, LOC659879, LOC103313785, LOC663516, LOC659649, LOC103313111, Y-e3, LOC656320, LOC663822, LOC664364
## LOC103313926, LOC657844, LOC100142553, LOC658512, LOC655420, LOC103312461, LOC659949, LOC654860, LOC658171, nAChRa10
## LOC656846, LOC664026, LOC662602, LOC662708, LOC659544, LOC660233, LOC661583, LOC662427, LOC661913, LOC656376
## Negative: LOC103315107, LOC103315121, LOC658333, LOC103315108, LOC660769, LOC661588, Tyr1, LOC661796, LOC654950, LOC103312806
## LOC656198, Arp1, LOC663147, LOC663025, LOC103315017, LOC100141837, LOC656761, LOC103313361, Y-b, LOC660394
## LOC103312803, LOC661488, LOC664282, LOC659613, LOC103315122, LOC661354, LOC662396, LOC660431, LOC659357, Tyr2
## PC_ 2
## Positive: LOC655492, LOC103313909, LOC660583, LOC656318, LOC100141741, LOC655420, LOC658821, LOC664266, LOC103314806, LOC103315122
## LOC103315098, LOC659676, LOC659649, LOC103313299, LOC657736, LOC103314134, LOC100141952, LOC656906, LOC655048, LOC655533
## LOC655796, Fim, Tcjheh-r4, LOC655157, LOC663912, LOC658824, LOC663220, LOC103312757, LOC660098, LOC662550
## Negative: LOC658140, LOC656464, LOC664368, LOC655756, LOC658478, LOC107397762, LOC661860, LOC654949, LOC659377, LOC661223
## LOC655378, LOC662223, LOC663871, LOC100142484, LOC661551, LOC100141601, LOC657695, LOC103312684, LOC656807, LOC656189
## LOC655614, LOC661185, LOC662396, LOC663178, LOC664143, LOC658894, LOC103312486, LOC658240, LOC657456, LOC658987
## PC_ 3
## Positive: LOC103313573, LOC663650, LOC100142175, LOC656538, LOC661278, Syx1A, LOC662642, LOC664222, LOC656067, LOC661354
## LOC658776, LOC657980, LOC103313738, LOC661812, LOC657452, LOC661444, LOC103313568, LOC656486, LOC658469, LOC103315156
## LOC662585, LOC661678, LOC663644, LOC661297, LOC663745, LOC663832, LOC656708, LOC661583, LOC103314979, LOC656579
## Negative: LOC103313530, LOC656279, LOC656377, LOC663141, LOC656983, LOC661203, LOC656855, LOC100142105, LOC103313707, LOC656993
## LOC662141, Lyz, LOC663663, LOC663391, LOC659272, LOC657130, LOC658207, LOC100142156, LOC103313746, LOC103314134
## LOC661164, LOC662551, LOC656549, LOC662028, LOC655328, LOC656560, LOC659556, LOC660771, LOC659494, LOC103313889
## PC_ 4
## Positive: LOC103313715, LOC659090, LOC657732, LOC659285, LOC664187, LOC662035, LOC656649, LOC103314322, LOC658740, LOC658720
## LOC656807, LOC103312928, LOC656993, LOC656350, LOC103313405, LOC658590, LOC662219, LOC656472, LOC107398173, LOC658108
## LOC103312846, LOC656003, LOC664436, LOC658192, LOC662857, LOC654890, LOC663023, LOC654930, LOC656381, LOC657096
## Negative: LOC654924, LOC657065, LOC658984, LOC656304, LOC663220, LOC657295, LOC100142234, LOC662708, LOC662265, LOC664086
## LOC664058, LOC658148, LOC663209, LOC659478, LOC661444, LOC660064, LOC656653, LOC663173, LOC100142122, LOC662785
## LOC662445, LOC662141, LOC103312143, LOC657360, LOC656070, Rpa2, LOC655398, LOC664222, LOC103314313, LOC662400
## PC_ 5
## Positive: LOC662402, LOC657826, LOC657584, LOC103313670, LOC656063, LOC659445, LOC657687, LOC100141810, LOC658150, LOC659526
## LOC659690, LOC103312803, LOC655529, LOC664225, LOC660804, LOC663934, LOC107397695, LOC103312115, LOC662214, LOC658964
## LOC655025, LOC657712, LOC664073, LOC661788, LOC659890, LOC663275, LOC100142007, LOC660294, LOC659258, LOC107398173
## Negative: LOC661589, LOC663147, LOC657662, LOC656649, LOC660889, LOC656934, LOC658590, LOC655019, LOC663180, LOC663695
## LOC656232, LOC103313198, LOC660822, LOC660375, LOC103313191, LOC661355, LOC103314638, LOC103312928, LOC100141706, LOC100187736
## LOC655533, LOC103315107, LOC664209, LOC656350, LOC103312775, LOC655772, LOC657290, LOC658554, LOC100141670, LOC662520## PC_ 1
## Positive: LOC659879, LOC658140, LOC664368, LOC655756, LOC656464, LOC654949, LOC662517, LOC103314322, LOC664086, LOC658478
## LOC664073, LOC662206, LOC656342, LOC103313715, LOC103312684, LOC660233, LOC103312461, LOC656003, LOC656807, LOC661223
## LOC661860, Y-e3, LOC103313926, LOC662335, LOC656189, LOC664436, LOC100141601, LOC107397762, LOC664555, LOC100142212
## Negative: LOC103315107, LOC103315121, LOC660769, LOC663147, LOC103313909, LOC661796, LOC103315122, Tyr1, LOC103315108, LOC103313579
## LOC103313361, LOC661588, LOC655232, LOC656761, Arp1, LOC654950, LOC663220, LOC660394, LOC655533, LOC103312946
## LOC660520, LOC664209, LOC660431, LOC664266, LOC658955, LOC658754, Tyr2, Y-b, LOC661968, LOC662785
## PC_ 2
## Positive: LOC658333, LOC103312806, LOC656464, LOC103315108, LOC100141837, LOC656198, LOC662396, LOC658140, LOC103315107, LOC661488
## LOC661588, LOC107397762, LOC664368, LOC656232, LOC662095, Tyr1, LOC663025, LOC655614, LOC655756, LOC663147
## LOC658955, LOC660769, LOC658914, LOC654924, LOC655649, LOC656003, LOC103312486, LOC658478, LOC654950, LOC662223
## Negative: LOC660215, LOC659302, LOC655420, LOC663066, LOC660583, LOC655492, LOC658171, LOC659229, LOC664364, LOC662907
## LOC103313498, LOC661791, LOC659649, LOC658356, LOC658002, LOC661756, LOC663141, LOC663655, LOC100141741, LOC659512
## LOC103314029, LOC658651, LOC657085, LOC655736, LOC103313854, LOC662139, LOC663803, LOC662176, LOC657830, LOC655896
## PC_ 3
## Positive: LOC659690, LOC658333, Arp1, LOC664315, Tyr1, LOC663695, LOC103315108, LOC663023, LOC664393, LOC664068
## LOC103315107, LOC662096, LOC656560, LOC656270, LOC663982, LOC100141706, LOC103313429, LOC654958, LOC663391, LOC655649
## LOC656303, LOC655413, LOC103315121, LOC657695, LOC657162, LOC656485, LOC661223, LOC659747, LOC663437, LOC661651
## Negative: LOC100142175, LOC103313573, Nag2, LOC660371, LOC103313568, LOC657980, LOC659879, LOC657095, LOC656250, LOC657844
## LOC661583, Syx1A, LOC661678, LOC658984, LOC661913, LOC103313926, LOC100142307, LOC103315156, LOC656376, LOC100141647
## LOC103314546, LOC659544, LOC656067, LOC657371, LOC663822, LOC100141776, LOC100142553, LOC103313607, nAChRa10, LOC656070
## PC_ 4
## Positive: LOC103312486, LOC659357, LOC662396, LOC656170, LOC103312806, LOC661651, LOC655796, LOC662746, LOC661480, LOC662095
## LOC100141621, LOC656464, LOC656983, LOC658333, LOC654949, LOC655736, LOC658140, LOC103314884, LOC660934, LOC656198
## LOC103315108, Nag2, LOC657757, LOC100141919, LOC660513, LOC654886, LOC655756, LOC658322, LOC658005, LOC661134
## Negative: LOC656270, LOC100142559, LOC664315, LOC103315122, LOC103312143, LOC664068, LOC103314012, LOC655533, LOC662666, LOC661223
## LOC657715, LOC664364, LOC659224, LOC100142578, LOC656303, LOC657076, LOC661033, LOC100142251, LOC662214, LOC655416
## LOC100141601, LOC103313909, LOC655337, LOC662315, LOC664209, LOC103312310, LOC100141896, LOC663110, LOC103314693, LOC661291
## PC_ 5
## Positive: LOC661185, LOC656653, LOC656276, LOC664058, LOC662743, LOC659478, LOC657360, LOC658195, LOC662400, LOC662265
## LOC659536, LOC662445, LOC664530, LOC655649, LOC656304, LOC655685, RpS6, LOC659112, LOC664315, LOC658148
## LOC663209, Rpl41, LOC661665, LOC656464, LOC664143, LOC662141, LOC662436, LOC658984, LOC661640, LOC656528
## Negative: LOC103312878, LOC659090, LOC656807, LOC100142549, LOC100142105, LOC656251, LOC657732, LOC656279, LOC657724, LOC655019
## LOC657918, LOC658108, LOC657501, LOC661369, LOC664436, LOC660470, LOC660418, LOC656476, LOC103313198, LOC658720
## LOC655985, LOC655190, LOC663232, LOC655576, LOC100141706, LOC661737, LOC657747, LOC655821, LOC662206, LOC103313003## PC_ 1
## Positive: LOC658333, LOC100141837, LOC103312806, LOC103315108, LOC656198, LOC662396, LOC656464, LOC661588, LOC661488, Tyr1
## LOC661860, LOC103315107, LOC659357, LOC658058, LOC662400, LOC664058, LOC657360, LOC661223, LOC662436, LOC664068
## LOC100142054, Rpl41, LOC103315121, LOC664143, LOC100141601, LOC655685, LOC658148, LOC659112, LOC662141, LOC654950
## Negative: LOC103313615, LOC664364, LOC655492, LOC655420, LOC664373, LOC103313111, LOC103314029, LOC657085, LOC658512, LOC660033
## LOC657844, LOC103315218, LOC660215, LOC658225, LOC661583, LOC662907, LOC658141, LOC662708, LOC664531, LOC100141741
## LOC657715, LOC664287, LOC656320, LOC103314663, LOC659226, LOC664026, LOC100142175, LOC663822, LOC663193, LOC660371
## PC_ 2
## Positive: LOC103315122, LOC103313909, LOC103315107, LOC103315121, Tyr1, LOC661796, LOC663147, Arp1, LOC660431, LOC103315108
## LOC659613, LOC655533, LOC100142559, Tyr2, cec2, LOC660769, LOC663390, LOC103313579, LOC661226, LOC103315017
## LOC663153, LOC654950, LOC659085, LOC103315256, LOC657969, LOC654924, LOC103313053, LOC661876, LOC660394, LOC664209
## Negative: LOC659879, LOC655756, LOC100142307, LOC660233, LOC103313785, LOC100142553, LOC664368, LOC664086, LOC658140, LOC662517
## LOC103312461, LOC664073, LOC662206, Y-e3, LOC103313498, LOC659949, LOC658478, LOC100141601, LOC656342, LOC103313715
## LOC664555, LOC103313723, LOC103314322, LOC656003, nAChRa10, LOC103312846, LOC657290, LOC662223, LOC662305, LOC659164
## PC_ 3
## Positive: LOC100142559, LOC661756, LOC654958, LOC100141718, LOC659777, LOC661409, LOC657880, LOC660215, LOC658841, LOC656237
## LOC656087, LOC655580, LOC660583, LOC655494, LOC654982, Scpx, LOC660829, LOC656066, LOC661791, LOC655736
## LOC657017, LOC661355, LOC657447, LOC655942, LOC662176, LOC660032, LOC656019, LOC658936, LOC659318, LOC107398075
## Negative: Arp1, chic, LOC103313579, LOC103313568, LOC103313573, LOC658955, LOC663390, LOC656298, LOC663083, LOC658987
## LOC663025, LOC103314806, Fim, LOC661343, LOC663147, LOC100141647, LOC656649, LOC664266, LOC660431, LOC659325
## LOC656464, LOC663644, LOC662421, LOC103314546, LOC662585, LOC656560, LOC100142030, LOC655614, LOC655694, LOC662654
## PC_ 4
## Positive: LOC103312853, LOC107398726, LOC661516, LOC660275, cec2, LOC660583, LOC661651, LOC663941, LOC103315256, LOC103313878
## LOC656797, LOC657295, LOC660518, LOC660328, LOC656594, LOC658100, LOC661204, LOC663266, LOC661369, LOC656565
## LOC661208, LOC655844, LOC661343, LOC658202, LOC103312313, LOC103312214, LOC662654, LOC664287, LOC661909, LOC658237
## Negative: LOC661355, LOC656170, Tyr1, LOC655329, Nag2, LOC103315108, LOC100141837, LOC660769, LOC661796, LOC103315107
## LOC103315121, LOC657715, LOC654950, LOC656761, LOC663850, LOC659949, LOC658333, LOC661588, LOC662785, LOC655232
## LOC103315164, LOC661488, LOC661297, LOC656637, LOC655706, LOC664305, LOC655533, Tyr2, LOC662933, LOC656198
## PC_ 5
## Positive: LOC100142175, LOC655157, LOC657672, LOC658148, LOC656276, LOC662265, LOC660379, LOC103314012, LOC656270, LOC659478
## LOC103313053, LOC103313032, LOC658195, LOC659272, cec2, LOC107398726, LOC659117, LOC655533, LOC663173, LOC664315
## LOC662315, LOC660417, LOC662203, Tyr2, LOC660576, LOC100142152, LOC656304, LOC660686, LOC660098, Rpl41
## Negative: LOC662396, LOC658262, LOC662095, LOC658754, LOC661651, LOC663025, LOC663982, LOC100141718, LOC656237, LOC103313429
## LOC664209, LOC663023, LOC663391, LOC659338, LOC654886, LOC660583, LOC663884, LOC100141896, LOC103315068, LOC658824
## LOC100187736, LOC661409, LOC655413, LOC661756, LOC664339, LOC655614, LOC655580, LOC107398075, LOC660394, LOC655809## Warning in irlba(A = t(x = object), nv = npcs, ...): You're computing too large
## a percentage of total singular values, use a standard svd instead.## PC_ 1
## Positive: LOC659879, LOC664368, LOC655756, LOC654949, LOC658140, LOC662223, LOC664555, LOC662206, LOC656807, LOC664390
## LOC660470, LOC103313785, LOC657513, LOC662305, LOC103312878, LOC657085, LOC658512, LOC657290, LOC662763, LOC658478
## LOC657732, LOC660754, LOC658108, Rpa2, LOC657179, LOC100142553, LOC663823, LOC656232, LOC656251, LOC664362
## Negative: LOC103315122, Tyr1, LOC103315121, LOC661796, LOC103315107, LOC103313909, LOC663147, LOC100142559, LOC103315108, LOC661355
## LOC661588, LOC655533, LOC659613, LOC103312143, LOC659357, LOC103315256, LOC660431, LOC660769, LOC103312806, Tyr2
## LOC103313053, LOC658936, LOC654950, LOC656761, LOC103312946, LOC656198, LOC654924, LOC661226, LOC655232, LOC658333
## PC_ 2
## Positive: LOC655492, LOC103315218, LOC100142175, LOC655420, LOC657844, LOC664373, LOC103313615, LOC664531, LOC660371, LOC660328
## LOC664364, LOC664266, LOC659226, LOC658141, LOC657715, LOC663832, LOC659849, LOC100141741, LOC660215, LOC103314663
## LOC663438, LOC662417, LOC657828, LOC658984, Mmp-1, LOC662035, LOC103312853, LOC103313111, LOC664287, LOC660576
## Negative: LOC658333, LOC656464, LOC100141837, LOC103315108, LOC661860, LOC655614, LOC107397762, LOC662396, LOC661488, LOC655649
## LOC656198, LOC103312806, LOC103315107, LOC658140, LOC659377, LOC658058, LOC103314322, LOC658914, LOC103312486, LOC658478
## LOC103315121, LOC656528, LOC662223, LOC659549, LOC659357, LOC662877, LOC100142484, LOC661665, LOC664068, LOC663209
## PC_ 3
## Positive: LOC655533, LOC660215, LOC655157, LOC100142175, LOC103313032, Tyr2, LOC103314012, LOC664315, LOC103313909, LOC661223
## LOC103314693, LOC658195, LOC659536, LOC662265, LOC658148, LOC654912, LOC661640, LOC663209, LOC662436, Rpl41
## LOC659849, LOC662141, LOC662315, LOC661229, LOC664058, LOC658058, LOC656303, LOC657076, LOC664068, LOC656304
## Negative: LOC662396, LOC662095, LOC661488, LOC655614, LOC103312806, LOC663982, LOC661354, LOC655796, LOC658333, LOC661968
## LOC103313200, LOC660431, LOC655329, LOC658986, LOC662585, LOC656170, LOC658140, LOC656298, LOC663147, LOC655809
## LOC656193, LOC659010, chic, LOC661089, LOC663025, LOC660190, LOC656649, Arp1, LOC655225, LOC660769
## PC_ 4
## Positive: LOC100142578, LOC659690, LOC656560, LOC103315068, LOC659747, LOC658651, LOC103315250, LOC103314176, LOC663982, LOC664447
## LOC103312806, LOC655736, LOC103312345, LOC659649, LOC663695, LOC659676, LOC655706, LOC664530, LOC661756, LOC663962
## LOC657017, LOC664393, LOC659318, Serpin5, LOC661686, LOC663391, LOC658333, LOC659512, LOC656302, LOC660038
## Negative: LOC103313568, LOC103313573, LOC100142152, LOC662585, LOC100142175, LOC103314693, LOC658188, LOC662818, LOC654933, LOC662214
## Tyr2, Rm62, LOC103313333, LOC663146, LOC655533, LOC658366, LOC656538, LOC662315, LOC655639, LOC660142
## LOC661070, LOC103313909, LOC103312586, LOC661396, LOC663025, LOC662100, LOC655273, LOC663833, LOC661278, LOC659405
## PC_ 5
## Positive: LOC103315121, LOC656170, LOC661355, LOC662708, LOC100141837, LOC661678, LOC658005, LOC656198, LOC103315108, LOC103313568
## Mae, LOC103313108, LOC662396, LOC103315107, LOC654958, LOC663823, LOC658165, LOC658754, LOC659949, LOC658936
## LOC657796, LOC663832, LOC657130, LOC656087, LOC100142175, LOC658366, LOC662642, LOC100142054, LOC103313573, LOC660483
## Negative: cec2, LOC100142235, LOC655373, LOC103313730, LOC659325, LOC656797, LOC103313361, LOC103313185, LOC103312853, LOC660584
## LOC662666, LOC658987, LOC656270, LOC660987, Lyz, Arp1, LOC658219, LOC656941, LOC659338, LOC103315256
## LOC655844, LOC103315218, LOC661357, LOC655028, LOC103313878, LOC658375, LOC660822, LOC661396, LOC660398, LOC658100## PC_ 1
## Positive: LOC659879, LOC103313785, LOC664364, LOC660033, LOC663822, LOC656320, LOC661583, LOC103313111, nAChRa10, LOC658512
## LOC657844, Y-e3, LOC100142553, LOC100142307, LOC103313498, LOC660371, LOC100142175, LOC103313615, LOC659949, LOC658987
## LOC664026, LOC660233, LOC659544, LOC658171, LOC103313926, LOC657095, LOC662708, LOC656067, LOC103312461, LOC661297
## Negative: LOC103313909, Tyr1, LOC103315121, LOC103315108, LOC103315107, LOC661355, LOC103315122, LOC100141837, LOC100142559, Tyr2
## LOC103312806, LOC655533, LOC661588, LOC658333, LOC663147, LOC660769, LOC661796, LOC656198, LOC662785, LOC661488
## LOC658936, LOC100142054, LOC654950, Rpl41, LOC659357, LOC103313789, LOC654924, LOC658366, LOC655232, LOC658262
## PC_ 2
## Positive: LOC103313579, LOC103315122, LOC663083, LOC664266, cec2, LOC103313568, LOC660769, LOC100141741, Fim, LOC655492
## LOC660431, LOC103314806, chic, LOC103315218, LOC660583, LOC103315107, LOC103313573, LOC103313615, LOC661588, LOC663147
## LOC658237, LOC658955, pain, LOC103313825, LOC655420, LOC656565, LOC662221, LOC658100, LOC660675, LOC660520
## Negative: LOC656270, LOC656276, LOC662265, LOC661223, LOC664068, LOC658195, LOC658058, LOC656464, LOC655157, Tyr2
## LOC655533, LOC103314012, LOC657672, LOC664315, LOC662400, LOC660417, LOC659478, LOC661185, LOC100141601, LOC655685
## LOC655756, LOC656653, LOC663557, LOC662315, LOC664368, LOC664058, LOC103312310, LOC658140, LOC656303, LOC654912
## PC_ 3
## Positive: LOC660215, Tyr2, LOC655157, LOC100142559, LOC655533, LOC103313909, LOC660576, LOC103313032, LOC100142175, LOC103315122
## LOC657672, LOC103313053, LOC657844, LOC655492, LOC660417, LOC658002, LOC655420, LOC661355, LOC662907, LOC660098
## LOC657715, LOC659085, cec2, LOC107398726, LOC659849, LOC663557, LOC664364, LOC659272, LOC103313615, LOC664294
## Negative: LOC658333, LOC662396, LOC103312806, LOC656464, LOC658140, LOC655614, LOC103315108, LOC656649, LOC656198, LOC103312486
## LOC655756, LOC100141837, LOC664368, LOC656232, LOC661488, LOC662095, LOC659357, LOC654949, LOC107397762, LOC661588
## LOC103314322, LOC660769, LOC656189, LOC103313715, LOC664555, LOC103315121, LOC663982, LOC662223, LOC103312878, LOC663025
## PC_ 4
## Positive: LOC107398726, cec2, LOC103313361, LOC103312853, LOC103315122, LOC103313579, LOC657295, LOC656565, LOC663372, LOC661516
## LOC655844, LOC659485, LOC660328, LOC661909, LOC659325, pain, LOC661343, LOC661369, LOC658237, LOC103314806
## LOC656797, LOC661651, LOC656270, LOC662221, LOC100142235, LOC103313185, LOC658100, LOC655260, LOC656594, LOC659085
## Negative: LOC103315121, LOC656170, LOC103315107, LOC103315108, LOC103312806, LOC661355, LOC662708, LOC103313467, LOC660033, LOC100142307
## LOC661588, Nag2, LOC103313825, LOC663832, LOC660379, LOC663962, Mae, LOC658333, LOC662933, LOC663822
## LOC663147, nAChRa10, LOC103313785, Y-e3, Tyr1, LOC658171, LOC657715, LOC663823, LOC664305, LOC103315156
## PC_ 5
## Positive: LOC103313568, LOC103313573, Tyr2, LOC655533, LOC103313909, LOC100141837, Tyr1, LOC103314693, LOC663832, LOC660769
## LOC100142175, LOC662470, LOC661354, LOC661488, LOC659849, LOC661355, LOC658366, LOC656637, LOC100142054, LOC662516
## LOC660804, LOC655115, LOC657980, LOC103314791, LOC654950, LOC655157, LOC657158, LOC662585, LOC103314569, LOC663025
## Negative: LOC661296, chic, LOC103312853, LOC103315017, LOC107398726, LOC663147, LOC661651, LOC103313615, LOC656560, LOC103314285
## LOC658237, LOC658333, LOC663220, LOC103315108, LOC664266, LOC662551, LOC100141741, LOC656565, LOC655614, LOC661516
## LOC103315256, LOC663390, LOC660518, LOC107399256, LOC103312486, LOC655649, LOC656649, LOC655492, LOC103312588, LOC658646anchors3000 <- FindIntegrationAnchors(object.list = runs.list, normalization.method = "SCT",
anchor.features = features3000, dims = 1:30, reduction = "rpca", k.anchor = 20)## Warning in CheckDuplicateCellNames(object.list = object.list): Some cell names
## are duplicated across objects provided. Renaming to enforce unique cell names.## Computing within dataset neighborhoods## Finding all pairwise anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 942 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 1011 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 930 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 824 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 813 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 846 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 1103 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 1046 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 1222 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 907 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 1047 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 1048 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 1066 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 910 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 1478 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 945 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 885 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 898 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 780 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 1019 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 1028 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 1112 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 1092 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 1256 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 895 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 1758 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 1801 anchors## Projecting new data onto SVD
## Projecting new data onto SVD## Finding neighborhoods## Finding anchors## Found 1135 anchorscombined.sct.3000 <- IntegrateData(anchorset = anchors3000, normalization.method = "SCT", dims = 1:30, k.weight = 64)## [1] 1## Warning: Different cells and/or features from existing assay SCT## [1] 2## Warning: Different cells and/or features from existing assay SCT## [1] 3## Warning: Different cells and/or features from existing assay SCT## [1] 4## Warning: Different cells and/or features from existing assay SCT## [1] 5## Warning: Different cells and/or features from existing assay SCT## [1] 6## Warning: Different cells and/or features from existing assay SCT## [1] 7## Warning: Different cells and/or features from existing assay SCT## [1] 8## Warning: Different cells and/or features from existing assay SCT## Merging dataset 4 into 6## Extracting anchors for merged samples## Finding integration vectors## Finding integration vector weights## Integrating data## Merging dataset 7 into 8## Extracting anchors for merged samples## Finding integration vectors## Finding integration vector weights## Integrating data## Merging dataset 6 4 into 8 7## Extracting anchors for merged samples## Finding integration vectors## Finding integration vector weights## Integrating data## Merging dataset 2 into 5## Extracting anchors for merged samples## Finding integration vectors## Finding integration vector weights## Integrating data## Merging dataset 1 into 5 2## Extracting anchors for merged samples## Finding integration vectors## Finding integration vector weights## Integrating data## Merging dataset 3 into 5 2 1## Extracting anchors for merged samples## Finding integration vectors## Finding integration vector weights## Integrating data## Merging dataset 5 2 1 3 into 8 7 6 4## Extracting anchors for merged samples## Finding integration vectors## Finding integration vector weights## Integrating data## Warning: Assay integrated changing from Assay to SCTAssay## Warning: Different cells and/or features from existing assay SCTcombined.sct.3000 <- RunPCA(combined.sct.3000, verbose = FALSE)
combined.sct.3000 <- RunUMAP(combined.sct.3000, reduction = "pca", dims = 1:30)## 09:43:45 UMAP embedding parameters a = 0.9922 b = 1.112## 09:43:45 Read 1281 rows and found 30 numeric columns## 09:43:45 Using Annoy for neighbor search, n_neighbors = 30## 09:43:45 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 09:43:45 Writing NN index file to temp file C:\Users\thoma\AppData\Local\Temp\RtmpQFINd1\file454c4521179f
## 09:43:45 Searching Annoy index using 1 thread, search_k = 3000
## 09:43:46 Annoy recall = 100%
## 09:43:47 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 09:43:48 Initializing from normalized Laplacian + noise (using RSpectra)
## 09:43:48 Commencing optimization for 500 epochs, with 56436 positive edges
## 09:43:52 Optimization finishedp1 <- DimPlot(combined.sct.3000, reduction = "umap")
p1
p3 <- DimPlot(combined.sct.3000, reduction = "umap", split.by = "Condition")
p3
Find Clusters
combined.sct.3000 <- FindNeighbors(combined.sct.3000, dims = 1:30, verbose = FALSE)
combined.sct.3000 <- FindClusters(combined.sct.3000, verbose = FALSE, resolution = 0.4)
DimPlot(combined.sct.3000, reduction = "umap")
Find Markers
combined.sct.3000.markers <- FindAllMarkers(combined.sct.3000, only.pos = TRUE)## Calculating cluster 0## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs wurden
## erzeugt## Warning in mean.fxn(object[features, cells.2, drop = FALSE]): NaNs wurden
## erzeugt## Calculating cluster 1## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs wurden
## erzeugt
## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs wurden
## erzeugt## Calculating cluster 2## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs wurden
## erzeugt## Calculating cluster 3## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs wurden
## erzeugt## Calculating cluster 4## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs wurden
## erzeugt## Calculating cluster 5## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs wurden
## erzeugt## Calculating cluster 6## Warning in mean.fxn(object[features, cells.1, drop = FALSE]): NaNs wurden
## erzeugtcombined.sct.3000.markers %>%
group_by(cluster) %>%
dplyr::filter(avg_log2FC > 1)## # A tibble: 3,174 × 7
## # Groups: cluster [7]
## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 3.15e-118 4.82 0.843 0.258 9.46e-115 0 LOC103315121
## 2 1.53e-112 4.59 0.835 0.267 4.58e-109 0 LOC103315108
## 3 1.86e- 97 3.77 0.802 0.303 5.57e- 94 0 LOC103312806
## 4 5.48e- 92 6.02 0.797 0.263 1.65e- 88 0 LOC656198
## 5 1.20e- 79 6.17 0.669 0.2 3.61e- 76 0 LOC658754
## 6 4.38e- 78 1.28 0.73 0.216 1.31e- 74 0 LOC660431
## 7 3.10e- 73 6.64 0.718 0.251 9.31e- 70 0 LOC103315017
## 8 6.57e- 69 4.87 0.622 0.163 1.97e- 65 0 LOC658262
## 9 2.10e- 67 4.25 0.655 0.224 6.29e- 64 0 LOC662785
## 10 4.81e- 67 4.70 0.679 0.207 1.44e- 63 0 LOC660060
## # ℹ 3,164 more rowsHeatmap of Top20 Markers per cluster
combined.sct.3000.markers %>%
group_by(cluster) %>%
dplyr::filter(avg_log2FC > 1) %>%
slice_head(n = 10) %>%
ungroup() -> top10
DoHeatmap(combined.sct.3000, features = top10$gene) + NoLegend()
How many cells per condition
table(combined.sct.3000$Condition)##
## Btt naive PBS
## 417 715 149What proportions of cells per each cluster by replicates
table(Idents(combined.sct.3000), combined.sct.3000$orig.ident)##
## A1 A3 A4 A5 A6 btt2022 naive2022 pbs2022
## 0 63 41 60 29 70 45 262 41
## 1 16 20 29 16 61 29 71 17
## 2 18 12 12 6 18 20 100 16
## 3 2 12 1 4 9 19 19 4
## 4 0 3 4 7 14 22 7 2
## 5 1 1 6 1 4 17 24 5
## 6 2 0 0 1 0 0 18 0same as above but %
prop.table(table(Idents(combined.sct.3000), combined.sct.3000$orig.ident), margin = 2)##
## A1 A3 A4 A5 A6 btt2022
## 0 0.617647059 0.460674157 0.535714286 0.453125000 0.397727273 0.296052632
## 1 0.156862745 0.224719101 0.258928571 0.250000000 0.346590909 0.190789474
## 2 0.176470588 0.134831461 0.107142857 0.093750000 0.102272727 0.131578947
## 3 0.019607843 0.134831461 0.008928571 0.062500000 0.051136364 0.125000000
## 4 0.000000000 0.033707865 0.035714286 0.109375000 0.079545455 0.144736842
## 5 0.009803922 0.011235955 0.053571429 0.015625000 0.022727273 0.111842105
## 6 0.019607843 0.000000000 0.000000000 0.015625000 0.000000000 0.000000000
##
## naive2022 pbs2022
## 0 0.522954092 0.482352941
## 1 0.141716567 0.200000000
## 2 0.199600798 0.188235294
## 3 0.037924152 0.047058824
## 4 0.013972056 0.023529412
## 5 0.047904192 0.058823529
## 6 0.035928144 0.000000000Top50 Marker per cluster
top50 <- combined.sct.3000.markers %>% group_by(cluster) %>% top_n(n = 50, wt = avg_log2FC)
write.csv(top50, "allconditions.csv")G2/M Genes LOC103313179 (CycB Ortholog Drosophila),LOC658006 (stg), LOC660822 (polo)
VlnPlot(combined.sct.3000, features = "LOC660822")
VlnPlot(combined.sct.3000, features = "LOC658006")
VlnPlot(combined.sct.3000, features = "LOC103313179", split.by = "Condition")## The default behaviour of split.by has changed.
## Separate violin plots are now plotted side-by-side.
## To restore the old behaviour of a single split violin,
## set split.plot = TRUE.
##
## This message will be shown once per session.## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
coexpression
FeaturePlot(combined.sct.3000, features = c("LOC660822", "LOC103313179"), blend = TRUE, split.by = "Condition")
pseudotime
library(monocle3)## Lade nötiges Paket: Biobase## Lade nötiges Paket: BiocGenerics##
## Attache Paket: 'BiocGenerics'## Die folgenden Objekte sind maskiert von 'package:dplyr':
##
## combine, intersect, setdiff, union## Das folgende Objekt ist maskiert 'package:SeuratObject':
##
## intersect## Die folgenden Objekte sind maskiert von 'package:stats':
##
## IQR, mad, sd, var, xtabs## Die folgenden Objekte sind maskiert von 'package:base':
##
## anyDuplicated, aperm, append, as.data.frame, basename, cbind,
## colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
## get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
## table, tapply, union, unique, unsplit, which.max, which.min## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.## Lade nötiges Paket: SingleCellExperiment## Lade nötiges Paket: SummarizedExperiment## Lade nötiges Paket: MatrixGenerics## Lade nötiges Paket: matrixStats## Warning: Paket 'matrixStats' wurde unter R Version 4.3.3 erstellt##
## Attache Paket: 'matrixStats'## Die folgenden Objekte sind maskiert von 'package:Biobase':
##
## anyMissing, rowMedians## Das folgende Objekt ist maskiert 'package:dplyr':
##
## count##
## Attache Paket: 'MatrixGenerics'## Die folgenden Objekte sind maskiert von 'package:matrixStats':
##
## colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
## colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
## colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
## colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
## colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
## colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
## colWeightedMeans, colWeightedMedians, colWeightedSds,
## colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
## rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
## rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
## rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
## rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
## rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
## rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
## rowWeightedSds, rowWeightedVars## Das folgende Objekt ist maskiert 'package:Biobase':
##
## rowMedians## Lade nötiges Paket: GenomicRanges## Lade nötiges Paket: stats4## Lade nötiges Paket: S4Vectors##
## Attache Paket: 'S4Vectors'## Die folgenden Objekte sind maskiert von 'package:dplyr':
##
## first, rename## Das folgende Objekt ist maskiert 'package:utils':
##
## findMatches## Die folgenden Objekte sind maskiert von 'package:base':
##
## expand.grid, I, unname## Lade nötiges Paket: IRanges##
## Attache Paket: 'IRanges'## Die folgenden Objekte sind maskiert von 'package:dplyr':
##
## collapse, desc, slice## Das folgende Objekt ist maskiert 'package:sp':
##
## %over%## Das folgende Objekt ist maskiert 'package:grDevices':
##
## windows## Lade nötiges Paket: GenomeInfoDb## Warning: Paket 'GenomeInfoDb' wurde unter R Version 4.3.3 erstellt##
## Attache Paket: 'SummarizedExperiment'## Das folgende Objekt ist maskiert 'package:Seurat':
##
## Assays## Das folgende Objekt ist maskiert 'package:SeuratObject':
##
## Assays##
## Attache Paket: 'monocle3'## Die folgenden Objekte sind maskiert von 'package:Biobase':
##
## exprs, fData, fData<-, pData, pData<-check drosophila genes Lozenge FBgn0002576
VlnPlot(combined.sct.3000, features = "LOC660060", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
VlnPlot(combined.sct.3000, features = "Dscam", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0029167 hml
VlnPlot(combined.sct.3000, features = "LOC659879", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
VlnPlot(combined.sct.3000, features = "Tyr2", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0262473 Toll
VlnPlot(combined.sct.3000, features = "LOC656158", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0034476 Toll 7
VlnPlot(combined.sct.3000, features = "LOC661135", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0003495 spaetzle
VlnPlot(combined.sct.3000, features = "LOC103314665", split.by = "Condition")## Warning: No layers found matching search pattern provided## Warning: Could not find LOC103314665 in the default search locations, found in
## 'SCT' assay instead## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0000250 cactus
VlnPlot(combined.sct.3000, features = "Cact", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0010441 pelle
VlnPlot(combined.sct.3000, features = "LOC654877", split.by = "Condition")## Warning: No layers found matching search pattern provided## Warning: Could not find LOC654877 in the default search locations, found in
## 'SCT' assay instead## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0014018 relish
VlnPlot(combined.sct.3000, features = "LOC659499", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0020381 dredd
VlnPlot(combined.sct.3000, features = "LOC661095", split.by = "Condition")## Warning: No layers found matching search pattern provided## Warning: Could not find LOC661095 in the default search locations, found in
## 'SCT' assay instead## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0261046 defensin
VlnPlot(combined.sct.3000, features = "LOC656717", split.by = "Condition")## Warning: No layers found matching search pattern provided## Warning: Could not find LOC656717 in the default search locations, found in
## 'SCT' assay instead## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0012042 attacin A
VlnPlot(combined.sct.3000, features = "LOC100142481", split.by = "Condition")## Warning: No layers found matching search pattern provided## Warning: Could not find LOC100142481 in the default search locations, found in
## 'SCT' assay instead## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0035976 LC
VlnPlot(combined.sct.3000, features = "LOC660982", split.by = "Condition")## Warning: No layers found matching search pattern provided## Warning: Could not find LOC660982 in the default search locations, found in
## 'SCT' assay instead## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0035975 LA
VlnPlot(combined.sct.3000, features = "LOC103312794", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0030695 LE
VlnPlot(combined.sct.3000, features = "LOC657369", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0030310 SA
VlnPlot(combined.sct.3000, features = "LOC658396", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0043575 SC2
VlnPlot(combined.sct.3000, features = "LOC657880", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0035976 LC
VlnPlot(combined.sct.3000, features = "LOC660982", split.by = "Condition")## Warning: No layers found matching search pattern provided## Warning: Could not find LOC660982 in the default search locations, found in
## 'SCT' assay instead## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0040321 Gram-negative bacteria binding protein 3
VlnPlot(combined.sct.3000, features = "LOC660764", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0260632 dorsal
VlnPlot(combined.sct.3000, features = "Dl", split.by = "Condition")## Warning: No layers found matching search pattern provided## Warning: Could not find Dl in the default search locations, found in 'SCT'
## assay instead## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0031213 Galectin
VlnPlot(combined.sct.3000, features = "LOC660408", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0013983 IMD pathway
VlnPlot(combined.sct.3000, features = "LOC660509", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0016917 Signal-transducer and activator of transcription protein at
92E
VlnPlot(combined.sct.3000, features = "LOC657965", split.by = "Condition")## Warning: No layers found matching search pattern provided## Warning: Could not find LOC657965 in the default search locations, found in
## 'SCT' assay instead## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0261046 DSCAM3
VlnPlot(combined.sct.3000, features = "LOC656717", split.by = "Condition")## Warning: No layers found matching search pattern provided## Warning: Could not find LOC656717 in the default search locations, found in
## 'SCT' assay instead## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0034246 Dicer2
VlnPlot(combined.sct.3000, features = "Dcr-2", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0087035 Argonaute2
VlnPlot(combined.sct.3000, features = "Ago-2b", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0250816 Argonaute 3
VlnPlot(combined.sct.3000, features = "LOC656427", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0262739 Argonaute 1
VlnPlot(combined.sct.3000, features = "LOC659936", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0003053 pebbled
VlnPlot(combined.sct.3000, features = "LOC662162", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0004657 myospheroid
VlnPlot(combined.sct.3000, features = "LOC657674", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0014141 cheerio
VlnPlot(combined.sct.3000, features = "LOC661488", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0003447 singed
VlnPlot(combined.sct.3000, features = "LOC661226", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0029167 hemolectin
VlnPlot(combined.sct.3000, features = "LOC659879", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0243514 eater
VlnPlot(combined.sct.3000, features = "LOC657844", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0015924 croquemort
VlnPlot(combined.sct.3000, features = "LOC664559", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0002924 non-claret disjunctional
VlnPlot(combined.sct.3000, features = "LOC655190", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0004378 Kinesin-like protein at 61F
VlnPlot(combined.sct.3000, features = "LOC103312801", split.by = "Condition")## Warning: No layers found matching search pattern provided## Warning: Could not find LOC103312801 in the default search locations, found in
## 'SCT' assay instead## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0001149 Glutathione S transferase D1
VlnPlot(combined.sct.3000, features = "LOC663147", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0002719 Malic enzyme
VlnPlot(combined.sct.3000, features = "LOC657686", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0034793 asrij
VlnPlot(combined.sct.3000, features = "LOC656418", split.by = "Condition")## Warning: No layers found matching search pattern provided## Warning: Could not find LOC656418 in the default search locations, found in
## 'SCT' assay instead## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0040068 vav guanine-nucleotide exchange factor
VlnPlot(combined.sct.3000, features = "LOC661526", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0036822 Ninjurin B
VlnPlot(combined.sct.3000, features = "LOC103312461", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
FBgn0013733 short stop
VlnPlot(combined.sct.3000, features = "LOC662585", split.by = "Condition")## Warning: Groups with fewer than two datapoints have been dropped.
## ℹ Set `drop = FALSE` to consider such groups for position adjustment purposes.
Pseudobulk DEG testing
pseudo.combined.sct.3000 <- AggregateExpression(combined.sct.3000, assays = "RNA", return.seurat = T, group.by = "Condition")
tail(Cells(pseudo.combined.sct.3000))## [1] "Btt" "naive" "PBS"