11 Confounding Factors
Before finalising the RNA embedding, it is important to evaluate whether technical or biological covariates are driving the clustering structure rather than true cell-type differences. The three main sources of confounding assessed here are cell cycle phase, ribosomal gene expression, and immunoglobulin (IG) gene expression. Mitochondrial gene expression is also scored as a reference but is not regressed out.
11.1 Cell cycle phase
Visualise cell cycle phase assignment on the initial RNA UMAP. If cells separate primarily by phase rather than by cell type, phase regression should be considered.
DimPlot(combined, group.by = "Phase", reduction = "umap.rna")
11.2 Gene set identification
Identify mitochondrial, ribosomal, and immunoglobulin gene sets from the feature names of the RNA assay. These sets are used to compute per-cell module scores, which quantify the aggregate expression of each gene set in each cell.
- Mitochondrial genes: prefix
MT- - Ribosomal genes: prefix
RP - IG genes: prefixes
IGH,IGK,IGL
DefaultAssay(combined) <- 'RNA'
mito_genes <- rownames(combined)[grepl('^MT', rownames(combined))]
ribo_genes <- rownames(combined)[grepl('^RP', rownames(combined))]
IG_genes <- c(rownames(combined)[grepl('^IGH', rownames(combined))],
rownames(combined)[grepl('^IGK', rownames(combined))],
rownames(combined)[grepl('^IGL', rownames(combined))])11.2.1 Mitochondrial genes
mito_genes
#> [1] "MTOR" "MTOR-AS1" "MTHFR" "MTFR1L"
#> [5] "MTF1" "MTF2" "MTMR11" "MTX1"
#> [9] "MTR" "MT1HL1" "MTRNR2L11" "MTA3"
#> [13] "MTIF2" "MTHFD2" "MTLN" "MTX2"
#> [17] "MTERF4" "MTMR14" "MTRNR2L12" "MTHFD2L"
#> [21] "MTTP" "MTRNR2L13" "MTNR1A" "MTRR"
#> [25] "MTMR12" "MTREX" "MTX3" "MTCH1"
#> [29] "MTO1" "MTRES1" "MTFR2" "MTHFD1L"
#> [33] "MTRF1L" "MTURN" "MTERF1" "MTPN"
#> [37] "MTRNR2L6" "MTMR9" "MTMR7" "MTUS1"
#> [41] "MTFR1" "MTERF3" "MTDH" "MTBP"
#> [45] "MTSS1" "MTAP" "MTPAP" "MTRNR2L7"
#> [49] "MTRNR2L5" "MTG1" "MTRNR2L8" "MTCH2"
#> [53] "MTA2" "MTNR1B" "MTMR2" "MTERF2"
#> [57] "MTMR6" "MTIF3" "MTUS2" "MTUS2-AS2"
#> [61] "MTUS2-AS1" "MTRF1" "MTHFD1" "MTA1"
#> [65] "MTMR10" "MTFMT" "MTHFS" "MTRNR2L4"
#> [69] "MT4" "MT3" "MT2A" "MT1E"
#> [73] "MT1M" "MT1A" "MT1B" "MT1F"
#> [77] "MT1G" "MT1H" "MT1X" "MTSS2"
#> [81] "MTHFSD" "MTRNR2L1" "MTMR4" "MTCL1"
#> [85] "MTRNR2L3" "MTG2" "MTMR3" "MTFP1"
#> [89] "MTRNR2L10" "MTMR8" "MTM1" "MTMR1"
#> [93] "MTCP1" "MT-ND1" "MT-ND2" "MT-CO1"
#> [97] "MT-CO2" "MT-ATP8" "MT-ATP6" "MT-CO3"
#> [101] "MT-ND3" "MT-ND4L" "MT-ND4" "MT-ND5"
#> [105] "MT-ND6" "MT-CYB"11.2.2 Ribosomal genes
ribo_genes
#> [1] "RPL22" "RPL11" "RPS6KA1"
#> [4] "RPA2" "RPS8" "RPE65"
#> [7] "RPF1" "RPAP2" "RPL5"
#> [10] "RPRD2" "RPTN" "RPS27"
#> [13] "RPS6KC1" "RPS7" "RPS27A"
#> [16] "RPIA" "RPL31" "RPRM"
#> [19] "RPE" "RPL37A" "RPUSD3"
#> [22] "RPL32" "RPL15" "RPSA"
#> [25] "RPL14" "RPL29" "RPP14"
#> [28] "RPL24" "RPN1" "RPL22L1"
#> [31] "RPL39L" "RPL35A" "RPL9"
#> [34] "RPL34-AS1" "RPL34" "RPS3A"
#> [37] "RPL37" "RPS23" "RPS14"
#> [40] "RPL26L1" "RPP40" "RPP21"
#> [43] "RPS18" "RPS10-NUDT3" "RPS10"
#> [46] "RPL10A" "RPL7L1" "RPF2"
#> [49] "RPS12" "RPS6KA2" "RPS6KA2-IT1"
#> [52] "RPS6KA2-AS1" "RPA3" "RP9"
#> [55] "RP1L1" "RP1" "RPS20"
#> [58] "RPL7" "RPL30" "RPL8"
#> [61] "RPS6" "RPP25L" "RPL35"
#> [64] "RPL12" "RPL7A" "RPP38-DT"
#> [67] "RPP38" "RPS24" "RPP30"
#> [70] "RPARP-AS1" "RPEL1" "RPLP2"
#> [73] "RPL27A" "RPS13" "RPS6KA4"
#> [76] "RPS6KB2" "RPS6KB2-AS1" "RPS3"
#> [79] "RPS25" "RPUSD4" "RPAP3"
#> [82] "RPS26" "RPL41" "RPL6"
#> [85] "RPH3A" "RPLP0" "RPL21"
#> [88] "RPGRIP1" "RPL10L" "RPS29"
#> [91] "RPL36AL" "RPS6KL1" "RPS6KA5"
#> [94] "RPUSD2" "RPAP1" "RPS27L"
#> [97] "RPL4" "RPLP1" "RPP25"
#> [100] "RPS17" "RPUSD1" "RPL3L"
#> [103] "RPS2" "RPS15A" "RPGRIP1L"
#> [106] "RPL13" "RPH3AL" "RPA1"
#> [109] "RPAIN" "RPL26" "RPL23A"
#> [112] "RPL23" "RPL19" "RPL27"
#> [115] "RPRML" "RPS6KB1" "RPL38"
#> [118] "RPTOR" "RPRD1A" "RPL17"
#> [121] "RPS15" "RPL36" "RPS28"
#> [124] "RPL18A" "RPS16" "RPS19"
#> [127] "RPL18" "RPL13A" "RPS11"
#> [130] "RPS9" "RPL28" "RPS5"
#> [133] "RPN2" "RPRD1B" "RPS21"
#> [136] "RPL3" "RPS19BP1" "RPS6KA3"
#> [139] "RPGR" "RP2" "RPS4X"
#> [142] "RPS6KA6" "RPA4" "RPL36A"
#> [145] "RPL39" "RPL10" "RPS4Y1"
#> [148] "RPS4Y2"11.2.3 IG genes
IG_genes
#> [1] "IGHEP2" "IGHMBP2" "IGHA2"
#> [4] "IGHE" "IGHG4" "IGHG2"
#> [7] "IGHGP" "IGHA1" "IGHEP1"
#> [10] "IGHG1" "IGHG3" "IGHD"
#> [13] "IGHM" "IGHJ6" "IGHJ3P"
#> [16] "IGHJ5" "IGHJ4" "IGHJ3"
#> [19] "IGHJ2P" "IGHJ2" "IGHJ1"
#> [22] "IGHD7-27" "IGHJ1P" "IGHD1-26"
#> [25] "IGHD6-25" "IGHD5-24" "IGHD4-23"
#> [28] "IGHD3-22" "IGHD2-21" "IGHD1-20"
#> [31] "IGHD6-19" "IGHD5-18" "IGHD4-17"
#> [34] "IGHD3-16" "IGHD2-15" "IGHD1-14"
#> [37] "IGHD6-13" "IGHD5-12" "IGHD4-11"
#> [40] "IGHD3-10" "IGHD3-9" "IGHD2-8"
#> [43] "IGHD1-7" "IGHD6-6" "IGHD5-5"
#> [46] "IGHD4-4" "IGHD3-3" "IGHD2-2"
#> [49] "IGHD1-1" "IGHV6-1" "IGHVII-1-1"
#> [52] "IGHV1-2" "IGHVIII-2-1" "IGHV1-3"
#> [55] "IGHV4-4" "IGHV7-4-1" "IGHV2-5"
#> [58] "IGHVIII-5-1" "IGHVIII-5-2" "IGHV3-6"
#> [61] "IGHV3-7" "IGHV3-64D" "IGHV5-10-1"
#> [64] "IGHV3-11" "IGHVIII-11-1" "IGHV1-12"
#> [67] "IGHV3-13" "IGHVIII-13-1" "IGHV1-14"
#> [70] "IGHV3-15" "IGHVII-15-1" "IGHV3-16"
#> [73] "IGHVIII-16-1" "IGHV1-17" "IGHV1-18"
#> [76] "IGHV3-19" "IGHV3-20" "IGHV3-21"
#> [79] "IGHV3-22" "IGHVII-22-1" "IGHVIII-22-2"
#> [82] "IGHV3-23" "IGHV1-24" "IGHV3-25"
#> [85] "IGHVIII-25-1" "IGHV2-26" "IGHVIII-26-1"
#> [88] "IGHVII-26-2" "IGHV7-27" "IGHV4-28"
#> [91] "IGHVII-28-1" "IGHV3-32" "IGHV3-30"
#> [94] "IGHVII-30-1" "IGHV3-30-2" "IGHV4-31"
#> [97] "IGHVII-30-21" "IGHV3-29" "IGHV3-33"
#> [100] "IGHVII-33-1" "IGHV3-33-2" "IGHV4-34"
#> [103] "IGHV7-34-1" "IGHV3-35" "IGHV3-36"
#> [106] "IGHV3-37" "IGHV3-38" "IGHVIII-38-1"
#> [109] "IGHV4-39" "IGHV7-40" "IGHVII-40-1"
#> [112] "IGHV3-41" "IGHV3-42" "IGHV3-43"
#> [115] "IGHVII-43-1" "IGHVIII-44" "IGHVIV-44-1"
#> [118] "IGHVII-44-2" "IGHV1-45" "IGHV1-46"
#> [121] "IGHVII-46-1" "IGHV3-47" "IGHVIII-47-1"
#> [124] "IGHV3-48" "IGHV3-49" "IGHVII-49-1"
#> [127] "IGHV3-50" "IGHV5-51" "IGHVIII-51-1"
#> [130] "IGHVII-51-2" "IGHV3-52" "IGHV3-53"
#> [133] "IGHVII-53-1" "IGHV3-54" "IGHV4-55"
#> [136] "IGHV7-56" "IGHV3-57" "IGHV1-58"
#> [139] "IGHV4-59" "IGHV3-60" "IGHVII-60-1"
#> [142] "IGHV4-61" "IGHV3-62" "IGHVII-62-1"
#> [145] "IGHV3-63" "IGHV3-64" "IGHV3-65"
#> [148] "IGHVII-65-1" "IGHV3-66" "IGHV1-67"
#> [151] "IGHVII-67-1" "IGHVIII-67-2" "IGHVIII-67-3"
#> [154] "IGHVIII-67-4" "IGHV1-68" "IGHV1-69"
#> [157] "IGHV2-70D" "IGHV3-69-1" "IGHV1-69-2"
#> [160] "IGHV1-69D" "IGHV2-70" "IGHV3-71"
#> [163] "IGHV3-72" "IGHV3-73" "IGHV3-74"
#> [166] "IGHVII-74-1" "IGHV3-75" "IGHV3-76"
#> [169] "IGHVIII-76-1" "IGHV5-78" "IGHVII-78-1"
#> [172] "IGHV3-79" "IGHV4-80" "IGHV7-81"
#> [175] "IGHVIII-82" "IGHV1OR15-9" "IGHV1OR15-2"
#> [178] "IGHV3OR15-7" "IGHD5OR15-5A" "IGHD4OR15-4A"
#> [181] "IGHD3OR15-3A" "IGHD2OR15-2A" "IGHD1OR15-1A"
#> [184] "IGHV1OR15-6" "IGHD5OR15-5B" "IGHD4OR15-4B"
#> [187] "IGHD3OR15-3B" "IGHD2OR15-2B" "IGHD1OR15-1B"
#> [190] "IGHV1OR15-1" "IGHV1OR15-3" "IGHV4OR15-8"
#> [193] "IGHV1OR15-4" "IGHV1OR16-1" "IGHV1OR16-3"
#> [196] "IGHV3OR16-9" "IGHV2OR16-5" "IGHV3OR16-15"
#> [199] "IGHV3OR16-6" "IGHV1OR16-2" "IGHV3OR16-10"
#> [202] "IGHV1OR16-4" "IGHV3OR16-8" "IGHV3OR16-12"
#> [205] "IGHV3OR16-13" "IGHV3OR16-11" "IGHV3OR16-7"
#> [208] "IGHV1OR21-1" "IGKV1OR1-1" "IGKV3OR2-268"
#> [211] "IGKC" "IGKJ5" "IGKJ4"
#> [214] "IGKJ3" "IGKJ2" "IGKJ1"
#> [217] "IGKV4-1" "IGKV5-2" "IGKV7-3"
#> [220] "IGKV2-4" "IGKV1-5" "IGKV1-6"
#> [223] "IGKV3-7" "IGKV1-8" "IGKV1-9"
#> [226] "IGKV2-10" "IGKV3-11" "IGKV1-12"
#> [229] "IGKV1-13" "IGKV2-14" "IGKV3-15"
#> [232] "IGKV1-16" "IGKV1-17" "IGKV2-18"
#> [235] "IGKV2-19" "IGKV3-20" "IGKV6-21"
#> [238] "IGKV1-22" "IGKV2-23" "IGKV2-24"
#> [241] "IGKV3-25" "IGKV2-26" "IGKV1-27"
#> [244] "IGKV2-28" "IGKV2-29" "IGKV2-30"
#> [247] "IGKV3-31" "IGKV1-32" "IGKV1-33"
#> [250] "IGKV3-34" "IGKV1-35" "IGKV2-36"
#> [253] "IGKV1-37" "IGKV2-38" "IGKV1-39"
#> [256] "IGKV2-40" "IGKV2D-40" "IGKV1D-39"
#> [259] "IGKV2D-38" "IGKV1D-37" "IGKV2D-36"
#> [262] "IGKV1D-35" "IGKV3D-34" "IGKV1D-33"
#> [265] "IGKV1D-32" "IGKV3D-31" "IGKV2D-30"
#> [268] "IGKV2D-29" "IGKV2D-28" "IGKV1D-27"
#> [271] "IGKV2D-26" "IGKV3D-25" "IGKV2D-24"
#> [274] "IGKV2D-23" "IGKV1D-22" "IGKV6D-21"
#> [277] "IGKV3D-20" "IGKV2D-19" "IGKV2D-18"
#> [280] "IGKV6D-41" "IGKV1D-17" "IGKV1D-16"
#> [283] "IGKV3D-15" "IGKV2D-14" "IGKV1D-13"
#> [286] "IGKV1D-12" "IGKV3D-11" "IGKV2D-10"
#> [289] "IGKV1D-42" "IGKV1D-43" "IGKV1D-8"
#> [292] "IGKV3D-7" "IGKV1OR2-118" "IGKV1OR2-1"
#> [295] "IGKV2OR2-1" "IGKV2OR2-2" "IGKV1OR2-3"
#> [298] "IGKV1OR2-9" "IGKV2OR2-10" "IGKV2OR2-7D"
#> [301] "IGKV3OR2-5" "IGKV1OR2-6" "IGKV2OR2-7"
#> [304] "IGKV2OR2-8" "IGKV1OR2-11" "IGKV1OR2-108"
#> [307] "IGKV1OR9-2" "IGKV1OR-2" "IGKV1OR9-1"
#> [310] "IGKV1OR-3" "IGKV1OR10-1" "IGKV1OR22-5"
#> [313] "IGKV2OR22-4" "IGKV2OR22-3" "IGKV3OR22-2"
#> [316] "IGKV1OR22-1" "IGLV8OR8-1" "IGLJCOR18"
#> [319] "IGLON5" "IGLVI-70" "IGLV4-69"
#> [322] "IGLVI-68" "IGLV10-54" "IGLV10-67"
#> [325] "IGLVIV-66-1" "IGLVV-66" "IGLVIV-65"
#> [328] "IGLVIV-64" "IGLVI-63" "IGLV1-62"
#> [331] "IGLV8-61" "IGLV4-60" "IGLVIV-59"
#> [334] "IGLVV-58" "IGLV6-57" "IGLVI-56"
#> [337] "IGLV11-55" "IGLVIV-53" "IGLV5-52"
#> [340] "IGLV1-51" "IGLV1-50" "IGLV9-49"
#> [343] "IGLV5-48" "IGLV1-47" "IGLV7-46"
#> [346] "IGLV5-45" "IGLV1-44" "IGLV7-43"
#> [349] "IGLVI-42" "IGLVVII-41-1" "IGLV1-41"
#> [352] "IGLV1-40" "IGLVI-38" "IGLV5-37"
#> [355] "IGLV1-36" "IGLV7-35" "IGLV2-34"
#> [358] "IGLV2-33" "IGLV3-32" "IGLV3-31"
#> [361] "IGLV3-30" "IGLV3-29" "IGLV2-28"
#> [364] "IGLV3-27" "IGLV3-26" "IGLVVI-25-1"
#> [367] "IGLV3-25" "IGLV3-24" "IGLV2-23"
#> [370] "IGLVVI-22-1" "IGLV3-22" "IGLV3-21"
#> [373] "IGLVI-20" "IGLV3-19" "IGLV2-18"
#> [376] "IGLV3-17" "IGLV3-16" "IGLV3-15"
#> [379] "IGLV2-14" "IGLV3-13" "IGLV3-12"
#> [382] "IGLV2-11" "IGLV3-10" "IGLV3-9"
#> [385] "IGLV2-8" "IGLV3-7" "IGLV3-6"
#> [388] "IGLV2-5" "IGLV3-4" "IGLV4-3"
#> [391] "IGLV3-2" "IGLV3-1" "IGLJ1"
#> [394] "IGLC1" "IGLJ2" "IGLC2"
#> [397] "IGLJ3" "IGLC3" "IGLJ4"
#> [400] "IGLC4" "IGLJ5" "IGLC5"
#> [403] "IGLJ6" "IGLC6" "IGLJ7"
#> [406] "IGLC7" "IGLL1" "IGLVIVOR22-1"
#> [409] "IGLCOR22-1" "IGLCOR22-2" "IGLVIVOR22-2"11.3 Module scores
Compute per-cell module scores for each gene set. These scores are stored in
the metadata as mito_genes1, ribo_genes2, and IG_genes3 and can be
overlaid on the UMAP to evaluate spatial distribution of each signal.
genes_regress <- list(mito_genes = mito_genes,
ribo_genes = ribo_genes,
IG_genes = IG_genes)
combined <- AddModuleScore(object = combined,
features = genes_regress,
ctrl = 5,
name = c("mito_genes", "ribo_genes", "IG_genes"),
search = TRUE)11.3.1 Mitochondrial gene module score
FeaturePlot(combined, features = "mito_genes1", label = TRUE, repel = TRUE,
reduction = "umap.rna") +
scale_colour_gradientn(colours = c("lightblue", "beige", "red"))
11.3.2 Ribosomal gene module score
FeaturePlot(combined, features = "ribo_genes2", label = TRUE, repel = TRUE,
reduction = "umap.rna") +
scale_colour_gradientn(colours = c("lightblue", "beige", "red"))
11.3.3 IG gene module score
FeaturePlot(combined, features = "IG_genes3", label = TRUE, repel = TRUE,
reduction = "umap.rna") +
scale_colour_gradientn(colours = c("lightblue", "beige", "red"))
11.4 Regression of ribosomal genes
Ribosomal gene expression can dominate the variance in immune cell datasets,
particularly in plasma cells and activated B cells, and cause biologically
unrelated cell types to cluster together. The RNA data are rescaled with
ribosomal module score regressed out, a new PCA is computed, and a new UMAP
embedding (umap.rna_regressedRibo) is generated for comparison.
11.5 Post-regression evaluation
Overlay cell cycle phase and each module score on the ribo-regressed UMAP to confirm that ribosomal signal has been reduced without distorting the biologically meaningful structure.
11.5.2 Mitochondrial gene module score
FeaturePlot(combined, features = "mito_genes1", label = TRUE, repel = TRUE,
reduction = "umap.rna_regressedRibo") +
scale_colour_gradientn(colours = c("lightblue", "beige", "red"))
11.5.3 Ribosomal gene module score
FeaturePlot(combined, features = "ribo_genes2", label = TRUE, repel = TRUE,
reduction = "umap.rna_regressedRibo") +
scale_colour_gradientn(colours = c("lightblue", "beige", "red"))
11.5.4 IG gene module score
FeaturePlot(combined, features = "IG_genes3", label = TRUE, repel = TRUE,
reduction = "umap.rna_regressedRibo") +
scale_colour_gradientn(colours = c("lightblue", "beige", "red"))