ELeFHAnt_Tutorial
library(ELeFHAnt)
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library(Matrix)
data("reference_PBMC")
data("query_PBMC")
reference = reference_PBMC
query = query_PBMC
query = NormalizeData(query)
query = FindVariableFeatures(query)
query = ScaleData(query)
#> Centering and scaling data matrix
query = RunPCA(query)
#> PC_ 1
#> Positive: CST3, AIF1, LST1, FTL, FTH1, TYMP, TYROBP, CFD, FCER1G, SERPINA1
#> FCN1, LYZ, CTSS, IFITM3, S100A9, LGALS1, COTL1, PSAP, IFI30, S100A11
#> NPC2, CFP, SAT1, RP11-290F20.3, S100A8, PYCARD, S100A6, PILRA, LGALS2, CEBPB
#> Negative: IL32, LTB, CD3E, LDHB, CTSW, GZMM, CD2, IL7R, CCL5, CD247
#> ACAP1, CST7, GZMA, STK17A, NKG7, CD27, PRF1, HOPX, GIMAP5, NOSIP
#> AQP3, GZMK, NCR3, FGFBP2, LYAR, KLRG1, SAMD3, CD8B, ETS1, GZMB
#> PC_ 2
#> Positive: PF4, SDPR, GNG11, PPBP, SPARC, GP9, TUBB1, HIST1H2AC, CLU, AP001189.4
#> PTCRA, ITGA2B, NRGN, RGS18, CD9, TMEM40, MMD, CA2, ACRBP, TREML1
#> F13A1, SEPT5, TSC22D1, PTGS1, CMTM5, LY6G6F, GP1BA, RP11-367G6.3, MYL9, RUFY1
#> Negative: RPS2, TMSB10, CYBA, NKG7, S100A4, GZMA, CST7, PRF1, CTSW, GNLY
#> FGFBP2, CD247, EIF4A1, GZMB, GZMM, ID2, IFITM2, GZMH, SPON2, ANXA1
#> CCL4, FCGR3A, PFN1, APOBEC3G, RBM3, S100A10, GIMAP7, IGFBP7, HOPX, CLIC3
#> PC_ 3
#> Positive: NKG7, PRF1, GZMB, CST7, GZMA, FGFBP2, GNLY, CTSW, SPON2, CD247
#> GZMH, GZMM, CCL5, CCL4, FCGR3A, SRGN, CLIC3, AKR1C3, XCL2, PFN1
#> ACTB, IGFBP7, TTC38, HOPX, APMAP, SH3BGRL3, RHOC, ID2, ARPC5L, ANXA1
#> Negative: CD79A, MS4A1, HLA-DRA, HLA-DQB1, TCL1A, HLA-DQA1, RPS2, HLA-DRB1, CD74, CD79B
#> LTB, HLA-DPB1, HLA-DMA, HLA-DRB5, HLA-DPA1, HLA-DQA2, FCER2, LY86, HVCN1, SNHG7
#> KIAA0125, P2RX5, IRF8, CD19, QRSL1, SWAP70, IGLL5, FCGR2B, C6orf48, POU2AF1
#> PC_ 4
#> Positive: S100A4, S100A8, TMSB4X, S100A6, S100A9, CD14, GIMAP7, FCN1, IL32, RBP7
#> LGALS2, S100A11, CD3E, TYROBP, ANXA1, LYZ, S100A12, IL7R, MS4A6A, GZMM
#> GIMAP4, FTL, CFD, LGALS1, S100A10, NOSIP, CD2, AIF1, FYB, TIMP1
#> Negative: HLA-DQA1, KIAA0101, TYMS, CD79A, HLA-DQB1, RRM2, TK1, CD74, CD79B, GINS2
#> MS4A1, HLA-DQA2, MKI67, HLA-DPB1, ZWINT, HLA-DRA, MYBL2, HLA-DRB1, BIRC5, HLA-DPA1
#> HLA-DRB5, KIFC1, TCL1A, CLSPN, HLA-DMA, CENPM, MZB1, AURKB, STMN1, NUSAP1
#> PC_ 5
#> Positive: LDHB, VIM, IL7R, CD3E, IL32, AQP3, NOSIP, CD27, RPS2, CD2
#> FYB, GIMAP7, CD40LG, RRM2, KIAA0101, S100A10, LTB, TYMS, GIMAP4, TK1
#> ZWINT, MKI67, PPA1, LDLRAP1, GIMAP5, BIRC5, GINS2, GAPDH, TRADD, COTL1
#> Negative: GZMB, FGFBP2, CD79B, CD79A, GNLY, TCL1A, SPON2, PRF1, MS4A1, CD74
#> HLA-DQA1, NKG7, CCL4, HLA-DQB1, HLA-DPB1, CLIC3, HLA-DPA1, HLA-DRA, CST7, HLA-DRB1
#> IGFBP7, PLAC8, TTC38, AKR1C3, GZMA, FCGR3A, XCL2, HLA-DRB5, FCER2, APMAP
query = RunUMAP(query, dims = 1:20)
#> 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
#> 00:14:25 UMAP embedding parameters a = 0.9922 b = 1.112
#> 00:14:25 Read 1358 rows and found 20 numeric columns
#> 00:14:25 Using Annoy for neighbor search, n_neighbors = 30
#> 00:14:25 Building Annoy index with metric = cosine, n_trees = 50
#> 0% 10 20 30 40 50 60 70 80 90 100%
#> [----|----|----|----|----|----|----|----|----|----|
#> **************************************************|
#> 00:14:25 Writing NN index file to temp file /var/folders/bw/whg3swn15jb08_f7v2y09xw9glk1wg/T//RtmpWY8t69/filee0be63731190
#> 00:14:25 Searching Annoy index using 1 thread, search_k = 3000
#> 00:14:25 Annoy recall = 100%
#> 00:14:26 Commencing smooth kNN distance calibration using 1 thread
#> 00:14:27 Initializing from normalized Laplacian + noise
#> 00:14:27 Commencing optimization for 500 epochs, with 54848 positive edges
#> 00:14:29 Optimization finished
human_tissues
#> [1] "Abdomen" "Abdominal adipose tissue"
#> [3] "Abdominal fat pad" "Acinus"
#> [5] "Adipose tissue" "Adrenal gland"
#> [7] "Adventitia" "Airway"
#> [9] "Airway epithelium" "Allocortex"
#> [11] "Alveolus" "Amniotic fluid"
#> [13] "Amniotic membrane" "Ampullary"
#> [15] "Antecubital vein" "Anterior cruciate ligament"
#> [17] "Anterior presomitic mesoderm" "Aorta"
#> [19] "Aortic valve" "Artery"
#> [21] "Arthrosis" "Articular Cartilage"
#> [23] "Ascites" "Ascitic fluid"
#> [25] "Auditory cortex" "Basal airway"
#> [27] "Basilar membrane" "Bile duct"
#> [29] "Biliary tract" "Bladder"
#> [31] "Blood" "Blood vessel"
#> [33] "Bone" "Bone marrow"
#> [35] "Brain" "Breast"
#> [37] "Bronchial vessel" "Bronchiole"
#> [39] "Bronchoalveolar lavage" "Bronchoalveolar system"
#> [41] "Bronchus" "Brown adipose tissue"
#> [43] "Calvaria" "Capillary"
#> [45] "Cardiovascular system" "Carotid artery"
#> [47] "Carotid plaque" "Cartilage"
#> [49] "Caudal cortex" "Caudal forebrain"
#> [51] "Caudal ganglionic eminence" "Cavernosum"
#> [53] "Central amygdala" "Central nervous system"
#> [55] "Cerebellum" "Cerebral organoid"
#> [57] "Cerebrospinal fluid" "Chorionic villi"
#> [59] "Chorionic villus" "Choroid"
#> [61] "Choroid plexus" "Colon"
#> [63] "Colon epithelium" "Colorectum"
#> [65] "Cornea" "Corneal endothelium"
#> [67] "Corneal epithelium" "Coronary artery"
#> [69] "Corpus callosum" "Corpus luteum"
#> [71] "Cortex" "Cortical layer"
#> [73] "Cortical thymus" "Decidua"
#> [75] "Deciduous tooth" "Dental pulp"
#> [77] "Dermis" "Diencephalon"
#> [79] "Dorsal forebrain" "Dorsal root ganglion"
#> [81] "Dorsolateral prefrontal cortex" "Ductal tissue"
#> [83] "Duodenum" "Ectocervix"
#> [85] "Ectoderm" "Embryo"
#> [87] "Embryoid body" "Embryonic brain"
#> [89] "Embryonic heart" "Embryonic Kidney"
#> [91] "Embryonic stem cell" "Endocardium"
#> [93] "Endocrine" "Endoderm"
#> [95] "Endometrium" "Endometrium stroma"
#> [97] "Entorhinal cortex" "Epidermis"
#> [99] "Epithelium" "Esophagus"
#> [101] "Eye" "Fetal brain"
#> [103] "Fetal heart" "Fetal ileums"
#> [105] "Fetal kidney" "Fetal Leydig"
#> [107] "Fetal liver" "Fetal lung"
#> [109] "Fetal pancreas" "Fetal thymus"
#> [111] "Fetal umbilical cord" "Fetus"
#> [113] "Foreskin" "Frontal cortex"
#> [115] "Fundic gland" "Gall bladder"
#> [117] "Gastric corpus" "Gastric epithelium"
#> [119] "Gastric gland" "Gastrointestinal tract"
#> [121] "Germ" "Germinal center"
#> [123] "Gingiva" "Gonad"
#> [125] "Gut" "Hair follicle"
#> [127] "Head and neck" "Heart"
#> [129] "Heart muscle" "Hippocampus"
#> [131] "Ileum" "Iliac crest"
#> [133] "Inferior colliculus" "Interfollicular epidermis"
#> [135] "Intervertebral disc" "Intestinal crypt"
#> [137] "Intestine" "Intrahepatic cholangio"
#> [139] "Jejunum" "Kidney"
#> [141] "Lacrimal gland" "Large intestine"
#> [143] "Laryngeal squamous epithelium" "Larynx"
#> [145] "Lateral ganglionic eminence" "Left lobe"
#> [147] "Limb bud" "Limbal epithelium"
#> [149] "Liver" "Lumbar vertebra"
#> [151] "Lung" "Lymph"
#> [153] "Lymph node" "Lymphatic vessel"
#> [155] "Lymphoid tissue" "Malignant pleural effusion"
#> [157] "Mammary epithelium" "Mammary gland"
#> [159] "Medial ganglionic eminence" "Medullary thymus"
#> [161] "Meniscus" "Mesenchyme"
#> [163] "Mesoblast" "Mesoderm"
#> [165] "Microvascular endothelium" "Microvessel"
#> [167] "Midbrain" "Middle temporal gyrus"
#> [169] "Milk" "Molar"
#> [171] "Muscle" "Myenteric plexus"
#> [173] "Myocardium" "Myometrium"
#> [175] "Nasal concha" "Nasal epithelium"
#> [177] "Nasal mucosa" "Nasal polyp"
#> [179] "Nasopharyngeal mucosa" "Nasopharynx"
#> [181] "Neocortex" "Nerve"
#> [183] "Nose" "Nucleus pulposus"
#> [185] "Olfactory neuroepithelium" "Omentum"
#> [187] "Optic nerve" "Oral cavity"
#> [189] "Oral mucosa" "Osteoarthritic cartilage"
#> [191] "Ovarian cortex" "Ovarian follicle"
#> [193] "Ovary" "Oviduct"
#> [195] "Palatine tonsil" "Pancreas"
#> [197] "Pancreatic acinar tissue" "Pancreatic duct"
#> [199] "Pancreatic islet" "Parotid gland"
#> [201] "Periodontal ligament" "Periodontium"
#> [203] "Periosteum" "Peripheral blood"
#> [205] "Peritoneal fluid" "Peritoneum"
#> [207] "Pituitary" "Pituitary gland"
#> [209] "Placenta" "Plasma"
#> [211] "Pleura" "Pluripotent stem cell"
#> [213] "Polyp" "Posterior fossa"
#> [215] "Posterior presomitic mesoderm" "Prefrontal cortex"
#> [217] "Premolar" "Presomitic mesoderm"
#> [219] "Primitive streak" "Prostate"
#> [221] "Pulmonary arteriy" "Pyloric gland"
#> [223] "Rectum" "Renal glomerulus"
#> [225] "Respiratory tract" "Retina"
#> [227] "Retinal organoid" "Retinal pigment epithelium"
#> [229] "Right ventricle" "Saliva"
#> [231] "Salivary gland" "Scalp"
#> [233] "Sclerocorneal tissue" "Seminal plasma"
#> [235] "Septum transversum" "Serum"
#> [237] "Serum exosome" "Sinonasal mucosa"
#> [239] "Sinus tissue" "Skeletal muscle"
#> [241] "Skin" "Small intestinal crypt"
#> [243] "Small intestine" "Soft tissue"
#> [245] "Sperm" "Spinal cord"
#> [247] "Spleen" "Splenic red pulp"
#> [249] "Sputum" "Stomach"
#> [251] "Subcutaneous adipose tissue" "Submandibular gland"
#> [253] "Subpallium" "Subplate"
#> [255] "Subventricular zone" "Superior frontal gyrus"
#> [257] "Sympathetic ganglion" "Synovial fluid"
#> [259] "Synovium" "Taste bud"
#> [261] "Tendon" "Testis"
#> [263] "Thalamus" "Thymus"
#> [265] "Thyroid" "Tongue"
#> [267] "Tonsil" "Tooth"
#> [269] "Trachea" "Tracheal airway epithelium"
#> [271] "Transformed artery" "Trophoblast"
#> [273] "Umbilical cord" "Umbilical cord blood"
#> [275] "Umbilical vein" "Undefined"
#> [277] "Urine" "Urothelium"
#> [279] "Uterine cervix" "Uterus"
#> [281] "Vagina" "Vein"
#> [283] "Venous blood" "Ventral thalamus"
#> [285] "Ventricular and atrial" "Ventricular zone"
#> [287] "Vessel" "Visceral adipose tissue"
#> [289] "Vocal cord" "Vocal fold"
#> [291] "White adipose tissue" "White matter"
#> [293] "Yolk sac"
mouse_tissues
#> [1] "Adipose tissue"
#> [2] "Adrenal gland"
#> [3] "Adventitia"
#> [4] "Afferent artery"
#> [5] "Airway"
#> [6] "Alveolar capillary"
#> [7] "Alveolus"
#> [8] "Amygdala"
#> [9] "Annulus fibrosus"
#> [10] "Anorectal junction"
#> [11] "Anterior lobule"
#> [12] "Aorta"
#> [13] "Aortic root"
#> [14] "Aortic valve"
#> [15] "Arm"
#> [16] "Artery"
#> [17] "Arthrosis"
#> [18] "Ascending aorta"
#> [19] "Auditory cortex"
#> [20] "Basilar membrane"
#> [21] "Bed nucleus of the stria terminalis"
#> [22] "Belly"
#> [23] "Bile duct"
#> [24] "Biliary tract"
#> [25] "Bladder"
#> [26] "Bladder mucosa"
#> [27] "Blood"
#> [28] "Blood brain barrier"
#> [29] "Blood vessel"
#> [30] "Bone"
#> [31] "Bone marrow"
#> [32] "Brain"
#> [33] "Breast"
#> [34] "Bronchiole"
#> [35] "Bronchus"
#> [36] "Buccal mucosal epithelium"
#> [37] "Caecum"
#> [38] "Capillary"
#> [39] "Cardiac neural crest"
#> [40] "Cardiovascular system"
#> [41] "Carotid artery"
#> [42] "Cartilage"
#> [43] "Cauda epididymis"
#> [44] "Caudal ganglionic eminence"
#> [45] "Central nervous system"
#> [46] "Cerebellar nuclei"
#> [47] "Cerebellum"
#> [48] "Cerebral cortex"
#> [49] "Cerebral motor cortex"
#> [50] "Cerebral organoid"
#> [51] "Cerebrospinal fluid"
#> [52] "Choroid"
#> [53] "Choroid plexus"
#> [54] "Choroid plexus capillary"
#> [55] "Cochlea"
#> [56] "Cochlear duct"
#> [57] "Colon"
#> [58] "Colon epithelium"
#> [59] "Colonic crypt"
#> [60] "Colorectum"
#> [61] "Conjunctiva"
#> [62] "Connective tissue"
#> [63] "Cornea"
#> [64] "Corneal epithelium"
#> [65] "Cornu ammonis 1"
#> [66] "Cornu ammonis 2"
#> [67] "Coronary artery"
#> [68] "Corpus callosum"
#> [69] "Cortex"
#> [70] "Dermal microvasculature"
#> [71] "Dermal papilla"
#> [72] "Dermis"
#> [73] "Diaphragm"
#> [74] "Distal limb mesenchyme"
#> [75] "Distal lung endoderm"
#> [76] "Dorsal forebrain"
#> [77] "Dorsal root ganglia"
#> [78] "Dorsal root ganglion"
#> [79] "Dorsal skin"
#> [80] "Dorsolateral prefrontal cortex"
#> [81] "Dorsomedial hypothalamus"
#> [82] "Ear"
#> [83] "Ectoderm"
#> [84] "Efferent artery"
#> [85] "Embryo"
#> [86] "Embryoid body"
#> [87] "Embryonic brain"
#> [88] "Embryonic breast"
#> [89] "Embryonic ectoderm"
#> [90] "Embryonic endoderm"
#> [91] "Embryonic heart"
#> [92] "Embryonic Kidney"
#> [93] "Embryonic mesoderm"
#> [94] "Embryonic stem cell"
#> [95] "Embryos"
#> [96] "Endocardium"
#> [97] "Endoderm"
#> [98] "Endodontium"
#> [99] "Endometrium"
#> [100] "Endothelium"
#> [101] "Enteric neural crest"
#> [102] "Epiblast"
#> [103] "Epidermis"
#> [104] "Epithelium"
#> [105] "Esophagus"
#> [106] "External genitalia"
#> [107] "Extra-embryonic ectoderm"
#> [108] "Extra-embryonic endoderm"
#> [109] "Extra-embryonic mesoderm"
#> [110] "Extra-embryonic tissue"
#> [111] "Eye"
#> [112] "Fat pad"
#> [113] "Femur bone"
#> [114] "Fetal hypothalamus"
#> [115] "Fetal kidney"
#> [116] "Fetal liver"
#> [117] "Fetal ovary"
#> [118] "Fetal skin"
#> [119] "First heart field(FHF)"
#> [120] "Flesh"
#> [121] "Focculus"
#> [122] "Foregut endoderm"
#> [123] "Ganglion cell layer of retina"
#> [124] "Gastric corpus"
#> [125] "Gastric epithelium"
#> [126] "Gastric gland"
#> [127] "Gastric isthmus"
#> [128] "Gastrointestinal tract"
#> [129] "Germinal center"
#> [130] "Gingiva"
#> [131] "Glomerular capillary"
#> [132] "Glomerulus"
#> [133] "Gonad"
#> [134] "Gut"
#> [135] "Hair canal"
#> [136] "Hair follicle"
#> [137] "Head and Neck"
#> [138] "Heart"
#> [139] "Heart muscle"
#> [140] "Heart valve"
#> [141] "Hind limb"
#> [142] "Hippocampus"
#> [143] "Hypothalamic brain slice"
#> [144] "Hypothalamic nucleus"
#> [145] "Hypothalamus"
#> [146] "Hypothalamus-POA"
#> [147] "Ileum"
#> [148] "Incisor"
#> [149] "Inferior colliculus"
#> [150] "Inner cell mass"
#> [151] "Inner Ear"
#> [152] "Inner nuclear layer of retina"
#> [153] "Interfollicular epidermis"
#> [154] "Intestinal crypt"
#> [155] "Intestine"
#> [156] "Juxta-cardiac field (JCF)"
#> [157] "Kidney"
#> [158] "Kidney cortex"
#> [159] "Knee"
#> [160] "Lacrimal gland"
#> [161] "Large intestine"
#> [162] "Large peritoneal"
#> [163] "Lateral hypothalamus"
#> [164] "Left ventricle"
#> [165] "limb"
#> [166] "Limb bud"
#> [167] "Liver"
#> [168] "Lobule VI"
#> [169] "Lower dermis"
#> [170] "Lower hair follicle"
#> [171] "Lung"
#> [172] "Lymph"
#> [173] "Lymph node"
#> [174] "Lymphatic vessel"
#> [175] "Lymphoid tissue"
#> [176] "Macrovessel"
#> [177] "Main olfactory epithelia"
#> [178] "Mammary epithelium"
#> [179] "Mammary gland"
#> [180] "Mandibular alveolar bone"
#> [181] "Meninge"
#> [182] "Meniscus"
#> [183] "Mesenteric lymph node"
#> [184] "Mesoderm"
#> [185] "Mesodermal precursor"
#> [186] "Mesonephros"
#> [187] "Microvessel"
#> [188] "Midbrain"
#> [189] "Molar"
#> [190] "Motor cortex"
#> [191] "Muscle"
#> [192] "Myenteric plexus"
#> [193] "Myocardium"
#> [194] "Nasal cavity"
#> [195] "Neocortex"
#> [196] "Nerve"
#> [197] "Neural tube"
#> [198] "Nodose"
#> [199] "Nodulus"
#> [200] "Non-Vasculature"
#> [201] "Nucleus accumbens"
#> [202] "Olfactory neuroepithelium"
#> [203] "Omentum"
#> [204] "Oral cavity"
#> [205] "Outflow tract"
#> [206] "Ovarian follicle"
#> [207] "Ovary"
#> [208] "Pancreas"
#> [209] "Pancreatic duct"
#> [210] "Pancreatic islet"
#> [211] "PeriBiliary cell gland"
#> [212] "Peribiliary gland"
#> [213] "Perichondrium"
#> [214] "Periosteum"
#> [215] "Peripheral blood"
#> [216] "Peritoneal cavity"
#> [217] "Peritoneum"
#> [218] "Peyer patch"
#> [219] "Pharynx"
#> [220] "Pituitary"
#> [221] "Placenta"
#> [222] "Pluripotent stem cell"
#> [223] "Polyp"
#> [224] "Posterior lobule"
#> [225] "Posterior second heart field"
#> [226] "Prefrontal cortex"
#> [227] "Presomitic mesoderm"
#> [228] "Primary motor cortex"
#> [229] "Primary visual cortex"
#> [230] "Primitive endoderm"
#> [231] "Primordial germ"
#> [232] "Prostate"
#> [233] "Proximal lung endoderm"
#> [234] "Pulmonary aorta"
#> [235] "Pulmonary arteriy"
#> [236] "Pylorus"
#> [237] "Red pulp"
#> [238] "Renal glomerulus"
#> [239] "Retina"
#> [240] "Retina vessel"
#> [241] "Retinal pigment epithelium"
#> [242] "Salivary duct"
#> [243] "Salivary gland"
#> [244] "Sciatic nerve"
#> [245] "Sebaceous gland"
#> [246] "Seminal plasma"
#> [247] "Serum"
#> [248] "Sinoatrial node"
#> [249] "Skeletal muscle"
#> [250] "Skin"
#> [251] "Skin of back"
#> [252] "Small intestinal crypt"
#> [253] "Small intestine"
#> [254] "Smooth muscle"
#> [255] "Soft palate"
#> [256] "Soft tissue"
#> [257] "Somatosensory cortex"
#> [258] "Spinal cord"
#> [259] "Spleen"
#> [260] "Stomach"
#> [261] "Striatum"
#> [262] "Subcutaneous adipose tissue"
#> [263] "Subgranular zone"
#> [264] "Submandibular gland"
#> [265] "Subventricular zone"
#> [266] "Superior cervical ganglion"
#> [267] "Suture mesenchyme"
#> [268] "Synovium"
#> [269] "Taste bud"
#> [270] "Tendon"
#> [271] "Testis"
#> [272] "Thoracic aorta"
#> [273] "Thymus"
#> [274] "Thyroid"
#> [275] "Tongue"
#> [276] "Tonsil"
#> [277] "Trachea"
#> [278] "Trophectoderm"
#> [279] "Umbilical cord"
#> [280] "Umbilical cord blood"
#> [281] "Undefined"
#> [282] "Upper hair follicle"
#> [283] "Urethra"
#> [284] "Uterine cervix"
#> [285] "Uterus"
#> [286] "Vein"
#> [287] "Ventral posterior hypothalamus (VPH)"
#> [288] "Ventral tegmental area"
#> [289] "Ventromedial hypothalamus (VMHvl)"
#> [290] "Vessel"
#> [291] "White adipose tissue"
#> [292] "White matter"
#> [293] "Yolk sac"
#### Copy the names of tissues you are interested. Multiple tissues are accepted by Validate Predictions.
out.CelltypeAnnotation = CelltypeAnnotation(reference = reference, query = query, downsample = TRUE, downsample_to = 1000, validatePredictions = TRUE, annotationCol = "Celltype", species = "human", tissue = "Peripheral blood")
#> Setting Assay of reference and query to RNA
#> Running Diagonistis on reference and query
#> Number of cells in reference:2019
#> Number of cells in query:1358
#> Downsampling reference
#> Number of cells in reference after downsampling per celltype:2019
#> Calculating ratio of number of cells in downsampled reference vs query
#> Ratio of number of cells in query vs downsampled reference:0.672610203070827
#> Centering and scaling data matrix
#> Centering and scaling data matrix
#> Finding common variable features between reference and query
#> Subsetting reference and query for common variable features
#> Preparing train and test datasets from reference and query
#> Scaling reference to obtain training set
#> Scaling query to obtain test set
#>
#> Setting up three classifiers: randomForest, SVM and LR
#> Initializing randomForest
#> randomForest Complete
#> Initializing SVM
#> ARGUMENTS SETUP
#> PROBLEM SETUP
#> FILL DATA STRUCTURE
#> SETUP CHECK
#> TRAIN
#> .*.*
#> optimization finished, #iter = 29
#> Objective value = 2.949909
#> #nonzeros/#features = 51/493
#> ..*.*
#> optimization finished, #iter = 39
#> Objective value = 22.066079
#> #nonzeros/#features = 188/493
#> ..*.*
#> optimization finished, #iter = 35
#> Objective value = 8.864476
#> #nonzeros/#features = 117/493
#> ..*.*
#> optimization finished, #iter = 33
#> Objective value = 22.589678
#> #nonzeros/#features = 186/493
#> ..*.*
#> optimization finished, #iter = 31
#> Objective value = 7.711503
#> #nonzeros/#features = 96/493
#> .*.*
#> optimization finished, #iter = 29
#> Objective value = 13.829589
#> #nonzeros/#features = 198/493
#> .*.*
#> optimization finished, #iter = 29
#> Objective value = 3.922960
#> #nonzeros/#features = 69/493
#> ....**.
#> optimization finished, #iter = 50
#> Objective value = 21.344268
#> #nonzeros/#features = 208/493
#> ..*.*
#> optimization finished, #iter = 32
#> Objective value = 15.986209
#> #nonzeros/#features = 154/493
#> ..*.*.
#> optimization finished, #iter = 40
#> Objective value = 19.200190
#> #nonzeros/#features = 159/493
#> .**
#> optimization finished, #iter = 18
#> Objective value = 4.516412
#> #nonzeros/#features = 50/493
#> COPY MODEL TO WEIGHT VECTOR
#> FREE SPACE
#> FREED SPACE
#> SVM Complete
#> Initializing LR
#> ARGUMENTS SETUP
#> PROBLEM SETUP
#> FILL DATA STRUCTURE
#> SETUP CHECK
#> TRAIN
#> .
#> optimization finished, #iter = 13
#> Objective value = -3.992689
#> ....
#> optimization finished, #iter = 40
#> Objective value = -18.939503
#> ...
#> optimization finished, #iter = 34
#> Objective value = -10.360630
#> ....
#> optimization finished, #iter = 40
#> Objective value = -19.051939
#> ..
#> optimization finished, #iter = 28
#> Objective value = -9.799596
#> ...
#> optimization finished, #iter = 36
#> Objective value = -12.786152
#> ..
#> optimization finished, #iter = 23
#> Objective value = -7.560311
#> ....
#> optimization finished, #iter = 43
#> Objective value = -18.135476
#> ..
#> optimization finished, #iter = 28
#> Objective value = -15.249190
#> ..
#> optimization finished, #iter = 27
#> Objective value = -17.553502
#> ..
#> optimization finished, #iter = 23
#> Objective value = -10.646397
#> COPY MODEL TO WEIGHT VECTOR
#> FREE SPACE
#> FREED SPACE
#> LR Complete
#>
#> Classifying cells in query using each classifier
#> Warning: Invalid name supplied, making object
#> name syntactically valid. New object name is
#> ELeFHAnt_RF_CD14..Monocyte.ProbabilityELeFHAnt_RF_CD19..B.ProbabilityELeFHAnt_RF_CD34..ProbabilityELeFHAnt_RF_CD4..T.Helper2.ProbabilityELeFHAnt_RF_CD4..CD25.T.Reg.ProbabilityELeFHAnt_RF_CD4..CD45RA..CD25..Naive.T.ProbabilityELeFHAnt_RF_CD4..CD45RO..Memory.ProbabilityELeFHAnt_RF_CD56..NK.ProbabilityELeFHAnt_RF_CD8..Cytotoxic.T.ProbabilityELeFHAnt_RF_CD8..CD45RA..Naive.Cytotoxic.ProbabilityELeFHAnt_RF_Dendritic.Probability;
#> see ?make.names for more details on syntax validity
#> Warning: Invalid name supplied, making object
#> name syntactically valid. New object name is
#> ELeFHAnt_SVM_CD34..Decision.ValuesELeFHAnt_SVM_CD4..CD45RO..Memory.Decision.ValuesELeFHAnt_SVM_CD14..Monocyte.Decision.ValuesELeFHAnt_SVM_CD4..CD25.T.Reg.Decision.ValuesELeFHAnt_SVM_CD56..NK.Decision.ValuesELeFHAnt_SVM_Dendritic.Decision.ValuesELeFHAnt_SVM_CD19..B.Decision.ValuesELeFHAnt_SVM_CD4..CD45RA..CD25..Naive.T.Decision.ValuesELeFHAnt_SVM_CD8..Cytotoxic.T.Decision.ValuesELeFHAnt_SVM_CD8..CD45RA..Naive.Cytotoxic.Decision.ValuesELeFHAnt_SVM_CD4..T.Helper2.Decision.Values;
#> see ?make.names for more details on syntax validity
#> Warning: Invalid name supplied, making object
#> name syntactically valid. New object name is
#> ELeFHAnt_LR_CD34..ProbabilityELeFHAnt_LR_CD4..CD45RO..Memory.ProbabilityELeFHAnt_LR_CD14..Monocyte.ProbabilityELeFHAnt_LR_CD4..CD25.T.Reg.ProbabilityELeFHAnt_LR_CD56..NK.ProbabilityELeFHAnt_LR_Dendritic.ProbabilityELeFHAnt_LR_CD19..B.ProbabilityELeFHAnt_LR_CD4..CD45RA..CD25..Naive.T.ProbabilityELeFHAnt_LR_CD8..Cytotoxic.T.ProbabilityELeFHAnt_LR_CD8..CD45RA..Naive.Cytotoxic.ProbabilityELeFHAnt_LR_CD4..T.Helper2.Probability;
#> see ?make.names for more details on syntax validity
#>
#> Obtaing Ensemble Predictions using RF, SVM and LR
#>
#> Celltype predictions are stored in query metadata. Please see: ELeFHAnt_RF_CelltypePrediction, ELeFHAnt_SVM_CelltypePrediction, ELeFHAnt_LR_CelltypePrediction, ELeFHAnt_Ensemble_CelltypePrediction
#> Ensembl celltype annotation completed. Starting validation of celltype assignments using GSEA
#>
#> Setting up Directory to write ValidatePredictions Results
#> Extracting Cell type Markers from CellMarker Database v2.0 [Experiment Based] and C8 Hallmark gene sets GSEA
#>
#> GSEA BASED VALIDATION
#> Obtaining markers per annotated cluster
#> Calculating cluster 0
#> Calculating cluster 1
#> Calculating cluster 2
#> Calculating cluster 3
#> Calculating cluster 4
#> Calculating cluster 5
#> Calculating cluster 6
#> Calculating cluster 7
#> Calculating cluster 8
#> Performing Gene Set Enrichment Analysis (GSEA) using gene sets from C8 Hallmark MsigDB
#> Obtaing GSEA statistics for cluster:0
#> Generating a Barplot with Normalized Enrichment Score for cluster:0
#> Obtaing GSEA statistics for cluster:1
#> Generating a Barplot with Normalized Enrichment Score for cluster:1
#> Obtaing GSEA statistics for cluster:2
#> Generating a Barplot with Normalized Enrichment Score for cluster:2
#> Obtaing GSEA statistics for cluster:3
#> Generating a Barplot with Normalized Enrichment Score for cluster:3
#> Obtaing GSEA statistics for cluster:4
#> Generating a Barplot with Normalized Enrichment Score for cluster:4
#> Obtaing GSEA statistics for cluster:5
#> Generating a Barplot with Normalized Enrichment Score for cluster:5
#> Obtaing GSEA statistics for cluster:6
#> Generating a Barplot with Normalized Enrichment Score for cluster:6
#> Obtaing GSEA statistics for cluster:7
#> Generating a Barplot with Normalized Enrichment Score for cluster:7
#> Obtaing GSEA statistics for cluster:8
#> Generating a Barplot with Normalized Enrichment Score for cluster:8
#>
#> GSEA VALIDATION COMPLETED
#>
#> CellMarker DATABASE BASED VALIDATION
#>
#> CellMarker DATABASE BASED VALIDATION FOR QUERY
#> Tissue of interest:Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Memory CD8+ T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Memory CD4+ T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Classical monocyte in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:T cell large granular lymphocytic leukemia cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Mast cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:M1 macrophage in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:CD8+ T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:CD4+ cytotoxic T1 cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Natural killer cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Activated CD8+ T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:CD4+ cytotoxic T2 cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Gamma delta(γδ) T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Monocyte in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Myeloid cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Macrophage in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:CD14+ monocyte in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Mature dendritic cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:CD14 monocyte in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Intermediate monocyte in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:CD16+ monocyte in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Intermediate monocyte cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Monocytic myeloid-derived suppressor cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Monocyte lineage in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Fully activated dendritic cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Non-classical monocyte in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Dendritic cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Monocyte derived dendritic cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Myeloid dendritic cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Polymorphonuclear myeloid-derived suppressor cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Dendritic cell lineage in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Myeloid derived suppressor cell (MDSC) in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Mature B cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:CD4+ T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Naive T(Th0) cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Exhausted CD4+ T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Regulatory T(Treg) cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Regulatory CD8+ T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Proliferative CD8+ T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Proliferative T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Naive CD8+ T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Cytotoxic T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Early effector T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Central memory T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Effector CD4+ T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Naive CD4+ T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Proliferative CD4+ T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Regulatory CD4+ T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Effector CD8+ T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Exhausted CD8+ T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Natural killer T(NKT) cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:CD8 T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:CD4+ central memory like T (Tcm-like) cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:CD4 T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Activated CD4+ T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Immature B cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:B cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Memory B cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Megakaryocyte in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Naive B cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Plasma cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Memory CD8 T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Monocyte precursor in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Abnormal myeloid cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:CD16 monocyte in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Platelet in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Effector memory CD4+ T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Plasmacytoid dendritic cell(pDC) in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Cytotoxic CD4+ T2 cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Responding conventional T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Cytotoxic CD8 T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Effector memory CD8 T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Mucosa-associated invariant T (MAIT) cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Memory T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Lymphocyte in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Neutrophil in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Germinal center B cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Patelet in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Activated T cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Polymorphonuclear myeloid-derived suppressor(PMN-MDSC) cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Circulating progenitor cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:CD16+ dendritic cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Granulocyte in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Eosinophil in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Suppressive monocyte in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Immature myeloid cell in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:M2 macrophage in Peripheral blood
#> Generating DotPlot/FeaturePlot for experimental evidence based markers for:Myeloid-derived suppressor cell in Peripheral blood
#>
#> CellMarker DATABASE BASED VALIDATION COMPLETED
#> Validation completed. Please see ValidatePredictions Folder for results
p1 = DimPlot(out.CelltypeAnnotation, group.by = "seurat_clusters", label = T, reduction = "umap", label.size = 6, repel = T) + NoLegend()
p2 = DimPlot(out.CelltypeAnnotation, group.by = "ELeFHAnt_Ensemble_CelltypePrediction", label = T, reduction = "umap", label.size = 6, repel = T) + NoLegend()
p3 = DimPlot(out.CelltypeAnnotation, group.by = "ELeFHAnt_RF_CelltypePrediction", label = T, reduction = "umap", label.size = 6, repel = T) + NoLegend()
p4 = DimPlot(out.CelltypeAnnotation, group.by = "ELeFHAnt_SVM_CelltypePrediction", label = T, reduction = "umap", label.size = 6, repel = T) + NoLegend()
p5 = DimPlot(out.CelltypeAnnotation, group.by = "ELeFHAnt_LR_CelltypePrediction", label = T, reduction = "umap", label.size = 6, repel = T) + NoLegend()
p1
out.DR = DeduceRelationship(reference1 = reference, reference2 = query, downsample = TRUE, downsample_to = 1000, selectvarfeatures = 2000, ntree = 500, annotationCol_ref1 = "Celltype", annotationCol_ref2 = "Celltype")
#> Setting Assay of reference1 and reference2 to RNA
#> Number of cells in reference1:2019
#> Number of cells in reference2:1358
#> Centering and scaling data matrix
#> Centering and scaling data matrix
#> Number of cells in reference1 after downsampling:2019
#> Number of cells in reference2 after downsampling:1358
#> Finding common variable features between reference and query
#> Subsetting reference1 and reference2 for common variable features
#> Preparing train and test datasets from reference1 and reference2
#> Scaling reference1 to obtain training set
#> Scaling reference2 to obtain test set
#>
#> Setting up three classifiers: randomForest, SVM and LR
#> Initializing randomForest
#> randomForest Complete
#> Initializing SVM
#> ARGUMENTS SETUP
#> PROBLEM SETUP
#> FILL DATA STRUCTURE
#> SETUP CHECK
#> TRAIN
#> .*.*
#> optimization finished, #iter = 29
#> Objective value = 2.949909
#> #nonzeros/#features = 51/493
#> ..*.*
#> optimization finished, #iter = 39
#> Objective value = 22.066079
#> #nonzeros/#features = 188/493
#> ..*.*
#> optimization finished, #iter = 35
#> Objective value = 8.864476
#> #nonzeros/#features = 117/493
#> ..*.*
#> optimization finished, #iter = 33
#> Objective value = 22.589678
#> #nonzeros/#features = 186/493
#> ..*.*
#> optimization finished, #iter = 31
#> Objective value = 7.711503
#> #nonzeros/#features = 96/493
#> .*.*
#> optimization finished, #iter = 29
#> Objective value = 13.829589
#> #nonzeros/#features = 198/493
#> .*.*
#> optimization finished, #iter = 29
#> Objective value = 3.922960
#> #nonzeros/#features = 69/493
#> ....**.
#> optimization finished, #iter = 50
#> Objective value = 21.344268
#> #nonzeros/#features = 208/493
#> ..*.*
#> optimization finished, #iter = 32
#> Objective value = 15.986209
#> #nonzeros/#features = 154/493
#> ..*.*.
#> optimization finished, #iter = 40
#> Objective value = 19.200190
#> #nonzeros/#features = 159/493
#> .**
#> optimization finished, #iter = 18
#> Objective value = 4.516412
#> #nonzeros/#features = 50/493
#> COPY MODEL TO WEIGHT VECTOR
#> FREE SPACE
#> FREED SPACE
#> SVM Complete
#> Initializing LR
#> ARGUMENTS SETUP
#> PROBLEM SETUP
#> FILL DATA STRUCTURE
#> SETUP CHECK
#> TRAIN
#> .
#> optimization finished, #iter = 13
#> Objective value = -3.992689
#> ....
#> optimization finished, #iter = 40
#> Objective value = -18.939503
#> ...
#> optimization finished, #iter = 34
#> Objective value = -10.360630
#> ....
#> optimization finished, #iter = 40
#> Objective value = -19.051939
#> ..
#> optimization finished, #iter = 28
#> Objective value = -9.799596
#> ...
#> optimization finished, #iter = 36
#> Objective value = -12.786152
#> ..
#> optimization finished, #iter = 23
#> Objective value = -7.560311
#> ....
#> optimization finished, #iter = 43
#> Objective value = -18.135476
#> ..
#> optimization finished, #iter = 28
#> Objective value = -15.249190
#> ..
#> optimization finished, #iter = 27
#> Objective value = -17.553502
#> ..
#> optimization finished, #iter = 23
#> Objective value = -10.646397
#> COPY MODEL TO WEIGHT VECTOR
#> FREE SPACE
#> FREED SPACE
#> LR Complete
#>
#> Classifying cells in query using each classifier and Generating scaled confusion matrix
#>
#> Using Relative Similarity from Normalized Confusion matrices to generate Reference1 vs Reference2 similarity
out.DR
reference$Celltypes = reference$Celltype
query$Celltypes = query$Celltype
out.LH = LabelHarmonization(seurat.objects = c(reference, query), perform_integration = TRUE, integrated.atlas = NULL, downsample = TRUE, downsample_to = 1000, npcs = 30, resolution = 0.8, validatePredictions = FALSE, selectanchorfeatures = 2000, ntree = 500, k.anchor = 5, k.filter = 200, k.score = 30, dims = 1:30, species = NULL, tissue = NULL, annotationCol = "Celltypes")
#> Downsampling seurat objects
#> Starting integration using Seurat Canonical Correlation Algorithm
#> Computing 2000 integration features
#> Scaling features for provided objects
#> Finding all pairwise anchors
#> Running CCA
#> Merging objects
#> Finding neighborhoods
#> Finding anchors
#> Found 4810 anchors
#> Filtering anchors
#> Retained 3602 anchors
#> Merging dataset 2 into 1
#> Extracting anchors for merged samples
#> Finding integration vectors
#> Finding integration vector weights
#> Integrating data
#> Integration Completed. Performing Scaling, Dimension reduction and clustering
#> 00:25:52 UMAP embedding parameters a = 0.9922 b = 1.112
#> 00:25:52 Read 3377 rows and found 30 numeric columns
#> 00:25:52 Using Annoy for neighbor search, n_neighbors = 30
#> 00:25:52 Building Annoy index with metric = cosine, n_trees = 50
#> 0% 10 20 30 40 50 60 70 80 90 100%
#> [----|----|----|----|----|----|----|----|----|----|
#> **************************************************|
#> 00:25:53 Writing NN index file to temp file /var/folders/bw/whg3swn15jb08_f7v2y09xw9glk1wg/T//RtmpWY8t69/filee0be13889d15
#> 00:25:53 Searching Annoy index using 1 thread, search_k = 3000
#> 00:25:53 Annoy recall = 100%
#> 00:25:54 Commencing smooth kNN distance calibration using 1 thread
#> 00:25:55 Initializing from normalized Laplacian + noise
#> 00:25:56 Commencing optimization for 500 epochs, with 143848 positive edges
#> 00:26:01 Optimization finished
#> Computing nearest neighbor graph
#> Computing SNN
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#>
#> Number of nodes: 3377
#> Number of edges: 168438
#>
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.8663
#> Number of communities: 14
#> Elapsed time: 0 seconds
#> Number of cells in integrated atlas:3377
#> Generating train and test datasets using stratification -- 70% for training & 30% for testing
#> Number of Anchor Features selected:2000
#>
#> Setting up three classifiers: randomForest, SVM and LR
#> Initializing randomForest
#> randomForest Complete
#> Initializing SVM
#> ARGUMENTS SETUP
#> PROBLEM SETUP
#> FILL DATA STRUCTURE
#> SETUP CHECK
#> TRAIN
#> ..*...**
#> optimization finished, #iter = 59
#> Objective value = 3.463748
#> #nonzeros/#features = 63/2001
#> .**.
#> optimization finished, #iter = 20
#> Objective value = 1.870714
#> #nonzeros/#features = 17/2001
#> .**
#> optimization finished, #iter = 18
#> Objective value = 8.515331
#> #nonzeros/#features = 171/2001
#> .*.*
#> optimization finished, #iter = 24
#> Objective value = 9.205882
#> #nonzeros/#features = 190/2001
#> ..*.**
#> optimization finished, #iter = 37
#> Objective value = 9.426450
#> #nonzeros/#features = 212/2001
#> ..*.**
#> optimization finished, #iter = 36
#> Objective value = 10.029644
#> #nonzeros/#features = 216/2001
#> ..*.*
#> optimization finished, #iter = 36
#> Objective value = 11.192939
#> #nonzeros/#features = 284/2001
#> ..*.*
#> optimization finished, #iter = 32
#> Objective value = 10.068798
#> #nonzeros/#features = 246/2001
#> ...*.*
#> optimization finished, #iter = 49
#> Objective value = 7.784389
#> #nonzeros/#features = 162/2001
#> **.
#> optimization finished, #iter = 10
#> Objective value = 2.089124
#> #nonzeros/#features = 18/2001
#> .*.*
#> optimization finished, #iter = 25
#> Objective value = 11.347513
#> #nonzeros/#features = 270/2001
#> ..*.*.*
#> optimization finished, #iter = 41
#> Objective value = 8.589253
#> #nonzeros/#features = 222/2001
#> .*.*
#> optimization finished, #iter = 21
#> Objective value = 4.318886
#> #nonzeros/#features = 82/2001
#> ..*.*.*
#> optimization finished, #iter = 42
#> Objective value = 8.714387
#> #nonzeros/#features = 199/2001
#> ..*.*.*
#> optimization finished, #iter = 41
#> Objective value = 6.275500
#> #nonzeros/#features = 112/2001
#> ..*..*
#> optimization finished, #iter = 41
#> Objective value = 10.596130
#> #nonzeros/#features = 249/2001
#> ..*.**.
#> optimization finished, #iter = 40
#> Objective value = 5.188268
#> #nonzeros/#features = 103/2001
#> .**
#> optimization finished, #iter = 14
#> Objective value = 2.501563
#> #nonzeros/#features = 28/2001
#> .*.*
#> optimization finished, #iter = 24
#> Objective value = 6.337268
#> #nonzeros/#features = 117/2001
#> ..*.*
#> optimization finished, #iter = 36
#> Objective value = 6.841068
#> #nonzeros/#features = 130/2001
#> COPY MODEL TO WEIGHT VECTOR
#> FREE SPACE
#> FREED SPACE
#> SVM Complete
#> Initializing LR
#> ARGUMENTS SETUP
#> PROBLEM SETUP
#> FILL DATA STRUCTURE
#> SETUP CHECK
#> TRAIN
#> .
#> optimization finished, #iter = 19
#> Objective value = -5.686856
#> ...
#> optimization finished, #iter = 33
#> Objective value = -6.688618
#> ..
#> optimization finished, #iter = 24
#> Objective value = -7.936455
#> ..
#> optimization finished, #iter = 22
#> Objective value = -8.181496
#> ..
#> optimization finished, #iter = 24
#> Objective value = -8.119265
#> ..
#> optimization finished, #iter = 20
#> Objective value = -8.384488
#> ..
#> optimization finished, #iter = 26
#> Objective value = -8.422691
#> ..
#> optimization finished, #iter = 25
#> Objective value = -8.130720
#> ..
#> optimization finished, #iter = 23
#> Objective value = -7.539691
#> ..
#> optimization finished, #iter = 21
#> Objective value = -6.777600
#> ..
#> optimization finished, #iter = 21
#> Objective value = -8.644200
#> ..
#> optimization finished, #iter = 23
#> Objective value = -7.524811
#> ..
#> optimization finished, #iter = 25
#> Objective value = -6.766292
#> ..
#> optimization finished, #iter = 27
#> Objective value = -7.640640
#> ..
#> optimization finished, #iter = 26
#> Objective value = -7.169847
#> ..
#> optimization finished, #iter = 21
#> Objective value = -8.331353
#> ..
#> optimization finished, #iter = 26
#> Objective value = -6.927301
#> ..
#> optimization finished, #iter = 25
#> Objective value = -6.634453
#> ..
#> optimization finished, #iter = 23
#> Objective value = -6.889984
#> ..
#> optimization finished, #iter = 21
#> Objective value = -7.274180
#> COPY MODEL TO WEIGHT VECTOR
#> FREE SPACE
#> FREED SPACE
#> LR Complete
#>
#> Classifying cells in query using each classifier and Generating scaled confusion matrix
#>
#> Harmonized Celltype predictions are stored in integrated metadata. Please see: ELeFHAnt_RF_HarmonizedCelltype, ELeFHAnt_SVM_HarmonizedCelltype, ELeFHAnt_LR_HarmonizedCelltype, ELeFHAnt_Ensemble_HarmonizedCelltype
#> Ensembl celltype harmonization completed.
p1 = DimPlot(out.LH, group.by = "Celltypes", label = T, reduction = "umap", label.size = 6, repel = T) + NoLegend()
p2 = DimPlot(out.LH, group.by = "ELeFHAnt_Ensemble_HarmonizedCelltype", label = T, reduction = "umap", label.size = 6, repel = T) + NoLegend()
p1
#> Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
out.Benchmark = BenchmarkELeFHAnt(reference = reference, query = query, downsample = TRUE, downsample_to = 1000, selectvarfeatures = 2000, ntree = 500, annotationCol = "Celltype")
#>
#> Downsampling reference cells to enable fast computation
#> Centering and scaling data matrix
#>
#> Deploying ELeFHAnt
#> Setting Assay of reference and query to RNA
#> Running Diagonistis on reference and query
#> Number of cells in reference:2019
#> Number of cells in query:1358
#> Downsampling reference
#> Number of cells in reference after downsampling per celltype:2019
#> Calculating ratio of number of cells in downsampled reference vs query
#> Ratio of number of cells in query vs downsampled reference:0.672610203070827
#> Centering and scaling data matrix
#> Centering and scaling data matrix
#> Finding common variable features between reference and query
#> Subsetting reference and query for common variable features
#> Preparing train and test datasets from reference and query
#> Scaling reference to obtain training set
#> Scaling query to obtain test set
#>
#> Setting up three classifiers: randomForest, SVM and LR
#> Initializing randomForest
#> randomForest Complete
#> Initializing SVM
#> ARGUMENTS SETUP
#> PROBLEM SETUP
#> FILL DATA STRUCTURE
#> SETUP CHECK
#> TRAIN
#> .*.*
#> optimization finished, #iter = 29
#> Objective value = 2.949909
#> #nonzeros/#features = 51/493
#> ..*.*
#> optimization finished, #iter = 39
#> Objective value = 22.066079
#> #nonzeros/#features = 188/493
#> ..*.*
#> optimization finished, #iter = 35
#> Objective value = 8.864476
#> #nonzeros/#features = 117/493
#> ..*.*
#> optimization finished, #iter = 33
#> Objective value = 22.589678
#> #nonzeros/#features = 186/493
#> ..*.*
#> optimization finished, #iter = 31
#> Objective value = 7.711503
#> #nonzeros/#features = 96/493
#> .*.*
#> optimization finished, #iter = 29
#> Objective value = 13.829589
#> #nonzeros/#features = 198/493
#> .*.*
#> optimization finished, #iter = 29
#> Objective value = 3.922960
#> #nonzeros/#features = 69/493
#> ....**.
#> optimization finished, #iter = 50
#> Objective value = 21.344268
#> #nonzeros/#features = 208/493
#> ..*.*
#> optimization finished, #iter = 32
#> Objective value = 15.986209
#> #nonzeros/#features = 154/493
#> ..*.*.
#> optimization finished, #iter = 40
#> Objective value = 19.200190
#> #nonzeros/#features = 159/493
#> .**
#> optimization finished, #iter = 18
#> Objective value = 4.516412
#> #nonzeros/#features = 50/493
#> COPY MODEL TO WEIGHT VECTOR
#> FREE SPACE
#> FREED SPACE
#> SVM Complete
#> Initializing LR
#> ARGUMENTS SETUP
#> PROBLEM SETUP
#> FILL DATA STRUCTURE
#> SETUP CHECK
#> TRAIN
#> .
#> optimization finished, #iter = 13
#> Objective value = -3.992689
#> ....
#> optimization finished, #iter = 40
#> Objective value = -18.939503
#> ...
#> optimization finished, #iter = 34
#> Objective value = -10.360630
#> ....
#> optimization finished, #iter = 40
#> Objective value = -19.051939
#> ..
#> optimization finished, #iter = 28
#> Objective value = -9.799596
#> ...
#> optimization finished, #iter = 36
#> Objective value = -12.786152
#> ..
#> optimization finished, #iter = 23
#> Objective value = -7.560311
#> ....
#> optimization finished, #iter = 43
#> Objective value = -18.135476
#> ..
#> optimization finished, #iter = 28
#> Objective value = -15.249190
#> ..
#> optimization finished, #iter = 27
#> Objective value = -17.553502
#> ..
#> optimization finished, #iter = 23
#> Objective value = -10.646397
#> COPY MODEL TO WEIGHT VECTOR
#> FREE SPACE
#> FREED SPACE
#> LR Complete
#>
#> Classifying cells in query using each classifier
#> Warning: Invalid name supplied, making object
#> name syntactically valid. New object name is
#> ELeFHAnt_RF_CD14..Monocyte.ProbabilityELeFHAnt_RF_CD19..B.ProbabilityELeFHAnt_RF_CD34..ProbabilityELeFHAnt_RF_CD4..T.Helper2.ProbabilityELeFHAnt_RF_CD4..CD25.T.Reg.ProbabilityELeFHAnt_RF_CD4..CD45RA..CD25..Naive.T.ProbabilityELeFHAnt_RF_CD4..CD45RO..Memory.ProbabilityELeFHAnt_RF_CD56..NK.ProbabilityELeFHAnt_RF_CD8..Cytotoxic.T.ProbabilityELeFHAnt_RF_CD8..CD45RA..Naive.Cytotoxic.ProbabilityELeFHAnt_RF_Dendritic.Probability;
#> see ?make.names for more details on syntax validity
#> Warning: Invalid name supplied, making object
#> name syntactically valid. New object name is
#> ELeFHAnt_SVM_CD34..Decision.ValuesELeFHAnt_SVM_CD4..CD45RO..Memory.Decision.ValuesELeFHAnt_SVM_CD14..Monocyte.Decision.ValuesELeFHAnt_SVM_CD4..CD25.T.Reg.Decision.ValuesELeFHAnt_SVM_CD56..NK.Decision.ValuesELeFHAnt_SVM_Dendritic.Decision.ValuesELeFHAnt_SVM_CD19..B.Decision.ValuesELeFHAnt_SVM_CD4..CD45RA..CD25..Naive.T.Decision.ValuesELeFHAnt_SVM_CD8..Cytotoxic.T.Decision.ValuesELeFHAnt_SVM_CD8..CD45RA..Naive.Cytotoxic.Decision.ValuesELeFHAnt_SVM_CD4..T.Helper2.Decision.Values;
#> see ?make.names for more details on syntax validity
#> Warning: Invalid name supplied, making object
#> name syntactically valid. New object name is
#> ELeFHAnt_LR_CD34..ProbabilityELeFHAnt_LR_CD4..CD45RO..Memory.ProbabilityELeFHAnt_LR_CD14..Monocyte.ProbabilityELeFHAnt_LR_CD4..CD25.T.Reg.ProbabilityELeFHAnt_LR_CD56..NK.ProbabilityELeFHAnt_LR_Dendritic.ProbabilityELeFHAnt_LR_CD19..B.ProbabilityELeFHAnt_LR_CD4..CD45RA..CD25..Naive.T.ProbabilityELeFHAnt_LR_CD8..Cytotoxic.T.ProbabilityELeFHAnt_LR_CD8..CD45RA..Naive.Cytotoxic.ProbabilityELeFHAnt_LR_CD4..T.Helper2.Probability;
#> see ?make.names for more details on syntax validity
#>
#> Obtaing Ensemble Predictions using RF, SVM and LR
#>
#> Celltype predictions are stored in query metadata. Please see: ELeFHAnt_RF_CelltypePrediction, ELeFHAnt_SVM_CelltypePrediction, ELeFHAnt_LR_CelltypePrediction, ELeFHAnt_Ensemble_CelltypePrediction
#> Ensembl celltype annotation completed.
#>
#> Deploying Seurat Label Transfer
#> Performing PCA on the provided reference using 1850 features as input.
#> Projecting cell embeddings
#> Finding neighborhoods
#> Finding anchors
#> Found 2258 anchors
#> Filtering anchors
#> Retained 1914 anchors
#> Finding integration vectors
#> Finding integration vector weights
#> Predicting cell labels
#>
#> Deploying scPred
#> PC_ 1
#> Positive: RPS2, RPS4X, RPLP0, LTB, NPM1, S100A4, S100A6, CD74, VIM, IFITM2
#> DUSP1, HLA-DRB1, IL32, HLA-DRA, HLA-DRB5, ZFP36, HLA-DPB1, PRELID1, FOS, CD7
#> TYROBP, LGALS1, PPIB, HLA-DPA1, HLA-DQA1, S100A11, GSTP1, HLA-DQB1, PFN1, HLA-DQA2
#> Negative: PF4, SDPR, PPBP, GNG11, TUBB1, GP9, ACRBP, CMTM5, SPARC, CLU
#> HIST1H2AC, NRGN, TREML1, RUFY1, NCOA4, ITGA2B, CLDN5, AP001189.4, PTCRA, AC147651.3
#> RGS18, TMEM40, MYL9, MAP3K7CL, CLEC1B, SNCA, MPP1, CD9, CTSA, FERMT3
#> PC_ 2
#> Positive: LST1, AIF1, SPI1, CST3, SERPINA1, LYZ, IFI30, CFD, FCN1, CFP
#> RP11-290F20.3, HCK, MS4A7, TYMP, PILRA, TMEM176B, FCER1G, TYROBP, LRRC25, HLA-DRB1
#> CTSS, HLA-DPA1, PSAP, HLA-DRB5, CD68, HLA-DRA, S100A11, FTL, HMOX1, SAT1
#> Negative: IL32, CD7, LTB, CCL5, CTSW, GZMA, NPM1, CST7, RPS4X, GNLY
#> NKG7, RPLP0, HOPX, AQP3, GZMH, GZMK, PRF1, CCR7, ITM2A, FGFBP2
#> CD8B, GZMB, CD8A, CCL4, SH3YL1, SPON2, CLIC3, RGCC, NCR3, KLRG1
#> PC_ 3
#> Positive: NKG7, GNLY, GZMA, CST7, FGFBP2, FCGR3A, CTSW, PRF1, GZMH, CCL5
#> GZMB, CD7, SPON2, IFITM2, HOPX, CCL4, S100A4, TMSB4X, ID2, IL32
#> TYROBP, SRGN, PFN1, RHOC, GPR56, CLIC3, ABI3, PRSS23, KLRC1, IL2RB
#> Negative: CD79A, HLA-DRA, HLA-DQA1, MS4A1, TCL1A, CD79B, HLA-DQA2, CD74, HLA-DRB1, HLA-DMB
#> HLA-DMA, HLA-DPB1, HLA-DRB5, LINC00926, HLA-DPA1, HLA-DQB1, FCER2, SPIB, VPREB3, IRF8
#> LTB, HVCN1, CYB561A3, HLA-DOB, BANK1, EAF2, FCGR2B, KIAA0125, BLK, CD19
#> PC_ 4
#> Positive: GZMB, NKG7, GNLY, CLIC3, CST7, FGFBP2, PRF1, GZMA, CTSW, GZMH
#> HOPX, PRSS57, IGFBP7, SPON2, CCL4, FCER1A, AKR1C3, CYTL1, PTGDS, GPR56
#> GSTP1, MZB1, C19orf77, CCL5, LRRC26, ITM2C, IL2RB, PFN1, LILRA4, PRSS23
#> Negative: LTB, CFD, RP11-290F20.3, SERPINA1, TMSB4X, TMEM176B, CCR7, S100A8, COTL1, MS4A7
#> AQP3, S100A9, SAT1, CDKN1C, FCN1, PILRA, HES4, GPBAR1, VMO1, LYPD2
#> C5AR1, HCK, HMOX1, C1QA, FTH1, IFI30, TYMP, RBP7, CDA, CD14
#> PC_ 5
#> Positive: CYTL1, PRSS57, C19orf77, SPINK2, GATA2, CRHBP, MYB, NFE2, RP11-620J15.3, SOX4
#> NPM1, FAM212A, IL18, EGFL7, IGLL1, RP11-354E11.2, CD34, GNA15, GATA1, ARMCX1
#> SERPINB1, HOXA9, H2AFY, HMGA1, IL1B, ID1, EREG, PTRF, RPLP0, ERG
#> Negative: GZMB, TMSB4X, CD79A, CLIC3, TCL1A, GNLY, HLA-DRB1, MS4A1, NKG7, FGFBP2
#> HLA-DRB5, HLA-DPB1, CD79B, CST7, PRF1, IRF8, CD74, CCL5, SPIB, HLA-DQA2
#> GZMH, HLA-DQA1, TYROBP, PTGDS, LINC00926, GZMA, FCER1G, HLA-DRA, HLA-DMB, HLA-DPA1
#> 00:26:51 UMAP embedding parameters a = 0.9922 b = 1.112
#> 00:26:51 Read 2019 rows and found 30 numeric columns
#> 00:26:51 Using Annoy for neighbor search, n_neighbors = 30
#> 00:26:51 Building Annoy index with metric = cosine, n_trees = 50
#> 0% 10 20 30 40 50 60 70 80 90 100%
#> [----|----|----|----|----|----|----|----|----|----|
#> **************************************************|
#> 00:26:51 Writing NN index file to temp file /var/folders/bw/whg3swn15jb08_f7v2y09xw9glk1wg/T//RtmpWY8t69/filee0be795267cc
#> 00:26:51 Searching Annoy index using 1 thread, search_k = 3000
#> 00:26:52 Annoy recall = 100%
#> 00:26:53 Commencing smooth kNN distance calibration using 1 thread
#> 00:26:54 Initializing from normalized Laplacian + noise
#> 00:26:54 Commencing optimization for 500 epochs, with 88246 positive edges
#> 00:26:58 Optimization finished
#> ● Extracting feature space for each cell type...
#> DONE!
#> ● Training models for each cell type...
#> Loading required package: lattice
#> DONE!
#> ● Matching reference with new dataset...
#> ─ 2000 features present in reference loadings
#> ─ 1850 features shared between reference and new dataset
#> ─ 92.5% of features in the reference are present in new dataset
#> ● Aligning new data to reference...
#> Harmony 1/20
#> Harmony 2/20
#> Harmony 3/20
#> Harmony 4/20
#> Harmony 5/20
#> Harmony 6/20
#> Harmony converged after 6 iterations
#> ● Classifying cells...
#> DONE!
p1 = DimPlot(out.Benchmark, group.by = "ELeFHAnt_Ensemble_CelltypePrediction", label=T, repel = T, label.size = 6, reduction = "umap") + NoLegend() + ggtitle("ELeFHAnt Predictions")
p2 = DimPlot(out.Benchmark, group.by = "predicted.id", label=T, repel = T, label.size = 6, reduction = "umap") + NoLegend() + ggtitle("LabelTransfer Predictions")
p3 = DimPlot(out.Benchmark, group.by = "scpred_prediction", label=T, repel = T, label.size = 6, reduction = "umap") + NoLegend() + ggtitle("scPred Predictions")
p1
