Open onurdanaci opened 5 years ago
Hi Onur, Thanks for the information that you provided about the upstream steps and the error message. I'm assuming that you are running DoubletDecon in-line with R, not the GUI--but if this is incorrect pls let me know. I'm looking into the likely cause(s) of this error message and hope to get back to you later today. If it becomes import, would you be able to share the expression and meta data? Dan Schnell
Hi Onur,
I'm not sure if this is related to the error message that you got on the 6th sample, but I'm wondering if the call of the top_n function is returning what we want...
It reads to me that the call is extracting the overall top 50 genes from the mrkrs dataframe, whereas we're trying to provide DoubletDecon with the top 50 markers for each cluster that Seurat IDed.
I have not used the top_n function, but the documentation makes me think that another argument can be provided to indicate top_n within each group/cluster.
Alternatively, I'm attaching a short script that I have used for doing this task with the dataframe outputted by FindAllMarkers().
Let me know what you think, and in the meantime I'll look specifically into the error message. To help this along, could you tell me how many clusters Seurat found for sample 6?
Best, Dan
Hi Onur,
The error is coming up in the step that is combining clusters for the purpose of creating synthetic doublets. A possible cause is that the binarized correlation matrix that it is attempting to use is not suitable for doing this (e.g., only 1 cluster).
I suggest examining the heatmap of the inter-cluster correlations that should be output prior to this step (red & yellow blocks, in the plot window if you are using RStudio) to see if it looks sensible based on what you know about the data.
You might also explore the effect of larger values of the rhop parameter--1.0 is the default. Larger values such as 1.5 or 2.0 tend to result in fewer or no clusters being merged, and this is sometimes helpful in figuring out what it happening with a dataset and/or effectively skipping the cluster merging step.
There are parts of the bioRxiv preprint that go into the cluster merging and changing the value of the rhop parameter matters--that also may be useful to (re)review.
Best, Dan
Hi Dan,
For 32k cells and 25k genes-per-cells (features) it found 28 clusters.
Unfortunately I didn't save all the markers while preprocessing the data. But, my top 50 is attached.
Onur
On Wed, Aug 7, 2019 at 10:50 AM DanSchnell notifications@github.com wrote:
Hi Onur,
I'm not sure if this is related to the error message that you got on the 6th sample, but I'm wondering if the call of the top_n function is returning what we want...
It reads to me that the call is extracting the overall top 50 genes from the mrkrs dataframe, whereas we're trying to provide DoubletDecon with the top 50 markers for each cluster that Seurat IDed.
I have not used the top_n function, but the documentation makes me think that another argument can be provided to indicate top_n within each group/cluster.
Alternatively, I'm attaching a short script that I have used for doing this task with the dataframe outputted by FindAllMarkers().
Let me know what you think, and in the meantime I'll look specifically into the error message. To help this along, could you tell me how many clusters Seurat found for sample 6?
TOP_MARKERS.txt https://github.com/EDePasquale/DoubletDecon/files/3477559/TOP_MARKERS.txt
Best, Dan
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2.14665494430489e-50 "0" "TMEM258" "166" 1.78568747282498e-54 -0.357879384451441 0.368 0.458 3.30155756850611e-50 "0" "ATP5I" "167" 3.51424230351726e-53 -0.257447905153872 0.221 0.333 6.49748259497307e-49 "0" "HNRNPDL" "168" 3.79052015082614e-53 -0.414560494704793 0.458 0.512 7.00829270686245e-49 "0" "CFL1" "169" 3.7979918562839e-53 -0.270847680971053 0.233 0.342 7.0221071430833e-49 "0" "UQCR10" "170" 9.92552425545101e-53 -0.423063532339779 0.211 0.317 1.83513017959034e-48 "0" "PA2G4" "171" 1.0791617277893e-52 -0.288187631743074 0.209 0.318 1.99526211850964e-48 "0" "HNRNPC" "172" 1.74046289384941e-52 -0.291490538042389 0.223 0.332 3.21794184443817e-48 "0" "SUMO2" "173" 2.42187425272971e-52 -0.330751560252183 0.194 0.302 4.47780330587197e-48 "0" "EIF5B" "174" 4.77965371877279e-52 -0.361760857688766 0.204 0.312 8.83710176063902e-48 "0" "C1QBP" "175" 7.73460640737748e-52 -0.474279738202705 0.314 0.41 1.43005137866002e-47 "0" "RAN" "176" 1.17156931204227e-51 -0.271623530593141 0.16 0.269 2.16611450103495e-47 "0" "MRPL20" "177" 2.1858095819121e-51 -0.274251734069514 0.229 0.339 4.04134333599727e-47 "0" "HMGN2" "178" 4.48744708042918e-51 -0.270079843429779 0.273 0.376 8.29684090700551e-47 "0" "COX5B" "179" 1.53440329779468e-50 -0.280682689886551 0.189 0.298 2.83695825729259e-46 "0" "HMGN1" "180" 1.60003775217733e-50 -0.250462465090953 0.263 0.367 2.95830980000067e-46 "0" "NDUFA3" "181" 2.46929607732403e-50 -0.272295082350303 0.216 0.322 4.56548151736441e-46 "0" "ATP5D" "182" 1.91890364098831e-48 -0.327609591826786 0.387 0.464 3.54786094182328e-44 "0" "MT-ND5" "183" 5.2878031708486e-48 -0.558887019744273 0.417 0.474 9.77661928258198e-44 "0" "YBX1" "184" 6.35954211025386e-48 -0.256195380715705 0.134 0.238 1.17581574076484e-43 "0" "PFDN2" "185" 2.96291784536954e-47 -0.371316722480566 0.211 0.316 5.47813880430374e-43 "0" "S100A4" "186" 4.07817443258387e-47 -0.56268364964704 0.139 0.246 7.54013670840432e-43 "0" "CCL2" "187" 1.45453756147962e-46 -0.491470083024013 0.235 0.337 2.68929449741968e-42 "0" "MT2A" "188" 5.07761391207068e-46 -0.556706905468924 0.351 0.426 9.38800036202747e-42 "0" "NCL" "189" 1.33718469072751e-45 -0.308782310799086 0.247 0.343 2.4723207746861e-41 "0" "TRMT112" "190" 2.25741237776257e-45 -0.254340980308662 0.144 0.245 4.17372974524521e-41 "0" "METAP2" "191" 3.78446484753457e-45 -0.317062252570742 0.348 0.431 6.99709705660667e-41 "0" "COX6C" "192" 5.60152636659225e-45 -0.321583591062266 0.213 0.31 1.03566620991924e-40 "0" "CBX3" "193" 1.26416276051342e-43 -0.686378072077265 0.482 0.513 2.33731052791326e-39 "0" "HSP90AB1" "194" 3.62047580372398e-42 -0.382386594252759 0.373 0.438 6.69389771350526e-38 "0" "HSPA8" "195" 5.99596111562884e-42 -0.275036057243248 0.231 0.324 1.10859325066862e-37 "0" "UQCRH" "196" 6.11828356482592e-42 -0.357480977829736 0.551 0.575 1.13120944830066e-37 "0" "MT-ND1" "197" 2.30806233634678e-41 -0.446688721099039 0.641 0.618 4.26737645367155e-37 "0" "ATP5E" "198" 3.70179830298401e-41 -0.316153340692315 0.286 0.37 6.84425488238713e-37 "0" "APRT" "199" 3.70589369067816e-40 -0.548533570579345 0.163 0.263 6.85182684469485e-36 "0" "IL8" "200" 7.91313326517155e-40 -0.28982889143628 0.322 0.398 1.46305920939757e-35 "0" "C14orf2" "201" 6.10425357310512e-39 -0.273859167586738 0.254 0.34 1.12861544313141e-34 "0" "TBCA" "202" 1.20699553506684e-38 -0.306284566313534 0.318 0.394 2.23161404478508e-34 "0" "SLC25A6" "203" 2.19568809555645e-38 -0.423536845743231 0.082 0.169 4.05960771987432e-34 "0" "IL1B" "204" 1.66076504766061e-37 -0.38082300118929 0.388 0.45 3.0705884966197e-33 "0" "HMGB1" "205" 2.55097611434958e-37 -0.274182047614325 0.285 0.366 4.71649973782094e-33 "0" "NDUFS5" "206" 1.23820278298885e-36 -0.36339620058558 0.447 0.486 2.28931312546809e-32 "0" "SRP14" "207" 2.15156488975611e-36 -0.539029994195355 0.701 0.648 3.97802832467007e-32 "0" "PFN1" "208" 3.20592263065644e-36 -0.383919730263981 0.834 0.739 5.92743035182069e-32 "0" "MT-CO1" "209" 5.41008178335453e-36 -0.352206077310109 0.687 0.651 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"578" 1.05028799122923e-59 -0.452181823073432 0.367 0.488 1.94187746698372e-55 "2" "HNRNPA1" "579" 1.26942533547611e-59 -0.591770518537769 0.877 0.793 2.34704050276179e-55 "2" "RPS6" "580" 1.6869527689487e-59 -0.317743055881452 0.143 0.289 3.11900697450925e-55 "2" "CSTB" "581" 1.95010948089463e-59 -0.585154588332228 0.914 0.806 3.60555741922607e-55 "2" "RPLP2" "582" 2.14191489487485e-59 -0.269701014990948 0.073 0.21 3.96018644913411e-55 "2" "CCT6A" "583" 2.3933131486441e-59 -0.256127483408109 0.13 0.275 4.42499668052808e-55 "2" "LAMTOR5" "584" 2.58050655841972e-59 -0.578787427977842 0.299 0.428 4.77109857586222e-55 "2" "NCL" "585" 2.90799999252516e-59 -0.317934338285482 0.206 0.348 5.37660118617977e-55 "2" "RBX1" "586" 4.90145394418966e-59 -0.275981540385339 0.11 0.252 9.06229819741226e-55 "2" "MRPL52" "587" 1.0424427402228e-58 -0.316872251541141 0.105 0.246 1.92737238239793e-54 "2" "EIF3J" "588" 1.53719465433217e-58 -0.3770674682122 0.246 0.382 2.84211919639476e-54 "2" "POLR2L" "589" 1.65875208601769e-58 -0.578458469885605 0.666 0.67 3.06686673183811e-54 "2" "RPL6" "590" 1.76729388982935e-58 -0.517014372636661 0.598 0.64 3.26754967290548e-54 "2" "RPS11" "591" 1.79722709335305e-58 -0.259277427835812 0.174 0.319 3.32289317290046e-54 "2" "GPX4" "592" 1.81235850228321e-58 -0.307392167536937 0.052 0.181 3.35086963487143e-54 "2" "EIF5AL1" "593" 2.03628917086854e-58 -0.281956371331427 0.139 0.284 3.76489504801884e-54 "2" "LSM7" "594" 3.12220722221137e-58 -0.256527867617159 0.098 0.239 5.77264893314661e-54 "2" "DNAJA1" "595" 3.37698164930449e-58 -0.289424901491291 0.081 0.218 6.24370137139906e-54 "2" "RPL22L1" "596" 4.19613814631553e-58 -0.285677907580354 0.072 0.207 7.75823981872279e-54 "2" "MT1X" "597" 4.21381562171955e-58 -0.569019476190006 0.702 0.694 7.79092370299727e-54 "2" "RPL29" "598" 4.67639315056881e-58 -0.605975945414031 0.841 0.779 8.64618329608667e-54 "2" "RPL35" "599" 6.66440293373239e-58 -0.256564809610795 0.1 0.241 1.23218145841778e-53 "2" "ANP32B" "600" 7.21576225982989e-58 -0.546015386134727 0.792 0.734 1.33412228421995e-53 "2" "RPS3A" "601" 8.21906469679461e-58 -0.274169026581447 0.153 0.296 1.51962287179036e-53 "2" "HSP90B1" "602" 1.46471211167157e-57 -0.54813190852878 0.851 0.773 2.70810622326956e-53 "2" "RPL19" "603" 1.60409190846351e-57 -0.334269783776125 0.156 0.299 2.96580552955818e-53 "2" "NHP2L1" "604" 1.75425440771004e-57 -0.29901858792829 0.157 0.3 3.24344097441509e-53 "2" "MINOS1" "605" 2.3600181139748e-57 -0.472580682250122 0.254 0.386 4.36343749092802e-53 "2" "SNRPG" "606" 3.59859675795289e-57 -0.344445580532759 0.233 0.366 6.6534455457791e-53 "2" "ATP5J2" "607" 5.43617430270305e-57 -0.564876862652102 0.937 0.829 1.00509426682677e-52 "2" "RPL13" "608" 5.57323391163215e-57 -0.258332907496462 0.127 0.268 1.03043521792167e-52 "2" "SRSF7" "609" 6.06894994784053e-57 -0.28376769069553 0.079 0.213 1.12208815585624e-52 "2" "HNRNPAB" "610" 6.60902772223746e-57 -0.273997628949817 0.116 0.255 1.22194313556448e-52 "2" "NDUFS6" 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"2" "GSTP1" "633" 6.6486466135333e-55 -0.305439441605517 0.179 0.317 1.22926827237617e-50 "2" "SHFM1" "634" 1.01342766656566e-54 -0.306207835195472 0.187 0.325 1.87372641271325e-50 "2" "UQCRH" "635" 1.03672895442021e-54 -0.302712302795705 0.18 0.318 1.91680816382752e-50 "2" "HNRNPC" "636" 1.1677794603671e-54 -0.417636433999917 0.291 0.415 2.15910744427273e-50 "2" "TPI1" "637" 1.50550354162589e-54 -0.348120152320146 0.225 0.355 2.7835254981121e-50 "2" "COX7B" "638" 1.60706538719573e-54 -0.576500470740389 0.782 0.733 2.97130319438618e-50 "2" "RPL15" "639" 1.68292969242319e-54 -0.306569255197319 0.101 0.234 3.11156870832123e-50 "2" "TOMM5" "640" 1.92102115469508e-54 -0.404385260794454 0.255 0.383 3.55177601291573e-50 "2" "SUB1" "641" 4.03611945464457e-54 -0.331357925049046 0.183 0.318 7.46238125969235e-50 "2" "NOP10" "642" 4.64428835044258e-54 -0.411896599675158 0.104 0.237 8.58682473113329e-50 "2" "HLA-DRB1" "643" 5.57910542661546e-54 -0.613842935104205 0.748 0.713 1.03152080232693e-49 "2" "RPS24" "644" 1.06154525143451e-53 -0.421147483124195 0.236 0.365 1.96269101537726e-49 "2" "PSMA7" "645" 1.28983102484019e-53 -0.300909473485025 0.156 0.292 2.38476858182702e-49 "2" "ERH" "646" 1.40182812534811e-53 -0.680657849886442 0.966 0.869 2.59184002095613e-49 "2" "RPS18" "647" 2.12256858738599e-53 -0.367158917855347 0.181 0.314 3.92441706121795e-49 "2" "ATP5G3" "648" 2.1726429898163e-53 -0.541677194197503 0.84 0.743 4.01699962387135e-49 "2" "RPS25" "649" 2.43755679563726e-53 -0.273451383855116 0.195 0.332 4.50679875945374e-49 "2" "HNRNPDL" "650" 2.71718901264096e-53 -0.292567538257345 0.246 0.375 5.02381076547186e-49 "2" "COX5B" "651" 2.8791689043374e-53 -0.314519694541465 0.151 0.285 5.32329538722942e-49 "2" "SSBP1" "652" 2.98416167748688e-53 -0.333396872867648 0.201 0.335 5.51741652550549e-49 "2" "ATP5J" "653" 4.73980384888445e-53 -0.366788545140341 0.151 0.285 8.76342333620245e-49 "2" "CYCS" "654" 6.17823143028951e-53 -0.567389695550685 0.917 0.801 1.14229320914623e-48 "2" "RPS15" "655" 8.53920488834134e-53 -0.253013292726673 0.123 0.257 1.57881359180543e-48 "2" "PSMB6" "656" 1.81199554122147e-52 -0.2860916431929 0.134 0.267 3.35019855616438e-48 "2" "COX5A" "657" 4.60659479415147e-52 -0.263052364127349 0.133 0.267 8.51713311490666e-48 "2" "NDUFV2" "658" 5.41435580436489e-52 -0.328010065291845 0.178 0.313 1.00106024466902e-47 "2" "H2AFZ" "659" 5.88121488907426e-52 -0.430750396507529 0.186 0.316 1.08737782084094e-47 "2" "PA2G4" "660" 6.26031278010494e-52 -0.254831834034554 0.118 0.25 1.1574692299136e-47 "2" "PSMA4" "661" 6.93436582037716e-52 -0.558888746372781 0.828 0.753 1.28209489652953e-47 "2" "RPL7" "662" 1.05245822893743e-51 -0.307457619465134 0.238 0.367 1.94589001948241e-47 "2" "NDUFS5" "663" 1.69492376423912e-51 -0.538430020081229 0.087 0.213 3.13374454770171e-47 "2" "LYZ" "664" 1.71699309313552e-51 -0.346541170469605 0.174 0.304 3.17454852989826e-47 "2" "HNRNPM" "665" 2.02347150432402e-51 -0.477068549134968 0.318 0.431 3.74119646434468e-47 "2" "CHCHD2" "666" 4.80134914061651e-51 -0.254664677277272 0.198 0.333 8.87721442608586e-47 "2" "EIF3K" "667" 5.95066396745973e-51 -0.274533860766081 0.109 0.237 1.10021826094363e-46 "2" "PFDN2" "668" 6.6915893692548e-51 -0.455316469033982 0.781 0.719 1.23720795848152e-46 "2" "RPL30" "669" 7.59902828629205e-51 -0.325545836099526 0.382 0.485 1.40498433985254e-46 "2" "OST4" "670" 7.80955933587432e-51 -0.287709186180902 0.1 0.227 1.4439094256098e-46 "2" "SRM" "671" 2.36052142344154e-50 -0.371183928935426 0.35 0.452 4.36436805980106e-46 "2" "TCEB2" "672" 3.58773924194495e-50 -0.326001709175667 0.214 0.342 6.63337108443201e-46 "2" "TRMT112" "673" 4.304529393643e-50 -0.376502685306741 0.197 0.323 7.95864439590654e-46 "2" "EIF2S2" "674" 4.6048558093935e-50 -0.285516019181093 0.165 0.295 8.51391790598765e-46 "2" "DDX18" "675" 4.94560408332156e-50 -0.514552407884178 0.831 0.757 9.14392738965324e-46 "2" "RPS4X" "676" 1.11659398232645e-49 -0.280769656388405 0.139 0.268 2.06447061392337e-45 "2" "MRPL20" "677" 1.22656208393687e-49 -0.252339058354943 0.074 0.195 2.26779063699088e-45 "2" "CTSS" "678" 1.69159762579045e-49 -0.283730498994 0.152 0.281 3.12759485032395e-45 "2" "SRSF3" "679" 2.71370841617331e-49 -0.386538156344983 0.333 0.436 5.01737549066282e-45 "2" "COX7A2" "680" 2.80253471431248e-49 -0.337487803176341 0.173 0.301 5.18160643329234e-45 "2" "EIF5B" "681" 3.27420890362925e-49 -0.338207786450484 0.182 0.31 6.05368484192011e-45 "2" "CBX3" "682" 5.85018082477535e-49 -0.345578117080801 0.326 0.436 1.08163993269272e-44 "2" "COX6B1" "683" 6.76480374475867e-49 -0.257772469377335 0.163 0.291 1.25074456436843e-44 "2" "COX8A" "684" 6.89518226326787e-49 -0.275772952069553 0.093 0.217 1.2748502486556e-44 "2" "HLA-DPA1" "685" 1.07905375044947e-48 -0.251216120049345 0.15 0.278 1.99506247920603e-44 "2" "GNG5" "686" 1.48677429290585e-48 -0.468867648714044 0.605 0.621 2.74889699015363e-44 "2" "ATP5E" "687" 3.39979995196431e-48 -0.258739995042616 0.171 0.298 6.28589013118681e-44 "2" "C19orf53" "688" 5.73994524124342e-48 -0.25936500623466 0.19 0.316 1.0612584756535e-43 "2" "YWHAB" "689" 5.76352567681533e-48 -0.481115913648144 0.467 0.528 1.06561826238639e-43 "2" "CD74" "690" 8.5006997155829e-48 -0.375190189549191 0.515 0.577 1.57169437041412e-43 "2" "MT-ND1" "691" 8.70695110979486e-48 -0.33736886152502 0.321 0.43 1.60982819068997e-43 "2" "COX6C" "692" 8.96084375892113e-48 -0.370517116110075 0.239 0.357 1.65677040258693e-43 "2" "S100A11" "693" 9.12664732646946e-48 -0.334762103874199 0.221 0.341 1.68742582419094e-43 "2" "SEC61G" "694" 1.01815109374114e-47 -0.297442812658045 0.164 0.288 1.88245955721799e-43 "2" "COX17" "695" 1.0374189682636e-47 -0.510114024126743 0.958 0.84 1.91808393042257e-43 "2" "RPL34" "696" 1.09958599328704e-47 -0.386009492188181 0.455 0.528 2.03302454298841e-43 "2" "ATP5L" "697" 1.72413619203724e-47 -0.289213250425807 0.199 0.326 3.18775540545764e-43 "2" "PARK7" "698" 3.51189890809848e-47 -0.251227554436408 0.069 0.186 6.49314989118329e-43 "2" "DUT" "699" 3.51873129278702e-47 -0.276044757063932 0.147 0.27 6.50578228723392e-43 "2" "COX6A1P2" "700" 4.05481087025847e-47 -0.304421232313025 0.238 0.36 7.49693981802088e-43 "2" "HNRNPA2B1" "701" 5.19334734057481e-47 -0.31368706645766 0.208 0.332 9.60197989798877e-43 "2" "PSMB1" "702" 7.41502784903393e-47 -0.339662204561775 0.206 0.327 1.37096449900788e-42 "2" "TUBB" "703" 8.38005249789437e-47 -0.250905718569864 0.12 0.243 1.54938790633569e-42 "2" "LSM5" "704" 2.78968178043515e-46 -0.285879775370574 0.146 0.269 5.15784264384654e-42 "2" "SNRPB" "705" 2.82621744982152e-46 -0.39838845922663 0.34 0.439 5.22539344297501e-42 "2" "HSPA8" "706" 4.62974778409818e-46 -0.25119130832889 0.126 0.248 8.55994067801913e-42 "2" "SNRPB2" "707" 4.79398431211645e-46 -0.600532856808045 0.683 0.645 8.8635975946721e-42 "2" "RPL17" "708" 5.96584686220453e-46 -0.29663756698617 0.586 0.616 1.103025426353e-41 "2" "PFDN5" "709" 2.42145783711608e-45 -0.328581384802976 0.203 0.32 4.47703339504392e-41 "2" "SEC61B" "710" 2.4626883021895e-45 -0.353261254353671 0.598 0.632 4.55326440191816e-41 "2" "MT-ND2" "711" 3.9
Hi Onur,
I only see markers for clusters 0, 1 and 2--maybe the file was truncated due to size.
There appears to be > 50 for each of the first 3 clusters.
Do you think the 28 clusters are reasonably unique? With just a quick scan I see the same marker in > 1 cluster.
Did the run of this sample generate the heatmap of the inter-cluster correlations (red & yellow blocks, in the plot window if you are using RStudio)? If so, please attach it to your response.
Three things that I think that you could try: 1) The default value of the rhop parameter is 1.0. Try the values 1.75 and 0.5 and see if it runs for either (or at least gets farther). 2) Reduce number of markers for each cluster to 50, or even 25, making sure that each cluster has at least 5 markers--if not, might need to decrease the number of clusters. 3) Subset the data to say the cells from the 5 largest clusters and run DoubletDecon to see if it runs all the way through. If it does, add more clusters. If it doesn't, there may be a problem with the input file(s).
Let me know what you try and please attach the cluster correlation heatmap if it is produced.
Best, Dan
Attached is an example of the heatmap that I'm talking about. Blacklist_11clusters.pdf
Hi,
For examples, one of the data sets that I'm analyzing is Satija group's (Seurat) Four.Cell.12.HTO dataset, it's among 3 data sets that DoubletDecon hit a snag: https://satijalab.org/seurat/v3.0/hashing_vignette.html
-So, basically, my expression file is their hto12.umis after CLR normalizing, scaling, clustering, finding features, PCA, UMAP etc -The clusters are from that seurat file -My Top50Genes file is right now implements the text file you sent me over All Markers (output of FindAllMarkers)
In order to get heatmaps and step-by-step debugging I downloaded each of your function files, and I go one step at a time. When I try to obtain Blacklist_Groups function output, here is what i get:
[1] "Unable to perform mcl function for blacklist clustering, please try a different rhop."
Error in Blacklist_Groups(data, groups, rhop, centroid_flag, log_file_name): Traceback:
The heatmaps for rhop=0.5 [image: image.png] rhop=0.75 [image: image.png] rhop=1 [image: image.png] rhop=1.5 [image: image.png] rhop=2 [image: image.png] rhop=2.5 [image: image.png] rhop=3 [image: image.png]
On Mon, Aug 12, 2019 at 1:44 PM DanSchnell notifications@github.com wrote:
Attached is an example of the heatmap that I'm talking about. Blacklist_11clusters.pdf https://github.com/EDePasquale/DoubletDecon/files/3493679/Blacklist_11clusters.pdf
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Hi Onur,
For some reason the heatmaps that you attached do not seem to be accessible. Could you please attach them to another reply? Otherwise, you can send them directly to me @ Daniel.Schnell@cchmc.org
Best, Dan
And, for the same data, when I add the 5 largest clusters it still isn't working.
rhop=1.1 [image: image.png] rhop=0.75 [image: image.png] rhop=0.5 [image: image.png]
rhop=1.4 [image: image.png] and all rhops above 1.4 leads to infinity. and you can't get any results
On Fri, Aug 16, 2019 at 2:30 PM Onur Danaci onur.danaci.ai@gmail.com wrote:
Hi,
For examples, one of the data sets that I'm analyzing is Satija group's (Seurat) Four.Cell.12.HTO dataset, it's among 3 data sets that DoubletDecon hit a snag: https://satijalab.org/seurat/v3.0/hashing_vignette.html
-So, basically, my expression file is their hto12.umis after CLR normalizing, scaling, clustering, finding features, PCA, UMAP etc -The clusters are from that seurat file -My Top50Genes file is right now implements the text file you sent me over All Markers (output of FindAllMarkers)
In order to get heatmaps and step-by-step debugging I downloaded each of your function files, and I go one step at a time. When I try to obtain Blacklist_Groups function output, here is what i get:
[1] "Unable to perform mcl function for blacklist clustering, please try a different rhop."
Error in Blacklist_Groups(data, groups, rhop, centroid_flag, log_file_name): Traceback:
- Blacklist_Groups(data, groups, rhop, centroid_flag, log_file_name)
- stop() # at line 50 of file
The heatmaps for rhop=0.5 [image: image.png] rhop=0.75 [image: image.png] rhop=1 [image: image.png] rhop=1.5 [image: image.png] rhop=2 [image: image.png] rhop=2.5 [image: image.png] rhop=3 [image: image.png]
On Mon, Aug 12, 2019 at 1:44 PM DanSchnell notifications@github.com wrote:
Attached is an example of the heatmap that I'm talking about. Blacklist_11clusters.pdf https://github.com/EDePasquale/DoubletDecon/files/3493679/Blacklist_11clusters.pdf
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Hi,
I've been following along with Dan trying to get this solved behind the scenes -- hopefully we can get this bug tracked down and corrected ASAP!
Would you be willing to get to the point in the Blacklist_Groups() function where mcl() is called -- Step #5, I believe, run the following commands, and post the results?
Additionally, if you don't mind, I think it would be helpful if I could get the head of your processed expression data after you ran Seurat_Pre_Process()? Sometimes there are unexpected errors in processing that can cause unintended downstream effects. As such, checking the versions of packages you are using (with sessionInfo) and the version of Seurat you are using (if you would be so kind), may give me an idea what is going wrong.
Thank you for bringing this to our attention, we will try to get it fixed soon!
Erica (careyea@mail.uc.edu)
Hi,
Sorry, I am not legally allowed to disclose any part of that data.
Best, Onur
On Fri, Aug 16, 2019 at 5:50 PM EDePasquale notifications@github.com wrote:
Hi,
I've been following along with Dan trying to get this solved behind the scenes -- hopefully we can get this bug tracked down and corrected ASAP!
Would you be willing to get to the point in the Blacklist_Groups() function where mcl() is called -- Step #5 https://github.com/EDePasquale/DoubletDecon/issues/5, I believe, run the following commands, and post the results?
- sessionInfo()
- class(blacklist)
- dim(blacklist)
- blacklist
Additionally, if you don't mind, I think it would be helpful if I could get the head of your processed expression data after you ran Seurat_Pre_Process()? Sometimes there are unexpected errors in processing that can cause unintended downstream effects. As such, checking the versions of packages you are using (with sessionInfo) and the version of Seurat you are using (if you would be so kind), may give me an idea what is going wrong.
Thank you for bringing this to our attention, we will try to get it fixed soon!
Erica
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Hi,
When I ran doublet decon, after the data cleaning and preparation steps (It was very hard to mimic your custer/top50genes/counts etc data, because clear instructions weren't given), it ran without any problem for 5 of my samples (each samples has around 30k cells). But, at the sixth it had a hiccup with the following error:
"Error : $ operator is invalid for atomic vectors In addition: There were 50 or more warnings (use warnings() to see the first 50) Error in Blacklist_Groups(data, groups, rhop, centroid_flag, log_file_name) :"
Calls: doubdec_outpipe -> Main_Doublet_Decon -> Blacklist_Groups Execution halted"
And the following output: [1] "Unable to perform mcl function for blacklist clustering, please try a different rhop."
I save my normalized count matrices out of Seurat object (e.g, by GetAssayData function) in a tab delimited format as counts.txt, I calculate all genetical markers by seurat FindAllMarkers function and grab the top50 of them with respect to avg_logFC (also save tab delimited). As well as get the clusters out of Seurat object in the following fashion:
" write.table(GetAssayData(pbmc),paste(name,"counts.txt",sep="."),sep="\t", col.names=NA)
write.table(pbmc@active.ident,paste(name,"Cluster.txt",sep="."),sep="\t",col.names=NA)
mrkrs=FindAllMarkers(pbmc)
write.table(top_n(n=50,x=mrkrs,wt="avg_logFC"),paste(name,"Top50Genes.txt",sep="."),sep="\t") "
I don't know what may be the problem, and why it worked for some while it didn't work for the rest.
Best, Onur