Dataset Processing: efficient ways to clean and merge sets for Life Sciences

Question

Dataset Processing (for Life Sciences)

Note: a related, but distinct task is posted here ID Swapping: Efficient use of a reference table to convert ID values.

A common task, at least for me, involves analyzing at least two different Datasets.

A common example, at least currently in biological sciences, involve gene lists. Depending on where they came from, who processed them, which pipeline they were processed with, etc they might vary in form.

For example, consider the following two Datasets:

In Dataset One (d1) we see that there are two conditions c1 and c2 with various replicates, e.g. {c1_1, c1_2, c1_3}. In Dataset Two (d2) we do not have the issue of replicates. However, in both sets we have duplicate ids, with varying values in their columns. Biologically this might arise if one converted the transcript id (a subset of the gene) to the gene id. Lastly, not all genes are in both sets. Therefore we have some preprocessing to do:

• find those IDs common to both sets
• combining into a singular Dataset
• find duplicates rows (e.g. same value in the ID column)
• average those duplicates rows by column value
• replace those duplicates with their average
• find replicates in the column headers
• average those columns together

i.e. applying the above transformations to given datasets, we should end up with:

There are a lot of ways to approach this problem. Below I am including what I made to answer this post. However I am sure it is not the most efficient (or most elegantly coded) method. Thus I would appreciate your assistance in finding better ways at this kind of pre-processing.

Shout out to @OneSquare 's answer on Variable named slots.

Here are the "datasets" used:

d1=Dataset@{<|"Gene" -> "a", "c1_1" -> 0.0862185, "c1_2" -> 0.591649, 
  "c1_3" -> 0.119653, "c2_1" -> 0.329605, 
  "c2_2" -> 0.953679|>, <|"Gene" -> "b", "c1_1" -> 0.0837976, 
  "c1_2" -> 0.408317, "c1_3" -> 0.427002, "c2_1" -> 0.373136, 
  "c2_2" -> 0.0670787|>, <|"Gene" -> "c", "c1_1" -> 0.331962, 
  "c1_2" -> 0.389325, "c1_3" -> 0.673205, "c2_1" -> 0.346972, 
  "c2_2" -> 0.784099|>, <|"Gene" -> "d", "c1_1" -> 0.460994, 
  "c1_2" -> 0.376045, "c1_3" -> 0.0499006, "c2_1" -> 0.165925, 
  "c2_2" -> 0.547476|>, <|"Gene" -> "e", "c1_1" -> 0.0474756, 
  "c1_2" -> 0.721516, "c1_3" -> 0.0866807, "c2_1" -> 0.754684, 
  "c2_2" -> 0.00415091|>, <|"Gene" -> "f", "c1_1" -> 0.258425, 
  "c1_2" -> 0.910458, "c1_3" -> 0.0203598, "c2_1" -> 0.267614, 
  "c2_2" -> 0.675246|>, <|"Gene" -> "c", "c1_1" -> 0.331962, 
  "c1_2" -> 0.389325, "c1_3" -> 0.673205, "c2_1" -> 0.346972, 
  "c2_2" -> 0.784099|>, <|"Gene" -> "d", "c1_1" -> 0.460994, 
  "c1_2" -> 0.376045, "c1_3" -> 0.0499006, "c2_1" -> 0.165925, 
  "c2_2" -> 0.547476|>, <|"Gene" -> "c", "c1_1" -> 0.331962, 
  "c1_2" -> 0.389325, "c1_3" -> 0.673205, "c2_1" -> 0.346972, 
  "c2_2" -> 0.784099|>, <|"Gene" -> "c", "c1_1" -> 0.331962, 
  "c1_2" -> 0.389325, "c1_3" -> 0.673205, "c2_1" -> 0.346972, 
  "c2_2" -> 0.784099|>, <|"Gene" -> "a", "c1_1" -> 0.0862185, 
  "c1_2" -> 0.591649, "c1_3" -> 0.119653, "c2_1" -> 0.329605, 
  "c2_2" -> 0.953679|>}





 d2=Dataset@{<|"Gene" -> "h", "f1" -> 0.93386, "f2" -> 0.684875, 
  "f3" -> 0.599702|>, <|"Gene" -> "b", "f1" -> 0.93083, 
  "f2" -> 0.735748, "f3" -> 0.586162|>, <|"Gene" -> "j", 
  "f1" -> 0.373753, "f2" -> 0.246, 
  "f3" -> 0.150022|>, <|"Gene" -> "d", "f1" -> 0.945271, 
  "f2" -> 0.553761, "f3" -> 0.658329|>, <|"Gene" -> "k", 
  "f1" -> 0.35108, "f2" -> 0.575718, 
  "f3" -> 0.337428|>, <|"Gene" -> "f", "f1" -> 0.525761, 
  "f2" -> 0.198373, "f3" -> 0.168825|>, <|"Gene" -> "d", 
  "f1" -> 0.525761, "f2" -> 0.198373, 
  "f3" -> 0.168825|>, <|"Gene" -> "d", "f1" -> 0.525761, 
  "f2" -> 0.198373, "f3" -> 0.168825|>, <|"Gene" -> "a", 
  "f1" -> 0.525761, "f2" -> 0.198373, 
  "f3" -> 0.168825|>, <|"Gene" -> "b", "f1" -> 0.525761, 
  "f2" -> 0.198373, "f3" -> 0.168825|>}

---

@Kuba's approach (prior to this update), is certainly more succinct and the syntax is a bit foreign to me. It does merge the data sets together and take the mean; however, it does not handle duplicate IDs. The replicate part of this question was added during the update so naturally it was not included in his answer.

Desired exact result

The desired results on the given example data in order of transformations is as follows.

• common ids of both sets: {"a", "b", "d", "f"}
• combining (tack on d2 to the end of d1
• find duplicate rows, e.g. in d1 there are two rows with the id "a", four with "c", etc
• average those duplicates together by id, e.g. for rows with the id "a", looking at only column "c1_1", then the average would be $(0.0862184+0.0862184)/2$.
• find replicates in the column header (e.g. "c1_1", "c1_2", "c1_3" are replicates of "c1")
• average them together, so for "a" and replicates of "c1", $(0.0862184+0.591649+0.119653)/3$.

e.g. produces the result of the example given above

Answer 1

We can get a long way by (inner) joining across the common gene keys using JoinAcross, grouping by gene, and then averaging across each group:

JoinAcross[d1, d2, "Gene"][GroupBy["Gene"] /* Values, Merge[Mean]]

What remains is to merge and average the similarly named columns. This is really the crux of this question. Here is a helper function that does the job:

averageKeys =
  KeyValueMap[<| StringReplace[#, n__~~"_"~~___ :> n] -> #2 |> &] /* Merge[Mean];

Here it is in action:

<| "Gene" -> "x"
 , "c1_1" -> 10, "c1_2" -> 40, "c1_3" -> 40
 , "c2_1" -> 10, "c2_2" -> 20
 , "f1" -> 1, "f2" -> 2, "f3" -> 3
 |> // averageKeys

(* <| "Gene" -> "x", "c1" -> 30, "c2" -> 15, "f1" -> 1, "f2" -> 2, "f3" -> 3|> *)

We can then supplement the original query to use this helper function and obtain the final result:

JoinAcross[d1, d2, "Gene"][GroupBy["Gene"] /* Values, Merge[Mean], averageKeys]

If row order matters, we can sort them after the fact:

%[Sort]

... or adjust the full query to include a sorting stage:

JoinAcross[d1,d2,"Gene"][GroupBy["Gene"] /* Values /* Query[Sort], Merge[Mean], averageKeys]

Sort is wrapped in Query to ensure that it happens after the inner operations are complete (i.e. to convert Sort from a "descending" operator into an "ascending" one).

More than two datasets

The technique can be generalized to accommodate more than two datasets. Given the additional dataset:

d3 = { <| "Gene" -> "a", "g" -> 1 |>, <| "Gene" -> "b", "g" -> 2 |>} // Dataset;

Then:

{d1, d2, d3} //
Map[Query[GroupBy["Gene"] /* Values, Merge[Mean], averageKeys]] //
Fold[JoinAcross[#, #2, "Gene"]&] //
Sort

This approach is superior to the earlier expression on two counts: 1) it supports multiple datasets and 2) it is more robust when it comes to handling duplicate keys within the source datasets.

Answer 2

Applying mergeData results in:

Define the conditions in the data set:

bioKeys = Normal@Keys@First@Bio
conditions = {"c1", "c2"};

Acquire position of replicates via string search:

replicates = 
 Flatten@Position[bioKeys, #] & /@ 
    Flatten@StringCases[bioKeys, conditions[[#]] ~~ __] & /@ 
  Range@Length@conditions

Merge replicates:

mergedReplicates = 
 Bio[All, Flatten[bioKeys[[#]] & /@ replicates[[#]], 1] /* <|
      conditions[[#]] -> Mean|>] & /@ Range@Length@conditions

Delete individual replicates:

Bio = Bio[All, Delete[Partition[Flatten@replicates, 1]]];

Add in the merged replicate columns:

Table[Bio = 
   Dataset@MapThread[
     Append, {Normal@Bio, Thread[Normal@mergedReplicates[[i]]]}], {i, 
   Length@mergedReplicates}];

Confirm

Let's look gene "a" which has both replicates and duplicates.

Getting gene "a" replicates for condition 1:

d1[All, {"Gene", "c1_1", "c1_2", "c1_3"}][Select[#Gene == "a" &]]

Averaging those rows together:

d1[All, {"Gene", "c1_1", "c1_2", "c1_3"}][
  Select[#Gene == "a" &]][Mean]

Average those columns together:

Mean[Normal[
   d1[All, {"Gene", "c1_1", "c1_2", "c1_3"}][Select[#Gene == "a" &]][
     Mean][Values]][[2 ;;]]]

and indeed that is the value in for gene "a" in column "c1".

Main Functions

mergeData[Data_List] := Module[
  {keys, data, common, dupData},
  data = Data;
  keys = Normal@Keys@First@data[[#]] & /@ Range@Length@data;
  data = Table[
    With[{key = keys[[d]]}, data[[d]][SortBy[#[First@key] &]]], {d, 
     Length@data}];
  common = 
   Intersection[
    Table[With[{key = keys[[d]]}, data[[d]][All, #[First@key] &]], {d,
       Length@data}]];
  data = Table[
    With[{key = keys[[d]]}, 
     data[[d]][Select[MemberQ[common[[d]], #[First@key]] &]]], {d, 
     Length@data}];
  dupData = Table[DealWithDuplicates[data[[d]]], {d, Length@data}];
  data = Table[
    ReplaceDuplicatesWithMean[data[[d]], dupData[[d]][[1]], 
     dupData[[d]][[2]]], {d, Length@data}];
  data = DeleteDuplicatesBy[#, First] & /@ data;
  Return[JoinAcross @@ Append[data, First@First@keys]]
  ]

Supporting Functions

DealWithDuplicates[data_] := Module[
  {keys, dupicateValues, duplicatePositions, duplicatesAveraged},
  keys = Normal@Keys@First@data;
  dupicateValues = 
   If[Length[#] > 1, First@#, Nothing] & /@ 
    Split@Normal@data[All, #[First@keys] &];
  duplicatePositions = 
   Flatten[#] & /@ (Position[Normal@data[All, #[First@keys] &], #] & /@
       dupicateValues);
  duplicatesAveraged = 
   data[duplicatePositions[[#]]][Mean] & /@ 
    Range[Length@duplicatePositions];
  Return[{duplicatePositions, duplicatesAveraged}]
  ]

ReplaceDuplicatesWithMean[data_, duplicatePositions_, 
  duplicateAveraged_] := Module[{temp},
  temp = data;
  Table[temp = 
    ReplacePart[
     temp, {First@duplicatePositions[[i]]} -> 
      Normal@duplicateAveraged[[i]]], {i, Length@duplicateAveraged}];
  Return[temp];
  ]

DeleteDuplicatesNotAveraged[data_, duplicatePositions_, 
  duplicateAveraged_] := Module[
  {minus, temp},
  temp = data;
  temp = Delete[temp, 
    duplicatePositions[[#, 2 ;;]] & /@ 
     Range@Length@duplicatePositions];
  Return[temp]
  ]

Answer 3

I still don't know what exactly is needed but this does most of what you seem to be after:

joined = Join[d1, d2];
merged = Values @ GroupBy[joined, #d &, Merge[Mean]]

merged // MaximalBy[Length]

and this is what your function does:

mergeDatasets[d1, d2]

Except it has strange value for (here) d=5, mine is an average while yours seems to keep the first record (for other d it takes average). Maybe that was your point, sorry but I lost the track.

Answer 4

No need for all that, try a functional approach: this gets you 90% there - you may want to delete based on missing values to match to your preferred format

{d1 // GroupBy[Query@"Gene"], d2 // GroupBy[Query@"Gene"]} // 
    Merge[Merge[Mean]] // 
   Query[All, KeyDrop["Gene"] /* KeyMap[stringSplit["_"]] /* 
     keyGroupBy[First] /* Map[Values /* Mean]] // Dataset // Query[Transpose]

The Transpose is not necessary, but is a workaround to V11's highly compressed formatting. You can replace it with:

// keyPushValues["Gene"] where

keyPushValues[key_]:=Query[{Keys/*AssociationMap[Association[key->#1]&],Identity}]/*Query[Merge[Apply[Join]]]/*Values

to get:

Note uses:

keyGroupBy[f_][expr_]:=Association/@GroupBy[Normal[expr],Keys/*f]

And

stringSplit, the operator version:

stringSplit[str_String][expr_]:=StringSplit[expr,str]

Answer 5

This answer takes a three step approach by querying the Datasetswithin Module.

1. Join and summarise the columns.
2. Get the sets of keys that need to be collapsed into one key.
3. Collapse the key sets from step 2 and drop the keys that form their basis.

mergeAndClean completes these steps.

mergeAndClean[ds1_Dataset, ds2_Dataset, idKey_] :=
 Module[{dsTemp, columnColapse},
  dsTemp =
   JoinAcross[ds1, ds2, idKey][
    GroupBy[#[idKey] &] /*
     Values,
    Transpose /*
     (Query[Join[{1 -> First}, (# -> Mean) & /@ Range[2, Length@Keys@#]]]@# &)];

  columnColapse = Normal@
    dsTemp[First /*
      Keys /*
      StringCases[LetterCharacter ~~ DigitCharacter ~~ "_" ~~ DigitCharacter] /*
      Apply[Join] /*
      GroupBy[StringTake[#, 2] &]];

  dsTemp[All,
   Function[{a},
     <|a, KeyValueMap[#1 -> Mean@a[[#2]] &]@columnColapse|>] /*
    KeyDrop[Flatten@Values@columnColapse]]
  ]

Then

mergeAndClean[d1, d2, "Gene"]

Additional parameters can be added to pass in the merging function, the key pattern, and so on.

Hope this helps.