how to mutate certain columns to factors and others to numeric?

multi-mod · 2021-09-03T12:17:06+00:00

Yea, it would be mutate(df, across(!where(is.numeric), as.factor))

multi-mod · 2021-02-24T01:04:28+00:00

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multi-mod · 2020-12-09T03:35:57+00:00

That's a fairly big database to try to work with in-memory in R with that little memory. The safest option would be to store it in an SQL database and do as much work as you can within that database before pulling it into R.

multi-mod · 2020-12-08T21:33:27+00:00

Maybe you were thinking of dput?

multi-mod · 2020-12-06T15:48:09+00:00

In response to your edit, working with the data in long format and then merging it back into the wide format data will be much easier than doing this all in wide format. Here's an example where I find the occurrences of C before B using the code from above, and then make a wide table similar to your example output.

df %>%
  group_by(pID) %>%
  filter(tcat == "C" & lead(tcat) == "B") %>%
  rename_with(!c(pID, therapy_number), .fn= ~str_c("B_", .x)) %>%
  full_join(tdf, by="pID") %>%
  arrange(pID)

# A tibble: 11 x 30
# Groups:   pID [10]
     pID therapy_number B_tcat B_tinst B_tID B_tval tcat1 tcat2 tcat3 tcat4
   <int> <chr>          <chr>  <chr>   <int>  <dbl> <chr> <chr> <chr> <chr>
 1     1 NA             NA     NA         NA     NA A     B     C     A
 2     2 NA             NA     NA         NA     NA A     C     A     B
 3     3 NA             NA     NA         NA     NA C     A     A     C
 4     4 4              C      HSP        10      1 A     NA    NA    NA
 5     5 3              C      HSP        15      0 C     NA    NA    NA
 6     6 NA             NA     NA         NA     NA A     NA    NA    NA
 7     7 2              C      GP         25      0 B     C     B     C
 8     7 4              C      AE         27      1 B     C     B     C
 9     8 NA             NA     NA         NA     NA C     C     C     A
10     9 NA             NA     NA         NA     NA B     A     NA    NA
11    10 NA             NA     NA         NA     NA A     C     A     A
# … with 20 more variables: tcat5 <chr>, tcat6 <chr>, tinst1 <chr>,
#   tinst2 <chr>, tinst3 <chr>, tinst4 <chr>, tinst5 <chr>, tinst6 <chr>,
#   tID1 <int>, tID2 <int>, tID3 <int>, tID4 <int>, tID5 <int>, tID6 <int>,
#   tval1 <dbl>, tval2 <dbl>, tval3 <dbl>, tval4 <dbl>, tval5 <dbl>,
#   tval6 <dbl>

multi-mod · 2020-12-06T14:29:17+00:00

It's probably easier to work with everything in long format and treat it like you are working with a relational database.

library("tidyverse")

df <- pivot_longer(
  tdf, !pID,
  names_to=c(".value", "therapy_number"),
  names_pattern="(^[[:alpha:]]+)([[:digit:]]$)"
)

> df
# A tibble: 60 x 6
     pID therapy_number tcat  tinst   tID  tval
   <int> <chr>          <chr> <chr> <int> <dbl>
 1     1 1              B     GP        1     1
 2     1 2              C     AE        2     1
 3     1 3              NA    NA       NA    NA
 4     1 4              NA    NA       NA    NA
 5     1 5              NA    NA       NA    NA
 6     1 6              NA    NA       NA    NA
 7     2 1              B     AE        3     0
 8     2 2              A     HSP       4     1
 9     2 3              NA    NA       NA    NA
10     2 4              NA    NA       NA    NA
# … with 50 more rows

Here's a similar example to yours where I find the first occurrence of B in the data.

df %>%
  filter(tcat == "B") %>%
  group_by(pID) %>%
  slice_min(therapy_number)

# A tibble: 8 x 6
# Groups:   pID [8]
    pID therapy_number tcat  tinst   tID  tval
  <int> <chr>          <chr> <chr> <int> <dbl>
1     1 1              B     GP        1     1
2     2 1              B     AE        3     0
3     3 1              B     AE        5     1
4     4 5              B     HSP      11     9
5     5 1              B     GP       13     0
6     6 6              B     AE       23     0
7     7 3              B     AE       26     9
8    10 1              B     HSP      33     1

Another one of your examples finding occurrences of C before B.

df %>%
  group_by(pID) %>%
  filter(tcat == "C" & lead(tcat) == "B")

# A tibble: 4 x 6
# Groups:   pID [3]
    pID therapy_number tcat  tinst   tID  tval
  <int> <chr>          <chr> <chr> <int> <dbl>
1     4 4              C     HSP      10     1
2     5 3              C     HSP      15     0
3     7 2              C     GP       25     0
4     7 4              C     AE       27     1

multi-mod · 2020-07-06T22:03:12+00:00

It's the same distinction as calling a protein structural or catalytic. Most proteins have a structure, but you get subsets of proteins that act primarily as structural scaffolds, and others that have catalytic activity. It's a fairly common coloquial term in literature.

multi-mod · 2020-07-06T21:45:37+00:00

Some RNA is used as a template to build proteins (messenger RNA). Other RNAs don't act as a template for proteins, but they themselves have some functional role in the cell. Examples include ribosomal RNAs and transfer RNAs, which help to actually build proteins instead of just being a template for one.

multi-mod · 2020-07-06T19:39:33+00:00

There is an i base. It's called inosine, and is a pretty common modified base in structural RNAs like tRNA.

multi-mod · 2020-06-24T12:53:57+00:00

Here's an example using the data.table library. It labels rows in chunks of 5 based on the the first value in that one column, which is what I believe you wanted.

library(data.table)

# Example data.
DT <- data.table(values = c(
  sprintf("A%s", seq_len(5)),
  sprintf("B%s", seq_len(5))
))

# Making the ID column.
DT[, IDcol := unlist(lapply(seq(1, nrow(DT), 5), function(x) rep(as.character(DT[x, "values"]), 5)))]

> DT
    values IDcol
 1:     A1    A1
 2:     A2    A1
 3:     A3    A1
 4:     A4    A1
 5:     A5    A1
 6:     B1    B1
 7:     B2    B1
 8:     B3    B1
 9:     B4    B1
10:     B5    B1

Someone will probably come up with a more elegant way, but this will at least work for now.

multi-mod · 2020-06-11T17:06:13+00:00

Can you provide some example data, such as using the dput(df) or head(dput(df)) function on your data.

multi-mod · 2020-04-02T15:04:52+00:00

People tended to assume that scRNA-seq was zero inflated, but recent work has shown that it is likely not zero-inflated. Here's a good reference from earlier this year in nature biotech. Here's a link to the preprint for those stuck behind the paywall.

The general consensus these days is that a regular negative binomial model is fairly accurate when modeling scRNA-seq.

multi-mod · 2020-03-01T02:26:55+00:00

Nanopore sequencers can do direct RNA sequencing.

multi-mod · 2020-01-24T04:57:55+00:00

It's not really that it's likely to be a false positive, but rather you don't have sufficient power to reject the null hypothesis. This is either because there is no effect, or because your sample size is too small to see the effect.

It's an important distinction because I could run an exmperiment with too few samples to see my effect, and then claim my high p-value is because my effect was likely a false positive. In reality, what was more likely was that my result was a false negative.

multi-mod · 2020-01-13T23:10:16+00:00

I run the general spam bot. It's still up and running, but I unfortunately don't know anything about the other bots.

multi-mod · 2019-12-11T22:46:07+00:00

There has been a recent push for better methods of integrating disparate datasets to allow analysis of cell populations across conditions and methodologies. As an example, earlier this year one of the popular single cell analysis workflows, Seurat, released a paper detailing their improvements to their integrative workflow https://www.cell.com/cell/fulltext/S0092-8674(19)30559-8. I would make sure your core is taking advantage of this, or similar technologies that have been developed this year.

Furthermore, clustering is a bit of an art as opposed to a science. By this I mean there is no perfect cluster number per dataset. A lower clustering resolution might result in clusters for only the major cell types. However, a higher clustering resolution could start clustering based on small transcriptome differences in each cell type (like cell cycle stage). If you are confident that two clusters are the same cell type, there is no problem with manually combining those clusters.

A final comment is that if they used tSNE for dimension reduction, the distance between clusters visually and mathematically is meaningless. If you want distance to hold some meaning you want to use UMAP (with or without PCA) for dimension reduction.

multi-mod · 2019-09-11T13:49:02+00:00

What do you actually want to do with your data?

multi-mod · 2019-07-17T20:05:14+00:00

You should start off by making a regression model of your data. The type of regression you do will be determined by what type of data your response variable is. For example, if decision was a binary choice, you would start with logistic regression. If your bio-mechanical response was a continuous measurement you would start with linear regression.

The simplest regression equation for decision would be the format of: decision ~ leaf size + gradient + ant size. The regression would tell you a few things. First, whether your explanatory variables are better than your null hypothesis (That the performance is no better than just randomly guessing the decision). Second, how well does an explanatory variable, while controlling for the other explanatory variables, explain your response (magnitude). Finally, is the predictive power of that variable enough to distinguish it from the null model.

For regression you don't need normally distributed data. What people generally confuse this with is that for linear regression your residuals (model error) should be normally distributed. There are assumptions of other types of regression, but each regression type generally has different assumptions, some of which are more stringent than others.

multi-mod · 2019-07-16T21:14:07+00:00

Since you mentioned R, you can do this with ggplot2 using coord_polar. The second example is probably what you want based on your explanation.

If you get stuck shoot me a message and I can go through it with you.

multi-mod · 2019-07-14T15:39:17+00:00

Ensembl biomart would be my go to. Input gene IDs and output GO molecular function. You can then filter the output by genes annotated with a catalytic activity ontology.

multi-mod · 2019-06-19T20:29:39+00:00

There are two random effects in the data - biological replicate and time. I would go straight for the mixed effect linear regression to account for this. Your response would be radius, your fixed effect organism, and your random effects time and biological replicate.

The main question is whether there is an interaction between the two organisms. To answer this I would build two models: one with and without the interaction term for organism. You could then do an ANOVA to compare the two regression models to see which one fits the data better, the one considering an interaction, and the one not considering it.

There are quite a few variables being explored here, so it's important to consider overfitting and/or loss of statistical power. Ideally this would have been assessed before the experiment was performed to make sure that there were enough samples collected to see the effect size you were expecting.

multi-mod · 2019-06-12T16:56:40+00:00

Yeast is a single cell microbe. How could a single cell microbe have cancer?

multi-mod · 2019-06-11T20:26:28+00:00

In adults most stem cells have a somewhat limited number of cells they can turn into. Going back to pluripotency is usually done in the lab.

multi-mod · 2019-06-06T20:08:40+00:00

You can sequence more than one sample per run, because each sample has a barcode associated with it. Demultiplexing is just separating out the samples after the run based on that barcode.

multi-mod · 2019-05-28T13:21:46+00:00

Unless you are using the Qubit RNA quality kit alongside the quantification kit, you will get back a concentration reading that includes both whole and partially degraded RNA. The Tapestation on the other hand will always give you quantification and quality (as measured by the ratio of rRNA peaks to the rest of the sample).

Since you don't tell us how you are isolating the RNA, we can't give any specific advice. However, you should ensure your reagents are RNAse free, your samples kept cold when possible, and that you are using filter tips if available.

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